w A -—’l’:~r&f.\u:\i >1.
1 .fi

. w
LIBRARY

   
 

This is to certify that the
thesis entitled
Development of a
Predictive Systems Model for

Course Research and Improvement

presented by

Richard Kenneth Brandenburg

has been accepted towards fulfillment ‘
of the requirements for i

Ph.D. degree in Secondary Education \

and Curriculum 1

aw i. M

Major professor

 

Date November 14, 1980

0-7639

 

 

  
  

. I: 5'
‘ J trig ‘Nhs‘

3., ‘9‘"!

 

 

 

  

OVERDUE FINES:

25¢ per day per item

RETURNING LIBRARY MATERIALS:
Place in book return to remove
charge from circulation records

 

 

 

T—— a

FOR COURSE RESEARCH
AND IMPROVEMENT

By

Richard Kenneth Brandenburg

A DISSERTATION

Submitted to

Michigan State University

for the degree of

 

DOCTOR OF PHILOSOPHY

Department of Secondary Education and

1980

 

DEVELOPMENT OF A PREDICTIVE SYSTEMS MODEL

in partial fulfillment of the requirements

Curriculum

 

 

Kt//é 3.4 7

ABSTRACT
DEVELOPMENT OF A PREDICTIVE SYSTEMS MODEL
FOR COURSE RESEARCH
AND IMPROVEMENT
BY

Richard Kenneth Brandenburg

In this study, the systems modeling approach is applied to the
investigation of the learning process in a college biology course. The
course is examined in systems terms, dissected and reconstructed ab—
stractly as a mathematical systems model. The model relates students'
background, prior knowledge and instructional behavior to concept mas—
tery based on data collected in the course during a term. Student per-
formance is then predicted using the model during a second or validation
term. The practical value of the model is also assessed by using it
to group students for special instructional treatment. The model is
examined for what it reveals about the instructional interactions occur—
ring in the course and also to provide hypotheses for future study.

This is a feasibility study and is intended as an experimental ap—
plication of the modeling technique to an educational process. The ex—
pectation was that this application could develop into a research tool
which would provide the educational researcher with the ability to dis—
sect and analyze highly complex instructional settings and processes.
This tool would provide a conceptual organization and analytically func—
tional framework to enhance the understanding of complexly interacting
entities and processes.

The major biological concept of Ecology was used as the base for
analysis and student performance evaluation. Two levels of Ecology

content analysis were performed. The first level provided a reference

 

 

Richard Kenneth Brandenburg

frame of disciplinary content and the second, from the actual con—
tent selected for the course, provided structure for the modeling pro—
cess.

A variety of background variables were collected for the students
in the course during the experimental term (n = 105) and the validation
term (n = 75). Variables describing the students' instructional be—
havior and attendance were also collected. Variables describing the
students' performance on forty—four (44) ecology concepts were calcu—
lated from testing data. The model,which is the functional relation—
ships between background, instructional and ecology concept mastery
variables, was developed using a series of multiple regression com—
puter runs.

The findings of the study indicate that the systems modeling pro—
cess appears to be useful in relating learning performance to back—
ground, prior knowledge and instructional behavior. Model variables
accounted for a significant percentage of the variation in the origi—
nal data and the power of the model is demonstrated by its ability to
predict student performance during a second (validation) term. The
practical utility of the model is also demonstrated using a student
grouping analysis based on the model components. The student groups
identified by this analysis have different academic and learning pro—
files. Also identified are a variety of hypotheses for improving

course instruction.

 

ACKNOWLEDGEMENTS

Many thanks are due the individuals who helped with this project:
Edward Smith for his guidance and support; Jean Enochs, James Gallagher
and William Schmidt for their participation; Margaret Lorimer for her
support and encouragement; Francis Fenn, Susan Anderson, Julie Courser,
Tena French, Barbara Stuart and many others for their typing and pre—
paration.

Special thanks are due to my wife, Susan, and son, John Paul, for

their patience and support.

 

ii

 

2.1
2.2
2.3

2.4

TABLE OF CONTENTS

The Problem

Introduction

Theory and Background

Theory

Background

Theoretical Model Development

The Instructional Session; A Simple Model
Possibilities

The Course and Content Analysis

The Course

Content Analysis

Quiz Content and Concept Mastery Assessment
Course Concepts Summary

The Model

Background Data

Instructional Data

Outcomes: the K Matrix

Viewing the Course as a System

Data Preparation, Data Analysis,and Limitations on this Modeling
Process

Data Preparation
Regression Analysis 1

Regression Analysis 2

iii

 

TABLE OF CONTENTS

5.3 Regression Analysis 3

5.4 Limitations

6 Results and Analysis

6.0 Introduction

6.1 Week
6.2 Week
6.3 Week
6.4 Week
6.5 Week
6.6 Week
6.7 Week
6.8 Week
6.9 Week
6.10 Week

6.11 Final

6.12 Summary

6.13 Synopsis

1

2

10

7 Applications - An Example

8 Summary, Evaluation, Recommendations and Conclusions
APPENDIX A Course Description
APPENDIX B Background Variables
APPENDIX C Instructional Variables
' APPENDIX D Factor Analyses
APPENDIX E Model Coefficients

iv

 

3.3
3.4

3.5

3.8
3.9
3.10

3.11

LIST OF TABLES

Phase One Concept Definitions

Ecosystems Concepts Presented in Lecture

BS 202 Ecosystem Concept Definitions

Ecosystem Concepts Covered in the Small Assembly Sessions
Ecosystem Concepts Presented in the Tape Laboratory
Ecosystem Concepts Contained in the Weekly Textbook Readings
Ecosystem Concepts Covered in the Weekly Practice Quizzes
Quiz Items with Ecosystem Concepts

Test Item Coverage of Ecosystem Concepts

Ecosystem Concept Coverage Summary

Ecosystem Content Matric C

Background Variables

Instructional Variables

Weekly Additions and Updates of the K Matrix

Phase One Regression Summary Tables for a Sample Test Item
Phase Two Summary Table for a Sample Test Item

Residual Error Rate for Three Sample Test Items

K Matrix Elements and Definitions for Fall Term, 1977

Phase Three Regression for a Sample Test Item

Tested Ecosystem Concept Coverage

Week 1 Regression Equations Goodness of Fit and Extrapolation
Week 2 Regression Equations Goodness of Fit and Extrapolation

Week 3 Regression Equations Goodness of Fit and Extrapolation

V

1-

 

6.5

6.6

6.7

6.8

6.9

6.10

6.11

6.12

LIST OF TABLES

Week 4 Regression Equations Goodness of Fit and Extrapolation
Week 5 Regression Equations Goodness of Fit and Extrapolation
Week 6 Regression Equations Goodness of Fit and Extrapolation
Week 7 Regression Equations Goodness of Fit and Extrapolation
Week 9 Regression Equations Goodness of Fit and Extrapolation
Week 10 Regression Equations Goodness of Fit and Extrapolation
Final Regression Equations Goodness of Fit and Extrapolation
Goodness of Fit and Extrapolation Summary

Frequency of Correlational Occurrences: Background Variables, (B)
vs. Concept Knowledge Indices (K)

Frequency of Correlational Occurrences: Instructional Variables
(I) vs. Concept Knowledge Indices (K)

Frequency of Correlational Occurrences: Concept Performance (K')
vs. Prior Performance (K)

Grouping Variables
Hierarchical Grouping Analysis Output
Values of Grouping Variables B18 through I70 by Group Numbers

Values of Grouping Variables B3, B6’ 1101

Groups

Pretest Factor Analysis

Affective Questionnaire Factor Analysis
Background Questionnaire Factor Analysis
Model Coefficients for Week 1

Model Coefficients for Week 2

Model Coefficients for Week 3

Model Coefficients for Week 4

Model Coefficients for Week 5

vi

LIST OF TABLES

E.6 Model Coefficients for Week 6
12.7 Model Coefficients for Week 7
E.8 Model Coefficients for Week 9
E.9. Model Coefficients for Week 10

12.10 Model Coefficents for Week-Final

 

 

 

 

LIST OF FIGURES

Instructional Session

Instructional Sessions

Ecology Concept Relationships

BS 202 Systems Diagram

Data Processing and Course Modeling

Sample Phase Three Regression Residuals for Fall Term Data Base

Sample Phase Three Regression Residuals for Validation (Winter Term)
Data Base

viii

 

 

CHAPTER 1

THE PROBLEM

1.0 Introduction
A basic assumption of modern education_is that as a student
experiences instruction something happens to change the student.
The student learns, that is acquires additional knowledge, develops
skills or masters processes. This conception of cause and effect
through instruction and learning serves as a primitive Kuhnian
(1975) paradigm of educational researchers. Given a simple learn—
ing experience or set of experiences, it is not too difficult to
investigate the effects. Such laboratory studies, complete with
control groups, are conducted by researchers in great profusion.
However, once the researcher moves from the lab to complex, real
classroom instruction, the investigation of learning phenomena be-
comes considerably more complicated and difficult. Unfortunately
the classroom is the arena of most concern to educators.
One form that such instruction takes is the college course;
a costly, sometimes effective and little studied instructional mode.
Large college courses usually require that the instructor and
assistants spend most of their time with material preparation and
presentation. The counseling and tutoring of individual students

account for the remainder of their efforts. The economics of such

a venture preclude any detailed research into the deep structure of

the course content or the development of analytical devices for

 

monitoring the student progress or what might be called the "quali-
ty control" of the course. In courses some initial effort usually
has been directed at defining the objectives, but the actual con-
tent of the course and methods of presentation are more often based
on previous experience and professional intuition. The common
testing routine is usually applied to assess the degree to which
each student has fulfilled the course objectives. Periodic course
revisions are made on the basis of the instructor's intuition and
experience, often from his or her personal and students! biased
feedback. Even in the unusual circumstances where the situation
permits a rigorous examination of course objectives, further de—
tailed study of actual student performance and understanding of the
concepts composing the subject matter is rarely possible.

What is needed is a research tool which will provide the edu—
cational researcher with the ability to dissect and analyze highly
complex instructional settings and processes such as those found in
the college course. Such a tool would provide a conceptual organi—
zation and analytically functional framework to enhance the under—
standing of complexly interacting entities and processes.

This study presents and examines a tool which may fit these
requirements. Here the system's approach and modeling techniques
are applied to a college course. The course is examined in systems
lJterms, dissected, and reconstructed abstractly as an artificial sys—
tems model. This model is examined for what it tells us about the
instructional interactions taking place in the real c0urse. The

model is also used to provide hypotheses for future study.

 

CHAPTER 2

THEORY AND BACKGROUND

2.0 Theory

Investigations of the empirical world are based on two basic or
primary presumptions:

l. The world exists, and

2. The world is, at least in some respects intelligibly

ordered, and is open to rational inquiry. (Laszlo, 1972)
Such rational inquiry of the real, empirical phenomena of the world
involve human conceptualizations which organize and lead to under—
standing. One such conceptual tool which has proven to be very
useful in the organization and study of events and processes is the
systems approach.

The systems approach may be defined as the logical analysis
and breakdown of a system, whereby the connections, interrelation—
ships, and organization of the constituent parts of the system are
made explicit. A system in the sense used here is a ”set of inter—
connected elements organized toward a goal or set of goals"
(Manetsch and Park, 1974a). The set of interconnected elements

which are the object of the systems analysis or approach may be as

large or as small as is necessary to understand the phenomena under

study. That is, the systems analyst chooses the relevant elements

of study and assigns everything else to the system's environment,

 

4

outside its boundaries. Interactions may Still occur between the
system and its environment across these boundaries, but the exter—
nal factors involved are considered to be completely independent

of the system itself. Within the system, the analysis is more than
a disection of parts, but rather leads to an understanding of how

a system may often be more than the sum of those parts.

The systems approach has been applied in many fields or disci-
plines where there arose a need to understand, and often control,
complex, multifeatured, interacting systems. This approach funda-
mentally allows a conceptual organizing scheme to be laid over what
otherwise would be considered a chaotic jumble of interacting enti-
ties and processes, the result being the orderly organization of a
complex system with definable characteristics, traceable processes
and predictable responses to external stimuli. It must be remembered,
however, that the structure imposed on the system by the analyst repre—
sents a subjective expression of opinion on its structure and behavior.
Although the manifestation of this expression may be a sophisticated
model, any model represents only one of many views of reality.

2.1 Background

This approach may be applied to existing systems or to the
planning and construction of new systems. The latter of the two,
systems planning, has enjoyed a wide popularity in educational develop—
ment and administration (Tanner, 1971; Silvern, 1965; Haefele, 1971;
LeBaron, 1969; Thompson, 1971), where enormous systems need to be
_ developed for things as diverse as state wide curricula and school
district food services. The systems planning approach usually devel—
Ops the system graphically, so that interactions and feedback loops

may be identified, but rarely goes into the mathematical or computer

 

modeling stage.

The application of systems analysis to existing systems, on the
other hand, usually includes the development and testing of a mathe—
matical computer model as well as experimentation with that model
(Manetsch and Park, 1974 a, b). Such analysis and modeling has been
applied to many seemingly unrelated areas including economics (Ellis,
1965; Abkin, 1965), the dynamics of world populations (Forrester, a,
b; Meadows, 1972), and ecosystem analysis, control and prediction
(Levine, 1975; May, 1974; Patten, 1972; Arnold and deWitt, 1976; etc.).
Silvern (1967) successfully applied systems methodology to an educa—
tional system in order to develop a model describing feedback signal
paths from outside the school or school district to an occupational
teacher. Although he did not develop a computer model, he concluded
that "models can be developed which have an immediate, practical
application (to education)." In addition he suggests, ". . . consider
a number of mathematical techniques, select promising mathematical
techniques and synthesize a set of expressions, using both flowchart/
mathematical model combinations similate to test the model. Using
problems from real—life simulate to solve problems on the model,
apply the problem solutions to real—life, improve the model based on
real-life experiences, and extend this technique of systems analysis
and synthesis (anasynthesis) to other problem areas."

In a paper unrelated to systems analysis, Carrol (1963) when
discussing his proposed model of school learning, states; "What is
J needed is a schematic design or conceptual model of factors affecting
success in school learning and of the way they interact." He then

goes on to propose his five component model of school learning;

aptitude, ability to understand instruction, quality of instruction,
learning opportunity (time related) and perseverance, and suggests
several mathematical or functional relationships between the variables.
In his conclusions he adds; ". . . it should be possible in principle
to describe the pupil's success in learning a series of tasks by
bummafing the results of applying the model successively to each
component task."

Carrol and Silvern have both implicitly suggested that a learning
experience may be simulated using a quantitative model, and that such
a simultation might provide useful insights into the learning process.
Additionally, such modeling might lead to mathematically predictable
learning performance.

It is the purpose of this study to follow the leads given by
Silvern and Carrol and investigate the application of some of the
techniques of systems analysis and modeling to a series of learning
experiences, and explore what the resultant quantitative model tells
us about such experiences.

2.2 Theoretical Model Development

Although any experience may be, and probably is, a learning
experience only formal or structured learning experiences such as
the lecture, laboratory and group discussion will be considered in
this study.

A student begins Such a learning experience with a certain level
of knowledge and understanding. During the experience something is
J transmitted so that the student emerges with a slightly altered state

of knowledge and understanding. The purpose of the experience was to

transmit or teach a certain amount of subject content, manual and

 

7
Although all are of importance to the

mental skills, and processes.
learner, the transmittal of subject content will serve as the chief

focus of the model building. The learning experience may then, for

the purpose here, be defined as a process of finite length through

which a student (or learner) may be said to "pass", and during that
passage is exposed to a certain amount of content knowledge, all or

a fraction of which is learned or added to the previous knowledge

base of the student.

The first step in this modeling process is to examine the sub?
ject content of the hypothetical learning experience. The subject
content of any intentionally designed learning experience is usually
a reflection of what might be called the "base" content of the relevant
disciplinary field. The subject content presented to the learner is a
simpler, less complex version of what is known by the experts in the
field. The more complex, underlying content will be referred to as
Phase I content, as compared with the Phase II or simpler content pre—
pared for the student digestion during the learning experience. The
Phase I content serves as a "reference frame" for the Phase II content
to be taught.

Suppose that the Phase I content consists of five concepts

(A, B, C»D, and E) which are related in several ways. The relation—

ships may be schematically illustrated as shown below:

 

 

8

Here concept C is defined in part by concept A (i.e., A is included

in the definition of C), concept B affects concept A (perhaps a direct
empirical relationship), B defines D, and E is a parameter of D

(D could be a physical low and E a physical constant).

The simpler Phase II or selected content for the learning exper-
ience analogous to the above Phase I could be concepts A, B, C, and D;
concept E being deemed by the teachers or curriculum developers to be
unnecessarily complex or otherwise unsuited for the student audience.
The phase II concepts may also receive simpler definitions or expla—
nations for presentation to the students.

Once the Phase II subject content has been determined it is
necessary to examine the match between it and the content actually
tested or evaluated for at some time after the learning experience.
This tested content, usually the only measure of student learning is
generally a subset of the Phase II content. Thus, in our modeling of
a learning experience the absolute or Phase 1 content, (all the subject
content which could be taught), the Phase II content (which is actually
presented) and the tested content must be examined and compared.

In the modeling of a system where entities pass through a series
of transformation processes it is necessary to describe those entities
with a collection of state variables reflecting the changes imparted
on them by the processes. Here the learning experiences is the process
and learners or students are the entities passing through the process.
To develop the necessary state variables the general method of variable

or information classification suggested by Tamir (1976) may be used.
Relevant information on student learners is classified according to
this method into one of three categories: antecedents, transactions,

or outcomes. This corresponds to the data describing the individual

 

9
student upon beginning the learning experience (antecedents), what
the student learner does and what happens to him or her during the
course of instruction (transactions), and the effects of that instruc-
tion upon the student learner (outcomes). Thus, each student complet-
ing the learning experience is described by a set of background data
(antecedents), a set of data describing the instruction (transactions)
and a set of data describing the knowledge obtained during the instruc-
tion (outcomes). These variables describing each set of data are the
students‘ state variables.

The partitioning of student state variables into antecedents,
transactions, and outcomes is based on Tamir's (1977) practical
adaptation of Bloom's Theory of school learning (1977). Bloom re—
lates learning outcome as a variable to student characteristics and
quality of instruction. Student characteristics may be divided into
either cognitive entry behaviors or affective entry behaviors. The
effect of the quality of instruction combined with the student's
cognitive and affective behaviors determine learning outcomes. Out-
comes are classed as affective level and type of achievement and rate
of learning. BloomhsTheory, based on extensive research review,
claims that up to 50% of the variation in school achievement may be
accounted for by cognitive entry behaviors, 25% by affective entry
characteristics, and up to 25% by quality of instruction. Taken
together he claims that cognitive entry behaviors and affective entry
Characteristics account for up to 65% of achievement variation. Adding
quality of instruction raises the limit up to 90% of the variation
(Bloom, 1977).

As quality of instruction is difficult to measure, this study

is concerned with the students' cognitive and affective behaviors

 

10
and measurable learning outcomes with existing levels of instructional
quality. Variations of Carrol's Time—on-Task Theme (1963) are also
reflected in the transactions variables.
For the purposes of this study all antecedent information on each
student is assigned to a "Background" data set or matrix B ;
—1

3. . . Bn ,

B = lBl, 32, B
where n = the number of antecedent or background variables describing
each student learner.

The only transactional state variables which can meaningfully be
measured for this type of study are attendance, methods of study, and
time-on-task. Such measured variables are assigned to the Instructional
Matrix I ;

-l

I - Ill, 12, I3. . .Im

where m = the number of such transactional variables.

After an instructional session a student learner may be evaluated
for knowledge or understanding of each of the concepts presented in
the instruction. Following the example introduced earlier, suppose
the concepts A, B, C, and D are presented in an instructional session.

These concepts may be arranged in an array' C such as;
C = I A, B, c, D ['1

Testing after the learning experience may allow a value to be assigned

to each variable in an analogous array.

= —1
Ks lKA’KB’ KC’KDis

where KA through KD are quantitative measures (such as test scores)
0f the knowledge or understanding of students of each of concepts A,

B, C, and D, respectively.

 

ll

Expanding this notion, all the (Phase II) concepts presented in

a learning experience may be represented in an array C ,
-l

C _ |Cl, C2, C3, C4. Cp ‘

and measurements of the mastery of the p concepts represented in an

analogous K matrix,

Thus each student "passing through“ a learning experience may be

described by three arrays of state variables,

B) I: and K ;
representing antecedents, transactions and outcomes, respectively.

2.3 The Instructional Session; A Simple Model

Consider a student learner passing through a learning experience
or instructional session. As a result of this experience changes occur
in the student's knowledge and understanding of the material presented
and discussed. In the model these changes are indicated as changes in
the state variables describing the student. Specifically, after an
instructional session, changes have occurred in the student's version
of C. These changes are influenced by the student's background
Variables, B , by previous instruction I , by previous learning
( Cimaged as K ), by unknown student idiosyncrasies, and by the nature

0f the instructional session itself. This is shown in the diagram of

 

of Figure 2.1 below:

 

 

 

Instructional
si( Bi’Ii’ Ki) Session Si(Bi’I i, K£)
I ==
1__€_J
K’=f€(B.,Il, i) ’ I;=11+I,c;oci<;
Figure 2.1

Student Si described by the state variables of Bi’ Ii (previous instruc-
tion) and Ki (reflecting entry level knowledge Ci) undergoes an instruc—

tional session represented by I As a result of this session the

5'
student's state variables are transformed; the student has new understand—
ing or mastery of particular concepts, described as alternations to Ci
reSulting in (:5 which are reflected as measurable alterations to Ki’
resulting in la. In addition, new instructional bookkeeping data is added
to Ii' Bi is unaffected by the instruction. The changes iIICi reflected
inl<i are influenced by Bi, Ii and previous concept knowledge (measured in
Ki). Thus, the instructional session may be described analytically by a

transfer function, fg’ operating on the state variables, and represented

by:

For any one student the transfer function may be viewed as exact,

reflecting both the effect of the instructional session and the exact-
neSs of concept mastery measurement. However, for the modeling process

we consider a large number of students passing through the same instruc—

tional sequence and the single transfer function representing the effect

 

13

of the instruction is based on an aggregate approximation and is
inexact.

Suppose now that students were exposed to a number of learning
experiences sequentially. That passage from one instructional ses—

sion to the next may be modeled graphically as shown in Figure 2.2

 

 

 

 

 

 

 

 

below.
Instructional Instructional Instructional
9
Si(Bi"Ii’Ki) S . s . Se. 31(31’12’Ki)
I essmn _. eSSion ssmn .
l 2' 5
Figure 2.2

Each instructional session could have a transfer function associ—
ated with it if student concept mastery was evaluated between sessions,
or the entire string of sessions could have just one transfer function
if concept mastery were tested for after session 5 . In either case
the transfer functions or function describe in some sense the effects
of the instructional sessions in this type of model.

In this study this simple concept of systems analysis shown in
Figure 2.2 has been applied to the instructional systems of a college
biological science course which consists of a series of lectures, labora—
tories, small group discussions, and other learning activities. Stud-
ents are viewed as the entities passing through the instructional
processes of the course, and the changes in their knowlege of a subset
of the content taught in the course as measured through their test
JPerformance is considered to be the result or product of the course.
Ecology, a main component of modern biology, is used as the subset of
content on which student performance is monitored. Each course compon—

ent is viewed as an instructional session contributing to the students'

 

14

knowledge of biology, and ecology in particular. The effects of the

instructional sessions are aggregated on a weekly basis and related

through the model to student performance in ecology concepts based
on items from the weekly quiz. Student concept performance is also
related through the model to a large array of background information
collected at the beginning of the course. Successively applied and up—
dated through the ten weeks of the course, the model provides a some—
what different view of a standard academic course. The model is also
tested for transferability across terms to validate the goodness of
fit between the model and the process.
2.4 Possibilities

The model presented in this study may provide the experimenter
with a new viewpoint on conceptualization of sequential learning exper—
iences. The model itself is a very simplistic, structured construct
which reflects the basics of the instructional processes of the course.
It is the intent of the modeling scheme to organize and uncover the
relations of content, background, instructional behavior, and learning
output. Such an organization may prove useful to future researchers
who need constructs for organizing and analyzing instructional exper—
iences and may lead to the study of previously unrecognized relation-
ships.

In Chapter 7 a possible application of the model is presented.
The parameters of the model developed during this study are analyzed
and student groups are developed which differ on instructional activ—

ity and performance in the course. Suggestions or hypotheses on

methods_of improving the learning performance of each group based on

the group characteristics was developed. This grouping analysis based

on the results of the model building process may be a link between a

 

 

 

15
research tool and classroom implementation. The systems model organizes
the process into an intelligible schema from which definite relation—

ships emerge, relationships that may be useful in course improvement.

1

CHAPTER 3
THE COURSE AND CONTENT ANALYSIS

3.0 The Course

The course chosen for modeling was Biological Science 202 (BS 202).
At Michigan State University BS 202 is a medium sized course serving
approximately one hundred students each term. It is one of six science
and mathematics courses required in the undergraduate science and math
option of the Elementary Education curriculum, and is the only opportu—
nity in this curriculum for the comprehensive study of biology. The
student population in BS 202 is composed of over 60% Elementary Educa-
tion majors and is an extremely heterogenous group, varying widely in
academic background, aptitude and attitude. Previous studies (Enochs,
1977; Tamir, 1977) have shown that the group as a whole is academically
representative of the lower half of the University's undergraduate
population.

The course typically consists of one hour of lecture, two hours
of group laboratory or small assembly session (SAS), open hours for
independent laboratory study using auto—tutorial cassette tapes, and
a quiz each week for ten weeks. A comprehensive final examination is
given at the end of the course. The student is expected to work with

an average of two cassette tapes for several hours each week. The 17

'tapes are in sequence following the general outline of the course.

There has also been a set of behavioral objectives covering a wide
area of biological content developed for the course. The course is

16

 

l7

closely centered around these objectives.

One of the problems with BS 202 is that although a fair proportion
of students do well in the course, a significant number perform at a
poor or mediocre level (Enochs, 1977). This occurs in many science
courses and is usually not considered a problem. However, the majority
of students in BS 202 receive Elementary Teacher Certification and go on
to teach in elementary schools. Since these students through their con-
tact with young children exert an influence on society disproportionate
to their number, it is particularly important that they develop a thor—
ough mastery and understanding of course material. The economic and
time boundaries of this course, as are most others, are finite and limit
what may be far reaching effects. Thus, any analytical findings which
point toward increased optimization of instructional resources and
student mastery are important.

A complete course outline for BS 202 as taught during the term
studied (Fall 1977) is included in Appendix A.
3.1 Content Analyses

The concept of Ecosystem represents one of the major unifying
themes of the BS 202 course. It was chosen as the concept focus on
this study not only because it is a central thread in BS 202 but also
because it is important to the understanding of many contemporary
political and social issues or problems. The concept of Ecosystem at
the level appropriate for the BS 202 student has the additional ad-
vantage of being composed of a small, finite number of simpler concepts.

There are a number of different techniques available to the
curriculum analyst for the decomposition of content. For this study,
it was necessary that the concept of Ecosystem be broken down into

Simpler, component concepts and the relationships between these

 

18

concepts elucidated. The analytical method used here is described by
Smith (1974, 1975) as the first stage in the process of developing
analytic and exemplary systemic networks for a specific discipline or
subdiscipline. The author has used this method (Brandenburg, 1976)
and it has proven to be a useful technique for the analytical decompo—
sition of a content area. Basically, the method requires that the
content analyst be conversant with the content area under scrutiny.
The analyst systematically decomposes the content, using reference
sources until the "fundamental" concepts and relationships are clearly
revealed. These relationships are then expressed diagrammatically.
It is an iterative process with much left to the creative ingenuity of
the analyst.

The content analysis for this study was divided into two phases.
In Phase I a complete content analysis of the concept of Ecosystems
was performed to a greater "depth" than appropriate for the students
in the BS 202 course. This was done by detailing the component concepts
of Ecosystem to a level appropriate for a course in the fundamentals of
ecology for college biology majors. From this in—depth analysis, the
origins and relationships of the Ecosystem concepts taught in BS 202
are apparent. Phase II consisted of analyzing the content presented in
each instructional component of the course using the first phase analysis
as a reference frame. In the lectures, small assembly sessions, tape
labs, textbook readings and practice quizzes a simplified, reduced
version of the content elucidated in the first phase was uncovered. This

was the level of detail deemed appropriate by the course designers for

a ten week course for preservice elementary education majors with little

Previous science experience. The Phase II ecosystem concepts serve as

19

a basis for the concept mastery or performance indices used in the
systems model describing the course.

Phase I Content Analysis

 

The first phase of content analysis consisted of detailing the
component concepts subSumed under the concept "Ecosystem", and illus—
trating their interrelationships. The level of complexity, or "depth”
chosen for this analysis was one which would be appropriate for a course
in the fundamentals of ecology for college biology majors; a level con—
siderably more sophisticated than necessary for the BS 202 course.

Eugene Odum, a noted authority in the fields of Ecology and
Ecosystems, has authored a textbook designed for three levels of stu-
dents. Fundamentals of Ecology (1971) is partitioned into three "Books"
by the level of difficulty. Book 1 (chapters 1, 2, 3, 4, 9, 15, 16 and
21) was designed for study by "lay students" those in the social sci—
ences, humanities, and other non-biological professions. It provides
a macroscopic View of ecology. For the undergraduate college course in
ecology for biology students, Book 2 (chapters 1 through 10, 15, 16
and 21) may be used. All twenty—one chapters (Book 3) may be used as a
comprehensive reference work or graduate course textbook. Book 2 was
subjected to content analysis in order to provide a reference frame for
the ecosystem concepts taught in BS 202.

Figure 3.1 presents the results of the phase one content analysis.
In this graphic representation each concept subsumed under "Ecosystem"
is represented as a node connected by propositional "bridges" to other

concepts. The total, interrelated web of concepts represents the com-
. plex concept of Ecosystem. Each of the subsumed concepts in the web,
may be broken down into simpler, more primitive concepts, and could be

represented by a smaller but equally complex web of concepts. However,

 

diiEfinyL-sopnr— ulhuEII-LU ‘ I llinuhuxr-{nl hIL‘p-N .l
\ utiEl!L—>EH )ILIIE ' ~IKE~E5L1>5 uIUHIhlh / urn- \

 

 

Aflmwfi .EDOOV mmflflmGOHumHmm uQmUdOD %wOHOUm H.m MMDUHM

_u=s—>:E.—

 

Ewan one:
ZOHHDAOE )

                

yo mucosa—w —
.«o 39:33 a
mo ocuun.n<

mEmfigoum
mmzauv

  

   
 

   

   

    

 
   
 

 

  

                          

:quuom
mussecsn mom> mucccm two:
mop—yumwa‘ cozufizeoe 3 E m _
mauve.¢.*
.«o mammawuomum; 9 mus—332w; "
>3. 7:wa

mmwzuqh w>quuat0nnam .
mmwcw>woncz< .
m:ov.:3ua.:..;u 13:3

          

mouuqmzm.

_ d.&

   

:oamaauxm
u>_oauoasnu

 

~.m
wfiiz
amudwofiou

 

mmmmmuo;
o>uuusv0nnwm

 

                          
                  

muauuaeom
3.39....

   

:3 53:33
a.” ~m<

  

 

m5: snatches...

20

35.5.: amusumz
.«o 33.
355.55

saamtu>ss

33:3
:oumoao;

vacuuming
amucmscongzu

 

g
\ 2.52:... 3.55 J a \

“cusses TEN awed—am.

(

unas:o~—>cm smuueu;u

. sutuzm
ozwecooq>cu «uuaamae nauseo.«>:u

 

21

it serves no purpose here to break these concepts down further. Rather,
a definition of each is presented in Table 3.1.

In addition to the definitions provided many of the concepts in
Figure 3.1 include a reference to a particular section of Odum's book
(1971) which more fully explains the principles and expands the rela—
tionships involved. For instance, in the relationships among the con—
cepts in Figure 3.1 the Ecological Niche describes an organism as
detailed in Odum, section 8.1: "The ecological niche...is a term
that includes not only the physical space occupied by an organism, but
also its functional role in the community and its position in environ—
mental gradients of temperature, moisture, pH, soil and other conditions
of existence.“ The Competitive Exclusion Principle further details the
complexity of the concepts; (from Odum, section 7.17) "Interspecific
competition is any interaction between two or more species populations
which adversely affects their growth and Survival...The tendency for
competition to bring about an ecological separation of closely related,
or otherwise similar, species is known as the competitive exclusion -
principle." Thus, the bridges shown attempt to represent the more
complex definitional linkages.

Phase II Content Analysis

Using the set of concepts detailed in the previous section, the
content of the BS 202 course was searched for Ecosystem concepts. The
ten lectures were first analyzed in detail for ecosystem concepts.
Then, each small assembly session, tape lab, textbook reading, and
practice quiz was thoroughly examined for references to these concepts
and catalogued by the nature of concept coverage. The weekly quizzes

and the final exam were similarly analyzed for concept coverage.

 

22

TABLE 3.1

PHASE ONE CONCEPT DEFINITIONS

Concept

Abiotic

Abiotic Materials
(Substances)

Allee's Principle

Antibiosis

Age Distribution
Area
Behavior

Biogeochemical Cycles

Biotic Cycling Rates
Biotic Environment

Biological Control

Biotic

(from Odum, 1971)

Definition

Not biological; not involving organisms or
that produced by organisms.

Basic inorganic and organic compounds, such
as water, carbon dioxide, oxygen, calcium,
nitrogrn and phosphorus salt, amino and
humic acids, etc., generally found outside
of living organisms. Abiotic materials
comprise one of the basic units of ecosystem.

For some populations the degree of aggrega—
tion and overall density which results in
optimum population growth and survival varies
with conditions. Undercrowding as well as
overcrowding may be limiting factors.

An interaction between two populations in
which one population produces a substance
harmful to the competing population.

The age structure of individuals within a
population.

The unit in which ecological interaction
takes place.

The response of an organism to its biotic
and physical environment.

The chore or less circular paths by which
the chemical elements, including all the
essential elements of protoplasm, tend to
circulate in the biosphere, from environ-
ment to organisms and back to the environ—
ment.

The rate of transfer of organic compounds
through the ecosystem.

The organisms and organic materials within
an organism‘s environment.

The use of biological factors to control a
population; the introduction of predators,
parasites, etc., into a particular ecosystem
to induce a population reduction of the
controlled organisms, causing an equilib—
rium condition with a smaller population size.

Biological, pertaining to organisms and their
products.

Concept

Biotic P<

Carrying
Chemical
Climax 3‘

Coevolut

c°mPetit
Princi

Confinensa
Commit
C"invent

C“upled
Trails:

Crowdin1

 

23

TABLE 3.1 (Continued)

Concept

Biotic Potential

Carrying Capacity
Chemical Environment
Climax State

Coevolution

Competitive Exclusion

Principle

Commensalism

Community (Biotic)

Competition

Protocooperation

COupled Metabolic
Transformations

Crowding

Definition

"The inherent property of an organism to
reproduce, to survive, i.e., to increase
in numbers." Maximum reproductive power.
The maximum population growth rate under
ideal conditions.

The maximum population that a given environ—
ment can support indefinitely.

The chemical elements, compounds, and
conditions within the ecosystem.

A relatively stable stage reached in some
ecological successions.

A type of community evolution (i.e., evolu-
lutionary interactions among organisms in
which exchange of genetic information among
the kinds is minimal or absent) involving
reciprocal selective interaction between
two major groups of organisms with a close
relationship, such as plants and herbivores,
large organisms and their microorganism
symbionts, or parasites and their hosts.

The tendency for competition to bring about
an ecological separation of closely related,
or otherwise similar species.

An interaction between populations of two
species in which one population is bene—
fited but the other is not affected.

Any assemblage of populations living in a
prescribed area or physical habitat.

In ecology, utilization by two or more
individuals, or by two or more populations,
of the same limited resource; an interaction
where both parties are harmed or limited.

Type of interaction between two species in
which both populations benefit by the
association but relations are not obligatory.

Coupled energy flows and matter cycles within
a community; where the organic compounds
produced by one segment are necessary metab-
olic inputs for another.

The situation arising when a large number
of organisms of the same species inhabit a
confined area; occurs in areas of high
population density.

 

Concept
Cycling 1

(Populat:

Detritus

Detritus

Diversit
Dominant

Ecologic

Edge in

Ecology

Ecosyst‘

 

24

TABLE 3.1 (Continued)

Concept

Cycling Rates

(Population) Density

Detritus

Detritus (food chain)

Diversity
Dominants

Ecological Equivalent
Edge Effects

Ecological Niche

Ecosystem

Ecotone

Emigration

Energy Conversion

Definition

The rate of process within the ecosystem;
the rate of a complete cycle of trans—
formations.

Population size in relation to some unit

of space-—generally assayed and expressed
as the number of individuals, or the
population biomass, per unit area or volume.

All the particulate organic matter involved
in the decomposition of dead organisms.

One of the two basic types of food chain,
which goes from dead organic matter into
microorganisms and then to detritus-
feeding organisms (detritivores) and their
predators.

The condition of being different. The
multiplicity of differences among organisms.

The organisms which exert the chief control
or influence over an ecological community.

Organism that occupy the same or similar
ecological niches in different geographical
regions.

The tendency for increased variety and dens—
ity at community junctions or areas of
interaction.

The physical space occupied by an organism,
its functional role in the community, and
its position in environmental gradients of
temperature, moisture, pH, soil, and other
conditions of existence.

Any unit that includes all of the organisms
(i.e., the "community") in a given area
interacting with the physical environment
so that a flow of energy leads to clearly
defined trophic structure, biotic diversity,
and material cycles within the system.

A transition between two or more diverse
communities as, for example, between forest
and grassland or between a soft bottom and
hard bottom marine community.

A form of population dispersal with one-way
outward movement.

The transformation of one form of energy to
another; usually involving heat loss.

can
Energy

Energy
Energy
Entrop
Enviro

Equfli
Evolut

Extinc

lst L:

Fluct'
Food

Food
Genet

Gran

Grow-

Gm“

 

 

25

TABLE3 .1 (Continued)

Concept

Energy Environment
Energy Input

Energy (Heat) Sink
Entropy

Environmental Resistance

Equilibrium

Evolution
Extinction

lst Law of Thermodynamics

Fluctuations

Food Chain

Food web
Genetic Characteristics

Grazing (food chain)

Growth

Group Selection

Definition
The forms and amounts of energy within an
ecosystem.

A source and amount of energy entering a
process.

'An element or area which collects unusable,

usually heat, energy.

A measure of the unavailable energy in a
closed thermodynamic system.

The sum total of the environmental limit-
ing factors acting on a population.

A state of balance between opposing forces.

The change in the genetic make-up of a
population with time.

The process or state of no longer existing
pertaining to a population or species.

For a closed thermodynamic system the total
amount of energy present remains constant
regardless of the transformations it
undergoes.

The uncertain shiftings about some point.

The transfer of food energy and organic
matter from the source in plants through a
series of organisms with repeated eating
and being eaten, through a final decomposi—
tion to inorganic materials.

The interlocking pattern by which food
chains are interconnected with one another.

Characteristics of an organism or popula—
tion caused by gene activity.

One of the two basic types of food chains
which, starting from a green plant base,
goes to grazing herbivores and on to
carnivores.

The increase or expansion of a population
or community.

The natural selection between groups of
organisms not necessarily closely linked
by mutualistic associations, leading to the
maintenance of traits favorable to popula—
tions and communities, but selectively dis—
advantageous to genetic carriers within
populations.

 

Concept

Growth For

Habitat

Honeostasi

Hman lnte
Innigratic
Intrinsic

Natural
Isolation

Law of th.

Limiting

Macrocons

MetabOIip

Microcom

“gratin;

 

 

 

 

Concept

Growth Form

Habitat

Homeostasis

Human Interference

Immigration

Intrinsic Rate of
Natural Increase

Isolation

Law of the Minimum

Limiting Factors

Macroconsumers

Metabolism

Microconsumers

Migration

TABLE 3.1 (Continued)

Definition

26

The shape of the arithmetic plots of the
population growth curves. May be J or
S-shaped.

 

The place where an organism normally lives.

The term generally applied to the tendency
for biological systems to resist change and
to remain in a state of equilibrium.

Human meddling and disruption of ecosystems.

One-way inward movement form of population
dispersal.

The growth rate of a population under ideal
or unlimited conditions.

Complete geographical separation from other
individuals or populations.

Liebigs law of the minimum: Under steady
state conditions the materials essential
for organism growth and reproduction avail—
able in amounts most closely approaching
the critical minimum needed will tend to
limit the organism.

Factor retarding the natural growth rate
of a population.

Phagotrophs (phago=to eat) heterotrophic .erwv
organisms, chiefly animals, which ingest

other organisms or particulate organic

matter.

The sum of the chemical reactions within
a cell or organism including the energy~
releasing breakdown of molecules and the
synthesis of complex molecules and new
protoplasm.

Saprotrophs osmotrophs, heterotrophic
organisms, chiefly fungi and bacteria,
which break down the complex compounds of
dead protoplasms, absorb some of the
decomposition products, and release by the
producers together with organic substances,
which may provide energy sources or which
may be inhibitory or stimulatory to other
biotic components of the ecosystem.

The periodic departure and return of indi—
vidual organisms from an area. A form of
population dispersal.

 

ma.

Modife
Mortal

Multid

Mutual

Natal:

Natur
Nutri
Nutri

0rgan

Paras

Parts

1’hys:

I’hya
.,. 1(Jill’s

I)‘iiiu

' Piiu

 

 

 

 

 

 

 

 

27

TABLE 3.1 (Continued)

Concept

Modifers

Mortality

Multidimensional Niche

Mutualism

Natality

Natural Balance
Nutrients
Nutrient Recycling

Organism

Parasitism

Pattern

Physical Control

Physical Environment

Physical Limiting Factors
Population

Population Dispersal

Definition

Factors limiting or restricting a process.

Equivalent to the "death rate" in human
demography. Refers to the death of indi—
viduals in the population more or less the
antithesis of natality.

A multidimensional space or hyper volume
within which the environment permits an
individual or species to survive
indefinitely.

A type of two-species population interac-
tion, in which growth, and survival of both

populations is benefited and neither can p
survive under natural conditions without
the other.

The inherent ability of a population to
increase. Natality is equivalent to the
"birth rate" in the terminology of human
population study. It is simply a broader
term covering the production of new indi—
viduals of any organism.

The dynamic equilibrium between the compo—
nents of an ecosystem with no external,
man made influences.

A substance usable in metabolism. v"'“‘“"

The continual passage and transformation
of nutrients through an ecosystem.

An individual living thing.
A symbiosis in which one organism benefits
at the expense of the other.

The structure that results from the distri-
bution of organisms in, and their inter—
action with, their environment.

The use of physical, non-biological means
to control a population.

The abiotic surroundings of an organism.

Factors of the physical environment which
limit a population's growth.

A collective group of organisms of the same
species occupying a particular space.

The movement of individuals or their dis—
seminules or propagules (seeds, spores,
larvae, etc.) into or out of the popula—
tion or population area.

um

Predat

Produc

(Print

Popul:

Pepul;
Repro.

2nd L

Sedin

SElec

Self

Seraj

.. SOci

Span
Spec

 

,4.

28

TABLE 3.1 (Continued)

Concept

Predation

Producers

(Primary) Produc tivi ty

Population Density

Population Structure
Reproductive Processes

2nd Law of Thermodynamics

Sedimentary Recycling

Selection Pressure

Self Regulation

Seral Stages

Social Attraction

Special Niche

Species

Definition

The feeding of freeliving organisms on
other organisms.

Autotrophic organisms, largely green
plants, which are able to manufacture food
or organic compounds from simple inorganic
substances.

The rate at which radiant energy is stored
by photosynthetic and chemosynthetic activ-
ity of producer organisms, in the form of
organic substances which can be used as
food materials.

Population size in relation to some unit
of space. Generally assayed and expressed
as the number of individuals, or the popu-
lation biomass, per unit area or volume.

The age distribution, stages and other
parameters which describe a population.

The processes involved in the replication
of organisms.

A natural process within a system that
begins in one state of equilibrium and ends
in another will go in the direction that
results in the largest entropy increase

for the system and environment.

The cycle of matter in which erosion, sedi—
mentation, mountain building, volcanic
activity, and biological transport are the
primary processes.

Factors within the environment which influ—
ence the natural selection of traits within
a population.

The control and regulation of a system
through feedback to maintain some equilib-
rium condition.

Each of the transitory communities or
developmental stages in the ecological
succession of an area.

Behavior resulting in the aggregation or
clumping of social populations.

An organisms microhabitat.

The largest unit of population within which
effective gene flow occurs or could occur.

Concept

Species

Success

Stabili

Standir

Survive

Territc

Total 1

Tmphi.
Tr0phi

Tmphi

 

 

Concept

Species Diversity

Succession

Stability

Standing States

Survival

Territoriality

Total Members

Trophic Levels

Trophic Niche

Trophic Structure

29

TABLE 3.1 (Continued)

Definition
The multiplicity of differences among
organisms.

The progressive change in the plant and
animal life of an area.

Resistance to change; the ability of a
system to restore equilibrium after being
disturbed.

Reserves of elements of compounds.

The continuation of existance, the
process of maintaining existance against
environmental pressures.

Any active mechanism that spaces individuals
or groups apart from one another.

The total number of individual organisms
within a population.

A nutritional level within the food chain.

The nutritional of food chain role and
position of an organism in the ecosystem.

The structure of the food web in an
ecosystem.

 

pc_ru_r

“I
concer
cancer
tions
3.2 cc
defini
lectu‘
conce
befor
tions
previ

Table

in t1

were

 

 

30

Lecture Content

Table 3.2 presents a summary of the lecture coverage of the
concepts subsumed under Ecosystem. For each of the ten lectures the
concepts covered, the method of treatment, examples, and the connec—
tions made between concepts are detailed. The first column in Table
3.2 contains the concepts presented in lecture. These concepts are
defined in Table 3.3 as they were used in the course. The set of
lecture Ecosystem concepts listed in Table 3.3 contains many of the
concepts defined in the Phase I content and several not presented
before. These new Ecosystem concepts are amalgamations or simplifica—
tions of the basic set of concepts. The relationship of these and
previously introduced concepts are contained in the definitions of
Table 3.3.

A discussion of the method of concept presentation is contained
in the second column of Table 3.2. The following presentation methods
were commonly used in lecture:

1) Definition; a concept definition was presented and several

examples provided.
2) Expanded Definition; a concept definition was presented
and discussed at length, with many examples provided.

3) Definition by example; a concept was defined by providing
a number of common examples——"water and silicon dioxide
(sand) molecules are common examples of inorganic molecules."

4) Review definition; definition restated often with restatement

of examples and addition of new examples.

5) Implicit definition; concept definition implicit in discussion

--the sun produces energy (therefore it acts as an energy

source).

 

the t

COME

able
lectn

in ti

 

31

6) Undefined; concept is undefined and is understanding presumed——
i.e., "energy flows into the ecosystem...," (without previously
providing a definition of energy).

7) Mentioned——a previously defined concept is referred to without
elaboration.

Examples mentioned in connection with the concepts are listed in
the third column of Table 3.2. Connections and interrelations between
concepts cited in lecture are contained in the fourth column.

The lectures were tape recorded and these tapes were made avail—
able for student use on the day after the lecture was presented. These
lecture tapes are, of course, identical in content to those presented

in the live lecture.

MHMHDHHUMWH ZH QNHZMHWVHMNnH “Hmmozoo WEHWMWOUMH

N c m. MH‘Hmudflhu

 

 

 

 

 

 

 

 

 

 

 

 

 

mmfisooaoz \/ a
235.30 m Mn N
memospoum mhm.n n. mamfinmmuoouofiz cowuficwmoa wovammxm mammOQEooom
.s
«on a m
mamfiamwuo nus m mHmEHe<
mumusvoum n.w W .msoo .wamssm cowuwmflmmm wmvcmmxm mhmfismaoo
T.1 .
o 8
mmasomaoa .mfiwflomwuo mwA musmam comma soauficflmon vopdmmxm mumoswoum
mmHmmemlmom Ammfiwfluo whosv
Eoum%moom mamfimxw mswfiab QOHufinHmom ouosmwOHm
Homomaooow .Hmfismaoo
.uooswoua .huflssssoo
mmwommw .GOHumasmom mnoumasz "uHoHHmEH woeswm< aOfluflsHmoQ Smfismwuo
uOHUooz "uHUflHmEH possmw< coaufisflwmm mmu<

 

.oum .mmHsE
madfimxofioa H .momuom

 

 

2 mamfiamwho .HmHHHDVm .wmmssm wEOfluflsfime 039 ofioomm
3
mowummm .mEmfiswmuo .mou4 Avowammxmv uoawooz coauwuflmmm hufludsfioo
mono .mmfioomm .meHaowho Eoou am museum SOfiUflanmQ coaumasmom

 

 

mHNWONEOUwV

.mHmESmaoo .mmmosvonm

 

 

 

 

 

.mmwoomm mono .uaosaon Awoumhosscov mamwuouma ....mo womoasou.
|fi>am wsfi>HHIaoz .mamfiammuo wafi>fialcoz mam mofiomnm :OHuHGHmoQ uuosm .3mw>om amum%moom
mZOHHomzzou mmqmz<xm HszH<MMH mHmmozoo
~5\m\oa "mama H mmmHomq ”onmmmm AmonHODMHmzH

 

MMDHUMA ZH mmHZMmMMM mHmMUZOU mEmHmeoom

N.m mflm<H

 

 

Anva—GWUFMOUV N I“ "HAHgH

 

3i

 

 

Emummwoom
mousom .BOHm wmcfiwmwcp kmumnm
muonoafioomw
.mumoswoua .mpmfismsoo Emuwhwoom ma maoxo
.moasumaoa .Eoumhmoom mamaflm coaufinfimmm macho Hmuumz

 

umuuoE mo 30am

mofisomHoE .Hflo

 

33

Emummmoom Hfiom .nmumz manemxm hp wonfimwo Houumz
owcowuocH
.oflcmwuo mamamxm so wonflmmn mmHSUmHoz

 

Homomfioomw .umo: .hwuoam
Hmofiamno .Emumzmooo
.hwumnm uswfia .muoamwofim

Eoummmoom GH 30am mamaflm

eoauaaamuo aueamaxm

hwumam mo 30Hm

 

Eouw>mooo "quHHaEH
.muoadwcoo .muoonvoum

Amwumam .umm:
Hmuasmao .usmaa "unuafleanv

 

hwumcm mo scam cam mamsmxm kn woafiwmn mousom hwuoam
Emumhmoom voESwm< cowufinfimmn nauseoufi>sm
mZOHHUMZZOo mmqmz<xm Hzmszmme mHmmozoo

 

Aemsaauaoov N.m mam<a

 

 

hh\OH\OH "UUWQ .N UHUHUMVH "ZOHMWWW ézohpbuuhkhvhflb
AVCIHEUCOUV Nun. Mnihmuawh

 

34

 

musumummEmH .30Hm hmumsm

 

 

 

 

.owopoum hwumcm .Esfiu HmoHEono
Inwaflsvm .COflmum>nou %whmam unwfiq voaflmmvcb mahH >wumsm
30am hmuosm .hwumdm Suumm mo manuoumaamH woaflwowcb ounuouomsoH
modem
HmoflEoso .mfimmsucxwouosm .ouo
coauoufiammm Amousom hwumsmv mumm
Acupudoov AGOHmHm>ooo xwuonmv moumuwhsonpmo mamamxm hp coauficfimmn owmuoum >wuonm
Amuowomsoooo
.mumESmcouv muoosuoum :Amumwoafioomnv was
cowumufimwom mamasmaoo mp mm: mwponm:
Esaunfiafisum :muoonwoum :owomso zwuocm:
.mmOH umm: .mocmfioflwmm kn monummo kwumam: ononm %n defiuwafimmn sowmuo>aoo kmuodm
nuumm
Asmumhwoomv mo ooanmn kwumcm :moamamm: ou wmumsvo
sueuaonmmm . . :uso amaziea uswaa: vagueness asaunamasem

 

mwumcm .mummomaooon
.mumfismaoo .mnmoswoum

 

30Hm zwumcm II: wmcfiwmvcb hummfiofiwwm
moanooaoz
unammso .asaunaaasem “seesaw ammom
zodowowmwm .3oam hwnmam ummm mamemxm ma nOHuflawwmo hwuoam

 

Enflunflaasvm .mumoswonm .mfimofiu
Iahmouonm .mvcom HmofiEoso

 

.moadooaoz owsowuo .Eoummmoom Aufiwfiav
Beam hwumcm cam mHmmem kn Gowuficfimmn monsom hwnmcm
monHUMZZOo mmumz<xm HZMZH¢MMH mHmmozoo
NN\0H\OH "some N musuoum "onmmmm q<onsopmsmzH

Asuscaunoov N.m mqm<s

hh\0nfi\o.fi

"P‘I-‘III III" Ill-Pin"!!! l.‘

u @annn

AuUMSFHHuUflHOMUV N o M qudepH

N. ”HSUUQH

“ZOE: mmnnhuv.

.h QECLI Fthhanth—EF

 

A%wumcmv mwaom
HmoflEmno .woanomaoz oaaowuo
Amuomoaaoomn .wumasmcoo

 

 

.mumonuoum "uonHmEHv cowufisflmmm aowumuamwmm
onSUoHoz ofismwuo
.oficmwuoaH .muooswoum moamamxm mamwuasz GOHuflnmen woodmaxm mwmmsuammouosm
Asowumuflammmv mumuufic .Noo

AnemonuahmOuocmv owououm

 

 

 

 

hwnoam .mvaom Hmuwamfio Asonuoov wcfi>fialaom EOHMV
wmasooaoz oflsmwuo .monm .on coauficfimon mucoEon ofiaowuocH
Asoam zwuoamv
mousom .mwwuoum swumcm
.mumfinwaoo .muooswoum
.mfimmnunhmouosm .mmaso . .
so Ioaoz ofinmwuonH .oflsmwno moamfimxm oHafiuHDZ noeuficwwon mwcom Hmofismfio
3
mEmHaowno .oum .mafimuoum
mvuom Hmofiamno .mumm .moumuwhnopumo
mumsnmcoo .muoonwoum “moaaaoxm maneuasz wmuammxm coaufiaflmom mmasooaoz aflcmwuo
ESEEEVE 5833mm
.aOHmHm>aoo %wumam
moasooaoz :uso poo:
oadmwuo .oousom hwumfim .aw ustH "nuumm:
muomomaoomo
.mumaswnoo .mHmUDVOHm :ummn mm umoa:
vcom Hmofifimso :mHuho uocz coauflcfiwmn Boa>mm Beam hwumam
monHomzzoo mmqmz<xm HszH<MMH mammozoo
~5\oa\oa “mung N musuomq "onmmmm A<onHoDMHmzH

AUGSGHUGOUV N.m mqm<H

 

 

wzo . Mun—Furvfwwrh £5qu a iii i l ‘
hh\OH\OH u UUNQ N WIN—JUUUH uZCFVVkV he‘s/ac n

Anvwnflufiuuoov N on MH‘HNAVh—u

 

6
3

 

 

wofisomaoz oflam Ho
.UHcmeocH .muomomaoomo
.muofiswcoo .muoosmoum

macho muonmmosm soauficfimoo 3mH>om

Auuuumzv
macho Hmwumumz

 

Auaoamonfi>cm waH>HHIaozv

 

 

 

 

:uHHQ: Goauaaflwmn onmnmmowwmm
Emuwhmoom AucmEdoufi>cm waw>fiaicozv
:umuwz: cowuficfimmn muonmwonufiq
Auaoeaoufi>cm waw>fiaicozv
:Hw<: defiufisfiwon ononmmofium
Ampsuamov cowm
lum>cou mwuonm .mumoswonm
.Boam hwnoem .maomo Hammond: II. mowuficfimmm Bmw>mm mumwomfiooom
Amusummov scam
|Ho>aoo hmumam .muoosvoum
.Boam kwumum .maoko Hmwuoumz II. :Ofiuwcflmma 3mH>mm muofidmaoo
macho mumuuoz
.mousom Acsmv %wuoam
.masomaoz oflammuo
.masooHoE aflaowuocH
.Boam hwuoam .mHoho Hoflumumz
Amusuamov mommuo>soo amuoam II coauflawwon Bmfl>om mumoavoum
Beam hmumam .maomu Hmwumumz
muonamosufia .muosmmouw>m AGOfluossm
. .mumsmmoeu< .mummonaooma II. was ousuosuumv wmnfimmpmm
muoESmaoo .muoosuoum 3oH>om aoumxmoom
mZOHHomzzoo magmz<xm HzmzH¢MMH mHmmozoo

 

NN\CH\QH “mama

N mhmuomfl "onmmMm A¢ZOHHUDMHmZH

Aemsaausoov N.m mam<s

 

WZOHHUWZZOU WMHHNEW HZNEENNH MFQECZCC
bh\hH\OH u UUND mu @HDUUCQ "ZOHWWWW .HG‘ZOHHUDMNHWZH
Ava-Jahihuvflnouv N on MHIHm—dvfi . .

 

_ ..

37

 

muoasmaeo «mumoewoum

 

 

Eeummmeom woeeflueoz muomemaoomn
GHNLQ poem Ufluflwmumm .uwumsmeuamm
Beam mwumem .mwpmcm uaoueae: .uwnmeuuenmum:
mumoevoum emfiem Umeflmowom muoESmseo
:oommmrbll.
mamawem‘ll muemam
wuesewcoo new: .mmoa use:
whoosweum .:oho>ficuwonllll ABmH>mmv Emuwmwn
mwuoem anew ‘II unmam: .voaofiummz aefluflaflmon Beam mwnmam

 

Aeneas eoom
ImHuHUfiHmfifiv mumoewoum
mammnuammouosm .mwumem

.ll Eoummfio mH> vchHusez

macho Hmuumz

 

 

 

 

335:
mflmonusmmouenm .muooevoum ...meo< ocflE< .umwnm vmeefiueoz moaeomaoz owcmwuo
meHeueHez oflaewuo
.mwmonuemmOuosm .muouewoum anemone:
AEMummmeomv ll: weaewuaoz ofidmwueaH
wmmmH mwumcm
.BeHm mwumam .Hmuumz
we mHomo .muueom mwumem
.mumueweum .mmadooaoz aflamwuo
.oflamwueaH .HmmomEequ
.umoawoum .uosemceo II. Emuwmfim mH> veafimowom Emummwoum
mueumuomaom AofinmeuueusmeaenoV
aOHmHm>Geo mwumam AUHzmouueuemeuesmv
Amm<v omoueum mwueem ..mme< Aofiseeuuoue<v
nefiumefinfioomu Houuoz mueoam compo coaufiefimon movemexm mnouswoum
mZOHHomzzou mmqmz<xm mzmzm<mmm mmmmuzoo

 

mh\na\oa "mama

m musuomq

uZOHmmmw A<ZOHHUDMHWZH

 

Aeusaauaoov ~.m mamas

 

 

NIP\~IF\CF Ilillr

AMUQJFHHUEOUV N a m "WHmJVH

 

 

mwwduoum mwumem
.mumaameoo .wHemo mound:
aomee>eeo .Beam mwuosmv

muemam somuu

 

 

 

 

 

 

 

Aumuumz afiamwueaH .oflamwuov aefiumaflafieomm
mwuoem .mpmoswonm .oouu woemem eefiuficwmen uwuwamfim Rowan:
amnesm .BeHm
mwuofim .moaeomaoz oHemwuo
moaeomaoz omemwuoaH
.eeflumeflnEooom sound: ii. woawmmm wfiwmauemmouonm
Amuoaemeeo .mnmoeweumv mmeH ummm
Beam mwumcm mHmEHG<
mwuoum mucmfim conflmmwcb musumuomEmH
Aaowmum>aoo
mwumcmu .mmmuoum mmumemv :ouoBfiCmu‘l Aa0flueea uuosmv
mwuocm .Bon mwnocm nuom‘luemam: eflmsu mwnmsm mm woeflmen damno weem
mango weom seamum>soo seem .
% .ooueem mwumem .weem Hooflfimso Amwmuoum mmuoeov meow Awmwumso coauo>wuomlv
maumo Hmuumz use: "xHoB ow ou mufloommo:
anew mwumem usmfla mm woefimma mamafim mwuoem
waem Hoowsenu mm<
mwnmsm mH< meaafimxm
whooevoum cam ma maufioflamefl wmaflmoo condom mwuocm
mueumummmmm Aaamfimmueafiov
mfimonuammeuefim .BeHm mwumcm
whenevoum muflm .eefiuoewouemm vmmwmmwep eeHmHeBGOU
mwuoem .weom Hoofiaoso .nuBeHw Hoofismfiu .cewmmeomfiw ou uHoHHmEH mmuoam

 

mwumam .Bon mwuoam
whooeuoum

mZOHHUMZZOU
NN\nH\OH "mumn

 

 

.ouo uwmumuwmnenumo
.eoom .msa
manage

Aeusaauaoov N.m mam<s

.SOHmmSUmHv eu ufiofiHmEH

woefimowe:
oMMHOum mwnmam

mHmMDZOO
uZOHmmMm A<ZOHHDDMHWZH

HZMZH¢MMH
m mueuoma

 

A—UWUEHUEOUV N um “Jug“.

 

 

39

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

.lwwmmMIdmmwmuomm wmn0fiucmz mummemmmmmm
:uamEeeuw>sm
deflumeuflm .m wefiwemno m GH ensue
II. we nocfimunwoz mm um>wq memeum: "aefiuwefimem wfimmumeoaom
Sammy 38“sz
Awumuewonmv OD wmfisumaez
whoseweoo emwouufiz woeowucmz owaowueam
...awouonm
Amumosvoumv umm .mumnvmaepumo Ammuumzv
muessmsoo mwflo< ecfls< wmeefluemz mofieoeaoz aflamwuo
Aamummwoom mmEOHuama

mumfiemeoo .mumuevoumv

 

 

Hound: oflaowuo

 

mHuomuflw uesluwofiamam

Beam mwumam

 

 

 

 

 

 

 

 
 

 

 

 

 

.mnmsemeoo .wnoespoum .Ii woeewuaez mmumam
AEeum moomv ueoseoufi>em
kuumz Aoflumsmeumow .eHeNoHomv
oHemwuosH .mmaeomaoz oHcmeo ..meEHe< .mmeem I moneuoum A:mpoo=veum
wumoewonm "Hmamsmxm onHuasz so monmweemowzv woefiwen unseemeou
Aanom Hoemv Asouwmmoomv
unmaaouw>em .mwuoam ...wuemam wemeBOHm .weumm
.oneomHoz omemwueem .ofieowho .mmwa< .muemfim eoouo venwumsasm coauoesm mumoewoum
monHQMZZOU mmqmz<xm mzmzm<mmm mmmozoo

 

ss\e~\OH "some

q mhduoma "ZOHmmmm A<ZOHHUDMHmZH

 

mewsaausoov N.m mamas

 

~I~I\ F0\(F :11:in

AuUQnJFHdnUHHOUV N In Muquhu

 

4O

 

 

 

ABeHm mwuocmv emoeeao
:eHmHeZHoo mwueem mgkmmm compasses moaowowmmm
mousem mwuoem
.mwuonm .cofloufimmmm mgtmmpw HoBQH umHeHHoo so @95me oaomu mwuenm

 

SOHumufimmmm .Hmuumz

ofiemwnoem .moueom mmuoem

 

 

 

 

 
 
 

 

 

 

.mwuoem .mflmeauemmeuonm muemam compo wmaefiuaoz muooeweum
Amofisumfioz ease Ho Aumuumzv
mweem Hmoflsesov ...o~m .Noo oneooHez
mwuwem managemenm oocmuommm eHammuoaH
seawaoaemm
Goamuo>eoo mmumem noes .uLwHHsem
mmaeomaez ofiemwuo mn< .mm< oufienomem .vmeflmmweb mwumnm
Asmumsmmumv cam .maa .ma<
someomao
modem HoowEmno omouosm .wmaeooaoz poem mauflowamam woousom mmuocm
wveom
amoesmfio .maeomaoz oflemwueem newcomao
coaumuammmm .vwo< oafiE< .mmeueem
mousom mwuoam muofihaoeumnmfieaez mononmmmm wmaeooaez eHemwuo
A oaoflofiwmmv aeflmum>ceo “mam
meadow mwumem .mwuoem
moaeumaoz oflemwueem .oflnmwuo ll. scammeemfia wmwcmdxm coaumufimmem
moueom Roam seam
aefiumufimmom .eowmxo .:v00m:
Aamummmeomv unoEeoHHBGM
.moasumaoz ofidmwuoeH .uwemwuo mamswem Hmaeaaoofluaez uncommon: sesameeo
mZOHHomzzoo mmamz<xm Hzmzm<mmm Hmmozoo

 

NN\HM\OH "mumn

m mHSuumA "ZOHmmmm A<ZOHHUDMHmZH

Awoscfiuaeov N.m mqm<H

 

 

PP\P\ Fr

ADCJGfiUEOUV NJm. HHNANH

 

muwufieufim

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Amamfiemwuov .m=o>umn .mBHuoewoummn umcfiwmwab AEmHemnuoEV
monsoon somnwomm .voeowueoz xomnwmem
mEeummm HmeoEuom

AmEmHemwuov mam wDOBHoZImHmEHe< woswmmwep mamfiemzomz

. .mEeuwmm Hmeoauom “muemam .wmeewuaoz owumumomSem
lame
.ueoEseuwBEm .eowuuewoummm wmnowueoz EmHHonmumE
.uemanuH>cm .Emwaonmumz wmeefiucmz coauosvoumom
l
.4 monHomzzoo mmqmz<xm HZMEH<HMH mmmmuzoo
NN\¢H\HH "moon n eunuoeq "onmmMm A<ZOHHUDMHmzH
image goes has smudge:
AEmHemmuov no UHunmnom ma HHmo .mmoamnm mm vow: Emfiufiwmuom
uemsaoufi>em
omaa .Asmflemmuov mammflup benefiuemz ueofieeufi>em
ueoaeeuHBem
omHH .Afimflamwuov mawmwun woaefiueoz Emflaonmumz
i3
.GOHuoeweummu mo names sowmmeomflw Annapomaozv
Asmfleowuov Hmofiseno mo em: wowemmxm .eOHuHeHmmw vowemmxm defiuonpoumom
mZOHHumzzoo mmqmz<xm Hzmzm<mmm mammozoo
BN\N\HH "moon 0 enuuooq "onmmMm AmzommobmmmzH

 

Qusaauaoov N .m mamas

.

 

“Ewanfiuuoov N I MN MTHMJV'H

 

 

 

 

memmfimwfli
.meHefifluw .Emficmmuov

.eefimmHEnnm .sOfimmonw< novuo enouu

 
 

 

 
 

 

 
 

 

 

 
   

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

vocflmwwep moemaflsoo
eonmou < Aomeo mom
.meassfiumv Emfiemmuo mmammww onEmm wocfimowd: cosmmflanem
mefimmwfinem Amoco mom
.meaeawumv Emflemwuo omemmon weeflmoa newmmmuww<
Auamseenfl>emv Emwem no Hana wefiuuom vmefimmn muoufiuuom
amass no mmeo mom Aamaflemv
.wsaeafium .uemaeoufi>em muoefiumem .wmcnmoq woeflmma ueeanom
gallant:
mDHDEHum .ufimficeHHBGM Amwncmmnv
Emflemwpo .xmammm .mHNmH wmcflmoo endomwmm
Asmaamae
uomusuv mmmammflw efismuseoo
mmeommmm oawuoom .Hmoflsono AHmuamEeeHH>emv
.Emwsmmuo .useEeeuH>em .Hmewfi> .muouflwem wmefimoo msasawum
mDHDEwum .Hefl>m£mm lilmmmmMwWmmI4Wwmmflwmwmlllllllmmwmmmmmwmm
9. iIIlilillmwmmmmmmiummmmmmwm
A. .Emflemwno .Eoummmoom mamowun woeowuemz Homemfiooom
some seven umoeweum
.Emwamwuo .Emummmoom mammwun pedoauemz “masmeoo
Home Eooom Hessmeoo
.Ewwemwuo .Emummmoom mammfiun wmaefiueoz Hoosweum
xooaeHxUfium
mefimm m .woum .nmsoumo
Hoummo .pumm emoemamu woeenmmmu usezufis soBHw
.HHSU wafinnom .MHnsme mofimsmxm .mfimmflnn wonfiuemz Ewfiemwno
mummomsoomo
.muosewaoo .mnoeemeou
.mnmeeveum .mfiwfidmwuo mamefiun woaefiuemz Emummmeom
monHomzzOo mmqmzmxm HzmzmmmMH mmmmozou
nm\HN\HH. “mumn w mueuumg

 

“ZOHmmMm A<ZOHHUDMHMZH

mewsnauaoov N.m mmm<e ,

"uh/PCP LLChFPl—F‘CL

 

 

hh.\wNm.fi.—.. "OUNQ

I‘ll]. -lllullilull

fivwafiulfiucouv N. um MNAQdVrH.

m ”HUMOUR.

u ZOHMWNW .HAVZOHHUDNH WZH

43

 

 

nos poem someone mom wfiu< muuom .mwfio< eafls<
Hmuumz we oaomu .mueoewoum macaw Hammom .umom
.mwueam .mflmosucmmeuonm wmumuvmfionumo meOHquZ measoeaez aflcmwuo
Beam mwuoem
.moemflowmmm .Houumz mo
macho .Eoummmoom .wmasumaoz
uwamwuo wan owemwweaH maumuumm BmHBmm mummemaoomn
nt uoom .BeHm mwueem
.monowofimmm .umuumz mo macho
Ewummmoem mmuo>Heuoo .mouo>wnumm Bmfi>mm muossmeou
Nmmwfiowmmm .Hmuumz mo oHomo
.mmfisomaea owcmwuoew
mam eflcmwue .moueem mwumao mummam ammuw
mwmonuammouonm .Emummmoom .mvom mafia .mowH< woeflmmo mumoevoum
nt poem .BeHm mwuomm
.aefiumuwmmou .ueoEeoufl>so
Hmoflmmzm .muowomfioomw

 

 

 

 

.muoEDmaoo .muooeveum odomo cemenufiz .mfiumo
.mEmflamwuo .measueaoz Gennmo .maomo cowmxo Bofi>mm
ofinmwuenw mam uaeomuo . . macho momma .weem wonBHeoz Houumz mo maumo

 

moamfioflmmm .udmecoufi>cm
Hmowwmnm .meueem mmumem

 

 

.mumnmwefin .Emummmeum Emummmeom mmafimmm Beam mmumum
souumz mo oaomu .nt poem
Beam mmuoem .mwumam mwmuoum Hmoflaeao voawmen
.Eouwmmoom .muooewoum Gem venefludoz moueem mwuoem
mnemommmoon

.wHoEemeoo .muoosveum
.oaomo umuumz .uemE
Iaeufl>em Hmowmmsm .umuumz

 

we maumo .BeHm mmuedm aefiufiafimmw popcmaxm
euofimmefim .ouusem mwumfim wdem BoH>mm Emuwmmeem
mZOHHomzzOo mmgmzmxm Hzmzm<mmm mHmmozoo
mm\wN\HH "memo m mueuomq "zommmMm AmonHoDmmmzH

 

Guseflnouv N .m mugs

 

WZOHHUMHZZOHV . WMH‘HEMH HZHEEMNH WHANHOZOU
hh\mM\I—nafl u wuNQ m MINDUUNR "ZOHWMNW QJVZOHHUDMIHWZH
Anmflufinflucoov N am MHJHMn<H . .

 

44

 

maomu
umuumz .measoeaoz ofiemwuo
new ofiemwueem .muosemeeo

mom
aefiumowfimeuuem

aeflmmsomfin wevsmuxm

 

 

 

 

 

 

 

 

.mumoeweum .mummemEooon moxoq .mweom wenflmon eeflwmooo=m
IIIIiIIIIIIIIIIIIIJmmmmNmmmm
.Boam mmumem .moasumae: owuouoom
eflcmmuoem use afiammuo wmflxwm flewouuwz :efiufiewmmo ufioflHmEH mfimefinfimm
moaoflofimmm .muoanmaoo
.muooepoum .Hmuuoz we ofiomo
.Boam mwuocm .Eeuwmwoum munmam Somme Bofi>em mfimosuammeuefim
wmaeomaoz omen Ream
was ownmwuo .muowomfieomn moueBfieumoafili woBeHBmm
.muooeveum .mquDmeoo mmuo>flnuo:.lii muemam veeflmmn neg woom
wueme Eooon defiumufl mom HeBoH ownmouu
.EmHHonmumz .Lmz poem suHB mwumam we mwoq
. muofismcoo . mHmmzuemmeuonm meet/«c.2341.
mueoecoum wouoBHnuo:.AI\ muemfim :ewuwcflmom ufloHHmEH mesoHUHmmm
IIIIIIIIIIIIIIIIImmmmNmmwm umwflm
mueosvoum HooHEenu moaOfluemz mwuoam
:eflumufimmom .Hmuumz me maomo Nz . moz mefieooaez
muouewoum Nov undefiueez ofiemmuoem
g , mg a g

 

mm\wm\HH "mama

“unassuaoov N .m mmmfi

m musuomq ”ZOmemm A<ZOHHUDMHmZH

 

WZOHHUMHZZOU mmqgm rts‘h-E<.HCE lllllllll
BB\M\NH UUNQ OH ”IN—JUUTJH uZOHWWMfiW NdeOHrHDDMNrNMZH

AuUQ-JHNHUNOUV Nam. MTHMH<yH

 

45

 

mwnmdm .aeflumasmom

fiuBeuw .moaumasmem .mwumam

mums Lumow\£upwm
.mmum .muflmcmv .mueflumaemem

mousem
mwmmam .suBeuw .chHumHSmom

muflmamw .SuBeHw .deflumasmem

meeflumaemem .muflmama

:uBeHw mm:
mwummo .nuBeHw .GOflumaemom

HHo
.mmw Houses: .Hmeo .cem

mHmEEmE .fimwm
.chem .mEOQMHm .memfism

HmHem .ofiuuomfim

.us\ooom\om amass

coauaaammn

coauaeamwn

mamamxm ma eewuficflmmn

coauaaamue nauaaasm

meoauaaamoa

soauacamwe uaoaaaem

:uBouo

meadow mwumam

Emfiamwuo

mwuocm

comm sumuo\nuuam

 

 

 

 

mou<
eewumuwHEo mflumuomn :efimmsomflw
defluMHwHEEH .mumu summv\£uufim .Hmanm .eemwflm .deSE wmvammxm .eefiuficflwmn muHmcoQ
.mHmEEmE .fimfim .mfimmuomp
wEmHGmwue .mmum .muwwdeo .Hmcem .eemem .cmesm BmH>mu .cewuficflmmn wGOHumHsmem
mZOHHomzzOo mmqmz<xm Hzmzmmmmm mammozoo
mm\m\NH some

 

 

Awesoaunoev N.m mmm<a

 

OH mumuomq "ZonmMm A<ZOHHUDMHmzH

 

COHCI

Age 3
Area

Auto

Beha

Bion
Bic

Bion

Biol
Bio:
Bio

Bit

Car
he
on

...Co1

 

 

 

46

TABLE 3.3

BS 202 ECOSYSTEM CONCEPT DEFINITIONS

(Keeton, 1973; Odum and Odum, 1976; Woolf, 1976)

Concept

Age Distribution
Area

Autotroph

Behavior

Biomagnification or
Biological Magnification

Biomass

Biome
Biosphere

Biotic Potential

Birth/Death Rate

Carrying Capacity

Chemical Bond

Climax

.Commensalism

Community

'Definitien

The relative distribution of age
cohorts within a population

The unit in which ecological inter-
action takes place.

An organism capable of manufactur—
ing organic compounds from inorganic
raw materials. A producer.

The response of an organism to its
biotic and physical environment.

Increasing concentration of rela-
tively stable chemicals as they are
passed up a food chain from initial
consumers to top predators.

The total weight or volume of all
the organisms, both plant and animal,
in a given area.

A major climax plant formation.

The part of the Earth in or on which
life can occur.

The maximum population growth rate
under ideal conditions.

The ratio of the number of births
or deaths divided by the number of
individuals in the population.

The maximum population that a given
environment can support indefinitely.

The energy or force uniting two or
more chemical atoms or molecules in
a molecular state.

A relatively stable stage reached in
some ecological successions.

A symbiosis in which one party bene-
fits and the other is neither
benefited nor harmed.

A unit composed of all the popula—
tions living in a given area.

 

@

Coin

Con

Con

Dec

Dec

EU

 

 

47

TABLE 3.3 (Continued)

Concept
Competition
Competitive Exclusion Principle

Consumer

Decomposer

Density (Population)
Density Dependent/
Independent Factors

Diversity

Ecosystem

Efficiency

Energy

Energy Conversion

EPEIgy Source

Energy Storage

Environmental Resistance

j2_

Definition

In ecology, utilization by two or
more individuals, or by two or more
populations, of the same limited
resource; an interaction where both
parties are harmed, or limited.

An ecological principle: Two species
cannot for long simultaneously occupy
the same niche in the same area.

An organism which feeds on other
organisms.

An organism responsible for the
breakdown of organic matter to
simpler forms.

The number of individuals per unit
area.

Factors which affect the growth of

a population, and are either related
or not related, respectively, to the
density of the population.

The condition of being different.
The multiplicity of differences
among organisms.

The sum total of physical features
and organisms occurring in a given
area, and their interrelationships
and interactions.

The ratio of the useful energy
delivered by a dynamic system to
the energy supplied to it.

A fundamental quantity, measuring
the ability to accomplish a process;
the capability to do work; the
ability to produce heat.

The transformation of one form of
energy to another (i.e., chemical
to heat).

A physical entity which produces or
stores energy in some form.

The maintenance of energy in a
single form (i.e., chemical energy
is stored within the ATP molecule).

The sum total of the environmental
limiting factors acting on a
population.

 

9pc
Equi

lint]

Evo
Flo

Foo

F0(

(in

 

 

48

TABLE 3.3 (Continued)

Concept Definition

Equilibrium A state of balance between opposing
forces.

Eutrophication The process in which body of water
becomes rich in nutrients but often
shallow and seasonally deficient in
dissolved oxygen.

Evolution Change in the genetic makeup of a

Flow of Energy

Food Chain

Food Web
Growth
Habitat

Hetertroph

Homeostasis

Immigration

Inorganic Molecule/Element
Life Cycle

Matter Cycle

. Metabolism

population with time.

The movement or passage of energy
through an ecosystem.

Sequence of organisms, including
producers, consumers, and decomposers,

through which energy and materials may

move in a community.

The complex interrelationships of
food chains.

The increase or expansion of a popu—
lation or community.

The kind of place where a given
organism normally lives.

An organism incapable of manufactur—
ing organic compounds from inorganic
raw materials; requiring organic nu—
trients from other sources; consumers
and decomposers.

The tendency of a system toward
maintenance of stability.

The movement of individuals into a
population area from other areas.

A chemical compound not based on
carbon.

The stages an organism passes through
between conception and death.

The movement or passage of matter
within an ecosystem.

The sum of the chemical reactions
within a cell (or a whole organism),
including the energy-releasing
breakdown of molecules and-the syn—
thesis ef complex molecules and new
protoplasm.

7?

Cone

Nutu

Nata
Natl

Nicl

Nut
Org
Org

Par

Phc

Phj

Poi

 

Concept

Mutualism

Natality

Natural Selection

Niche

Nutrient

Organic Molecule
Organism

Parasitism

Photosynthesis

Physical Environment
Population
Predation

Producer

Reproduction

Respiration

' Saprophyte

49

TABLE 3.3 (Continued)

Definition

A symbiosis in which both parties
benefit.

Birthrate.

Differential reproduction in nature,
leading to an increase in the fre—
quency of some genes or gene combin-
ations and to a decrease in the
frequency of others.

The functional role and position of
an organism in the ecosystem.

A substance usable in metabolism.

A chemical molecule containing carbon.
An individual living thing.

A symbiosis in which one party bene-
fits at the expense of the other.
Autotrophic synthesis of organic

materials in which the source of
energy is light.

The abiotic surroundings of an
organism.

A group of individuals belonging to
the same species.

The feeding of free—living organisms
on other organisms.

An organism responsible for the con—
struction of organic molecules from
inorganic matter, facilitating the
capture of energy within the
ecosystem.

The process by which organisms give
rise to offspring.

(1) The release of energy by oxidation of
fuel molecules.

(2) The taking in of O and release of

. 2
C02; breathing.
A heterotrophic plant or bacterium
that lives on dead organic material,
a decomposer.
9a”

Lona

Speci

Suoci

Ten

Ton

Tro‘

 

Concept

Species

Succession

Symbiosis

Temperature

Territory

Trophic Level

50

TABLE 3.3 (Continued)

Definition

The largest unit of population within
which effective gene flow occurs or
could occur.

The progressive change in the plant
and animal life of an area.

[Gk bios life]. The living together
of two organisms in an intimate
relationship.

The degree of hotness or coldness
of a physical object measured on
a definite scale.

A particular area defended by an
individual against intrusion by other
individuals, particularly of the

same species.

A nutritional level within the food
chain.

 

ass
by

ant
eas
uaI
cor
be

th

51

Small Assembly Session Content

The Ecosystem concepts covered in each of the nineteen small
assembly sessions are presented in Table 3.4. Each SAS was taught
by one of the three assistants assigned to the course. The assist—
ants were provided with an outline of the topics to be covered in
eash SAS, but the details of the session were left to the individ—
ual's initiative and teaching style. Consequently, there was a
considerable variation among concept coverage. Although we can
be certain that the concepts were covered, no parameter describing
the extent of that coverage can be assigned to the SASs. l
Tape Laboratory Content

A catalogue of the Ecosystem concepts presented in the prere—
corded tapes used in the individualized instructional sessions is
presented in Table 3.5. This listing was developed by analyzing
the scripts of each of the seventeen tapes and the objectives for
each of the individualized instructional sessions.
Textbook Reagipgs

The main textbook used for BS 202 was Keeton's Elements of
Biological Science (1973). Burnett's Zoology (1973) and Botany (1970)
were also used as supplementary resources. The Ecosystem concepts
covered in each of the ten weekly reading assignments are presented
in Table 3.6. No attempt was made to quantify the extent of concept

coverage in the reading assignments.

Practice Quiz Content

A practice quiz was made available to the students each week
several days before the official quiz. The practice quizzes were not

V a mandatory requirement of the course, but served as an exercise for

 

MZOHWWMMW NHEAUHWMAV Hgm NEH. 2H QMHNNWNVOU Mr—ethmCh/ncc EPUPUC‘N

N In MH.HMH<.H

 

 

52

   

Hoonwoom COHuoeweu mm
nauseouH>em

         

  

meme eHo>oQ emaum

 

 

 

 

 

 

             

 

 

 

 

Hoonmfim .seHuoeweHmmm .wueoemeum unmEmeHoBmQ HoEHe< mH a ¢H\HH
.uaoacepH>em Hmonmnm .mmnmcm acme eHmBmQ
.eOHuoeveumom .muomemaoomn .muooswoum can nuBouo uemHm OH a m\HH o .
mnemmHQ HmHsHHoo NH mmm
moHeomHoZ oHcmwuo .COHumusz pew oHuoemo .<zn w a
cOHuoeweu mm N\HH 0 HH m4m
.mEmHameo .HeHBmsem .aeHumHHmmmm wHMEEmE woo wvHHm m a N\HH
GOHuoeveH mm :
.mEmHammmo .HeH>m£mm .GeHumuHmmmm wfimummm Heummem H\HH a HM\OH m

mmmmmHQ was Eeuwam

Hmfieweou RN w oN\OH
mommmHQ mam fieumfi<

EmHamwuo .eeHumHHmwmm .uefismsoo

 
 

Emflfifl HO fiOHUNHH mQM Hwaflwfloo

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

HQEDmCOU mN w VN\OH
GOHuoewou om aoHomo e N m<m
oMHH .eeofieouH>:m HmonmLm .EwHomwuo moomumeoo woo muommem ON a aH\OH m o m<m
oHomo
mwuoem .BeHm mmumsm .mwumem .mmHsooHoE
eHnmwMoeH mam oHemeo .mHmonuamweuosm mHmmSucmmouefim wH w mH\OH m m m<m
oHo o Hound: Bon seem Eouwmmoom
.wuoosmeum .EmHemwno .muowomfieoom .mHmESmnoo mHHH wHon MH a NH\OH N a m<m
aeHunHHHsmm mmHH we mHmmm Hoonmam HH a 0H\OH N m mum
GOHuoewoumom nouoz wdem eH
.eOHumww< .mHmoumomEom .eeHumuHmwmm mEmHaowuo waHmmHmwMHo
.EwHHopouoz .Hmsemeeo .Hoosweum .Echmmuo moamHom\mMHH mo oueumz o a m\OH H N m<w
II. weHnommm NweHOHm
mHMuamEon we mHewoz q a M\OH H H m<m
moHoBOU wumeodoo GOHumHnowmn AmmmHv wwumn xmez eOHmmom
Smummmoum HMdOHuoeuumeH

 

mZonmmm WHmEMmm< HH<Em mmfi ZH QMMM>OO mHmmozoo EHHmNmoom

¢.m.MHm<H

Wiilliii . i
EUUMKAHOUMH RNCC$¥E:¥CNIF

AENSIHHGOUV Q .m HHNAVH

 

 

 

53

 

 

 

 

 

 

 

.mEmHemmHo

.mmHommm .wneuomm oHuOHm .ucmaeouHBGm

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Hmonmnm .mamsemeoo .mmeoevoum .nuBouu unmfimwmemz ucmHm w a N\NH OH mH m<m
gel

.muoaeweou .mumoewoum .SuBouw GOHuanmom mam woem o a m\NH 0H wH mmm
IllllllmmwmmmmmwmthWuooeweum .mumESmeoo
.ueeaaoHH>em Hmonmnm .muouomm UHHOHm

.eeHmmeoosm .GOHuoeHmHumHuw .Eouwmwoom mmeHeom H\NH a OM\HH m NH m<m

111mmmmfilemwmnB0Hm

wumusvoum .mmHsumHoE eHameoeH .UHcmmuo muHHmeo HH< wee umumz mN a wN\HH m 0H m<m
nauseouH>em

Hmonmfim .ueHBmsom .muooeweum u0H>m£mm HmEHa< NN a HN\HH m mH m<m

woueBoo mu mosoo eOHumHHomoD AnmmHv momma Moez =0Hmmom
Emummmoum HmceHuoauummH

 

$055308 a .m mamas

ENUC>WOUN I I
“MOM—wgoaq Hugh... ”in... ZH hum—”Hzmmmmm WHmMHHUh/HOO EHWMWOUH

n I M; uihmdwhu

 

 

A.
5

 

NmeHHo .eEOHm .mwneem .mHmmsuemmouenm .Hm>mq oHsmoum .mHomo
Hound: .eeHmmeeoem .GOHueHOBm .mummomEooom .mumfiemeoo

.mumoswoum .aoHuoHemom .muHasEEoo .oEOHm .EwHumnmonmwm .EmHu

IHmonm .eOHumweum .EmHHMmemsEoo .GOHuHummEoo .amHHmeueE
.BeHm mwuodm .mmoEOHm .suBouw .ucmE:0HH>em Hmonmfim
.mosmHemem .nmz use chno weom .mnon .uManmm .Emummmoom

 

 

 

 

 

 

 

  
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

H swoaoom m 3 mg
eeHummmmmmwM .muooevoum muemHm weHuoBeHm a mH mm<m
uamfifleufl>cm
HMUHmmnm .eeHuoewoummm .umanmm .mumfismaeo .wumeeweum N EOHuonmowmmm m «H mmmm
a .398 83 .amumsmoom .umfigoum H 353 a S. as;
GOHumHH mom moueom “mam mHmoumomEom EmHHonmuoz
.suBouu .mnoasmeoo .muooewoom .coHuoemoummm .oHomo mmHg H eOHuosmoumem o NH mmmm
uamseeuH>am Hmonmcm
.moHeomHez QHemeeaH new oHammuo .suBouo .mummomfieooo wuomomsoomn 0 HH mm<H
mwmueum mmuoem duoH>m£om .mHomu umuumz
.BeHm mwumem .ueeseouH>em Hoonmsm .GOHumHHmmom UHnoyemn< N muessmeou m OH mm<m
mummNWMMMldmmmmmmwuo .muesswCOQ N mmumvu0£o m m mm<m
eOHuoeuon em. mHmMumeoEom EmHHonmuo:
.meeoeHoz oHameoeH wee oHemwuo .aoHueuHmmom .ueoseeMH>em
Hmonmnm .oHumnmonmmm .oHnmequHom .oHuHmoumm .muoEDmeou museumcou q w mm<m
mamwmmwwo .mHmESmeeo mmumwuono q n mm<m
pea woom .meeoHonwm .oumsmmeHm .BOHm mwwonm
.moHeooHoE oHemwuosm wee oHemeo .mmumnm .mHmmcuemweuonm
.Emuwmmoom .oHomu Heuumz .ueoaaosH>em Hoonmnm .muoosweum mueoscoum m o mm<H
oceanouH>em
Hmonmnm .mHomu mmHH .mumanom .EmHamwuo .muoeemcoo mwomomzuu< m m mm<m
mammmmwmm .muofiemaoo .mnmonweum wHHoo N a mm<m
mfiuouoaHnom
mEchmwuo .muofismeoo muHHmne< .wmeHHoE N m mm<m
.<.z ammomouon mam H H mad.
wentoo mumwwmmu ceHumHnowmn x003 GOHmmem
Emummmeom HmeeHuoeuumaH

 

VMOH<Mom<H mm<H mmH ZH QMHZMmmmm mHmmozoo ZMHmMmOUm

\1 o) 1.11.1111

 

AnvMIJ-HHHUNHOUV WIN MNHMHAVHH

 

 

55

 

 

eeHumHemom .wummommmwna «mamasmdoo fimuooevoum .muouomm

ueoweomowem wee ueomeomoa muHmamn .uaoaeouHBGM Hmonmzm
.munsom .oHomo House: .BOHm mwsoem .meoz woom .maHmno

woom .cOHumoHMHuwwEOHm .HOHanem .nuBeno .muHommmo

 

 

 

 

weHmHHmo .moeoumHmom HousmanonH>em .HdHueouem oHuon N mw0Hoom OH NH mm<H
eOHusHQBm
.ummomfiooon .Hoaemeoo .Hmoewoum .Echmeo .mHommm BmH>em EmHemwuo OH mH Mm<m
wouo>oo mumooaoo eoHumHHomma Moez eonmmm
Eoummmeom HoueHuosuumcH

 

Gazaaunoov mam .

magmas

EUUQPWOUW MENU-uh. C‘Eitfiihfi iihnxiin

 

 

 

WUZFCAVIL.“ MEN-“B hHuflyH. ZH CkZFAVrFV-CL WHHHMNUZOU Erflmhfimoom
win HNQ¢H

 

 

iiillillliiliiiiliiiiiii

mumoewowm .wuoaemmoo

.eHomu mmHH .¢0Huoeweummm .mHmHocHHm GOHwnHoxm e>HuHueumEoo

.uouHQmm .mnon .EmHHmsusz .EmHHmmeoEEeo .mHmOHnEmm .mousom

mwuoem .muHHmHHouHHHmH .HOH>m:om .GOHuHummEoo .sowummoum 0H
.EmHuHmmHom .muHommwu meHmHumu .muouomm wsHuHEHH ucewemmoo ommlwwm
muHmemo mam ueovcemowemlmuHmemn .moemumHmmm HouamEeeuH>am mlq mHINH lNoq

.HMHuemuom oHuOHm .muom Summo\nuHHm .muHmceo .cOHuoHemom qu

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

nOmeoooem uHmHe>Hn ueoEGoHH>nm Hmon Lm
.muamHUHmwm .Hw>oH oHsmouH .QOHuoeweHmmm .oEeHm .meHHo .GOHumo
IHnmeHuem .muomemfieuen .mumEchoo .mpmeswenm .Hmz weem .eHmso m
weom .mwumsm .oHomo Hmuuoz .BOHm mwnmem .mumammOHm .meHeooHoE mHHIoOH quleq
oHemeoeH mew oHemwwo .muHaSEEoo .deHumHeeem .Emummmoom molom NoqtNmm
mHoo m eoHunHo>m meHsomHeS oHem no can oHem HoeH .Il
.EmHsmwno .uceeeouHBam Hmonmnm .H0H>m:om .muosemeoo 1!. quleN w
mumoswoum .ueoeeouHBsm mOHIHOH oQMIqu N
Hmonmam .mwumem .mouzom mmnmem .mmESmeou .50Huosvoumom ll. mmlmq omHHmmHlllllll
EmHHmnusz .mHmOHnEmm .wuomeNEMWoQ .mHsooHoz mmHimoH
oHemeoaH .uamEGoHH>em Hoonmsm .muueom mmumsm .oHeooHoZ II. quwN HQMINNm o
UHGNwHo .mHoEDmeou .mumosvonm .mwueam .EmHammuo .GOHuoemeueom mlemmN
5 no: poem .COHusHo>m oNINw II. HMNIoNN m
.uemEGoHHBGm Hmonmsm .HeH>w:em .mnosewaeo .muooewoum wONiooN
ueeaaouH>em Hmonmnm «pom woom .Hooewoum .Hefiemeoo NfioHumHHm ll HoHlmmH.oNHiaHH q
loom .mmuoem .BOHm mwuoem .oneuoHoz UHemeo was oHemmHoeH Hwimo mHHINOH.¢miHm
BOHmrwmeem .oHomo Houumz .mmHeo NmHINNH
IoHoz UHnmeeeH .Hosemeeo .nmeauonmuom .smouuou:< .uemHHuez II. mHHIwHH.HOHImm m
.mwououm mwuoem .muuaom mwumdm .mHmoauemmouefim .muoonuOHm colmq qwin.NoImm
mueaswaeo .meesusmwOuOSm .muooeweum .II ooINo.om|cq N
.mwuoem .owmueum mwuoem .moHeooHoz oHcmeo new oHcmmuoam qqtoq welmm.Hm|MH
unmeaeuH>um
Hmonmsm .muomomfieomo .muofiemeoo .wnooewoum .oHuHmmHmm
.wHoso 83 .33 fiamfififim .coEoseoEom .fiHmSa mmuem 2in $13 :TH H
.meHHMHemom .oHommm .EmHemwuo .GOHuomHom Housumz .eeHusHo>m
pmueBou mumwwsoo wudAddM1IiJwmwwdflllllllllldflflflflMfllMMMfli
Emuwmmeom "mmwmm nemEewwa< wememm

 

 

 

mUZHQ<mm MoomHNMH MHMMMS WEB ZH QMZH<HZOU mHmmozoo ZMHWWMOUM

 

 

they
of H
pm

the

OCCU

list

cour
ecos
aPPE
list
cone

logu

whet
res;
Spoi
Nun

P08

. . Corn
are

Hat

 

57

those students who elected to use them. The content covered in each
of the nine practice quizzes is catalogued in Table 3.7. All the
practice quizzes were available for student review in preparation for
the final exam.

In each of the practice quizzes all the ecosystem concepts which
occurred in the questions and in fill of the possible responses are
listed in Table 3.7.

3.2 Quiz Content and Concept Mastery Assessment

Table 3.8 contains the results.of the content analysis of the
course quizzes and the final exam. 'Each test item was examined for
ecosystem content and catalogued accordingly. Only those concepts which
appeared in the body of the question and in the correct response are
listed in Table 3.8. Many test items included additional ecosystem
concepts in the incorrect responses, but these concepts were not cata—
logued. If a student answered an item correctly, then it may be as—
sumed that either the student has some understanding of the concepts
in the body of question and the correct response, or made a good guess.
If an incorrect response was chosen, it is not possible to ascertain
whether the student did not understand the concepts in the correct
response, did not understand one or more of the concepts in the re—
sponse chosen, did not understand the concepts in the body of the
question, or did not understand some combination of these three
possibilities.

Table 3.8 lists only these test items which contained ecosystem
concepts. For each test item the concepts covered and examples used
are cited. Under the Comments column the possible methods or combi—
nation ef methods employed by students to answer the question are

listed. For instance in the quiz item below (item 11 of Quiz 10),

 

m

 

58

TABLE3 .7

ECOSYSTEM CONCEPTS COVERED IN THE WEEKLY
PRACTICE.QUIZZES

 

Practice
Quiz Ecosystem Concepts Covered'

 

 

Population, Species,Community, Ecosystem, Biosphere,
l Organisms, Energy Source, Physical Environment, Energy

Flow, Reproduction, Behavior, Producers, Censumers

Organic and Inorganic Molecules, Organism, Decomposer,

 

 

2 Energy, Physical Environment, Energy Storage, Producer,
Consumer
Energy, Energy Source, Inorganic and Organic Molecules,
3 Energy Flow, Photosynthesis, Physical Environment,

Producer, Consumer, Respiration

Holotroph, Organism, Producer, Consumer, Decomposer,

4 Organic and Inorganic Molecules, Nutrients, Photosynthesis,
Energy Source, Physical Environment, Respiration
Respiration, Energy Storage, Efficiency, Organic and

5 Inorganic Molecules, Energy Flow, Photosynthesis, Consumers,
Producers, Physical Environment
Organic and Inorganic Molecules, Energy Storage, Evolution,

 

 

 

 

 

 

 

 

6 Producers, Consumers, Reproduction, Photosynthesis,
Decomposer, Organism, Biosphere, Autotroph, Heterotroph
7 Reproduction, Consumer, Physical Environment, Organic and

Inoganic Molecules, Producer, Habitat
Behavior, Physical Environment, Matter Cycle, Organic and
Inorganic Molecules, Producers, Consumers, Decemposers,

9 Respiration, Eutrophication, Nutrient, Ecosystem, Food Web,
Biomass, Energy Flow, Competition, Commensalism, Mutualism,
Symbiosis, Parasitism, Trophic Level, Biome, Niche,
Succession, Climax, Diversity, Community, Reproduction
Birth/Death Rate, Age Distribution, Immigration, Growth,
Population, Density, Life Cycle, Biotic Potential,

10 Environmental Resistance, Behavior, Density Independent
and Dependent Factors, Physical Environment, Predation,
Competition, Food Chain, Producers, Consumers, Decemposers,
Biomagnification, Biotic Potential, Natality, Energy
SourceJ Organism

 

 

WHAHMHOZOU EMHWNWOUH EHHB WEHH NHDO

mom MHJHNJGVMH

 

 

iiiiiiliiiiiiliiliilil

 

 

 

 

 

 

 

 

 

 

 

 

 

 

, ZmHZ<OMO
omen: Soon uoouuooeH\uomHHoo wcHNHewoomm mmommomm
masses onémo
eOHuHaHmow o meHmHmm< mmmm<z 0Hz<omozm ooo
mmmZszou
mmmmomzoomo
mMWODQOmm
mason uo meHmmem me ommme uomuueoeH weHNHemeomm MMmmmmOHm
mmHam:0HumHoHHmuaH weHNHewooom ZMHmwmoom
.fiOHUHEwafi m wfiHHAHQan 30gb“ VUMMZW woo
mwmmmrsmou poopuooeH waHNwmwmwMMii mHDomHoE omz<omo
.GOHuHusoo o wnHmHmm< , MMmomEoumo moo
HZMHMHDZ
Mmubmomm
ZmHz<Umo
EMHmwmoom
aeHuHeHmmv m meNHewoowm MHowo MMHH<E ooo
. 832ng
onmeolaoz weHNHewooom .mHmEmNm so weHNHemeomm muemHm compo mmmopoomm moo
mMWmomZOUmQ
wuumm ueoGemEoo weHNHsmoomm mMMZDwzoo
wMMDDQOMm
ZHHmeoom
:oHanHmov m weHmHmm< MHowo mmmm<z coo
eoHuHaHmmw uomppooeH cm wdHnHMMMdmm Houumz MHDQMHOZ UHz<omo
oHemwuo Noo
mHmmem do weHuHsmeoom o MHowo mmmm<z moo
so
onmeo so wdHNHewoooH He aeHuHaHmoo e weHmHmm< o m ZWMMWWWWW Hoo H
mucosfioo meHmmmmm‘ mumwoeoo EmuH NHDO

NHno

 

mmoH HHmm
mfimmozoo EMHmeoom EHHB mZMHH NHDO

COflU HCHN 0“. N Nufixnafi IAIN
muU .

 

EN .. CENnrk

 

 

 

AnufludfluuuUuhnvan mi NH MNJHMHNIH

 

 

 

 

 

 

mmeoeHo mo

MU<MOHm womm2m

 

 

 

 

 

 

 

 

 

 

 

 

 

 

GOHuHeHmmo m we HHmomm mmumem HmoHano BOHm wommzm ooo
neHH
EeHmmMHem .EeHoem
fimwouqu .Heo
Imosmmecm HemHem
emwmxo
newenomm .eonumo
aoHanHmue m waHsHaam Ham :83 £8on 3.5030: 325% moo
omOHeHHoo
.moHom oeHE<
£33m 688% momma: onaomozH
mGOHuHchmo waHmHmo< one HHooom emmoomHo mHDQMHoz 0Hz<omo woo
oONHmNo
.00 N a
o m . mz
aoEHaEme m @123. sowzm Nos 3830: 3398 N8
momma
.Hmmem .umm
moumuomzonumo
eHououm manomqoz omz<omozH
AHHmoomo 53333 a. $1ng 3:09:00 .3830: 35.30 Hoo
AHHmommv neHuHaHmmw m eHNH: comm mZmHz<omo HNo
AHHmoemv mmwawmmWIlill
do meHNHewoooH Ho aOHuHeHmmo m wsHmHmm< ZmHHom<mmz mHo
EmHzmomo
omen: Ehmu uoeuuoouH\uomHHoo meHNHewooom mmoooomm
maoomqoz omz<omo
neHquHmmm m wchHmm< HZMHMHDZ wHo
muaoEEoo . V moH

 

 

AVNQEHUEOUV mu m. NHMIVH

““50

 

 

61

 

mswfimufi>

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

mvwo¢ muumm 30am Mummzm
mwwo< oaHE< mqowo mmHH<z
mmamwoafigm Ho moauwcwmww mo sowumofiaaa< mmumuwhnonpmo MMODQOMm
mcowuwawmww mo Hanumm mucmam MMZszoo moo
summwmmmnmmmmmww
AwmwvvanMIcHoE
:mHMInmfim
wownxowy
mHmmem am wfiwuwawouwm umeIuawvnum
coaufldflmww m wdfikamm< .HHmowm EmwuksmoummM MMZszoo Hoo ¢
umuuflq
“mumscmwum
HmflnumwuuwH
.memew am maflmamm< ‘ wcwumz
Emficmmuo
mamfimxw cm waHHHmomm owmwuwam H<HHm<m “No
ucmam
maflamasm unwecouw>cm
memew :m wcfimamuwm .UQH3 .wom A<onwmm mHo
waafiocwua Ho sowufiaawmw m mswmammflllllmmmmwlflflmmmmw
35533wa mama—gm am mo 33% N8 mHmmmazwwoeomm woo
mwmwfiuczwouOJm
wdoaufldfimww uomuuoucfi can uowuuoo mo Hamowm wmumuvhaonumo
.wamfimxm am we HHmuwm cmwouwzm mqowo MMHH<E moo
wHQwaw aw mo HHmuom mwumuw%£onumo
.GOfiumeu m mo Hamumm Hahsmouoano
.¢0fluflcfimww m Hamowm wmwa< mqowo mmwedz moo
mGOHumeu we wmfiumm m wo Hamowm
.uoflwfimmw m mo 5383?? .339” 30am wommzm moo
muamwm mm< mapumqoz on<omo
coauwawwww m wafikaaa< o z oo mu¢moem vamzm
magmaOHuwHou m we Hamoom hmuwam umflom zogm wQMmzm Hoo m
wunwafioo \JIIIAWWWflEMMWID\w Ktmmmmmmmmr\w EwuH NHJml

 

 

 

me ENNN WU QUHHOU ENUH NHSO
NfiSO

m HawEOU

Amuwnuflflufloov m.m W‘HMJVH

 

 

62

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

mmfimmmmw\wauuooaH wmwmwmwmwwm oHo>Hnuwm
:Oflufidfiwww m mcH>HQQ¢ .wHameo am wafiHHmowm HwamocmmMHo mmon NNO
comeCHva m wafihammd nowum>wam MSOHm
ammEoE
memew cm wnflaamuwm .uw oooH ZOHmmmooDm HNo
ummuom
msosvflumw MEOHm
83233 m @3183 .mfifiuwoomm 33353 283825 0.8
mmz Doom
wozmHUHmmm
Gowuwcwwov m wcwhfimm< 30am wwmmzm mHo
Goaufiafimww w wafihamm¢{l‘ ©m0H ZOHH<Qmmm
awstOHumeu m wawuwcwouwm van aooumm mmz moon mac
wH mew Ho coaufiawmwu uuwuuoocfi am :HNH: comm mmz Doom ofio
doauwnfiwmu m wawhammm wdw mafiawmomm mmz moon mac
so Lwcw-w \MJfimmmmmmflwam wcwfimmowm mm: moon qdo
coguHGfiHMWIMImeNwmmmenm mwflammuwm mxmg ZOHmmmuopm mac
dowuaawmww m meflmmowm sowumuwfi onusm onmmmooDm Ndo
CCMLTCwmww m wfiH>Hmm< HZMZZOMH>ZM UHHOHm
wamamxm aw mafiaamuwm HzmzzomH>Zm q<onwmm
GOHufiaHmww uumuuoocw aw wawnwcmoomm mHmmmszwOHomm
30am wQMmzm
EOHuHaHmwv uuwuuoo m wawuwawoowm mqowo MMHH<Z 0H0
:flwuopm
aoflflaflww m maifiam BE mcflflmuom Swofifiz mapumqoz 3598 moo
coauficwmwv m wafiHHmomM moo mHmmmHZWmOHomm moo
cofiufiaflww m mafiamomm xumnmaxuflm 3m: EHQEOHHmmmH
ImoH><mmm Noo
:oauwafimwv m waHHHmowm ucmHm mmoz mqowo mmHA “No
coauficfimww m mnfizaan< wmuumcwm .meH
39.55 Em mfidmowm 26: .Eom ezmzzomHEm 12835 m8
wamfimxm am mnflaamuwm waom mmz moon
.coaufiswwww m wcwmaam< mmwfi< cmmhu mMmoDQomm «No

mucwafiou

 

mmHmamxm

 

 

NHSO

uHOflUHd—flmwwu .fi wuHHuanuafic‘

 

WIUIAHIIIHWEHSU MMH. Nunmn

humafiflusoov m o m MHIHMHAVHH

ZmHmmNUUDm
§GOU EQUH Nun-00
. Nuuaflv

 

 

63

 

 

UCC T— :VCCU

 

 

 

 
 

 

 

 

 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

wwaouo
.mwuo>flcwmo
.COHHNUflwHG
Immz .:Ofluwwwwm onH<gDmom
.muouomm udw wHHA<HmOHHMMMH
mmHmmewlcoa cum memmew waflwfiawoowm locmmww zuflmdwn IMOH><mmm NHo
. mufimx4mmmmmmwumm
uoa>mnmm
coauwdwwww m wnwhHaa< coaufluwmaoo mMOHu<m
mmaafimxwlcoa wcm mmHamew maHNHawoowm coaumwwum Hzmmzmmmm WHHmzmm HHo
(mmwmmmmflmwwuuoocfi\uuwuuou m awuwa comm wHHAHm<Hm\mHzomo
coauflaflmwv m wcflhamm< .smmuw m mcfluwuaHMuaH onH<Abmom 0H0
\tJMwmmmmM!mmeHouaH\uumuuoo m wmwmwmwmmwmw
832va a mug??? .gmmuw m 339:3qu 33:5 oszmmg ooo
ma mew uuwuuouca am mamas comm m>uau wHHQHm<Hm\mHzomo
.50Huficfiwmv m waflzaam< .nmmuw m wnfluoumpwucH :uaouw amass onH<Abmom noo
\wwmmmmwxwuwauooafi cm wdflummwmwwm WHHAHm<Hm\mHSOMO
.cOfluflcfimmv m wnwhamm< .fimwuw m wnfluwumuounH ZOHH<ADmom ooo
. ANEMMMImwwMuouafi am wmwmwmwmwwMIll{k
.smmuw m wswuwumuwuaH .aOHuflnflwwv m mcfl%aqm< wHHo<m<o oszmm<o moo
flow+wCHmww m mafimflam< ZOHHDmHMHwHQ mo<
£2593 5 waflamomm 39% 3583 205338 coo
MH<M mHmHm
.wamfimxm cm waHHHmowm mawadm ZOHH<ADmom Noo
onH<ADmom OH
defiuflnflmmw m waflmamm< NMOHHMMMH .WHHmzmn Hoo
mwmwm A<mmmmmHQ
GOHUHnfimmw m wcwkaaa¢ .wamfimxw mm wGHHHmuwm coawauwumz wmmzbmzoo wNo
sofldflgmg “$5 32:5
.wmflnmaowumawu wcfinfimwa .uumsu m wawuwumkwudH cam muamHm mmz Doom mNo
Goauwaflwow m wawhamm< unmso mHmEHa< ZOHH¢ADmom
.mmfismaOflumeu wswcflmwm .uuMAU m wafiumumumuaH wan mucmam mma noon «No
WHHZDEZOU mwo m
flOHuHuswv m wnfihamn< onmmmooDm

 

 

 

mufiwagoo

a

 

 

{I‘l‘llllll‘n \

AMkun-GHUHHOUV m 0 Mn “\HdeH

NHSO

 

 

64

 

 

aOHuflaflwmo m oaflHHmowm

 

 

 

 

 

 

 

 

 

 

 

 

 

 

mmmmooZoomo omo
\Jlllmmwmfi‘amouwlmsfio
NHmGOS
wagmxw cm wafiuficwoomm ownmouuoufiw mmmopoomm mmo
cofiufiafiwwo m saw“: comm zmHa<oHoz .zmHz<omo oNo
omm .Noo
acouflcflwwo m waflmaoom can mafiaamomm mwoosao mo<Mon Monmzm Nao
Noo onH<mHmmmm
own .No mHommszmoHomm
maoflufiafimwo wcfizfioom ocm waflfiamomm wmoosao mmeaoz on<omozH oHo
mnwoa<
. ‘ aoflum wozmHonmm
muofiufiafimmo mafizaoom cam wsflaamumm -HHommm oflpouw< mo<moam wommzm moo
om: oneomHmmzH
aowumamm awwouoom mmaa<z on<omozH
nowuficfiwmo m wcflfiamomm ammoxo mqoomgoz on<omo qoo
onH<mHmmmm
uofiuoaonuoufiz Zoom wommzm
mowuflaflwww m wGHHHmowm manomqoz UHz<omo moo
:OHUHafiowmlmnwmwwmmmmw>\
.memew am wcfiuwcwoowm wozmHonmm Hoo
...m&3mm
...wcޣoub mwm
. . . mwxmfim
...mxumnm
...mH0ummHHH<
.maoHUHafiowo mo Hamomm wusumngEwa HZmzzomH>Zm q<onwmm “No

 

wUfiUESOO memn—mvmm

 

 

AVUSCHUCOUV w - mu Managua.

 

65

 

NMHQGO

umaow .Hmums

wHQwaw am wdwaamuom mafiaamm .mmm
HMHSum: .HHo
scammflw ucowmnm

 

 

 

 

 

 

 

 

 
 

 

 

 

 

 

 

 

 

304m wommzm mwo
30mm mwfioflwmm onnEoEHzooz
mansaom umm ZMHmwmoom HNo
wwflUHummm
memem cm msHHHmuwm :mE amwm mmz aoom
.Goauwcfimwv m w¢«%ama< mBOGSHE mme< ZMHmwwoum ONO
coauflufimww m usfizamm¢ wHHAHm<Hm\mHzomw
ZOHH¢ADmom
waaamxw.am wcflaamowm dmfism MH¢M MH<MQ mao
ma Smxm cm afiaamowm smasm wHHAHm<Hm\mHzomo
onH<ADmom oao
lewNEWMWImMImmWHHmomM amaam wHHAHm<Hm\mH30mo
onH<ADmom mHo
JIIWMNmmmmlmmMszwm
mucumkum mo xumq wHHAHm<Hw\mazomo
wmmwuuafl onH<ADmom
mHmmew am MSHHHmuwm mumpoHHongwz NHHmzmn
.SOHuwdfimww m wdwhamm< GOfluDHHom onH<quom
.GOHquflwwv m wdfiHHmumm Goaum>umum WHHA<HMOHHMMmH
ImoH><mmm «Ho
wmmuo monHo<mmyzH
Iona: wozwsoam ZOHH<ADmom
nowuflcfimwv m mafihama< Ixnhfilmsufl> mmB Doom mac
mufiwEEoo xtimwwmmmmwli\‘ \k1mmmwwmmm11IAmmflWlJflunfin

 

0H

HHHmUOHnH MHAHBMHQOE UHZdVMVNHQ N00

 

 

 

 

 

MUAHUEOU mWHNHWunMH WUNQUCOU . EWUH EMMA“.
Human—“LN.

 

Auum—JHHJHUFHOUV m y n mqmdvflrfl

 

 

66

 

mMS Doom

 

 

 

 

 

 

 

 

 

 

 

 

 

macauficfiwmo wafimfiamm wan wawaamowm ZOHmmmooDm mNo
MUZMHonmm
coaufinfimmv m mnfihama< mmB 000m
.wfimfimxm am wcHHHmumm 304m wommzm «No
defiufiaflwoo m wawhaam¢ mm3 ooom
Scam wummzm mmo
N N manomqoz 0Hz<omo
8:233 m mfiSmowm o m + 8
‘lllmmousao mw<moem wommzm wHo
ma mxmlaoa\mH mem mmz noom
am mawuficwoomm .aowufifiwmmw m wafiHHmowm 304m wUMmzm mHo
wGOHumumumuwucw uomuuooafi wcwuflawouom wHHo<m<o quwmm¢o
mmz noon
mafiuwdfimwu m wcflmama< mZOHHo<MMHzH onH<Abmom
.nmmuw m wafiuoumpoumH ZOHHoquom «Ho
mmowo<m
Hzmnzmmmn WHHmzmn
.memmew uoouuooaw wafiuwcwouwm MHHAHm<Hm\mHBomo
ZOHH<ADmom
.GOHuHaHmwo m mafimaaa< .aOfluHaHmmo m mcflaamowm wHHmzmo .ZOHH<ADmom Hao
HzmzzomH>zm A<0Hm~mm
coauficfiwmo m waflaamuwm wwmum xmaflao onmmmouam moo
mmmmmmmwwmmmwm
.mEoum conumo mqbumqoz 0Hz<omo
coauficflwmv m wafiHHmomm .mamamxw cm wcHHHmowm mwaom .ma< mo<MOHm Mummzm qoo
“wumz
umwdm .umm
.wHamew am mafiafimumm mwumuwhnonumo
aflwuoum manomqoz UHz<umo Noo

 

 

WHGUEOU

llWWflNHMWWII\w mummucoa

53)....

inc:

HNHHHHN

AmUmvuJHuHUHu—OUV mw - Mu MTHMHAVH

'5:

 

67

 

 

me memlcon afiufla comm .cOfluHaHmmw m wcfimwmmd

 

WHHo<m<U UZHMMM<U

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

wHHAHm<Hm\mH30mo
ammuw m wnHumganucH ZOHH<ADmom wmo
dowuflcwMMWIMImeNwmm< wHHu<m<o Uszmm¢u
wHHAHm<Hm\mezomu
nmmuw m wcwuwuauwucH ZOHH<ADmom nmo
MMHH<E on<omozH
awmofiwz 586 mama:
coauflcfimwo m wcwaamomm wax mfiuwuowm mamomzooma Nmo
ummmwMMNMM1mm‘wcflumumuoucH
.maafimxm cm wGfiHHmuwm .EOHufiaflmww m wuHHHmowm mHmmmszmoeomm moo
mamammWmewmmwmmwmwwm
.aowuflafimwo m mawmaaa< .GOfluflafiwww w wcHHHmowm mwuflauwa zmHA<DHDz wqo
wammmxm uuwuuoocfl wHHAHm<Hm\myzomo
\uomuuou m wcfluflcwouwm .nmwuw m mafiuwuauwucH amasm onH<ADmom coo
ma 38 am nfiflmoom 83333 m wsafimfiw HzmzzomHEm 1283mm moo
coauflaflmmw m wnwfiamuwm mama ownmouusm ZOHmmmuoDm qqo
coaufiawmoo m . H < coaufinwwwv m wcwuamuwm muamam ammuo ZOHH¢MHmmmm mqo
ma mew cm :Huflc comm wHHo<m<o oszmm<o
.:0Hufiawmwo m mafiaamowm Sufism VHHqu<Hm\mH30mo
ZOHH<ADmom nmo
cowufinflmww m wdHHHmuwm meEHa< vam
madman meHHo ZOHmmmooDm .mZOHm omo
ZOHH<mHmmmm
mu<MOHm wommzm
aOHuHafiwmw w wnflhaa9< mmqaomqoz UHz<wmo mmo
2223433
wHHAHm<Hm\mHBOMU
onH<ADmom
my<m mH<ma
chHuHawwov mafihaaa< .aamuw m mmfiuwumuwucH MH¢m mHmHm wNo
wucwefioo mmflmammw mwmwmmOo EmuH

 

Bmxm

4.64:4. .1

 

68

a student might recall that responses 1 through 4 are examples of

density dependent factors, or the student might apply the definition

Deywlty dependent Meta/vs efifiec/téhg popuzaLéon/s inc/Kude aid:
the flak/towing except

1 . medallion.

2 . competition.
3 . behavio/L.

4 . pa/LaAWAm.
5 . flue.

of density dependent factors against the definitions of items 1 through
5 and find that the 5th response's definition is not in agreement with
that required by the question. Either method would result in the cor-
rect choice of response 5.

In another instance a series of operations might be required to
answer a question correctly. For the following item (item 9, Quiz 10),

the student was first required to inter ret the ra h presented, apply

 

Which 06 the anus below but Wanda whim/ing capacity?

 

1. 0.6 b

2. be

3. ad Numbe/L 06 [\Avd
4. ed Individuw

5. e6 0

 

Time
the definition of carrying capacity, and then recognize the incorrect
examples. Response "ab" represents (exponential) population growth,
"b0" rapid population collapse, "ef" slow population decline, "ad"
Pépulation equilibrium about "ed", the carrying capacity of the popu—
lation's environment. Thus, 1, 2, 3, and 5 are incorrect responses,
and 4 is correct.

Table 3.9 presents a Summary of the test item concept coverage.

 

N. h¢du EH0“ Ann—um.“

mHAHmHUZOU EHWNMOUN .HO NU§EOU EHH HWNH. -. -

ml “mug“

' ‘vw

 

69

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

mN om
mN .oN.oH .mN.HN
.QN.MN NH.oH .ON.oH
£12 8.2 .23: «N 2&8 om: woos. m.”
we 0N EwHHNSuSE N.
m¢.mm OH.q.N Nm coaumHHmmleIIIIWI.
«N S m; 8.8 ago: S
N “33% m:
UGNEGOHHNVGW
3 . o S N N S N . N Hmfimba 3
mo OH.w 0H m mHmoSuahmouonm mH
mm owmuoum
.wH.q NH.m H o m owuoam «H
3;: amflonfimz NH
3 . o 233% 2
Houumz
Nm OH.q m.¢.H o oflcmwuoaH OH
. .eN . om£N
2 2 .NN S S N N N; o o 8.2 302 $35 o
mm We 3 38.30:
odiN o iN N .NJ ago m cosmos o
lo ouo>Haumo N
Nm om wan.¢ MN Hummmmmwoo o
wN N.H w.¢ “maswaoo m
o.w
«N mN N .o.m.q MN.m Hoosooum w
No S N Tm oéa 2 296 Show: m
w
HN.oN .o.q.H wN.wH Eoumwmoum N
HN
cN m.o.H Ewfiamwuo H
Hmnfim an oaHz am>mm me 0>Hm usom omusw 038 moo umwumum umwocoo Hmnfisz
mEouH uon unwocoo

 

\\hn_l HHHHDJ. 4.4.“.4.

3.1 4:)pd\r> 1411.!11)>\r1t

HNfifih

man—Hz

NHNKVQW

 

“Hum

mKVHhH
MEMUH

HDOHH 00kg .0be
u QUE

AuvflnuuhflUnHOnvv m q n MH‘HMHdVH

”no

umwuwkm

 

70

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

HN flewbwcwwwg qq
wN ma Eswunwawsvm m¢
macauumuwuuH
NH ma NH aowumadaom Nc
muouuwm
.HH «Ha ea .mwm huwmamn He
.wm.nm .wH.oa mufiafi£mum
uoq.~m mm ma \auzouu
mm HH OH n o coaumfismom ow
wm.~m aH NUHummmo
.nm.NH .m.m NH wsfihuumo on
doaudn
: 123,8 a? mm
mm wJ mumm numwn mm
wN N 8% figs 8
J“ huoufiuuwa mm
HH «H.a 0H hufimfiwn é”
wN ammuwmmfln mm
ma
.¢H.NH om.na
NH.HH .q.N.H q“ .0H.¢ fiowumasmom Nm
ma m“ muwcsfifioo Hm
N.“ NNNNN QUE om
om HN.O¢ ma mfiowm mu
NH cu Coaumwmum mm
mm
3. cm NN6N «N
.mN.m .mH.NH .ma.m cowmmwouam mm
unwacoufl>cm
3 333m 0N
zufiHwHHOufiuumH
<H.NH N uoa>msmm mm

 

.II N

Ba

1111‘

CO'

ma

CE

ch

of

0D

 

71

1 of the concepts tested for in the course has been assigned a
Jer and is listed in the left hand column of this table. Each
:ept is then cross referenced with the relevant test items. This
:ix of concepts versus test items serves as a basis for the con—
: mastery or performance indices discussed in the next several
)ters. Not all of the ecosystem concepts presented in the course
a covered in the Weekly tests, thus any assessment on the degree
mastery or understanding of the total Ecosystem concept is based
>nly a subset of the subsumed concepts.

Course Concepts Summary

The Ecosystem concepts treated in the course have been presented
fables 3.2 through 3.9. Table 3.10 summarizes the concept coverage
)ughout the various instructional sessions of the course. Of this
a1 set of concepts, student mastery or learning indices could only
leveloped for those concepts that were included in the questions
:he weekly quizzes and the final exam. Although students must have
sly mastered other concepts, no evidence was collected to measure
extent of that learning. Thus, for the purposes of this study the
ent's mastery or performance on those quiz items testing Ecosystem
epts provides the only measure of the students' understanding of
total Ecosystem concept.

These measured concepts were arranged in a matrix format follow—

the convention developed in section 2.2;

_ —1
c -|cl c2 03.....c44

é.each element, Ci’ is.defined in Table 3.11. C contains the

 

72

 

OH . m MAME.

 

73

 

$953.58 3. m 35:.

 

74

 

$253.88 3 .m HEB;

Nutrient

Behavior

 

75

TABLE 3.11

ECOSYSTEM CONTENT MATRIX C
C

Density

 

conten
for, a
Matrix

outcou

76

ent which was both taught in the course and effectively tested
and will be referred to as the Content Matrix. The Content
ix will serve as a central basis for the discussion of the

ome components of the course model.

 

4.0

info
info
info
deve
one
1113‘
desl
beg
hap

and

bat

til

in

-' 'eE

 

CHAPTER 4
THE MODEL

Student Information

In order to efficiently gather, compile, organize, and analyze
ormation on students in BS 202 the general method of variable or
ormation classification suggested by Tamir (1976) was used. This
ormation scheme is discussed in section 2.2, the theoretical model
elopment. Relevant information on students was classified into

of three categories: antecedents or background, transactions or
tructional outcomes or performance. This corresponds to the data
:ribing the individual student upon entering the course at the
inning of the term (antecedents), what the student does and what
)ens to him or her during the course of instruction (transactions),
the effects of that instruction upon the student (outcomes).
:, each student after completing BS 202 is described by a set of
:ground data, the B matrix, a set of data describing the instruc—
t, the I matrix, and a set of data describing the knowledge ob—
,ed during the instruction, the K matrix. The variables describ—
each set of data are referred to as the students' state variables.
Background Data

For the purposes of this study all antecedent information on

Student was assigned to a Background Matrix, B , where:

33......13 '"1,
236

236 background variables are listed and defined in Table 4.1.

B == Bl’ B2,

77

The in
standa
variab
4.2 l

B
Each 5
sessic
expec1
lab 1:

the S'

and s

Sessi
tape

expos
mEthc

were

Matt:

Stat

,. hi
and

How.

 

78

instruments used to collect this information and the means,
ndard deviations and other statistical information for these
iables are given in Appendix B.

Instructional Data (Transactions)

Biological Science 202 has a fairly rigid instructional routine.
1 student must attend one lecture session and two small assembly
sions each week for approximately ten weeks. Each student is also
acted to spend some additional hours in the individual instructional
listening and working with 17 prerecorded lessons. In addition to
structured class time, the student also spends some time reading
studying.

Attendance data was recorded for each lecture and Small Assembly
ion. Time spent in the instructional lab was also recorded by
number for each student to give an indication of the individual's
sure to taped instruction. Data on the number of hours spent and
od used for reading and studying the textbook and practice quizzes
also solicited on each student's weekly exam.

This attendance and study data were assigned to the Instructional

_ —1
I-I,I,I,.....I
l 2 3 108

The 108 instructional variables are listed and defined in Table 4.2.
,stical information for these variables is given in Appendix C.
Random student discussions or tutoring sessions with the instruc—

re difficult to accommodate within the framework of this model,

uch data was not to be entered into I for reasons of economy.

Er, each instructor was encouraged to keep a log of such interactions,

mmHn—NHHNKV Hv§0hnwv~omvm~ m.

In..- oV MH‘HMH<H

 

79

n

o u uomuyooaH H n wwaoammm uowupoo .m EouH umouwum moommm u mHm
o u uoouuoosH .H n wmsommwm uooupoo .m EmuH umwuwum woomMm u me

o u uomuuoucH .H u wmaommmm uomuuoo .n EmuH amououm Roommm u NHW

o n uumuuooaH .H u omaommmm uuouuoo .o swuH uwououm ooomMm u on

o u uoouuoucH .H u owaoamom uoouuoo .m EwuH umououm moommm u mHm

o u uuouuoosH .H u owdoamoM uowuuoo .q EouH umououm qoommm n «Hm

o u uoouuoonH .H n wwnommom uomuuoo .m EouH umououm mOOMMm n MHm

o u uomuuooaH .H u omdoamwm uowuuoo .N EmuH umoumpm NOOMMA u NH@

0 n uooupousH .H u meOAmwm uoouuoo .H SwUH umouwum Hoommm u HHm
wowusou uflwwuo oGIunwuo aH vacuum munwHu sz ozmo n OHm

kumomou momusoo sH wofiumm munHom sz mHmm u an

$322 ufiom 2.55 pm: .20 H mm

mmu oo.N m soHom muaHom pm: omHm u Nm

wvmuw x quouo mo wwmusou HHm uo>o saw I vmcumm musHom 3m: mHmsz H mm
hwsum mo EnoH ou m30H>wum vacuum munouo sz zmmmU H mm

hwsuw mo EuoH ou msow>oum voHHHmo wunouo sz m<omo u cm

pm: 0» coupommamufi munwuo McMH H mm

wmsumm munwuo wmoHHoo Hauoe mMoH H mm

Ho>mH mmmHu pm: mm<Ho u Hm

 

 

 

 

 

 

 

 

 

 

:owuHcmeo anMHHm> shcouo< unoEme

)iislll.

O H UUfiMHOUfluH «H H @MCOQQGM UUNHHOU «OH. EwuH UWUUUHAH OHONMHN “ (Cm

 

o nuojhunfiukfimmnn ”HQWHHWKP gnuOcHUdV . U finmEmvHNN
”HANNHHIHNKP .m UHHINU SN
AHMHDh/HHHZOU IF] I q MHAQ<H .

 

80

O O O C) C> C) C> C) O C> O C) O C> C> C> O C) O C) O C

H

H

H

H

uuwuuooaH
quHHOUCH
uomuuoocH
uowuuouaH
uowwuoodH
uoohuoosH
nowuwooaH
uoouuoosH
uowuuooaH
uumuuoocH
uuwuuoufiH
wowuuoodH
uuwuuoocH
uuwHHoosH
uumupoudH
uomuuouaH
uowupouaH
uoousoofiH
uowuuoofiH
uuwuuoocH
uowuuouGH

uomnuoodH

a a a A a A a a a a

0

II I A h F I
F4 F: F: F: r: F4 F1 H H Hi F4 F1 F4 Pi H H Fl H H Fl H H

a

H

wmcoemmm
wmdommom
uncommon
owdoamom
uncommom
wmsoamom
wmsonmwm
wmcoamwm
wmaoammm
mwaommwm
omaoamwm
wmaoamwm
omsomwwm
wmdoawmm
wmaommwm
omdoammm
omsoamom
wmdoammm
uncommom
uncommon
wmsoawwm

wwaoamwm

uuouuoo
uuwnuoo
uuouuou
uowyuou
UUOMMOU
uomuuoo
uowhwoo
uowuuoo
uowpuoo
uowuuou
powhuoo
uowuuoo
uowuuou
uuwuuop
uomuuoo
uumuuoo
uuouuoo
uomuuoo
uomuuoo
uuwuuoo
uowkuoo

uowhuoo

EwUH
EmuH
EmuH
EouH
EmuH
EwuH
EouH
EwuH
EwUH
EwuH
EwuH
EwuH
EwuH
EmuH
EwuH
EwuH
SouH
EwuH
EouH
EwuH
EwuH

EOHH

umwuwum
umouohm
amououm
umoumum
uwoumpm
“mououm
umouwnm
umwuwsm
umwuopm
uwouwum
umwuosm
ummuoum
umwumum
umoumum
umouowm
umouopm
amouwum
umoumum
umwuopm
amouwsm
amouwum

uwmuwun

Hmommm
omomMm
mNommm
wNOMMm
NNOMMA
oNOMMm
mNommm
«NOMMm
MNOMMm
NNommm
HNOMMm
ONommm
mHOMMm
wHOMMA
NHomMm
oHommm
mHOMMM
«HomMm
MHQMMm
NHOHMm
HHommm

Dn-INIJIi 1.

a) O\ C) F4
W) F) Q"d
m m

o H N r) <- m ‘o n
:3 $3 2} «a «a w» «u r) «w a) r

F4 Ci F) <3 V) \D .
N filfiflflfqflflflfilm m m m m m m m m m m m m

C)
C“
l m m

“OHUHHNHMQQ NHDNMHNKV fiflOHHUAV
@HDWHh—nqb
QMDZHMFZOHV HteV mqm<fi

UGQEMV Hum
mm unflh U use

 

81

Hm.mN.mN.NN.HN.wH.NH
mN.NN.sH.OH.e.m.N
«N.NN.wH.NH.mH.m.N
mm.wN.NN.HN.wH.sH.e.m.N
NN.0N.NN.NH.NH.w.o
em.Hm.NN.oN.NH.NH.N
om.sN.mN
NN.HN.wH.eH.NH.w.o.m.e
am.om
.mN.oN.aN.mN.NH.m.m
em.Hm.eN.sN.wH.NH.m
Nm.om.qN.HN.ON.NH.N
mm.0m
.NN.eN.sN.HN.NH.m.N.H
NN.HN.¢H.N.m.N.H
om.wH.NH.m.m.N.H
sm.mN.Hm.eN.sN.mN.HN

wEwuH
meuH
meuH
mEouH
meuH

mEmuH
wEmuH

mEouH
meuH

wamuH

mEouH
wEmuH
mEouH

mEmuH

umoumum
umwuoum
amououm
umwuoum
uwouwum

umwuoum
umouwum

umwuwum
umouwum

umouwum

umwuoum
umwuwum
umouonm

amouosm

o u uoouuousH

O H UUUHHOUGH

MO
“HO
MO
W0
MO
MO

mo
mo
mo

W0
HO
NO
NO

a A
H H

o n uowupoosH .H

IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII[

vmeQEoo
vwmoafioo
pmmoafiou
meomfioo
wwwoaaoo

vmmoaaoo

cwmoafiou

pwmoafioo
wowoaaoo

meoafioo

wwmomEoo
wwwoafiou
cwwoafiov

vwmomEou

ouoom
whoom
ouoow
ououm
ouoom

whoom

muoom

whoom
whoom

ouoom

whoom
ouoom
wuoum

ouoom

umououm oau

neuomm
scuomm
pouumm
uouomm
pouomm

Heuomm

Mouomm

uouomm
Houumm

Houomm

Houomm
uouumm
Heuomm

uouumm

ummuwum
umwuoum
umououm
uwououm
ammuoum

uwouwum

umwuwum

amouwum
amouwum

umouwum

umouwum
amououm
uwououm

uwououm

do ououm Houoy

owdoamom uuouuoo .qm EouH amououm

omaoammm uowunoo .mm EwuH umwuwum

mwcommwm uuwuuoo .Nm EouH umououm

qummm
MHOMMm
NHommm
HHommm
OHommm
mOOMMA

woommm

BCOMMm
ooom¢m
mOOMMm

qoommm
moommm
NOOMMA
Hoommm
HOHMmm
«mommm
mmommm
Nmommm

mm
mm
um

mm
«m

:fi
m m m m m H

mm

Nm
Hm
om

no

me
wq
he
mq
me
«Q
mq
Nq

commences-1mm

 

CCFLYruranC. UHDNflHNNV HHkflEOHUAV UENEWHR
HHn—NNHme , m HHNUH:

QMHEHHZOU 1H I V thmdfiun

 

ON
mH
wH
RH

U10
HH

 

OHqu
HHHHH

82

HNm<an\Ol\w0\

Nm.Hm.om.mN.sN

EouH
EmuH
EwuH
EouH
EmuH
EmuH
EmuH
EwuH
EwuH
EwUH
EwuH
EouH
EwuH
EwuH
EwuH
EwuH
EmuH
EmuH
EouH
EmuH

mHHMCEOHumwSO
wuHmauOHumoso
wuflmGGOHquDO
oHHmd¢0Hummao
mHHMSSOHumeO
wHHmSGOHumwso
wHHmGfiOHumwSU
wHHmGGOHuwoDO
wuHmcGOHumoso
mHHdeowumeO
wHHmCSOHuwwsc
wHHmCdOHuwwDO
mHHmfiGOHumeO
muHMGSOHuwwso
wMHmSEOHumoSU
wHHNGGOHumeO
wHHmGEOHumoDO
wHHmGGOHumeO
wuHdeOHumwvo

muHmccOHuwoso

w>Huommw<
0>Huuwwm<
0>Huowwm<
0>Huuwmw<
0>Huummw<
w>Huowwm<
w>Huowmm<
w>Huomwm<
w>Huuwmw¢
w>Huommm<
0>Huumwm<
0>Huommm<
w>Huowww¢
w>Huowwm<
m>Huowwm<
m>Huuwww¢
m>Huowmm¢
w>Huowmw<
0>Huowmw<
m>Huuwmw<

MN.mH.~H.HH.0H.n.o.m mEouH umwuwum mo wwmoafioo .mH muoom Houomm umouwum

f

ONohm<
mHOMh<
wHomm<
mHomm¢
wHomm<
mHomm<
«Homm<
MHomm<
NHomm<
HHomm<
OHomm<
moomm<
woomm¢
moomm<
woomm<
moomm<
«oomm<
moommm
Noomm¢
Hoomm<

mHommm

hoooxo
mmmmmmm

\‘f‘
m

HNM

H N m \‘1’ In 0 r\ 00 Ch 0 '\ l\ l\
00000005

moment: CO CD a: no {1'1 CC on PO an ‘13

O
0
m

 

 

fiOfiUflCHNHQ UHflNrLKKP

hum—DZHHZOU H- .V MAHAVH

lllllll <
HHQNMHHKV

 

N UAHNUWIVM

83

Hq
oq
mm
mm
mm
mm
mm
qm
mm
Nm
Hm
om
oN
wN
NN
0N
mN
qN
MN
NN
HN

EouH
EwuH
EmuH
EouH
EouH
EwuH
EouH
53H
EwuH
EouH
EmuH
EwuH
EouH
EouH
EouH
EwuH
EouH
EouH
EwuH
EwuH
EmuH

muHmcGOHumoDO o>Huowww<

ouHm::0Huww=o
wHHMGCOHumwSO
muHmccOHuwoso
muHNGGOHumwso
mHHNGGOHumwso
muHMCGOHumoso
oHHmsGOHumwso
mHHmGEOHummDO
OMHNGGOHumoSO
muHMCSOHumwSO
muHm:COHumw=o
mHHNEEOHummDO
wuHmc:0Huwwao
wHHNGGOHumoDO
oHHmaCOHuwwso
mHHmacOHuwwDO
wHHmsaOHumwso
wHHMGGOHumMSO
wHHMSCOHummDO

wHHmcGOHumwso

w>auumow<
m>aouwmo<
o>auowuma
w>HoomoN<
m>auomoo<
m>aoowmo<
w>Huuwwo¢
w>auommo<
m>auuoma<
m>auowwm<
m>auowao<
u>auumaoa
m>auuwwo<
m>auuwou<
o>Nuoon<
w>auomau<
w>auoomm<
w>aoomum<
w>Nouwmu<

w>Nouwmu<

 

qumm<
oqomm<
mmomm<
wmomm<
nmomm<
omomw<
mmomm<
«momm<
mmomm<
Nmomm<
Hmomm<
omomm<
mNomm<
wNomm<
RNomm¢
oNomm<
mN0hm<
«Nomm<
MNomm¢
NNomm<
HNomm<

mm

II
on
ONON

some:

II
In
OK

In

H
\T
ON

m

0")
ON

I
eI W) q- n ¢> E; g g g g! g
m xI w w m) m m m m

H
H
(I)
mmmmmmmmm

 

ice... rErLQC @Hr—fithxr

QMFHZHLHZOO IFII QV MHHEthu

.II <
m HQNNIN WK?

Ham-EH...“
. m ufifihug

 

 

84

os.es
Ne.oe.Nm.mm.mm.Hm.wN
.mN.eN.mN.NN.qH.OH.m.N
oq.mq.Nm.em.mm.mN
mN.mN.mN.oN.oH.w.m.N
ma.os
.mm.om.em.mm.qN.m.N.N
we
as.Nm.em.qH.mH.oH.m.H
Na.oq
am.Nm.sm.mN.mN.a.q.H
as.ma.He
mm.Nm.mN.mH.oH.m.s.N
eq.mq.Nq.em
mm.Hm.mN.NN.ON.OH
Hq.mm.Nm
.om.mN.N.o.m.m.N

meuH

mEouH

mEouH

meuH

meuH

meuH

mEmuH

mEmuH

.wwDO mm<
.mwdo mm<

.moso mm<

.mmso mm<

.wwso mm<

.mmso mm<

.mwlo hm<

.mwdo mm<

m

w

m

m

m

0

O

O

O

O

momoafioo .

womomfiov .

a

0

wwmoafioo

wwmanoo .

wwmomEoo .

vwmanoo .

wowanou .

bomomaoo
wq EouH
me EmuH
we EouH
we EmuH
«q EouH
me EouH
Ne EouH

 

[\

Ln

<3

(‘0

N

GHOUW HOHUNW

whoom uouomm

wuoom HouUMh

whoom uouomm

wuoum Houomm

whoow Houomm

ouoom Mouomm

.H whoom Houomm

oHHmccOHummso
wuHms:0Humoso
oHHMGEOHumeO
myHmaGOHumwso
wHHmGCOHumodo
mHHmGEOHumwso

wuHmaaOHumwdo

w>aouwom<

m>auomum<

m>aoumum<

m>auuwmu<

w>Huuomm¢

m>aoomom<

w>auomNN<

w>aoumom<
m>aoomou<
m>aoomma<
m>auuoom<
w>NuomNN<
o>aoommma
w>aoomom<
m>auowmm<

woomm<

BOOMM<

roommm

moomm<

woomm<

moomm<

Noommfi

Hoomm<
wqomm<
nqomm<
oqomm¢
mqomm<
#qomm<
mqomh<
Nqomm<

m m m m m

m m fl

 

GOHUHEHNQQ UHQNHHN>

QWDZHHZOU HIT HAQ<H

:erOIH .UdV
HHAHHHHb

 

mm EmuH wMHmsEOHuwmso wasoumxomm smooMm Ime

 

I m
H mm EwuH muHmaGOHumwso wsdouwxomm omooMm ummHm
mm EouH oHHmGGOHumoDO unsouwxomm mmoomm nemHm
em EouH wHHMGGOHumwSC wsnouwxumm «mouxm ummHm
mm EmuH wuHmcGOHummDO uc50pmxumm mmowmm uNmHm
Nm EwuH oHHmcGOHummso wnbouwxomm NmouMm nHmHm
Hm EwuH wHHmuaOHumwso Undouwxomm Hmome nomHm
om EwuH wuHmCGOHumwsd vasouwxomm omooMm nqum
mN EwuH wuHmGGOHumodo wanouwxomm aNouMm quHm
wN EwuH ouHmasOHumoao vqnoumxomm wNooMm uanm
. KN EmuH wuHmGCOHumwso wssouwxomm BNome uqum
% 0N EouH ouHmaCOHumoso wcaouwxomm oNouMm uqum
mN EwuH muHmCGOHummDO wasoumxomm mNouMm nqum
qN EwuH ouHMCGOHuwoDO vasoumxomm qNome "meHm
MN EmuH muHmasOHumwso wssouwxomm MNome "Nqu
NN EouH ouHchOHuwwso wasouwxomm NNooMm HHqu
HN EmuH wHHMGGOHumoSO vqsouwxomm HNOUMm HOQHm
ON EouH ouHmsSOHumwsc majouwxumm ONOUMm umMHm
mH EouH wuHMGGOHummDO wssouwxomm mHooMm HwMH

mH EwuH oHHmccOHumeO wasouwxomm wHooMm "NMH

0

m

H
manna

RH EwuH muHmssOHumoso vcsoswxumm NHOUMm

 

GOHUHGHMNQ Ufiflmflhm>

QMHAHZHHZOMV IF. c ¢ mqdepH.

 

86

ea awUH
mH awua
«H amoH
NH smuH
EouH

HN
HH

EouH

O
H

EwuH
EouH
EouH
SmuH
EwuH
EmuH
EwuH
EwuH
EmuH

HNMQ'LO0NOOON

EmuH
wq.Nq.oq

wHHmncowumwdo
ouHmEfiOHumeO
wHHmuSOHummso
wuHm:GOHumeo
wHHm¢GOHumw=O
wMHmcSOHumwso
wuHMCGOHumeO
wuHmccOHuwwDO
wHHMGGOHumoDO
oHHmcsOHumwso
ouHma:OHumwso
wHHmc¢0Huwwso
mHHmCGOHumwSO
muHNCSOHumeO
wHHm¢GOHumwso

muHmdaOHumoso

baaoumxomm
vasoumemm
waaouwamm
wusouwxomm
vssopwxomm
wssouwxumm
wasouwxomm
wasouwxumm
ccsoumxomm
wasouwxomm
vcsouwxomm
vasopwxumm
vasouwxomm
vdsoumxomm
unsouwxomm

vnsouwxumm

mm.mm.mH.qH.NH.m.N mEmUH .mwdo mm< mo wwmanoo .HH whoom Houumm o>Huowww<

oq.qm.mm.mN.mN

oH.wH.NH.oH.mH.w.m meuH .mwso mm< mo wwmomEoo .OH whoom acuomm m>Huowww<

oq.m¢

.N¢.Hq.oq.mN.MN.NN.m.m meuH .mwdo mm< mo wwmomEoo .m whoom nouomm m>Huuoww<

 

oHooxm
mHOUMm
«HOUMm
MHooMm
NHOGMm
HHOUMm
OHooMm
moouxm
wOOUMm
noooxm
woooxm
moooMm
«OOUMm
moouMm
Noome
Hooomm

HHomm<

0Homm<

moomm<

ImmH
usma
"NNH
uNmN
INMH

ONH
INNN
uwNH
"NNH
ueNH

I H I I
In CG CG m m g; m m m

In

N

H
m

\‘f
N
,_I

umNH

N
N
H

H
N
H

l
O
S
mmmmmm

 

 

 

CCflL .rr. mute: UHQNHHNka

DMHNHZHHZOU .HIQ MHIHMHAVH.

CKWCOH 04V
H HAHN—PH Nb

 

 

 

87

wm
mm
mm
mm
«m
mm
Nm
Hm

mq
wq
we
we
me
He
me
Nq
Hq
ow
mm
mm

EmuH
EwuH
EwuH
EouH
EwuH
EouH
EouH
EouH
EwuH
EmuH
EwuH
EouH
EouH
EmuH
EmuH
EwuH
EmuH
EwuH
EwuH
EouH

53H

oHHmsa0Humw=o
mHHmssOHumond
wHHmEEOHumoDO
wHHmSSOHumeO
wHHmEGOHumwwo
wuHmcsOHumoso
wHHmaHOHumeO
wMHmSSOHumwSO
wHHNCGOHumwno
wHHMSGOHumoSO
wuHmGGOHumoSO
wuHmafiOHumoso
wHHmnCOHuwwso
wuHmCGOHumeo
wuHmGGOHuwwso
wunaaOHumwao
ouHmssOHumwso
wRHm:COHumwso
mHHmcsowumwso
mHHmdaOHummno

wHHmsGOHummso

manouwxomm
vsbouwxomm
wnsouwxomm
vasouwxomm
wusouwxomm
vssouwxomm
wasouwxomm
panouwxumm
vcsouwxomm
unsouwxomm
vasouwxomm
mssouwxomm
caaouwxomm
wnzoumxumm
wssouwHUMm
vcsouwxumm
vcsouwxumm
wcsouwxomm
vasouwxomm
wasouwxomm

pcaouwxomm

 

wmoosm
Nmooam
omooam
mmooam
«modem
mmooam
Nmooam
Hmooam
omouam
mqooam
maooam
Nqooam
osooam
mqouam
«sooam
Nqouam
Neooam
Hsooam
osowam
mmooam
amooam

uNNH
ueNH
umNH
ueNH
umNH
"NNH
"HNH
uoNN
"sea
"weH

m m m m m m m m m

mcH

uwma
INmH

 

 

when... rfiranC QHQNHH—wxw CNCOHUAV UWQEHHMH
mHamflhfib .m Kuhn—N:

DHDZHIHZOU HIM MN‘HMHJVIH

 

 

 

88

ee.am.em.mm.wa
.eq.mm.em.mN.QN.mH.N

mm.nm.wm.mm

.Hm.mm.qm.mm.om.wN.N.N
mo.mm.am.Na.sq
.mm.sm.mm.Nm.Hm.mN.e
Ne.Nm.Hm.om.wq.Ne.eq
.ms.oq.wm.Hm.om.NN.s
N©.He.om.mm.am
we.Ns.em.mm.mN.wN.e
N©.Hm.Na.me.qq
Ha.mm.mm.eN.NN.N.m
No.Nm.em.Hm.om.Nq
Nm.Hm.om.sN.NN.N

mfiouH

meUH

wEouH

mEouH

mEmuH

wEouH

mEouH

.mwso UMm wo vwmoafioo .
.mwsc uMm mo vmmomaoo
.modo UMm mo vmmomEoo
.woso UMm mo vmmoafiou .

.wodo QMm mo wmwomaoo .

.mwso oMm mo wmmomfioo .

.mwso me mo vwmomfioo

we
no
we
mo
«0
me
No
Ho
00
mm

EmuH
EouH
EmuH
EwuH
EmuH
EouH
EmuH
EwuH
EouH

EouH

I\

<1-

M

N

macaw

wuoum

muoum

whoom

wuoum

mwoom

.H ououm

Houumm

wouomm

Heuomm

Houumm

Houumm

Houomm

Heuomm

upHmaEOHummso

mHHmGCOHumeO

wHHmGCOHumwSO

wuHmdaowuwwsc

oHHmaGOHumwso

mHHmGGOHumoDo

wuHms:OHumwso

oHHmGGOHumoDO

wuHmGsOHumoso

oHHNCCOHumeO

vascuwxumm

vascuwxomm

vasowwxomm

cadopmxumm

ccsouwxomm

wssoswxomm

wasouwxomm
unsopwxumm
vcsoumxumm
wdsouwxumm
vssonwxomm
wssouwxumm
masonmxumm
vssoumxumm
Unsoumxomm
wssouwxumm

vasouwxomm

 

noomMm

mochMm

mOOhMm

woomMm

MOOMMM

Noommm

Hoommm
woowxm
moome
coowmm
moooMm
qoooMm
mooomm
Noowxm
Hoooxm
oooomm
amouxm

HmH

"NNH
"Nma

\0
w
H

mmmmmmmmmm

Imma

\‘f
(13
H

“NNH
"NwH

H
(X)
H

O
00
H

"NNH
"NNH

DJ

GOHUHCHNNQ WHKHHHHNKV HHNrHuOIHUHV “Cmufinwhwn
HHAHHHMS. . mm unfihuuw:

QMHHHZHHZOU 4... § mqmdfifi

 

89

qe.mo.mm.oq.am
om.qN.NN.aH.NH.N.m
oo.wm.Nm.om.mm
am.am.ms.sa.as.am.mm
sN.Nm.NN.oN.aN.NH.N.m
I No
ma.Ho.Nm.mm.Hm.wq.Ns
.mm.om.sm.Hm.qN.m.s.N
No.mc
.Nm.mm.am.wq.Ns.mq
.os.qm.HN.wN.mN.qN.N
me.¢m.aq
Nq.aq.mm.Hm.aN.wN.NN
Nm.ws.Ne.Ns
.oe.em.am.om.NN.wN.q
He.oe.wm.Nm.mm.me.qq
.oe.em.mm.Nm.mN.wN
.mN.qN.NN.NH.NH.e.m
ow.mm.am.Ne
ma.me.aq.oa.am.Hm
NN.¢N.NN.¢H.N.N.¢

 

maouH

meuH

wEwuH

wEwuH

mEouH

mfiouH

mEouH

mEmuH

.mmso mam
.mmso can
.wwso can

.mmso cam
.mwso can

.mwso cam
.mwso cam

.mmso cam

W0

HO

HO

M0

M0

MO

NO

NO

wuoom HouOH ume wnH>Hom EmHnoum

ououm N umouQSm wfiH>Hom EmHnoum

whoom H

wwmoafioo

vwmomfiou

womoefioo

wwmanou

womanoo

womomaoo

wwmanou

wmmoafioo

umounsw wGH>Hom Eanoum

ouoow

whoom

ouoom

whoom

whoum

whoom

ouoom

ouoom

Houomm

uouumm

nouomm

Heuomm

scuomm

uouomm

Houomm

Houuwm

wnsoswxomm

wasouwxomm

vasosmxomm

vasouwxumm

vssouwxomm

pasouwxowm

wsaouwxomm

vnsoumxomm

HOmmmm
NmmmMm
Hmmmmm

H<Hm

mHomMm

«Hommm

MHOMMm

NHOMMm

HHomMm

OHomMm

@oomMm

woomMm

 

 

fiOHU dung

AHMHAEHHZOU H n V MTHMHIGSH

$56.0“wa UHWBUHN
HHAHHRNb . mm unfihumes

 

 

90

wuoom ummH HmHHwfim

ououm umoH wwaHnEoo

oHHusoouom msoom umoH oocmHow Hapdumz

oHHucouuwm ouoom ume mmesum HmHoom

oHHquouom muoum umoH sum:

wHHufioouwm .mEoo wusmom was kaHsnmoo> wodHnfiou quEwumHmIGOHuMusoHpo
oHHucoonm wuoom umoH smHdem ufioEmomHmIsOHumusoHuo

wuouw umoH hHmHnfimoo> udoEwomHmIGOHumuaoHuo

ouoom ume anHwnm uswfioomHmIGOHuMucoHuo

mHHucwoumm SOHmGwswuano wavaom ucoEoUMHmI¢OHuMuGoHHO

wuoum uon COHmEoJoMQEoo wusmom uGoEoUMHmIGOHumucoHuo

oHHusoonm uwwH moHumeJuMZIoHuosbuHu< woanEoo unoamomHmIGOHumusoHuo
oHHuaoouom ouoom uwwH moHumaosumS unwamomHmIGOHumusoHuo

MHHuSmouom muoom umoH oHquLuHu< unoEoUMHmIcOHumusoHHo

ouoom uon moHumEofiumz ufiofioomHmICOHumuuoHuo

whoom ume UHuoEnun< unwEoomHmICOHumucoHuo

HU¢

HU<
DmE
sz
sz
HHmHH

sz

ouoom msoanHuuoo wfiH>HwoMmmecprDh wwwHHMImuwhz

wuoom wnosfiHucoo wcHHmwMIwaHstJH wwwHumImuo%:

whoom mDODcHucoo wchsomIm>HuH5uCH wwwHumImum%Z

whoom msosafluaoo uuo>ouusHquo>muuxm wwwHHmImum%2

umoH couuHHB mmHanum> msHHHouunou anwHwoz so whoom

 

mmZHU<
mmeU<
B9HD<
mo>bm£
mumpmz
900ng
Emupwz
OO>DmZ
uzmbmz
mMDmZ
Mbmz
mE<mE
mmeE
m<DmE
mez
<Dm2
mm
MB
mz
Hm
MUmmm

 

 

rack... vCruTeC HHAHflHN>

HH <
UHQNHINWKV . m UWHIHUNVA

DHEHEOU IHloN MQQAVIH

 

91

 

 

 

 

wcchmm oHHuawupwm Hoonom anm MHHmmm noMNm
mmmum>< ucHom wwmuo Hoofiom ame <m0mm umMNm
mHHusonwm ououm umoH :mHchm 80¢ mUMHo< "VMNm
wHHudouuom wuoom ume vmaHnEoo Hu< mzofio< umMNm
ouoom ume moamHow Hmusumz Ho< mZHu¢ "NMNm
whoom ume ooaoHum HMHuow Ho< mmao< "HMNm
muoom umoH moHumenumz H04 mHEHU< HQMNm
ouoom umoH amHHme Ho< uzmHo< umNNm
muoum umoH wwaHnEoo Ho< UHU< uwNNm

ccwaHCHme oHpmHum> cmcouo< newsman

WMHHMHAVHNH<> HQZO HHUDMHMZH

N‘ V MHJHNdVH.

.H

 

 

m SOHmmom thEwmm< HHmEm um ouampswuu< moom<m qHH

N nOHmmwm thEomm< HHmEm um monmvsouu< Noommm mHH

H :onmom mHnEwmm< HHmEm um wonmwaouu< Hoowmm NHH
oHH ou HH mo Sam waHmsouu< muauooH HmuOH QH<UMH HHH
S 3303 um 8533: 0883 SH

a ounuuoq um mosmvcouu< mooomH mH

w ousuooH um wosmwaouu< wOOUMH wH

N ousuooH um oosmwaouum mOOUMH NH

0 3303 um wuamcawuua. 88mg 0H

mu m 3303 um mocmwcwué mooumq mH
¢ mnnuuoH um mofimwdouu< «OOUMH qH

m ousuooq um monmwsouu< mOOQMH mH

N wHSuUwH um moamwsouu< NOOUMH NH

H ousuooH um wosmwsouu< HOOQMH HH

onHHszma mHm<Hm¢> EVZOMU< Hzmzmqm

MHH<HM<> xHMH<z H

 

 

mMHm<HM<> H<ZOHHUDMHmzH a

 

AHU-UFHOUV N- «V “Agra...

 

 

93

 

NH aOHmmmm anammm< HHmEm um moamecwuoa mHom<m ONH
wH cOHmmmm anammma HHmam um muamwamuu< wHowam mNH
NH aonmmm anammm< HHm5m um woqmecmou< NHom<m mNH
0H aOHmmmm anammma HHmsm um muamsaoou< oHom<m NNH
mH aonmwm sHaaomwa HHmam um mommeawuu< mHomam eNH
«H aOHmmwm anammma HHmam um woamsawuu< «Hom<m mNH
NH :onmwm sHpammm< HHmam um nonmeamou< mHom<m «NH
NH cOHmmwm a35mm... HHmam um mesmeamuu< NHom<m mNH
HH aonmmm anawmma HHwam um moanecwuu< HHom<m NNH
OH conmmm anawmm< HHmam om augmeamuu< OHomam HNH
m :OHmmmm %HnEwwm< HHmEm um moamwswuu< moom<m ONH
w :OHmmwm thEmmm< HHme um oucmwsouu< moom<m mHH
n GOHmmom thEwmm< HHmEm um oosmvsouu< moom<m wHH
o sOHmmom anEwmm< HHmEm um wocmwsouu< ooom<m NHH
m GOHmmwm mHnEmmm< HHMEm um muamwcouu< moom<w 9HH
6 HOmewm anEmmm< HHmEm um wofimufimuu< «oom¢m mHH
onstHamm mHm<Hm<> zNzomo< azmzmHm

mHm<Hm<> meH<z H

 

WV . UHMOUV N IoV MH‘HQH

 

94

 

« NHso mUHuomNm we was unwesum «OONom o«H

m NHuo moHouwum Ho amp “magnum NOONON m«H

N NHDO moHuumHm mo om: uamwSum Noowom qu

H NHso onuomum mo amp unmesum HOQNom m«H

HQH ou NmH mo Efim .wmb wmmH onsuooH HmUOH HOHmHH NqH
OH mama assoumH No mm: unwesum OHONHH H«H

m mama mssuumH we own unmesum moomHH o«H

w many munuumH we own uawwSum woomHH mMH

N mama wusuowH we was usmesum NoomBH me

o mQMH musuuwH mo me ufiwwnuw OOOMHH mmH

m mums onsoowH H0 mm: unmesum moomsH omH

q oamH wuauooH we own ufiovpum woomHH mmH

m mama musuowH we own uamesum moomeH «NH

N mama eunuumH mo om: “condom NoomHH mmH

H mamH wusuowH we was uawwnuw HoomHH NMH

OMH ou NHH mo 55m .mosmwnmuu¢ SOmewm thEwwm< HHmEm HmuOH QH<m<m HmH
onstHmmn mHm<Hm<> zwzomo< HzmzmHm

Jziudvddd n

 

RHO- UfinOUV N I 0V MHRQHANH

 

 

 

 

HH mama OmH some unmesum maHH Ho HowamH HHOH NOH

OH mama an OwwO “seesaw waHH Ho HumawH OHOH HOH

O mams OmH sows unwenum oaHH Ho euwcoH OOH OOH

O mums an comp unassum weHH Ho HowaoH OOH OmH

N odes an New: namesum maHH Ho nowamH NOH NNH

O mama an Ohm: ucmvsum waHH Ho suwamH OOH NNH

m wama pmH some unmesum maHH Ho numcwH NON ONH

« mama an some ucmcsum maHa Ho nuwawH «OH mNH

N mama OmH OmmO oawesum maHH Ho cowaoH NON «mH

mu m H mama an wow: uamesum oEHs Ho nuwamH HON NNH

HNH ou.N«H Ho saw .mmO NHso moHuowHH Hausa NONNOH NNH

Namxm HmcHHO OH NHso moHuomHH H0 mm: namesum OOONOH HNH

m NHDO wUHuomHm mo mmD ucowsum woowom omH

N NHsO onuumHH Ho «NO namesum NOONOH O«H

O NHso moHuumHH Ho mm: unmesum OOONOH O«H

i m NHso moHuomHH Ho om: unwesum NOONOH N«H
W onHHzHHHO HHN<HH¢> zNzomo< HzmzmHm
mHm<HH<> NHHH<2 H

 

I??? fizz—23:15 a; 7'

AHU. nun—00v N .6 WINNAVH.

hr hfln<bhn<kv

.NVLII-ILVVI Ir

 

 

96

m

N

H

m NHad How wEHH HOHumHmmmHm can zwzum

NHDO How vogue: HOHumummon and thum

m NHDO wUHuomum mo om: mo wOSuoz

N NHDO How wEHH GOHuMHmmoum paw hwsum

NHDO new wosuwx HOHumummem wcm hvSHm

N NHDO moHuomum mo mm: mo wonuwz

H NHDU How wEHH GOHumHmmmHm cam %w=um

NHSO How venues HOHumummoum cam homum

H «Had ooHuomum mo own mo wosuoz

wH

NH

0H

mH

«H

MH

NH

wmmH
wmmH
wQMH
mama
mama
mamH

mmmH

£3

£3

wouuomwu ucmvdum

wouuomwu uaowsum

wouuomou unobsum

wwuuomwu unowsum

kuuomwu ucowsum

wousoawu usowsum

wouuoawu unovsum

wouuommu ucowsum

wwuuoamu unopsum

womb ucowsum wEHH

wow: unocnum oEHH

womb uaovsum mSHH

womb uawwdum mEHH

womb unmwaum wEHH

womb unmvsum oEHH

womb uamvsum oEHH

Ho nowamH
mo suwGwH
mo nuwaoH
mo SuwdoH
mo :uwawH
mo SuwaoH

Ho sumaoH

ZOHHH 7H,...Hfl2. flJBHd/HHHN.

mmomMH

NmomMH

Hmommw

mmOOZH

NMOOBH

HMOOBH

mmomzo

Nmomzo

Hmomzo

wHUH

NHOH

oHoH

mHOH

QHUH

mHoH

NHUH

«Ha. rd>a<>11

 

 

 

 

 

A—V. UEOUV N: {N "Hugh-u

 

 

97

 

0H NHDO owHuumum mo mm: mo menus: wmuuomou ucowsum Hoosz «mH

a NHDO How oEHH GOHumHmmoum was >w5um wwuuomou umovsum mmosz mmH

m NHSO How wonuoz HOHumHmmon was hwaum wouuomou usowsum Nmosz NmH
m NHso ooHuomHm mo own mo venue: wouuoamu unowSum Hmosz HmH

N NHDO new wEHH HOHumHmmoum was hvsum wouuomou uawwsum mmoz>m omH

N NHDO How vosuoz GOHumHmamum was NApaum wouuomou unocnum Nmoz>m me
N NHDO ooHuomHm mo omD mo wonumz uwuuonwn ucowsum Hmoz>m wwH

o NHSO How oEHH COHumHmmem was >v5um kuuomou udovdum mmome NwH

o NHDO Mom wonuwz GOHumHmmmHm vfim hwsum wmuuomwu ucovsum NmoNHm owH
o NHao.oUHuomum mo omb mo VOSuoE wouuomwu udopdum Hmowa me

m NHSU How wEHH SOHumHmaon was musum wmuuoaou ucowsum mmom>m «wH

m NHDO new venue: SOHumummem can zwsum wouwommu usmwsum Nmom>h me
m NHso ooHuomum mo mm: mo voauoz wouuomou uswwsum Hmom>m NwH

q NHso How wEHH GOHumquoum was hwdum wwuuommu unobsum mmomom HwH

q NHDO How coaumz :OHumemon paw kcsum wouuommu unmv=um Nmomom owH
q NHSO ooHuumHm mo mm: mo posuoz wouuommu uaowsum Hmomom mNH
onHHszma mHm<Hm<> zwzomu< HzmzmHm

34113444.!“ >

411’! .1414 .1

 

Avun-FHOUV NuoN ”Hahn

 

 

OH NHno >ono3 do opoom 0H0 wOHH
m NHSO thwwz do muoom mo NOHH
N NHDO Nonmz so muoom No 00HH
0 NHSO szowB no muoom mo mOHH
m NHDO meme co ouoom no qOHH
q NHSO %onm3 Go wuoom <0 mOHH
8 m NHDO thwoB so muoom mo NOHH
o. N NHDO szwoS no whoom No HOHH
H NHDO %me03 do mHoom Ho OOHH
mem HmsHm Mom oEHH GOHumHmawum pom zwsum wwupoaou unopsum moosz mmH
mem Hmch How wOSuoz COHumemwum van wwSHm wouuomou unopnum moosz me
mem HmsHm moHuumHm mo mmb mo wozuoz wwuuoawm ucmwSum «oosz NmH
0H NHDO How mEHH COHumummem use thHm wouuomwu uswwsum moosz mmH
0H NH30 How wosuoz COHumHmame was mpnum kuuomou uGQUSum Noosz mmH
onHHszmQ MHm<HM<> szomu<

record
length
inatiI

4.3 _I

on a'
(orga
spons
Respc
Respc

Vectc

Each

item

racc

ent:

Eat

C0!

:1- -
H1
’5

 

99
Irding the student(s) name, topic of discussion, and approximate

;th of the session. Such information may be useful in future exam—
.ions of the data.

Outcomes: The K Matrix

During the course of the Fall 1977 term each student was tested
, number of items related to the concepts subsumed under Ecosystem
.anized within C ). For each test item the student chose a re—
se which he or she considered to be the correct answer. This Item
onse, IRi for item i, ranged between 1 and 5. The set of all Item
onses for an individual student is the student's Item Response

or:

-—- -l
= . . . . . IR
IR 1R1, IR2 p
p = total number of test items related to C .
.student's IR vector serves as a record of that student's test
.choices.
The Item Performance vector, IE, is designed to serve as the
rd of the individual student's performance on each of the items or
ies in IR.
__. *
IP is defined:
IPi = 1 when IRi = correct response
IPi = 0 when IRi = any other response
1 = l. p

element of IT, IP,, is an indicator variable, indicating the
1

actness of choice of the corresponding entry in IR, IRi'

 

example; suppose 3 is the correct choice for item 14, then;
IP14 = 1, when IR14 = 13
= = 4, 5
1P14 0, when IRl4 l, 2,

each student.

repr
pria
unde
stan
deg:
and

stuI

Wh

 

 

100

As indicated previously the Matrix

_ -l
C — CICZ . . . . .C

asents all the concepts associated with Ecosystem at the appro—

te level of instructional difficulty. The student's mastery or
rstanding of each element of C contributes to his or her under—
ding of the total, complex concept. The concern here is with the
ee of understanding or mastery of C by each student in the course
the only method available for assessing this mastery is through the
.ent‘s response and performance on each of the test items related to
'ecorded in IR and IE, respectively.

The understanding or mastery of each Ci is theoretically measured
)ne or more test items, recorded in IR and TB. Each test item may
sure the understanding of more than one concept, however, and in
7 instances if a test item is answered incorrectly it is difficult
ascertain whether it is because the student has not mastered all the
:epts present, or has mastered all but one. The student's mastery

;nowledge of a particular concept, Ci’ is defined as:

“e q = numbers of items related to Ci’ and n = number of such items.
j This yields a Ki’ such that 0 j-Ki-i l.

1probability that the student has mastered concept Ci increase as
hPProaches 1.0. Conversely, as Ki approaches 0.2 (the probability of
Hsing the correct answer) the student may be assumed not to have

‘ered Ci' The certainty of separating mastery from guessing increases

‘increasing n.

 

Init:
on s.
set .

item

VioI
Val

new

for

fiI

101

The computation of a series of Kis based on IE, produces a matrix

_ —1
K— K11<2.....K44

r each student, Ki corresponding to the student's mastery of an element,
, of the concept matrix. C. l<is thus a measurement of the mastery of

, and as any meaSurement tool represents a distorted reflection of the
:udent's true understanding.

The K matrix was updated each week as the term progressed.

litially Pretest (Week 0) items provided an indication of understanding
1 some of the 013. The first weekly quiz (Week 1) provided the first
at of items on which to update some of the elements of K . As the
tems on each weekly quiz were added to the students' IR and I? vectors
he K matrix was gradually filled in. Table 4.3 illustrates the pro—
ressive addition of information to the K matrix as the term progressed
rom the Pretest to the final exam. After Week 1 (column 1 in Table 4.3)
1 through K6 and K8 through K12 were computed from the appropriate items.
see Table 3.9). After Week 2 no new information was available on K

1

hrough K6, K11, and K 2, so they retained the same values as in the pre—

1

8 and K9 were recomputed based on new information, and a

Blue for K14 was computed from the new information. Each week whenever

ions week. K

éw data was available from the quiz, the appropriate Kis were recomputed.
f no new information was available, the previous Ki values were brought
hrward. This process was continued weekly for the nine quizzes and the
final. Column 10 in Table 4.3 represents the final version of the
Imatrix.
{4 Elgyigg_the Course as a System
This study, following the theoretical directions discussed in

laPter 2 applies the structure and techniques of system analysis to a

m - NV MHINNHAVH

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

OHM III. NOOH I OH
2.. . I III :
OHM OHOH NOOH EI «OOH NOOH OOOH OH
OHM OHOH NOOH I NOOH NOOH JI I OH
«HM OH«H a I NO«H NO«H NO«H I OO«H «H
NHVH g IIIAI . I NH
NHN WWII .I HONH I NH
HHM l IIII.I HOHH I HH
OHM OHOH . HI NOOH . NOOH HOOH I OH
NM OHNO NONO OONO I OONO «ONO NONO NONO HONO OONO N
OOH OHNO OONO NONOI NONO NONO HONO OONO O
NH .. I .II N
m OOH OHOO .II OOOO .I HOOO OOOO O
NH I OONO I «ONO .II HONO I N
«a .I NO«O OO«O «O«O .I HO«O OO«O «
NVH OHNO OONO I I «ONO NONO HONO OONO N
Na NONO .III I .I HONO OONO N
is III I OOHO AI HOHO H
L H
OH N N N O N « N N H O H O
I H.463 uwmumum mummoaoo

 

 

NHMB<E v— mme .mo
mmH<QN5 Q72 mZOHHHQg WHMmmS

n. . 3 “35¢”:

 

AwwdflnHUfluOUv M, I V MTHNWJQH

 

 

103

 

co
m
M

[\
m
M

\D
M
M

m
M
M

¢
«'3
M

m
m
M

N
m
M

H
M

O
m
M

o
N
M

00
N
M

[x
N
M

\o
N
M

Ln
N
>4

\‘r
N
M

m
N
M

N
N
M

O‘Dr-I
v-INN
MMM

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

_ _

 

 

 

 

 

_

I mowm I I I I I I I mm
OHNN NONNIII II I I NN
OHON NOONITIII II I I ON

NONN I I I I I I I NN

OH«N NO«N I I I I I I OO«N «N
NONNIIIIII I NN

OHNN NONN OONN .I I I I I I I OONN NN
NOHN NOHNIIIIII I HN

I OOONIIIIII I OOON ON

OHNN I NONN I I I I I OONN NN

I NONNIIIIII I OOON ON

OHNN II. NONN T I I I I I I OONN NN

I NOON I I I I I ON

OHNN NONN OONN j I I TI I I NN

NO«N I I I I I «N

OHNN NONN OONN NONN I I I OONN NN

OHNN I I OONN I I I NN

I I I I I II I I

HN

OHON I I I I NOON I I OOON ON

OHNH I OONH II NONH I I OONH NH
OH N O N O E « N H O / HO

_ I mez ummuwum mummocoo

 

deafiucoov m. w mania

 

AnUQDuHHUfl-OUV m - ON "NJ—ugh...

104

no 0H

 

 

 

 

 

 

 

 

 

 

 

 

 

 

NOOH v_ n OHV_ .N x33 H0O < <
H "ZOHHZHOZ

VH3: O
NO«« ILILI I I ««
oamq mom.« I mq
3% momq II I oomq «q
23 mo: I co: 1»
oaoq mooq oq
OHNN NONN .I II I OONN NN

OH N N N O N « N N H O H HO
I mes uwwuwhm mummonoo
_ _ N

 

 

 

 

 

 

 

 

 

 

 

 

St!“

St!

the

105

structured college biology course, BS 202. To do this, a system
structure analogous to that shown in Figure 2.2 was imposed upon
I the course. In reality the course exists only as a difuse group
, of students passing with time through a series of instructional
activities organized in some manner. The system structure, defined
'as "the set of interactions of elements, and related variables that
intervene in the causal chain that links system outputs to system
inputs" (Manetsch and Park, 1974a), is the abstract analogue to the
physical entities and processes of an automated assembly—line. The
mathematical model of the system developed is in effect a catalog
of the mathematical or statistical relationships between the ele—
ments of the system through action on the students state variables.
Although the mathematical computer model developed for this
study is an important tool in itself, most notably through its use
in identifying "weak spots" in instruction, its chief contribution
to educational research is as an organizational framework for stu—
dent information and data on instructional processes. Such organiza—
tion places the rigorous definition and analytical usefulness of
Systems methodology on the somewhat nebulous variables suggested by
Bloom's Theory. 1
A student enrolled in BS 202, may be viewed as undergoing a
.sequential instructional process. As a result of the student's
Ldaily or weekly "processing" through various instructional sessions
~and activities changes occur in the students knowledge and understand-
ing of the material presented and discussed. These changes are indi—
,cated as changes in the state variables describing the student;

Specifically, after each instructional session (lecture, SAS, LAB,

 

outs
f

(
nat
dla
ab]

um
Stl

Es

 

106

outside study, etc.) changes have occurred in the student's version

of C . These changes are influenced by the student's background

variables, B , by previous instruction I , by previous learning

( C imaged as K ), by unknown student idiosyncrasies, and by the
nature of the instructional session itself. This is shown in the

diagram of Figure 2.1 where student Si described by the state vari—

: ables of B i’ Ii and K1 (reflecting entry level knowledge Ci)

'undergoes an instructional session. As a result of this session the

student's state variables are transformed; the student has new under—
standing or mastery of particular concepts, described as alternations
to C1 resulting in Ci which are reflected as measurable alterations
to K,, resulting in K i. In addition, new instructional bookkeeping
data is added to I i' B1 is unaffected by the instruction. The
changes in C i (reflected in Ki) are influenced by Bi} Ii and
previous concept knowledge Ci (meaSured in K i). The instructional
session may be described analytically by the transfer function, f .
The process may then be described by K; = fg ( Bi} Ii’ Ki)'
This development is discussed in more detail in Chapter 2.

Following the archetypal system's model introduced in Figure 2.2

a model was developed for BS 202. Figure 4.2 illustrates a ‘systems

:model‘ describing the BS 202 instructional plan based on the fundamental

instructional session unit. The students were formally exposed to

‘approximately 10 lectures and 19 small assembly sessions (SAS), as

‘described in Appendix A. Each student was also urged to spend an
,undetermined amount of time listening to 17 tapes and working in the
‘individual instruction lab. In addition, each student spent some time

Loutside of class reading and studying. The model allows one to

   

   

UZ _ D<WK
fi
>OD|PW
mun . WFDO

«stnmNuhnvww

 

107

Emuwmflm mEQumhm NON .m.m

2 neonaum .3“— _

 

 

N .NN ounmfim

 

       

   

     

 

 

 

 

 

 

 

 

 

 

 

       
      

        

   

 

 

 

 

 

 

 

 

 

 
 

 

     

 

 

 

 

 

 

 

 

 

 

 

 

 

 

   

 

 

 

 

0 :0 330...”. 05 no occur—o ciao—:53 on» do 0.5305 05 \l\ 7” v:
u .
5;: s 3;: : OZONE . I n
q
o. O N
ONE. 3 £3 E EN :53
I'ly . , .III'I
O O ,
1 NOW... 2.. N<N 9: 35:6 .22: A .NV. N .NHLmYNN
.Z _z
_
_
_
-
—
a . 4 II
. .3 r. Z N: .5
:3 OEOEN ._ . _ ON a 2 2
O
N - a
N3. BEN a A , .25 H ON I. .
Om: NO _ NSO N<N OS N<N $583
. . _
ocozicm _
_
_
. u —
._ a 2:93 m N My.
NH NH was. m N30
7 IV. , II. :55 II.
mxakumg m<m 93 we _ PSO >4xum3
. . ._.mH+:u_. M .wa; Ha .wmv—m _. a: _. N .N .N 1. v j >maoocmu35m
N N N MnfiNv— . H+.Hn.NM A.v_ .H .m “v.1. v. A. _ .
N 3305 25
293:: mmmn 2..
. .1 I I . Ir mu¢0taum Nu acuvflum
. N. H 5. 3H .3 v. _ SN .39. 35332 3
Ozsfix . :80 . ._ N _
O N NE: N
:55 N N NE: N

 

 

 

 

visu‘

simu

The

assc

anal

sim

the

onl

108

visualize and comprehend the process of a large number of students
simultaneously passing through distinct seduential instructional units.
The change of each student's state variables by the transfer functions
associated with each instructional session provides a mathematical
analogue to the learning process inherent in a course of this type.

This model is a theoretical construct designed to mimic and
simulate the function and processes of the BS 202 course. In many ways
the model does not resemble the "reality" of the course — but it is
only a construct meant to simplify reality and aid in our understanding
of a complex process.

Condensing the Model

In the BS 202 course the students were tested only once each week.
Thus, the performance variables were updated once each week and not
after each instructional session. For any individual student perform—
ance on a particular concept Ci’ measured in Ki’ reflects the
aggregate effect of the week's instruction and study. For a typical
week, student performance on any Ci is related to previous background
and knowledge, the lecture, two small assembly sessions, two tape
lab sessions, outside study and reading and work with the practice
test. For the purposes of this study the effects of each week's instruc—
tion and study were aggregated as a series of transfer functions relating

the performance meaSures taken from the weekly quiz ( K ’) to the per—

. - .
formance measures taken the week before ( K ), the week 5 instruction

( I ’), previous instruction ( I ) and background ( B ).

 

 

109

The "weekly” transfer functions are represented in the

following matrix equation:

K’ = F (B. I” K) = BB+AI’+uK+ oz

'
K1
'
K2

 

 

821

- 441

31

 

 

8L1 B12 '

Ikll AIZ '

B1.236 I31 I
. B2

 

 

 

+
° ° B4¢23JI3323§
v - K
' I108 I11 ”1-1 “12 ° ‘ ' “1:44 I1
'
I2 . , K2
+ .
' 0 U
MAI-10% I10% u41.1. . . . . .“44-44 J K44

 

 

 

 

 

 

 

Where K'j is the performance related to con—
cept Cj shown in Table 3.11; 8 ij is the coeffici—
ent of Bj shown in Table 4.1; I’ is the update in—
structional vector reflecting the student's in-
structional behavior during the week; Aij is the

coefficient Ij shown in

 

Table 4
perform
additiv

A

110

Table 4.2; Aij is the coefficient of Kj reflecting the previous
performance on concept Cj defined in Table 3.11, and “j is an
additive constant.

A sample equation that could be expreSsed with this notation

is

K1 = f1 (31’ B18’ I14’ K2 K22)

= BllBl+Bl.18318+Al717+A114Il4+ul.2K2+“122K22+“1

Although for any individual student the effects of each model
component are blurred together, using multiple regression techniques
to find coefficients 8, 1:11 and a for the student population
(N = 105) produces relative weights of each component. By expressing
the relationship of antecedents, transactions and outcomes in this
manner it is possible to examine the relationships and relative

contributions of each component of the model.

 

5.0 y
W
Scienc
deal w
necess
ysis,
This f
are u:
cant
analy
numbe

Pessi

anal)
Small
ence
howe

labo

 

CHAPTER 5

DATA PREPARATION, DATA ANALYSIS, AND LIMITATIONS
ON THIS MODELING PROCESS

5.0 Data Preparation

When dealing with large and varied amounts of data in the Social
Sciences the most important initial analytical steps are those that
deal with correctness and error. Too often it is assumed that it is
necessary to move as quickly as possible from data collection to anal—
ysis, without the important intermediate steps of data preparation.
This is especially true when computer scored and coded instruments
are used for information collection. Very often the lack of signifi—
cant findings in a study is not due to inappropriate instruments or
analyses, but rather to sloppy data. Thus, this study incorporates a
number of inspections and checks to keep the data base as "clean" as
possible.

Figure 5.1 illustrates schematically the data preparation and
analysis used in this study. This type of analysis also requires a
Ismall amount of intuition, adding a touch of practical art to the sci—
ience, which is not representable on a diagram. Figure 5.1 does,
‘however, give a true picture of the steps, although not the amount of
Vlabor involved.

All the data analyses detailed in this chapter were done on the
IMichigan State University CDC 6500 computer using standard SPSS

I(Statistical Package for the Social Sciences, Nie et al, 1975; MSU

111

Que‘

1m . :vm. : aw. : :P mil W—[M

DATA 1

COU‘

 

Background
Questionnaire

112

 

 

 

Problem Solving
Test

 

 

Springs Test

 

 

Pretest

 

 

Affective
Questionnaire

—_____._————-——— I

 

I

 

Data From
University Files

 

 

Myers-Briggs
Test Data

 

 

h——-———‘—_——

 

Enter Raw Data
Computer File

   

 

 

Scoring and
Variable
Manipulation

 

 

?IGURE 5.1

PROCESSING AND
JRSE MODELING

Visual Check

Yes-

_4_‘

l Scoring Office

  
 

Summary
Inspection

Data
OK

Yes

  
 

 

Card DeCk Sorted

In Case Order

 

 
  

 

Enter Raw Data
Computer File

I }

Add To SPSS
Master File

 

«I»

 

Data Validity
Check

 

 

Correct Case
Sequence Check

 

Sequence
OK

?

 

 

Correction;

 

 

 

 

Delete
Incorrect
Variables

 

 

 

 

Delete
Incorrect
Variables

 

 

 

 

 

hLO F. uh in II n I

113

FIGURE 5.1 CONTINUED

 

l = Correct

  
   

 

PRETEST CODING

O = Incorrect

 

 

 

 

 
 

Of Pretest

Background

Affective

Questionnaire
Items

Factor Analysis

  

 

 

Output

Factor File

 

  

 

 

Addition of
Factors To
SPSS
Master File

 

 

 

I

   

Factor
I,t.

  

 

 

Lab Tape Check
Out & In Times

 

 

 

Hand Transcrp. Card Deck
& Coding — Keypunching Sorted In
Vicnal Phnh flrflnv

 

 

 

 

 

 
     
  
 

Lecture
Attendance

      
 

 

 

Small Assembly
Session
Attendance

 

II

 

 

   
  

   

Enter Raw
Data File

 
 

SPSS Variable

 

 

 

 

 

I Lecture
Tape Use

Practice Quiz
Use

 

 

 

Weekly Quizzes
and
Final Exam

 

 

 

  

Hand'Transcription
and

 

 

Codinq

 

 

Keypunching

Card Deck Sorted
In Case Order

 

Enter Raw
Data Computer
File

 

 

 

 

Scoring Office

 

    

Corrections

 

 

 

 

 

 

 

Summary
Inspection

 

 

 

 

114

 

 

Data
Validity
Check

Correct Case
Sequence
Check

 

 

 

Sequence
OK
?

 

 

 

NO

 

Delete

®4—-— Incorrect
Variables

Delete

Incorrect 1

Variables

 

 

 

 

 

 

FIGURE 5.1 CONTINUED

 

 

Total LEC, SAS
PQZ Use Variables

 

I

 

Coll Unusable
Test
Items

 

 

l

 

Code Test Items

1 = Correct
= Incorrect

 

 

Final
Inspection
And
Verification
Of
SPSS Master
File

 

 

 

Master

Data File

 

 

Test Item =
First Test
Item

 

 

 

Test Item =

Item

 

 

 

 

 

* Dependent
Variable =

Test Item

Aggregate Tenta—
tive Performance
Variables For
Previous Weeks

 

 

Variables

 

Regression:

Dependent VS
Independent

Variables

Next Test l—-\

 

115

FIGURE 5.1 CONTINUED

Master File
Structure
Test

 

 

 

 

 

Creation Of
Concept Performance
Variables

 

I

 

 

Addition To
Master File
of Winter Term
Data Imaged To
Fall Data
Structure

Final
Data Completeness
Check

 

 

 

 

Select To Rele—
vant Independent

 

Substitution Of
Mean Values For
All
Missing Case
Variables

 

 

 

 

 

Select Most
Significant
Independent

Vari ah'l no.

 

 

 

Add To
Significant
Variables List

 

 

 

   

IR]
Indgpghggnt
Variables

   
 

 

   
 
 

Variable Against
_ 70 Most
Significant

 

 

Winter Term Data
File Construction &
Variable Manipulation

 

 

 

Refine

» Significant Variables

Group

 

 

 

 

Roundoff
Predicted Value
To 0 or 1 —

Compare To
Actual

 

Regress Dependent.

 

 

Independent
Variables

    

 

Output
Actual And
Predicted
Dependent
Variable

 

 

 

 

 

 

 

 

 

................
El I K eeeeee
K [FIILFIDLFEBLFNILFDIILE

116

FIGURE 5.1 CONTINUED

 

I)

Select First

 

K Matrix Element END
KOlOl

 

 

 

 

 

 

 

 

Dependent Next
Variable = Performance
Performance Variable

Variable ‘

 

 

 

5 I
e ec es '
Items From Quiz =
Week Which Compose 1
Dependent Variable
Week =

pTfiifif—‘fig—rom ac es Week + l

tem' 5 Group Of
ignificant Va
Se ect Varia les

 

 

 

 

 

 

 

 

 

 

From This Group Of
Variables Select MOdel
The 70 With The Complete

Largest F's

 

 

  
 
 
 
 
 
 
 
 
 
 
    
 

 

e ress The
Depen ent Variable
Against This Group

Of Variables

Yes

 

No Week
10 ?

 

 

i
utput Goodness Of
Fit Measures And
Predictions Of

inter Term Data

 

 

 

   
      

Reexamine
Independent
Variables

    
 

gression
Satisfactory

 

 

Add Coefficients Yes
To Model Re-

Do um ' maining
c entation Pe “forman e No
Varigbles

 

 

 

ee

 

Con
de\
the

(u

116
Computer Lab Supplement, 1978) routines and several Fortran programs
developed by the author. The Pretest, the Background Questionnaire,
the Affective Questionnaire, the Problem Solving Test, and the Springs'
(controlling variables) Test were all administered to the student pop—
ulation within the first several weeks of the term. All of these instru—
ments except the last, consist of multiple choice items. The students
answered each item by selecting and marking a number choice on a comput—
er answer sheet. Responses to the Springs' Test were transcribed to the
same type of answer sheet by research assistants. It was necessary to
first visually check and correct these answer sheets before submission
for computer coding. Often students would neglect to fill in their
names and student numbers, or fail to adequately darken the answer boxes.
The answer sheets were then submitted to the MSU scoring office which
provided a computer card deck containing the test information and a sum—
mary. The summary provided a variety of information which was checked
for data anomalies such as unexpected or meaningless responses. If no
problems were found with the test data, the card deck was manually
sorted into the proper order for the 105 Fall term cases ()5 for Winter).
Data from the University's records (ACT scoeres, High School GPA and
Percentile Rank, MSU Orientation Test Scoeres, MSU class level, MSU
credits earned and transferred, MSU points and points below 2.0 and
MSU GPA on computer cards and cards with Myers—Briggs Test scores
(which had been administered early in the term but was scored by the
MSU Counseling Center) were also sorted into the proper case order.
Each card deck was then entered as a raw data file into the Computer
(MSU's CDC 6500). The Problem Solving and Springs‘ Tests raw data

were then processed using a series of SPSS programs. The raw data for

EI'

fi

fi

1|

 

117

each of the other instruments were entered and added sequentially to

a SPSS Master file created to hold the term data. Problem Solving and
Springs' Test processed variables were also added to the Master file.
After each set of data was added, the new variables were checked for
errors. The mean, standard deviation and range of each variable was
examined for any anomalies which might suggest data error or incorrect
file entry. When errors were found the variables were purged from the
file, the errors corrected, the data reentered into the Master file
and the variables reexamined. The case sequencing was also checked
after each set of variables was added to the file, and the data purged
and correctly reentered if any individual case was out of sequence.

At this stage of file construction the thirty four Pretest items
represented all that was known about the students' entry level know?
ledge of biology and ecology. Here the student responses to the Pre—
test items were recorded in the IR vector format; a series of choices
of one of five possible answers. The next step was to convert IR to IP
by assigning a correct or incorrect code to each item. If a student
chose the correct answer for Pretest item 1, then IPi was set equal
to 1. If the incorrect response was chosen, 1?1 was set equal to 0.
(If data was missing for any student case the SPSS "missing value"
designation was retained for IPi).

After the Pretest items were recoded and checked for errors, the
values of the elements of the Pretest IP vector were assigned to ele—

‘ments Bll through B of the Background Matrix, B . The total score

44
,on the Pretest was assigned to 345. The forty—eight item responses
on the Affective Questionnaire were assigned to elements B61 through

B108’ and the sixty-eight item responses on the Background Questionnaire

WEI

WEI

abc

anc'

D3

re:

Pr

 

118

were assigned to elements Bl through (see Table 4.1)

20 B187‘

At this point in the data preparation a series of Factor Analysis
were performed on the three sets of Background variables discussed
above. It was hoped that by rearranging these sets of variables, new
and useful background indices might be generated. Tables D1, D2, and
D3 in Appendix D summarize these Factor analyses for the Pretest,
Affective Questionnaire, and Background Questionnaire variables,
respectively.

Each of the three analyses performed applied the techniques of
Principal Factoring with Iteration and Varimax Orthogonal Rotation,
(Nie et al, 1975). This is the most commonly used method of reducing
a data set to a smaller set of factors which account for the variance
within the larger set and point of some underlying regularities within
that data set.

Table D1 presents the results of the factor analysis on the
thirty—four Pretest variables. This data set was reduced to 15 factors
after 21 iterations of the factoring procedure. The contributions to
the explanation of the variance of each of the 15 factors is shown in

,the top right section of the table. Commonalities, the total variance
Lof each variable accounted for by the combination of all common fac—
tors (Nie et a1, 1975) are shown in the top left portion of the Table.
Below, the major component Pretest items are shown with the mean, range
and standard deviations of each of the 15 factors. The major component
variables are those which significantly contribute to the computation
.Of the factor. Part of the outprint from a SPSS Factor analysis is a
:Factor score coefficient matrix, listing the component coefficients

0f each standardized variable (in this case for the Pretest variables)

for

vari

devi

ere

POD

ele

dex

SCI

t0

na

f0

 

119

for each factOr. Since the coefficients are computed for standardized
variable values (the variable minus its mean, divided by its standard
deviation), the relative magnitude of each coefficient is a measure

of its contribution to the total factor score. Thus, if three of
thirty-four factor score coefficients for a particular factor are great—
er than 0.1 and the remaining thirty—one are less than 0.01, the three
variables corresponding to the three large coefficients may be consid—
ered the major component variables of the factor.

The 15 Pretest factor scores were prepared using the major com-
ponent variable questions for additibn to the Master file or B matrix
elements B46 through B60.

Tables D2 and D3 show the results of the Factor analyses for the
Affective and Background Questionnaire variables. Eleven factors were
developed for the Affective variables (B61 through B108) and factor

scores were prepared for addition to the Master file as elements B

109
to B111. Fifteen factors were developed for the Background Question—
naire variables (B120 through B187) and factor scores were prepared

for addition to the Master file as elements B188 through 3202. Sev—

eral Background variables were omitted from the factor analysis

(3120’ 3122’ 3127’ 3129’ 3132’ B142’ B187
of O.

) because they had a variance

These factor analyses were done in the hope that the factors
would reduce the number of variables entered into the regression anal—
yses described in sections 5.1, 5.2, and 5.3. This was not the case,
.however. The Factors developed did not contribute to a reduction in
variables. Individual variables played an important part in describ—

‘ing student characteristics; the factors scores did not.

fi

ab

th

 

120

After each of the three Factor analyses were completed, an output
file containing values for each of the eleven to fifteen factor vari—
ables for each student case in the correct case order was generated.
These three files were visually checked for bad data, then added to
the SPSS Master file. Data validity and correct case sequence checks
were performed again and bad or improperly sequenced data purged,
corrected, reentered, and reexamined until the Master file was in order.

Throughout the term data on the instructional variables, listed
in Table 4.2, were collected. Lecture and Small Assembly Session at—
tendance data, information on the use of tape recorded elctures and
the practice quizzes, and the length of time each student spent using
the lab tapes in the carrel session were recorded by hand. After the
course was completed this data was transcribed and coded manually from
the term records, and then keypunched onto computer cards. Lecture
(Il through 110) and SAS attendance (112 through I30) variables were
coded 1 if the student attended, 0 if he or she did not. Similarly,

Lecture Tape use (I32 through 141) and Practice Quiz Use (I through

43
151) variables were coded 1 if the tape or Quiz was used by the stu-
dent, 0 if it was not. Total Lecture attendance (Ill), total SAS
attendance (I31), total Lecture tape use (142) and total Practice Quiz
LUse (152), variables represent the sum of the respective components.
Missing values were again maintained in standard SPSS convention.

The Lab tape—time variables, 153 through 168’ representing the
amount of time each student spent using each Lab tape were computed
’frOm Carrel Laboratory records. To use a Lab tape each student would
Check a tape out through a lab attendant. When finished, the student
would check the tape back in. The Lab attendant would note the times

Of tape check-out and check—in. Often students would use the same

tap
and
alt
the
enl
abI
hm
ta]

val

as
an
in

C0

 

121

tape on several occasions. When the Lab tape records were transcribed
and coded at the end of the course check—out and check-in times were
altered to reflect any multiple tape uses by students. The data was
then key punched, sorted in case order, visually inspected for errors,
entered into a raw data computer file, and processed with a SPSS vari—
able generating program. This program produced Lab tape times, in

hours and fractions of hours, for each student and Lab tape. This

tape time data was then entered into the SPSS Master file with the usual
validity and case order checks.

The weekly Quizzes and Final Exam were processed in the same manner
as the Pretest. Each computer answer sheet was checked and corrected
and submitted to the MSU Scoring Office, the test summaries were exam-
ined for anomalous data and the tests rescored if necessary. The
computer card decks were sorted in case order, with blank cards inserted
for those students who missed the test, and entered as a raw data com—
puter file. The test data was then entered into the SPSS Master file
with validity and case order checks. Bad or improperly sequenced data
was purged, corrected and reentered.

The last three items on each quiz (usually questions 31, 32, and
33) and on the final exam, requested information on the methods and
manner of study for that test. The values of these variables were
assigned to elements 170 through I99 of the I matrix. Likewise,
total scores on each of the quizzes were assigned to elements 1100
t0‘1108.

At this point the Master file contained about 900 variables. All
the test items from the nine quizzes and the final exam which were not
related to the Ecosystem concepts delineated in the content analysis

liscussed in Chapter 3 (see Table 3.8) were purged from the Master

fill

the

63C

385

den

ref

dal

ste

th

th

85

122
file (excluding I70 through 199). The remaining test items constituted
the IR vector for each student, and as was done with the Pretest items,
each test item was then transformed to the IP vector format; 1 was
Iassigned to IPi for a correct response, 0 for an incorrect. Each stu—
dent's IP consisted of 97 variables each having a value of 0 or 1,
reflecting the performance on Ecosystem related test items.

The Master file now contained a relatively error free, fundamental
data set describing each student with variables suitable for the first
step in the regression analysis.

5.1 Regression Analysis 1

The main hypothesis of this modeling process maintains that every—
thing a student experiences prior to answering a question influences
that answer. Most of the student‘s previous experiences other than the
instructional ones must be assumed to be insignificant in order to model
the learning experience. The variables developed for the study are
assumed to have at least a potential effect on the outcomes of instruc—
tion, as measured in the performance variables.

The intention of this process is to search for those variables that
Play a large role in reflecting student performance, incorporate them
in the model and test the model for accurate predictions using another
set (winter term) of data.

In the first stage of analysis each Ecology related test item
(represented as a variable with a value of 0 or 1 in IP) Was regressed
against all variables representing possible effects as hypothesized in

the model. Using the SPSS Multiple Regression routine (Nie et a1, 1975)
”each test item (IPi) was regressed against the entire B lhtrix, those

Velements of the I Matrix which represented instructional events or

act:

SCO'

WOU

6C0

Mat

83C

V31

reg

811E

are

of

WE

p1

th

 

123

actions which had occurred previous to the test including the item
(a test item from Quiz 5 would not be regressed against the total
score on Quiz 7 or the length of time Lab Tape 17, as it

would not be used until the eighth or ninth week), and all other

K

ecology concept related test items aggregated to tentative

Matrix elements, from previous quizzes in the course. Due to the large

amount of core storage necessary for the Multiple Regression routine

each test item was regressed against the relevant set of independent

variables divided into groups of about 70. This required six or more

regression runs for each element of the IP Matrix at this stage of the

analysis. The limitations introduced into the model by this necessity

are discussed in the last section of this chapter.
From the output of the six or more initial regression runs a set

of up to 70 significant independent variables was developed. These

were the variables which contributed the greatest amounts to the ex—
plained variance of the dependent variable, the test item, on each of

the initial regressions. It was also required that the "overall F"

value of the regression at the point of the significant variables ad—

dition be at least 1.0. Table 5.4 shows a set of Regression Summary

 

l
The test items from the previous quizzes which were related to
ecology concepts, as shown in Table 3.9, were aggregated using the

following formula;
Ki = 2 1P5
6

 

N

,aé defined in section 4.2. This was a tentative aggregation because
missing data was scattered throughout the variable set.

124

TABLE 5.4
PHASE ONE REGRESSION SUMMARY TABLES

FOR A SAMPLE TEST ITEM

"‘.IS'XOI Cotonou-llflaonoOIttooot

louelllDeon-ohoIod-Iul;:I nul'xcgg

l
VIILI

Run

I U I I I I V
uaurxcuu IILYFLI R Q IOU“! R PHASE ’1‘?“ I

"DIM

I'DII“ uuun“

"Ellncuct

OIKIILL 7

“NF

I‘ IV
llT‘l 0 um:

"I, [ll‘lIlfi‘x‘ugfiD

€55
2 =5

55!
i S;
2
I

.
‘11}
2‘

.. :,
o"ab

I"
l

.556}
:EIIII‘I
'zmu
_ll0lllll.. IOU-ooooooao-ooooooaooo

o 155’ -c
23.52;:
:Ii ’1

.‘cnuooleoollouooloolfieo gutyxyggl

VIII-l

Run 2

SUIIIIV

A
V ‘0': 8101!”: I“ ILTXPL! '. l "mu I egg! 8!.“ .

[I'll 0

'0“!

Km"! III! In!”

Slfllfltla

”um. I

I untfdu‘lgam

m

9. it! loszllterlsslltigol.’
Qui-LlfiflIN57OIN7tNON9§IIJ|N03s91~3
iiixllksslii‘iii
no... .aou:.o:o.N.....N:...:~o.:

INBiOSZ’I-NnAOXIllifliilitoillhsi
INYI YYZIINDNI N5: 1562.: 76151;}..69‘1
tibglrs‘iu‘ .52. 31617372. Ollihbbl
Itfi53Y§919579hIIIZGSi 30‘
0.00.9 N NSSIIII’sniE wife-56.91.57.555.
a o ........... can-ouco-ucnco

  

inlet-.1111;

Lissaoiu‘o’N21035l’611001tloozoil
lrnluolubsuunnxflunhsa ”I21“ enillsu I
n I E. I n...
"v N A Vlvurefivlliau 353:6..3 520‘ N “52».
Illxdl‘lllllslogxivxllllilltl
NONN-o...-.-..:::::N....a::...
N... .N .-.No.—N N N

11.635551997131072! 1...!!!» EhralsaYN I
(L 99.,its-FL.....£!7¢?ze-.L5252.N.§313
I: CITE-.509:NNLESSZISIlIEDDIflcCE

.332llzil: NIC’.EBIG.N:E&B.I: 50°09an
i‘i'gltifllltll'
N . N

o...-loo...-NONNNNO-noa-no-Nuonug

32572311505. A’blssbllu: .9906 N91 509
60355 55!.» ~606§1i05ntl¥67395371I251
IN, A1“... Izrrblaithrilz: N Nibevrl.‘
leg! #01:!!! N stlossrrvvry 1777 71
:IIIIIEIE-fitgif
u...o....on.onooonon¢uuo.o..oNN-NI-o

h810§l§¥l609ts°a12§1TSUN‘OJ9.I
ODSAZNKYVSJN'fias 0,-79EIIN9N051N01
15-...oexau-‘o‘2716592N¢l01v§-§i6777|
glgul’lilfio-AIIIIZSIIIZI

SiNtoethh N~§Szl§§li§i
ouOa....no-ooo-nnuoueo-auu..oooc-Nu.

INNfiu‘szn’lasvvloxzizallllu .7055; 2n
ngdbg.§§N¢-zgiflllx$znsiu7l
IO‘EIStht-N‘sglutullli
..-.o.....:o:.o...:.o. ..... o......

V.‘ ifQINO’Iwizz-U r- :.Z:’N,N .
0...». 515.72.. nullifabacQJhNBVHHQanuuvu
IsN awuhhfisu‘duunuuhr‘n it... 51%.»...15 5.9.2:.
N as ..
at”. . . .zbnt'uSnN,Ns‘!rt,!1.-§I'eoull
status“:................c..:..

1r}.

.29.~.~I-I.1”Iv3‘-’ELQI‘ 7 II

. l 565...... .4 .
"IlfivioauliununnMannililalhih
Firigfiesiihnunhrhbc

"flu?nnnuunufiununflnnnafinfinfi

I

9157
Ital
Ella.
r-rrr.

nun

run“.

....................... an”... “nu“... ..
Run 3

“WWI-1 "HAIL!”

VOILE

“unfit!“ mun: l C mum I W‘BE I!" R

.UIIIIV

1‘"!

'7" “mugs-us...

“mucus!

"mu. !'

KIWI

[ITII'D‘

livisnsnrzsssezsev: \viuottvwb’zlg all
i~lit11111t0000§90c=e¢rl|12>§32.5.1159 9...“!
Isiesuceouvloaooolsgvnlllhhiot
.N..........:. ............... u... ....... N.....

77701561‘5235571339NE.‘ N 162 N b gnlt‘gzlioil N I
ll..- .Nv 5 . N\...711§L(.Nv 9! ESPN... 7oz...) IZIIIZQJVIIO
lubt’li‘lsvotvvs .JSJ‘V :31 N7157XSIN 1.1))0057'ctl
IN £29026! 1 abhsfinNrsN-illsl’l‘ Nfihog N ‘51,) XI
Near-7 653556;; Nbllalxlb.! (11‘s.... N tail!!!
. n... .....................................

ihg'glii‘tii' l 0 ¢

.

2!!!!26‘7267’II56N02091‘. u.- .. Nil-66 \tabvrl Ns‘IO
OXDCOOIuLSEYSlIIZLZZIEGJ-i ’TIJsSJIr-NDINIB
9519:! 11.1.2: .sISVa-ivi‘ZiNecr .lsubficiissiu‘
lul5lnl‘.0563§511£7|52\ PEIvelaNcuI-xq.‘ ill a... .
1|!!!III!!!UUC§IIIIEO|I§OOCOII|OIOOD
ouoooaooN..-.o-.- ........ O .............. eon...-
NN NNN N. NN. N. NO... N

TIYG-Ntl-‘NIOOZOIIO133%560011 N90355:: 3967361 :5
«(it‘ll NIOII‘iNiitr e... .N (9627 01:92.. I 195 . .11).; ll!
6651!!!!0222115 00607 85553212~231111§221000tl
Striat1czrlllllllt....ccr .19. .n!?.!n.5§.lax.naflhl
iQIIIIIIFIlII‘UI|In-IOIIUIEIIIOOSIIIIIIII
s...¢ao..u.. ........ N ...... .o.....o.~..~c.o..-N

791:5!!!1732 N hghzew’lllslxgrfln 9335.. N9295Ni5
66!;921631 N79295!0.§9§§5l960075l9512 2.7: a... 7

”zllélgv NYHZsllzlbvatchbaZL-i 5579a)?!
‘05:... N7eal§sr759nblittftvi§l S
BIIIIS‘JIIJSSS .N. 5. Nb th e h
o

3
5:35....
.- b .N
soon-ooeoo-uou.o...-.u-~.un.~cc-o

Nut; N
N ANNhN hb~s
nN-NOouo-no-

2,."VifiazSLQBO',5‘.2‘§$£~399'°~I°5&‘
.Iszoz-2...§#Z3.€I§i.375-2 N
31.521.5335.l..--..3€d:l.u.nuly
2.53.1323: 3-1.2.3: s 3“ it, 1 I
.Oh‘fii’gzOEDOOOOJJz !‘

cos-cos....oN-NINo-o-c-NN-o NONo-uooNNoONNNooo

I 05.097559‘gifiln‘13015 3307235.: ect-5‘50
6h” NII’ANISIBIYI N1¢§ILZSDINS 0N7 N57)!
Oftlliigghi’oiegz
on-N.¢.Noo. .............. N ....... .N.........-

:anst.~l.’u31:$§9~iss!cs297’; Ilia 3360...!

’1‘EEEIIIIJSUA‘1 Nl‘195btsalxllyho... In!

.5!“lz$1l:9oéo ~55069ifaba9 N (uz.-511stze sun
81 N! ~1‘Jfl97:631.ieln 9‘15. Nlhl .790... if): I

ogholltlhs «rail‘bbgitllflui

N .oo...o.....:.:..-:o

7

......................
III-.11 I

 

\LIIIJQ

5v!!! .llaaN623927711!7. Iiitalsiixslunl

Phi‘fifiuflllnaN .N x~3Nsh3alch°ntlitlu that: N IN...
3u5€.5c:1«5:vl.nl«x.271}.)01ul.l!l..3~5..7)nl1u

nrrrvrrvrrrrrrtv: r 7.. turret... S»: 7...»: ”are r rrnnrr
rirrrrvrrlrrrrrtr rrrrrvrrvrcir .. rvrrr VIM
AAllat“L151LAAAJA‘:AILIAAtAAJJAAIIILAlAIJID

It

. *"Significant" Variables for Phase No Regression Analysis.

125

TABLE 5.4 cont'd.

'(ls-luson N-NNNNNN-NNN-NNNNNNNNN.

~~~~o~ ulLvi'oLI

~a0-ouoo-obuoonQN

 

Run 4

S 9 i I I D V
”alumna: WL‘I‘I'LK l l nun! t flag: 8]“! C

M"!

KEN In In Int“...

71.5!

81"! "cl-Cl

DWQILL I'

I P
[IVER D '1:an

' “” rug-{Smlkfiom

Ilslallnrsilitbzll I‘IIECSN-61uzl'53li
03123Nsfivi‘iin‘lt‘5’ItlI .01N7n1N567!az
IIIINleIQOIIltlullNS-N‘NS§VVVIIAOQ:QOOQIO
NNNNoNNNooNNNooooNN.oNNNNoN-NNNN.N.N:.NNNN

ills}? N13513:! n1!!!» n1 3 3 2173119
I925. 3792;131:3911 N N NiantuN “has“; S6183»...
157913365757]!!! Sine. .. liszvxxzsasu,s N3!
(In Ilasnelzp1-i ! N 93!... 1739922319638I631u1l
I .55. NZ..O!1|§NN~h!l|xIOOQOQ-i.7tv1tesliit N.
............... .N oNNNNNNN-NNNNN1.....0...
‘liiitia

{Iiiltlllbl‘lllti Ziblufitzzz uhizlnaifi?
IZXSQQBIOJINIQNYJIN NJN 631‘!th07293‘
7.9.?!- 31615 N. lira! v 3.1.32? |...7.|...a397 IN. V 9“)“
VibrainniifiaNOI NfifiaNilNzll .ilelNci3N33:N 3

11113108390... Itilvtillolnlfllllt
oNoooNoN-ooNnNNNNN-NnacN ..... Nu. NNNNNNNN n.-
NNNN N N NN N. N N. N NNN N N...

N ¢M3I
{561.213.}. N3L§EE Stir: iglxx’tnill
N’nglixa-liis N7 N3 NNNZ’NZIlgl3llltIEXOII N
3N3 1111111 323:3:th03394333033993nfll0030Bil
'Ofllliti "Oll'll'iillli -
N ..... Nun-NuoN-NN.N.NNoo-NNNNNNNNNNNNNNN-NN

v.1 ilh‘OiINOVQZISI Gilt NVYTSIIGIi

0
1101993263.. Nitllzgzvi nougIZS-Ssllla

N NOSGAIN’GNitulftxnl NatirNunla .11351567313 3
‘SSNEIZNI. .‘NllchvoscszatstuS No.3!» 7090 1....
3'! NsevsiGIZIJIN 22.89! 777 .3!- was. 993.33. I
"lib‘lilgigg‘glggi
a.no.vow-nNooeuutuovuoeuaccount-ao-nIcon... '
.I
‘
glslzabul’lzltll NafiN NN NlNgtzltu: 7

2v N351... eilaovgxou. .733..1N.N3711..551fp N“! 6
"2 D’Nv‘N7' h,"‘olhlvl.sh’9l .73, 15,-3527'
7,257’9gl‘.l$’l.‘|“‘llv‘££ll!llh ~§hbib I n I
III-i § .i A '5 ‘5. .O‘ h h‘l‘lgiigign
.-N...N..NN:N::NNNNNNN ......... :NN..N:I

1223;3323353361‘1t “Engage: u AKRON!!!”
933919355le II. 3‘ 31...: 25h

Zilégflhhbpltl‘lfiiZT,"

N.co-NNuNNoNNNo-No-aNNNNNNNNN... .NNNNNNNNN.

15 N \1I5333‘51253ND NII£15N3A 17 3.35.72 0131?,
‘N‘choésssllbDISIJDECQNIX’INOU9IIZ..NISZL
.1.5Sn.322 NJNSSQNN... 3.79: .Néib726677ufirb .7 N3
29016823331 {032:1 352.3... 55033095932L1323
OOilhtlfl, 63,-; 22!!£§§i

a. NNNNNN NNNNN-NN-NNosoc-vounooooco-NoooNo-N

M! N

3!: N‘Z‘Iii”llthNlNN 3962s....1512N1i

3.1.2281}!!!.........-........i...........z......!..

8.335333] Biz... .3352. .2515...
r... E 3.... 5.5.... ..i.:.. . 9:... is. in. s:

alufiK'rl'VllK llullllrl v. VIIUK‘VI ’(VKIINilIK

:I:§§.’ I'E£E§PIES 9‘

11:11 I

1

I

I

l
>NNNNNN-oco-NNoaooNo-NN IULVXDtt

I’D’Dn' Illllfl. I.-

Run 5
I I O I. I:
cannula ULTX'LI I I [MK I “IMF: I1“! I

SUIllkV

VIII!

its-menu

mm». |‘

I“! NC

I '0
[ml DR 'IRWE

"mus...

DIVE":

S"?

i Se.iysi.y§..i$.i..23§:§
2.. znuniziiilcit .33113...
.32.ls§..§xis!txszttlll.
...... ..... ..

hzlzhnani33335755fi52'636393957i‘9l1180 3
{335.7 92961525§ 1:53.. $35.33 N6 .79.;IZN5 Q.
16761116|N 5 :3! .N 11665953317 I‘lfizb‘NgllDDz
39155556..“2369290N31 29 N07296:! N I! no? .N ill
96 .332222110- 5166.....5 N33N¢llltldivlll11Y63
NNNNNNNNNNNNNNNNNNNNNN .NNNNNN .Nooco-uoNo-
gilllilfillllillt’ul

VIISN32l>Ntlf92N07IOIISIIOUDSGQII;I7.N6§”
US!(NIM7§3§N3}DJ‘NJAZ$3 713755.551-iN

\1‘333 19:.ZN3535N5..317 lZlleu .932735391NI
7 52.523.10.15 .v- beIN NlNclannzliNNld
Illa...ltllittl‘iflllltlllliiizliblll
.oNNNNN-NNNNNNNNNN.o....oo ..... NNNNNNNNNNN
N N. N N N N. NNN NNN N N NNN. N

.N1Sgls‘NE7519216t356N’fiizzgYNiSENébl
25!...{352 971:1 .N 1.. (I... N .51.. Entitrz‘iaN 5332:
0A QIZVQLLINENrr 7b.?iN155oN 2636223333’ulctn...
31111131211. ......:3N«t_t4€_ .IDE.-§.NN5993n3:flfll
Inlllllllllliil’ulfllloiii
Ns..NNNNNNNNNNNNNN-N-NNN.NNooN-N-NNNNNNNNN

N 1333378305315939.,31309573l55103; 365x.
ll? N66953:}, 36297;...Nil ~539§ll3929|9 I
I“ .Nxszlizibv ..... 53:52753’319‘Nb70hn63fll
3 7.113.271.3323N N9m677£q,!u1|¢22333313 3 N I
lgllllilssgliliiil‘!!!
a.NNNNN-NNNNNNNNNNNNNNNNNNNNNNNNoNoNNNNNoo :-
I
I
1.32.2.0.33.23523026500757195irbiE3NN227
0.2131037355N661ni INSN..»52! N 1.336.132.5635? 1-
INIlKN sznliilsi31uo 3573th ‘IQ‘IIJIJSI
1375N61‘N $743933)?! N Illiotrv7711-iiuiitl I
121333.; N N NN ~N nggfiil‘ii
NNNNNNNNNN-No NNNNNNNNN uuNNNNoNooNN-Nuoaoou

I3... NYIISQ. N5 .N lfiSSN .N 9x97 5:73.356}... N‘VIY N 565
9251316~366111703N901 lbdlltlllt‘ltlvNZiYIl
Illalllx‘v‘SSSNNNlb-a Nba6h§fiivzllxI
..... 0......-¢.N...NNNNNNNNNNNN-NNNNNNNNNN

27932.59 .51.}. 3726759....957399 153836.951.

usgssNurlNlazxizou‘. . 1913.2.N7957N22..)N.29:3

1139.. N9715S7.71N4.043(31N§73.Dto. 223.55? N .5

3x015N0t266e:990..911093919 Ohtvtl’ici N22,...

ihilviu‘lrzn‘vrvlSYNYbsb c... lIIIIOONiI
. o N

NNNNNNNNNNNNNNNNNN
c c... oNNoN-oNoNNNN-NNNNNN

 

3.1.3.27 leiufir..§2e!17:rN-. 51...!!1 .N, .. 32!

N606..§.NN 55555 NS9N101N.l.353u3.t.Nlnih.Nuln6ntu
73U231:3..I))..0))I)7333333131~§N.IJDI....9333
up... a: grin Ericnozr r rot 5957:: 6.1.... 67.. car...-
ervxllir y .5... 5? 7.. (v.2. (1.3;: 7-: [5.5.5:-
lenl reg-i: Fir-.- Erooh PDP’QEIKIiiGOII
.

0|.O'IDIIDOOOOUICIID‘I. uuLIIDLl

I'll-1n Ins-up.

[Elli

IIULE

’"Illl'

stunned-N: I’L‘N’Ll R l "Milt l gulls II“! l

'1“! 'l :1“

OVERALL I

nvu‘z’fiwlflom
”ISM.
E5.

I'll!

Ant.

I '2
SITE! ll “HOV!

!
I
J.

126

TABLE 5.4 cont'd.

IIIIIISXOI .ltobhOIDOOIOQCOCDODDI.

uI‘ouoootangno|.oo.cu;ouo “uLy‘OL‘

' uvgunu "mun“

'IIL!

“5“".th M71“! I l nun! I wgi "fill I

Run 7

IIIIAQ'

unu'ol'um:

3!“! nun:

“Ian I

V‘lX l‘blnno

I‘VEIEQ

"I!

AJ‘IL91V2650366I3155
1‘85150.O7\.;ILOIA6!90
Illiifinl;“9i

- . . a o n a o n ...... u n o ooooo .

.ll'

Nihvi‘ilil 350!) Sn .913500
tl359h§1$l671I|622I319l
n-Ytés‘zsll‘l uliuzlh shit
52-35193. 155.13.71.97?!“
ilbllnllhoai (I! 56:53 A :-
............. . . u u . n n u a u o

tuOH“

Igiflz'?ighir:!
‘l‘sl‘gr‘hsulu' IORQO‘b‘S-IIVHLI
unnflfiuavué liner.uz..s..292.nnoa
iti‘szili:g'
...........~.....n.~....... .....

vz’llfilblfifllli’...» SIS...‘
8390...?- llflt?!.~$..§2 .-A6¢~Ll~!ub
IODDJGXIAK.’llltl9ths313x111£t0G
ICE-2.31.51!=3I|cn.93..n:uall..lnfli
liulilulllllnlflglill
. . c uuuuuuuuuuuu a .............. u .

YOXQESOISJGOIa;QQZI~I7IIIIh89
Inhil.fll609al.9-9~661930~I!§nt16
ll12565¢fiI!~S§JIII§G)69.\;§AISI
00.13:). Idau|3~§~§bbeéirl~17711
OOI!...ZZ§IE§I§I§§)J
onus-co... ...... ogoo‘oo-a-octonu

izz§lzh§bg§l?§b“ll§0
13541251.; xii 63% .-Y $011221; 1!): EC]
litissvlihttiln; ~136g>090lvvlu§$
It) urflbllrllslblliivflnllt 111111
OI: ~§k;2§i5§§!fi|tbtlt:0‘
con-ouoc.co-.o-¢.unnoo-uc.-.coo

‘30!!! .Oladll’lu’u .9. 3,26h90
lltosblhsitbfixu‘) nl ~0‘51257 II
I’D; Yt’l’ib‘tzl":
o o ~ - a n a o ooooooo n . ....... a n u .....

lailivr‘l ~55]!’556.\.55I%1~b72

l b‘OI-u’fiitbrlflz.’ ~93, 1560:.

Isilhzfilszuafx :5 71273775 h 33.5.:

‘3’:?’97:151!0‘936th. 5.»)le

tl‘hlz nilfllflblh .lltlilltllll:

.uoououuugnon-onuauuo.-acog...uc
u u. I

O O O V
25:51:!!!)11...‘ nu 16 c ..
Ilagxflllallflzkpvcou 0! ti .SDBA
g..a1.32.l5..ol:vt 1.5:... (1A2i151vp
rxu’ 1535953....3.‘ - ultsvuuuYsuuvv u.-
lvraAu an n .. I. Llllgrxti.g.cvs:g.m
nii'sigottruniin urnlsnvllu

1 s:-&1:0‘$‘~<t7lcotl!
lint-lot}:

ltl

X
I

0 llIL

OIOOOJICOIUI

RM! “nu-LL.

o
o
O
I
o
D

Iltttxtl "'0-'O’§oouo-oooooo~

1 A I L l
Il‘lll'lfllla‘ "1&le I t lull! \ :0)”: $llfil I

nu
nu
Du

.UIIA‘V

"Hint!“

WI‘ILL '

II“

I I!
(I‘ll! DI RIM!

“I I L!
50 X {ENVID

m
n
u

0:91.95!!!
0120‘s}...
igh
. u uuuuuuuuu

{2&7 nil)-
3l~6921015|
62:0!!!) ~ 2
53900;: ~ I.
ll~789§ nil-
- ..........
lulsinuttltl

97!;5!“
“"1nltI-SI
builltiti
Ylt 96.316
lfllttll
- u u n u u ‘ . . . .

1 Gui-5.05!
LB”? .6152)...
5565312000.»
71.1.0 aaaaaa

7..- 3.37722
“92531.37;

2.31....
{€011}...
oouiziix
. . . . . . . . . . .

967717632...
43.90.57.726
10;?133hh
1:333!!!
{Kill-14!!!!
o a u o ...... a

02311.1 150!
0|.I733I56
'ZI bb||IIOI
a n . ........

ZlfiilZshvfil
367702.715
tuiioisflen.
h 1),: )2
II’AYISJII

I.

01692:..lllh
V35 lll In [[[
3224:1559!
M!!! r .1 7r r .. I

"wwwnwunnu

i=0? A $11
[A

.--.g--.-9.-...--.g-..---g,g_--o-.---.----..u

---.-o.--..-.

PFF008

K0101

KO80 1 , PFFOOl

vari

and

frm

the

5.2

eac

VHI

8H

uaI

ea:

WE‘

of

nu

Vi

pl

127
ables from the initial regression runs for a sample test (1P1)
the group of significant independent variables that were developed
n the inspection of these runs.

This process was repeated for each test item or each element in
IP vector.

Regression Analysis 2

The second phase of the regression analysis consisted of regressing
h test item or T? element against the appropriate set of significant
iables. The Multiple Regression routine was again used with each
‘up of approximately 70 variables. ‘From each regression run the act—
, and regression "predicted” values of the test item were output for
:h student case to a separate file. The regression predicted values
'e then rounded off to 0 or 1 using the standard criterion, and a set
residuals (equal to the actual minus the predicted) generated. The
1ber of residuals with a value equal to a plus or minus 1 for a test
:m was the error rate for 105 cases (or approximately the percentage
‘or rate). Tables 5.5 and 5.6 show, respectively the Summary Tables
‘en of a sample test item against a "significant"* set of independent
iables and the resulting residuals.

If the second phase regression resulted in an error rate of ten
cent or more, the set of significant independent variables and the
vious phase one regressions were reexamined and the set of signifi—
t variables refined. For over ninety percent of the test items it
not necessary to reexamine the significant variables. No more

a one iteration of this process was required for any test item to

 

Lgnificant" is not used in the statistical sense here. What is
:ended is that the variables labeled as significant hold the promise
being useful in the prediction of performance through the applica—
’n of the model.

 

"' : ____

$I‘ll'chCI

OVIIILL '

TABLE 5.5
PHASE TWO SUMMARY TABLE

128

Qgslgggxa. DOQOOIOIOUDIOOIOIQDO-oo

TAIL!

SUIIARV

SZGIXHUIK WLTX'LE l I WI! l nun: sun: I
wltt

FOR A SAMPLE TEST ITEM

occu...ooo-vsu.ou--. IULVZ’LI

at“ unmh.

nan
I 7
human SITE! Jon-cove

VIII!

EIYIIEG

  
     

           

  

6 .- 52!.6255s...!v52\.515792._. 2517335. I..9=.12.!§

’ ' I‘M I.‘.‘_!‘.hhl.§7l “.§I.!.I.§u‘gigh in .IL 575!

' I i 3.1:.i9‘irsfiflfiz.‘ .31....1‘

. . ... ........................... .. .......

.l ‘ 31.327‘JEBQ h'59!.§3355h7096n.7. fan-59's n‘lvl‘.’ .- h H

r. .- :Lh‘n.‘327!l.5..5|.7r3§ L- ut‘SG (.2: 39-All..“§l.

I t Lz5t5551‘307eurisllattpiv.. .Ilh?1!’l~bv”

u. | ‘1 nislaua =9 nucla~993~iso 155.3205". 3 a

I I sl1rlhll111ii$§2ah .351!!!) Yiblgzoul q u.

. . .......... ...... . ............. . ...:......‘

‘- I Int-till‘ll‘oilu‘lilltllltll‘

71591152039723.33327’tingeo‘s’tzntzrssls.gnligstestnnx

{3313“hlugh...‘33?!”‘53"!ltl’llfll‘i‘fI‘Iczfilwfil-Y[lullglgA-Kgil‘ria'

Darwin—5287méflxlwxvehng(fin-hulauwunfififlfltflfiubfiflkmac"gfifimfinnmfiiDE“.

OIJJIUII~BIIIIJJIAJIXA|§nIddltiaadixxzanounldfilltomlanofl .0. . . . .

.. .........- . .. .. . ..... .... . . 3.... ........ I .. . . . . . . .
il.|votfi‘titbnlul¢l¢ila ill: «its: ll...

. . ... .. ... .. . ... . .... . a. . .21.. (11.1: 0‘:

abiboitgslaigtlfilglfleilis’.‘.5525552331» 11.5.!!! :1 .511
01.1563155931I5102535618 .9695) -7 s!!!l.-A§bi?12u.lllul|.5u 219306.... .
g1§2hfl$lfl§§lk15¥lli2l1l91Iginlzrtulgllidnc. 00.»... . n32?!
in.50¢.ulllgsoagsrlttiliIttlifltlfltfiflbtnir‘o..Jctlolrattllaltll
‘il'iliil‘lgilillligi‘tslunlllll
n..u.....u....-.o...u.ua.....................o...............o

15967.9. JDQMM 55663l3l5077llnzo .0533915695h1l155.31316|611
~i2~tfl§i§115 Slxhir‘ov'Ihulola~lalssz9slauin1621.1365616695
finlsillav 2)§t61§fib|§tl)1§76§t1i . ’31.,301353113566114337 ) .10!
‘5... 3.79.3330 invitysnlrl.SLIYIQICIIlIIPSESSI-ibslr£09.22. .
Iltlllfigllsgobohhzggilttbbtt¢ltbttltsbb61r
.n....:....:.....o.-............-........................-:,

‘lhnllhits~.\.lth h! I ;9~55?99§62ni‘:‘:t.&ntbxgilnlSMuu‘
5056717. 22555141 13$k1~lltlstdlnl¥7€§11 .56i35.2?§911bv.17279 1
0°62- . 145903339-516lbo165304-uicn:6e15*§9013 .5b11133506u IJShO
1|7l1l91~ .5559315135v 0941.6!) .9566!!! :4! 54.13.3313 9.11)!)27!S‘(.I
‘5‘!"oszxtiib‘hozvz‘777171777711'71117VYIOQOIIOAQIO .
.u........-.:..u.............-..-..o..........2............u

     
 

           

 

 

ggéganzhélézlb ~7 .5‘I-la‘rZh22!

antitafiozgh * . sggbgllflns :5 3’“. 357.11.339‘) 6‘3: . . n a. -

L . . . kn... .-. .. . 24.13.... . . . . «.53 .35.... . v.73 .J1 o. 51.... n.»

.. iv: . . . 3:33.. . . . .l... .1. ... 3. .. vl . .. 2.. -1- y- - - 1.2: {5:125}, - £31--.) it..- . , ..-
. Emu“ xufinl-ILFH......: Show... gunfight .xmflfifivvflu ......._4....1.¥....\1...L.::. E1113:

075,561,505??? l~efrun ~§1Y539$1sgh h ~15§IE9031§555§935l52605
2:52.. A 9.5.91.2! 15715 .’ .9; 5200 s _. 6x. . «3.21721:va ..Libs.nuv.u 2.651)
500533.:5315933!‘ 393-.236150l3 .5512 ~ liais526ta~ ‘ (..l.162-:. @926!
giaisnigtitg, .2757 lilgfl70I§~l6659$5§Ll :3 :95?!
1.1., ~i~lffl§rit§36€132210¥6l¢$7~ h afaalil§§u~lhzal
o-nu-.-.oouo-.oo:..o....o......oov-u.-.o...o.......o..-o.-.o
x it: I
I .

 

 

'
u \IKIIJ
1...... 2.3... x255232rl$2.323315%..952372 .. as ..i
.52... 11.2. 3.12135....z...§3x2..saz...lii.x.i.is :37 a a

It"'
"IS

30.535531)! 5.... .DBEHIHDG!JQ.§¢A a.@
5:31. ..litrncvrtr c n... : If! it.» EEK 666...»...

   

 

flair: ..srrrxurrirr «(5.5.5.5. (r 5:. 1.2.5.... SS .
DDOLIDII'AI AIIIIUIAIAIAIADIAltu-Iu’qvvnl

J ‘ ‘ I >5 F I!1Lv‘h§§7$5|ih3l§)"fl~§§r|

W
i!

18

ficant" Variables for Phase Three Regression Analys

igni

HS

are

thl
im

I

E

129
TABLE 5.6
RESIDUAL ERROR RATE FOR THREE SAMPLE TEST ITEMS
Below the actual and the rounded regression estimated responses
are shown as 0 or 1,
0 = incorrect responses to the test item; 1 = correct.
The error is computed by subtracting the estimate from the actual;

the number +15 and -1s in the error summaries, indicate the number of
incorrect estimates

 

TEST ITEM ACCURACY OF ESTIMATION

 

 

 

 

 

 

 

 

 

 

 

Two 5 RELATIVE ADJUSTED CUM
ABSOLUTE FREQ. FREQ. FREQ.
CODE FREQ. (PCT.) (PCT.) (PCT)
Actual 0 9 8.6 9.2 9.2
Response 1.0000000 89 84.8 90.8 100.0
-9.0000000 7 8.7 Missing
Total 105 100.0 100.0
RELATIVE ADJUSTED CUM
ABSOLUTE FREQ. FREQ. FREQ.
CODE FREQ. (PCT.) (PCT.) (PCT.)
0 9 8.6 9.2 9.2
Reg’Eessmn 1.0000000 89 84.8 90.8 100.0
Estimate
—9.0000000 7 6.7 Missing
Total 105 100.0 100.0
RELATIVE ADJUSTED CUM
ABSOLUTE FREQ. FREQ. FREQ.
CODE FREQ. (PCT.) (PCT.) (PCT.)
* 0 98 93.3 100.0 100.0
Ermr‘ —9. 7 6. 7 Missing
Total 105 100.0 100.0
Based on: Valid Cases 98 Missing Cases 7

 

*
0 Estimation Errors = 0%

I?! Q

130

TABLE 5.6 (Continued)

RESIDUAL ERROR RATE FOR THREE SAMPLE TEST ITEMS

 

 

TEST ITEM ACCURACY OF ESTIMATION
Two 4 RELATIVE ADJUSTED CUM.
ABSOLUTE FREQ. FREQ. FREQ.
CODE FREQ. (PCT.) (PCT.) (PCT.)
0 29 27.8 29.9 29.9
Actual 1.0000000 68 64.8 70.1 100.0
ResP°nse -9.ooooooo 8 7.6 Missing
Total 105 100.0 100.0
RELATIVE ADJUSTED CUM.
ABSOLUTE FREQ. FREQ. FREQ.
CODE FREQ. (PCT.) (PCT.) (PCT.)
0 25 23.8 25.8 25.8
Estimate 1.0000000 72 68.6 74.2 100.0
RegrSSIOH -9.0000000 8 7.6 Missing
Total 105 100.0 100.0
RELATIVE ADJUSTED CUM
ABSOLUTE FREQ. FREQ. FREQ.
CODE FREQ. (PCT.) (PCT.) (PCT.)
-1, 4 3.8 4.1 4.1
Error * 0 93 88.6 95.9 100.0
—9 8 7.6 Missing
Total 105 100.0 100.0
Based on: Valid Cases 97 Missing Cases

g

 

*
4 Estimation Errors = 4.1%

 

 

 

 

 

 

 

 

 

 

 

TES']

My;

Ac

 

131

TABLE 5.6 (Continued)

RESIDUAL ERROR RATE FOR THREE SAMPLE TEST ITEMS

 

 

 

 

 

 

 

 

 

 

 

 

 

TEST ITEM ACCURACY OF ESTIMATION
1
1Five 4 RELATIVE ADJUSTED CUM.
ABSOLUTE FREQ . FREQ . FREQ .
CODE FREQ. (PCT.) (PCT.) (PCT.)
0 43 41.0 46.2 46.2
Actual 1.0000000 50 47.5 53.8 100.0
Restse -9 . 0000000 12 11.4 Missing
Total 105 100.0 100.0
RELATIVE ADJUSTED CUM.
ABSOLUTE FREQ. FREQ. FREQ.
CODE FREQ. (PCT.) (PCT.) (PCT.)
0 41 39.0 44.1 44.1
Regression 1.0000000 52 49.5 55.9 100.0
Estimate —9.0000000 12 11.4 Missing
Total 105 100.0 100.0
RELATIVE ADJUSTED CUM.
ABSOLUTE FREQ. FREQ. FREQ.
CODE FREQ. (PCT.) (PCT.) (PCT.)
—l. 5 4.8 5.4 5.4
Error:* 0 85 81.0 91.4 96.8
1. 3 2.9 3.2 100.0
-9. 12 11.4 Missing
Total 105 100.0 100.0
Based on: Valid Cases 93 Missing Cases 12

 

*
8 Estimation Errors = 8.6%

 

produc

secom

K M

as de
Maste
in OI
cases
variz
Regn
vari
case
Matr
the

cont

132
roduce an error rate of ten percent or less.
This second phase of the regression analysis was repeated for each
f the items in the IP vector.
.3 Regression Analysis 3
After each test item had been successfully processed through the
ecOnd regression analysis phase, the test items were aggregated into

Matrix elements using the following equation:

3 defined in section 4.3. Before this was done, however, the SPSS
aster file was altered somewhat to aid computation. Up to this point,
1 order to use as much of the available information as possible, any
Ises with missing values for a variable had had the mean value of that
triable substituted for the missing value (using Option 20 of the SPSS
:gression routine). Before summing and normalizing the IP vector
Lriables using the above expression, the mean values of any missing
Lse variables were substituted throughout the entire file. The

.trix elements were then generated2 using the above expression and

1e concept—test item relationships outlined in Chapter 3. Table 5.7

‘ntains the mean, standard deviation and rage of each Ki°

 

n the two previous phases of regression analysis the K elements had
een updated on a ”weekly" basis, and test items regressed, in part,
gainst prior K elements. This was a tentative arrangement used to
educe the total number of variables stored in the SPSS Master file.
ecause of the large number of variables necessary for analysis the
aster file was maintained as a SPSS Archive file. The reduction in
he number of variables necessary for analysis in the third phase of
egression, allowed the Master file to be stored as a "regular? SPSS
ile with about 490 variables, which made the computer analysis
onsiderably easier.

.lnlt r nllll

h in MAMHQH.

 

133

 

 

 

 

 

woN.o ooo Hao.o omm.o HoNHM NoNHM NH
mNN.o ooo Nuo.o Hmo.o aoNHs NoNHM Ha
ooN.o ooo.H omN.o sos.o m .o .H ose sane N+ Nooas Nooas oH
NoN.o ooo Nuo.o oos.o o ooo Mano + Hooos Nooos o m
mmmmo ooo.N mmmwo mmmwo m .o .N .N oss saao w+ Hbmmu Nowox o m.
.o.z a
mMN.o ooo.Nno.o NNo.o Hooos Nooos o m
mNN.o ooo.Huo.o Hmo.o Nomos Nomos m
noa.o ooo o-o.o mmm.o Hooos Noaos a
NoN.o soo.o-o.o osN.o Homos Nomos m
moa.o oms.ouo.o omm.o HoNoM NoNos N
oNN.o ooo.Huo.o woa.o HoHoM NoHoM a
mwmwmdouu EouH umouwum ou Hmswm uwm «Olmwwoucu «H
.o.z ma
woN.o ooo HIo.o omm.o oa .wa ooo sano N NoNHM NH
mNN.o ooo Huo.o Hmn.o ma .o ooo nano N Hoaam NH
mom.o ooo Huo.o ooo.o o oso naao N Hooax oa
msm.o ooo Huo.o on.o o ono saao N Hooos o m
ooN.o ooo Huo.o Noo.o ma .o .s .m ooo saao N Hooos o m.
.o.z a o
mMN.o ooo H-o.o NNa.o o .N .a ooo sane w Hooos o w
mNN.o ooo Huo.o Hmn.o w .a ooo nano N Homos m
soa.o ooo ouo.o mom.o o .o .o .m .4 ago saao N Hoaox a
NoN.o moo o-o.o NAN.o o .o .m ooo Mano N Homos m
mwN.o omN o-o.o omm.o o .o .n .H ooo naao N HoNos N
oNN.o ooo Huo.o woa.o HN .o .o .H ooo naao N Hoaos a
onsaHomo moz<m z<mz u as ons<eoz
om<oz<em mzmHH Home Hzmzonzoo mamonoo a

 

 

 

 

 

 

hum." Emma. Aim mom mZOHHHZHmmn Q73 mHZmSMAm NHMHSA v_

 

 

AUQUEHUEOUV h.“ "NJHMHJVH

134

 

 

 

 

 

 

 

 

 

.o.z a
ooN.o ooo.Huo.o NNo.o mooom oooos o m
ooN.o ooo.H-o.o oHo.o N .H Loom NHno N+ momos somos m m
NEH.o mmw.onaoH.o swm.o N soon sHao + mooom soaos a E
NoH.o mmw.o-o.o smo.o N soon ano + momog somos m m
moH.o ooN.ouo.o wmm.o moNos noNos N a
oNN.o ooo.H-o.o woo.o moHoM ooHos H
m :Hmsouo EmuH umwuoum ou Hmsmm umm «q nmsouzu ma
“mmwo ooo.Hnmwo wmmuo NN noses NHso :11. mooHs wH
.o.z NH
osm.o ooo.Huo.o oHo.o oH noses sHao mooHM oH
omm.o ooo.H-o.o oso.o o noses NHoo momHM mH
ommwo ooo.Hnmho mmmmo H noses ano + mems mooHM oH
.o.z NH
ooN.o ooo.H-o.o amm.o NoNHM moNHM NH
mNN.o ooo.H:o.o Hmo.o NoHHM moHHs HH m
ooN.o ooo.H-omN.o son.o NooHM mooHM oH m
NoN.o ooo.H-o.o ooN.o N .H noose anoN + Nooos mooos o I
mwwwo ooo.H-mmmmo nmmwo H noses saao + memu mowom o m
.o.z N %
ooN.o ooo.H-o.o NNo.o Nooos mooos o
mNN.o ooo.H-o.o qu.o Nomos momos m
NnH.o ooo.ouo.o mmm.o Nonos mooos o
sHN.o ooo.o-o.o mom.o s .m noses NHnoN + Nomos momos m
me.o oms.o-o.o omm.o NoNos moNox N
oNN.o ooo.H-o.o woo.o NoHoM moHos H
w EH SOHO EMUH UWQUQHAH Cu HMS mm “mm flaw S DOHQH WH
oNN.o ooo.flnmwo wwwwo o osa nHao NooHM 8H m m
.o.z MH o M
onsaHsmo mozam zams u HM onsosoz
om¢oz<om mzmsH Emma azmzomzoo mHm<Hm¢> H

 

 

 

 

 

 

 

 

ADUDEHUCOUV hum. WHQH

135

 

 

 

 

 

 

II II II I: .o.z NH
woN.o ooo.Hlo.o 0mm.o wooHM mooHM oH
mwN.o ooo.Hlo.o mHm.o 0H w>Hm NHSO womHM momHM mH
DNWMO ooo.HIDMWDo Nwwho NH .m o>Hm NHDO w qquM mOQHM «H
.Q.z MH
woN.o ooo.Hlo.o qmm.o OONHM mONHM NH M
mNN.o ooo.Hlo.o qu.o «OHHM mOHHM HH %
OON.o ooo.H|mmm.o HHn.o OH.q o>Hm NHSO w+ OOOHM mOOHM OH H
nuH.O ooo.HlnoH.o wom.o N o>Hm NHSO + qomoM momoM a H
NMHMO ooo.HINMMMo OHWWO q .N o>Hm NHDO w+ qubM mowoM w m
.Q.Z N
mMN.o ooo.Hlo.o NNq.o ooooM mooom o
OON.o ooo.Hlo.o OHo.o qomom momoM m
an.o mmw.0|moH.o nmm.0 qoqom moqoM c
NmH.o mmw.oto.o qmq.o oomoM momoM m
mmH.o omH.OIo.o wmm.o «0NOM mONoM N
mNN.o ooo.Hto.o wmq.o «OHoM mOHoM H
w aHmDouo EmuH umwuwum ou Hummm uom q¢1flflioy£u aH
Nmm.o ooo.Hlo.o wan.o mbmHM wowHM wH
III III. .III .Q.z NH
moN.o ooo.HIo.o omw.o mN usom NHDO + mooHM comHM 0H m
omm.o ooo.Hlo.o wow.o momHM nomHM mH m.
OMMMO ooo.HHbMo mMMWo mbNHM oquM «H J
.92 HH m
woN.o ooo.Hlo.o qmm.o MONHM «ONHM NH 1
mNN.o ooo.Hlo.o qu.o mOHHM «OHHM HH
oON.o ooo.H|omN.o mwn.o MOOHM «OOHM 0H
©ON.o ooo.HIOON.o an.o N udom NHSO + comoM «ooox m
MHH.o ooo.H|mnm.o Nmm.o mowoM qowom m
ZOHH<H>MQ muz<m Z<ME M HM ZOHH<HOZ H
QM<QZ<Hm mEMBH HmMB Hzmzomzoo mam<Hm¢>

 

 

 

 

 

 

 

 

E

ADUUEHUGOOV h.fi MHIHMHQH.

 

136

 

 

 

 

 

 

 

omN'o ooo.Hko oIomMo oN EH... :3 I ooNNvH NN
.Q.z HN
NQN.O OOO.HImmm.O MOm.O mOONM OOONM ON
NON.O OOO.HIO.O mHn.O mOOHM OOOHM OH
Nomo ooo.HEo ooho SE 8st NH
.Q.Z RH
OON.O OOO.HIO.O Omw.O mOOHM OOOHM OH
MON.O OOO.HIO.O mHn.O mOmHM OOmHM MH
ooN.o ooo.HIoRo NNIoIo 3E oooHoH oH
I .o.z NH
OON.O OOO.HIO.O cmm.O mONHM OONHM NH W
mNN.O OOO.HIO.O Hm¢.O mOHHM OOHHM HH W
OON.O OOO.HImmm.O HHn.O mOOHM OOOHM OH S
an.O OOO.HIBOH.O wOm.O mOOOM OOOOM O H
Nfio ooo.Hunho oFo 8% 8on w
.Q.z m
NOH.O OOO.HIO.O mqm.o Om me NHSG moooM OOOOM O
OON.O OOO.HIO.O OH0.0 momOM OOmOM m
qu.O hmw.Olm¢H.O OO0.0 ON me NHDO + mOOOM OOOOM q
NOH.O mmw.OIO.O qmq.O mOmOM OOmOM m
me.O Omn.OIO.O wmm.O mONOM OONOm N
OwH.O OOO.HIOwH.O mum.o ON me NHSO + mOHOM OOHOM H
433 3 Hang Com. 3 dwfloufi NN
NON.O OOO.HImmm.O mon.O OH .q .N NHSGN MOONM ON E M
NON.O OOO.HIO.O mHn.O m .H NHSON mOOHM OH M.%
Nmm.O OOO.HIO.O OON.O «OOHM mOOHM OH 9 X
onaaHEHH mag 23% u HVH onsfiHoz H
2953 mZmHH Emma ezmzomzoo

 

 

 

 

mqm<Hm<>

 

 

 

 

AHUNUEMUCOUV him. NAM—”din“

137

 

 

mwchsoso umouosm ou Hmavm umm qq nwsounu mN

 

qu.O

ONn.O

 

 

 

 

 

 

 

 

O O H O O RN Gm>wm NHDU anNM «N
OON.O OOO.HIO.O mN0.0 «N Gw>ww NHSO HOMNM MN
ofio ooo.Hfio oofio ooNlNM BNNOH NN
.D.z HN
NON.O OOO.HImmm.O MON.O OOONM NOONM ON
NON.O OOO.HIO.O mHn.O OOOHM NOOHM OH
melo ooo.HHRo Poho oomHloH BNHVH oH
.D.Z NH
OOH.O OOO.HImmm.O mow.o mN Gw>om NHDO + OOOHM KOOHM OH
MON.O OOO.HIO.O QHN.O oomHM nOmHM mH
om o ooo .Huole. o NF o 8E Bog EH
.Q.Z MH M
OON.O OOO.HIO.O. cmm.O OONHM NONHM NH %
mNN.O OOO.HIO.O qu.O OOHHM nOHHM HH 3
OON.O OOO.Hlmmm.O HHN.O OOOHM HOOHM OH %
NNH.O OOO.HINOH.O OON.O OOOOM nOmOM m %
NMHI. o ooo.HLoR. o Sh o 8% Boos o u
.D.z m
NOH.O OOO.HIO.O mqm.O OOOOM 5000M O
OON.O OOO.HIO.O OH0.0 oomOM nOMOM m
NMH.O mnw.OImNH.O mqo.o ON Gm>mm NHDO + OOQOM anOm q
NmH.O mmm.OIO.O qu.o OOMOM mOmOM m
me.O Omn.OIO.O wmm.O OONOM HONOM N
OOH.O OOO.HIOOH.O wmm.O OOHOM mOHOM H
m CH Soho umoumsm ou Hm: m uoWImwlmwmmmnu MN
onsfismo mug is: n HM onafioz H
QM<QZ<Hm

 

 

 

mZmHH Emma Hzmzomzoo

 

mqm<Hm¢>

 

.

 

 

ADUDCfiUr—OUV htmn Eda...“

138

 

 

 

oNN.o ooo.Hno.o Noo.o NH onHz NHao oooNs NN
mN.HN
ooN.o ooo.HuooN.o moo.o oN.mH.NH oon NHao N ooNNs NN
mon.o ooo.Huo.o moo.o oH onHz NHao wooNM oN
moN.o ooo.Huo.o «Ho.o N oaHz NHao oomNM mN
oNo.o ooo.Huo.o oNN.o NooNM wooNs 4N
mN.oN.oH
oHH.o ooo.HnmNm.o Nmo.o .NH.oH.mH.sH ost sHao N + NomNM oomNN NN
ommwo ooo.Hnwwo ommwo Nommm. ooNNM NN
.o.z HN
NoN.o ooo.HnmNm.o ooN.o NooNM oooNs oN
oNN.o ooo.Hno.o ooN.o oH ost NHao + NooHs oooHM oH
ommwo ooo.Hnmwo mommo Nomwm wooHM NH
.o.z NH
HNH.o ooo.H-omN.o mHo.o oH ocHz sHao + NooHM wooHs oH
oNN.o ooo.H:o.o oNo.o oH .o onHz NHao N + NomHM oomHM NH
owmwo ooo.Huommwo Nmmmo Nowmm. moaHs aH M
. .o.z NH m
woN.o ooo.H-o.o oNN.o NoNHM woNHM NH E
mNN.o ooo.H-o.o Hmo.o NoHHM onHm HH m
ooN.o ooo.HumNm.l HHN.o NooHM wooHH oH NW
NnH.o ooo.HnmNm.o oNN.o oH .oH onoz sHso + N Nooos oooos o 2
Hmmwo ooo.H-mmmuo obmmo o onHz NHno + Nombm oooos o
.o.z N
NoH.o ooo.H-o.o mom.o Nooom oooos o
moH.o ooo.Hno.o omo.o wN oon NHao + Nomos momos m
NmH.o mNo.onmNH.o ooo.o Nonom wosos o
moH.o Nmo.ouo.o ow4.o oH onHz NHno + Nomos oomos m
moH.o oNN.o-o.o wmm.o NoNos ooNos N
oNH.o ooo.H-ooH.o oNN.o NoHoM NoHoM H
onN<H>mo Noz<m z<mz n HM oneasoz H
om<oz<am mzNNH Homo azmzoozoo

 

 

 

 

MAMEHMSV

 

 

 

E

AUUUCHNUCOUV hum. Hagan...

139

 

 

 

 

 

 

 

 

MHH.O OOO.HIOO¢.o ONM.O ON .MH :oH NHDO w + MOMNM mOMNM MN
mnN.o OOO.HHIMO QWMMO Mommw mONNM NN
.Q.z HN
NON.O OOO.HIMMM.O MON.O MOONM MOONM ON
ONN.O OOO.HIO.O OON.O womHM MOMHM MH
NMM.O OOO.HHmMO mmwwo wommm. MOMHM MH
.Q.z NH
HwH.o OOO.HIOmN.O MHM.O MOOHM mOOHM OH
MMN.o OOO.HIO.O «No.0 MOmHM momHM mH
ONWMO OOO.HIDMWMO NMMMO womww mquM qH M
.Q.z MH %
MON.O OOO.HIO.O qmm.o MONHM MONHM NH fl
MNN.O OOO.HIO.O qu.o MOHHM MOHHM HH m
OON.O OOO.HIMMM.O HHN.o MOOHM mOOHM OH w
MMH.O OOO.quqq.o MMN.O MN :OH NHDO + MomoM momom m
Hmmhb OOO.HIMmMMO thhb Mommw moweM w
.m.z m
NMH.o OOO.HIO.O mom.o wooom moooM o
mMH.O OOO.HIO.O mmo.o womoM momeM m
NMH.O MAM.OIMNH.O moo.o woqox moqoM q
MMH.O NmM.OIo.O «No.0 MOMOM mOMOM M
MNH.O MMM.OIO.O mmq.o HN .ON cow NHSU w + MONOM MONoM N
me.o OOO.HIOMH.O MHm.O MOHOM MOHoM H
mwchsouo umououm ou Hmsvm uou «q fiwsoun «M
NNM.O OOO.HIO.O wa.O MN oaHz NHDO MOMMM MM
MHN.O OOO.HIO.O qu.o ON OGHZ NHDO MONmM NM m M
woq.o OOO.HIO.O Mom.o MN ocHz NHDO MOHMM HM fi.%
qu.o OOO.HIO.O OOm.O NN ode NHDO MOOMM OM 3
OON.O OOO.HIO.O OON.O HN .ON ocHz NHdo w MOMNM ON
ZOHH¢H>mQ moz<m z<mz n HM ZOHH<HOZ H
OM<QZ<HM

 

 

 

mzmHH HmMH HZmzoszo

mgm<Hm<>

 

 

 

 

 

 

 

 

ADUDEHUCOUV h.“ EQNAVH.

140

 

 

 

 

NNH.o ooo.H-o.o oNN.o oomos onos N m
NNH.o NNN.o-NNH.o ooo.o oooos oHoos a m
NNH.o NNN.ono.o NHN.o NN Hoan + ooNoM oHNos N I
NNH.o NNN.ouo.o mon.o NoNoM oHNos N m
oNH.o ooo.H-oNH.o NNN.o ooHos oHHos H
NNo.o ooo.H-o.o NoN.o HN nos NHno moons on
ooN.o ooo.Hao.o ooN.o NH nos NHno ooNos No
ooN.o ooo.H-o.o ooN.o NH nos ano ooNqs NH
oNN.o ooo.H-o.o Hoo.o HH nos NHao ooHos Ho
NNH.o ooo.H-HNN.o oNN.o NH.oH.NH

.HH.oH.N.o nos NHno N moons oo
NHN.o ooo.H-o.o oNN.o oH .o .N nos NHno N oooNs NN
Noa.o ooo.H-o.o oNN.o 8 nos NHno ooNNM NN
Noo.o ooo.H-o.o Noo.o NH nos NHao ooNNs NN
noa.o ooo.H-o.o ooN.o N nos sHao oooNM oN M
NNH.o ooo.H-o.o omo.o nos ano NoNNM NN m
oNH.o ooo.H:ooN.o Hoo.o oH .H nos NHoo N ooqNs nN M
NNN.o ooo.H-o.o NNN.o NoNNs ooNNs NN m
oH u
NoH.o ooo.H-ooN.o NoN.o .oH.NH.o.N.H nos NHno N + NoNNN ooNNM NN a
oNN.o ooo.H-o.o oNN.o oH nos sHao + NoHNs ooHNs HN
oNa.o ooo.H-o.o ooN.o NooNs oooNs oN
ooN.o ooo.H-o.o ooN.o NooNs oooNs NN
NNN.o ooo.H-o.o Noo.o NoNNM ooNNs NN
ooN.o ooo.H:ooN.o Noo.o NoNNs NoNNs NN
Nan.o ooo.H-o.o Noo.o NooNs oooNM oN
NNH.o ooo.H-NNN.o NNN.o EH .NH nos NHao N + NoNNs ooNNs NN
oNo.o ooo.H-o.o oNN.o NooNs oonNs «N

zosansso moznm znsz u HM onsnsoz H

omnozosm stss smms szmzo

 

 

 

BLOC

 

MHMHHNHMHHS

 

 

 

ADUUEHUCOUV h . M MmaHm—HVH

141

 

1.1411111111111111

 

 

\1‘1111111
HNH.o ooo.HINNN.o NoN.o HH Hoan + oooNs oHNNN HN
.o ooo.H-o.o NNN.o NoNNN oHNNN NN
NNN. . I . N.o NH.HH Hoan N + $me 3me NN
HoH o ooo H non o NN N oHHNN HN
oNN.o ooo.H|o.o oNN.o NoHN oN
oNN.o ooo.H-o.o ooN.o oooNN oHoNN
HnN.o ooo.H-o.o NoN.o oN Hoan + NoNNN oHNNN oN
NNN.o ooo.H-o.o NoN.o ooNNN oHNNN NN
NNH.o ooo.H:NNN.o NoN.o on.oN.NN.o Hoan N + ooNNN oHNNN NN
NoN.o ooo.H|o.o NoN.o ooNNN oHoNN oN
NNH.o ooo.H|NNN.o NNN.o oomNN oHNNN NN
HNN.o ooo.H|o.o oNN.o ooNNN oHoNN «N
NN.¢N
oHH.o ooo.Huoon.o NNN.o .NN.NH.NH Hoan N + NoNNN oHNNN NN
NNH.o ooo.H-o.o NoN.o No Hans + ooNNN oHNNN NN
.o.z HN
NHN.o ooo.HnooN.o NNN.o Nn.NN Hoan N + oooNN oHoNs oN M
NNN.o ooo.H-o.o NoN.o «N Hoan + oooHN oHoHs NH %
Nmmwo ooo.Hnwwo. mmwwo NoNHN OHNHN NH 8
.o.z NH n
NoH.o ooo.H-NNN.o NNN.o No.o Hoan N + NooHN oHNHN NH u
NHN.o ooo.H-ooN.o NNN.o on Hoan + ooNHN oHNHN NH
mmwwo ooo.H-Nwao Nmmwo NN.NH.H Hoan N + Nowmm. oHoHs HH
. . . . .o.z NH
NoN.o ooo.Huo.o oNN.o ooNHN oHNHN NH
NNN.o ooo Hno.o HNN.o ooHHN oHHHN HH
NoN.o ooo HINNN o NHN o NN Hoan + oooHs oHoHs oH
NNH o ooo.H-NNN.o HNN.o oN.NN.NH Honas N +
NHH.o ooo.HnoNN. . . . . . oooos oHooN o
III. Iu.o NoN o NN NH o N Hoan N + ooNoN oHNoN N
. . I . . .o.z N
NNH o ooo H o o NNN o NN Hoan + ooooVH oHooN o
ons<H>No Noznm H
omnoznsm z<mz u N onsnsoz H
NNNsH sNNs szszoszoo

 

 

 

 

MHQHMHHS

 

 

 

A—UQUCHUCOUV him. MEHNJVH.

 

142

 

 

1|||.|I|...I\lll1llll1||||11|

 

 

 

 

 

 

 

 

 

 

 

 

11111111111111.1111. . N . 2 NH
. . . NN.N umouosm w OOOHM OH
NNN.o ooo Hnm1o wmm.o .o.z ooNHN NH
. . . M umoumsm OOHHM «H
oNN.o ooo HH11o H1m1o .11. .o.z NH
.11. .11. .1.. .o.z ooNHN NH
.11. .11. 111 .o.z ooHHs HH M
..1. 11.. 1... .s.z oooHN oH %
. a a a CM m v...
. oNN.o-o.o ooo o oN NN oN NH ooooons N ooo
MMM.W ooo.H1o.o HNN.o N ooooons ooNoN N O
111 11. 111 .11. .o.z N
NHN.o ooo.H1 .o HoN.o NN ooooons oooos N
111 ..1. .11. .o.z ooNoN m
NNN.o ooo.H-o.o ooN.o NN.N ooooons N oonoVs a
NHN.o ooo.H1o.o HNN.o NN ooooons ooNoN N
HNN.o ooo.H-o.o NNN.o NN.NH onooosn N ooNoN N
.o.z ooHoN H
NoszooNo stsNNs
NNo.o ooo.H1o.o NoN.o moons oHonN no
HoN.o ooo.H1o.o NHN.o NN Hoan + NoNnN oHNoN NH
NoN.o ooo.H-o.o NHN.o NH Hoan + NoNsN oHNnN No M
HHN.o ooo.H-o.o NNN.o HH Hoan + ooHoN oHHoN Ho m
NN.NN M
nHH.o ooo.H1NNm.o oNN.o oo.NN.NN.HH Hoan N + oooos oHonN on n
MWMHW oommWHMNmnw MMMHW NN.NN.NN.NH Hoan N + oooNN oHNNN NN u
- NoNNN oHNNN
oNN.o ooo.H-o.o NNN.o NN Hoan + ooNNs oHNNN WM
NoN.o ooo.H1o.o oNN.o NN Hoan + NoNNN oHoNN NN
NNH.o ooo.H1o.o NNN.o
NoNNN oHNNN NN
zossnssmo H
goznsm $an as: 1 Vs onsnsoz H
stsH sNNs szszossoo NHN<HN<>

 

 

E .

AHuvafifiUCOUV k. m. mHJHmndVH.

 

 

 

 

M11111...“ 1... 11. .92 8432 no
1.. ..1 ll .92 ooNoN Na
. mgwsm OONSN No
oNN.o ooo.H1o.o NNN.o m woooonn ooHHN Ho
NNNIo ooo.HubMo NNN.o .92 oooss on
. oooon ooNNoH NN
oNlolo ooo.Hnbwo NNN.o NH o o.“ ooNNoH NN
1| 1.. 11. .92 8:2 NN
1.. II 11 .22 oooNoH 8
ll ...1 II 2.2 ooNNN NN
. . 1 . . NH umouoss 033 «N
NNHIo ooo H.b.o Nwolo 2.2 ooNNN NN
. . 1 . . 0N.NH.NH:N smouoss 8me NN 1m
NNN.o ooo Hlp1o NFo .o.2 ooHNN HN m
m. oNN.o ooo.H-o.o _ NNH.o NN.NN noooonn oooNos 8 0
1 NHN.o ooo.H-o.o aCmoo NH ooooons ooNNVH NN
ooN.o ooo.H-o.o oNN.o NN ooooonn ooNNN NN
NNN.o ooo.H-o.o NNN.o lCN.NH.N Hoonoss ooNNoH NN
l1 ..1. '1 2.2 oooNvH NN
.1 1|l1 H .92 ooNNN NN
.1 2.2 ooaNN HN
Nleo NNN.o-NWHbo HEB oN.NN.HN.oN.NH.NH.N.N noonoss ooNNN NN
I. 1.. 1| .o‘2. ooNNoH NN
.92 HN
NHN.o ooo.H-o.o NNN.o NN ooonons 8on 8
nfio ooo.HhPo HolNlo oN.oN Hoooons ooNHoH NH
22 ooNHN NH
2oHs<H>Nn 82,2 239 . H2 2oHs<so2 H
smoozosm NEE sNNs szszonzoo NHNnHNfi

 

 

 

 

 

 

 

 

 

 

were
sing
in t1

used

thel

case
and
valu

eque

run
Err.

var

144

The set of variables describing the students for Winter term 1978
Here also added to the SPSS Master file at this juncture. The proces—
sing of this data into model variables was identical to that described
in the first sections of this chapter. The winter term data was not
lsed to develop the model, but rather to test and validate it. That
is after the model was developed from the Fall date, it was applied to
:he Winter data and the predicted and actual performance scores compared.

The Master file was once again subjected to a data validity and
:ase order check and the final irregularities sorted out. The third
1nd final set of regression routines were then run on this missing-
ralue-free Master file (omitting the Winter data from the regression
equation development).

Starting with the first K Matrix element for the first week

Kl = K0101) the significant set of variables for the last regression
'un of each of the component test items of K1 resulting in a residual
:rror rate of 10% or less were aggregated into a group of independent
‘ariables. For instance, the component test items for K0101 were items
, 6, 9, and 21 from Quiz One (from Table 5.7). From inspection of the
econd phase regression runs (each with an error rate less than 10%),
ach test item was seen to be correlationally related to ten or eleven

ignificant independent variables. As before, only those variables

hich contributed significantly to the explanation of the variance in
he test items were selected. For K0101 there were 42 Such independent
ariables. The K Matrix element was then regressed against the set

f significant variables. This final regression produced the regres—
ion coefficients B, A , n and the constant a which relate concept

astery to background, instructional behavior and prior knowledge.

for
(the
mod.
the
£11
val

mea

The

var

1(

145

This process was completed for each element in B not equal to 0,
for week one. The process was then repeated for weeks two through ten
(the final exam). Simultaneous with the final regression runs, the
model was applied to the variables within the Winter term, although
the regression coefficients were not based on the 75 cases in the Winter
file. Thus, the final coefficients of the model were developed and the
validity of applying the model to an independent set of data were
measured concurrently.

Table 5.8 shows the output from a sample phase 3 regression run.

The resultant equation relating K10 (K1002, week 2) to the model

variables is;

' — — .—

KlO 0.041 B39 0.030 B43 0.121 B63 + 0.053 B69
— 0.104 B79 + 0.193 B81 — 0.062 B85 + 0.084 B87
- 0.037 B88 — 0.034 B104 + 0.045 B109 + 0.056 B137
— 0.063 B153 — 0.046 B158 — 0.135 154 + 0.136 Kl
+ 0.257 K10 + 0.6047
corresponding to

K I

10 = B B + B B B B
1039 39 1043 43 + 1063 E63 + 1069 E69
+ 81079 B79 + 81081 B81 + 81085 B85 + B
1087 87

+ B
1088 B + B B + B 3
88 1081 81 1085 E85 + 1087 B87
8 B A U
+ 10 153 B153 + 10 158 B158 + 10 54 I54 + 10 1 K1
1.] 0L
+ lo 10 K10 + 10
from section 4.4

Figure 5.2 shows a plot of the residuals computed for the actual value

of K1002 minus the regression estimated (from the above equations)

 

 

 

146

TABLE 5 . 8
PHASE THREE REGRESSION
FOR A SAMPLE TEST ITEM

..o...... son-1.1121}
u tum nun-L!" OHZ
.m- c rm at Jun 110. I". .1111"
Inn "If.

.l 4" ‘ISII'EIS
.3128 51:113.:

———~——-——-——-- VAIKAILIS II 'I! IIMYIHI ——-"~
0 ‘1. "I" I

 

g N VAIIIIKI
1

N. I
:3 1:1. .....
$70 "I II!!! AIlfllLl"

     

   

UL! _ u

, n 111711;
"10111 .15719111 .451198442-01 11.111?“ .«rgg
umu ' .uomu .uuum-M 2. 117i 2 . .1! t
nun -.1nzuu .411101741-61 5. 1116.1}: -.1‘0I-2§$
"sou ~.nsunu~u .zozsanu-OI 1. 1.694? c .112 13’!
nun -.1ozuz41I-cl .u-Asssn-m .‘ruzifi: 3'9
1:1 -.usmn .Ssmunrvu 1. :73511'; 5:
anon: -.1211 411211991411 71173;: 3:21
Anon .uuauu-u .uuuxu-u L152!" - 33
"1°27 .ucxouu-M .35270‘1Sl-M 5.17823}: .§
men .1025»); .19302401 3.4911”! g,
""25 -.n.nn11-o1 .suosuu-u 1. “8'3, 3
"61m .uenuu-al .41323193l-Ol 1.1116?” g;
211111 -.Lo§1732:(-n1 .31212554l-o1 1.3179 3 11
use“ -.u1nc921 o .111111191-01 1.111153%o :1
IIIOOO .SSTVVSQII-Dl .ISYISL‘ITI-U‘l 2.301“;§ g?
nun -.111:111u-o1 .111130571-01 1.4114” 1
m __1411 1111 o .Josnmr-n 1 o b 9§ i;
mun-m .umu: .uuuu ‘ fig

 

'lIlL Ili.llilm,'l‘lfl2

'OtIVIL Fl '0“ I'CIKUVIL IIIU'IIIIIIY In '-'I" {M‘AVI..
a o o o o n o I
MOIIDIIV Illllul.. '1'“!

      

. 1 11 I o n a v I n I 1 l
u" nun-u r m swxnuul nun-u a I mun
ulnn “no". u".- o- 111-11.1111
; :g' a; 1111: 131::
a I 3.. “11 Mac
4. Fl ' ‘1”? {AGL 55‘?
1 r1 ' Sn“ (R 1‘62;
1 1: - g a: g, 3;.
3 I: t 7 22 #456 132;)
‘ H ' Al- 5‘" 0“
:1 1' '1 '1" 1:1;
1 :7. 59 11$; 71'.
1g ‘15. )6 OZ 5“;
1 .1. 1" ‘33; 3:.
19 a. 1 a? 31 «be

         

PZ1001 K1001

’73! '(IM 0' 7????

03".!” P
‘ DH?”

 

u . - v - . - . u
nnooo“-.-w
vows-WM
Hum-woweuuo
Wf‘umwtn-a

380000006500

 

non-II-ooco'o'on‘

21.111. 4:

nun-Lu In! II VI! nun

'llilll. IOLHII‘I

50"" .0200!
' 6|

 

V1.0 41.91.12. "it
..Oll" JO."

 

Il" "I‘LL.
2‘“ '1 53
4.2% 1. g:
121? 12’ i3,
1;.” :3 '2'
)1 ‘ Cl
12% 1;. 1:,
1131 i 2:

 

nulls-Hun“

no. _.-__

'
1.1:- «11737713:

. .
s z
.. ..
O O -‘
”NW”...

N
Name-mm»:

v
r o- w

a-acwouo» prth-WWMMW

.-vwrooq—‘Nu Haw-wag.“ w.«~won<—uo—ww-

 

Wu I-uorov‘Nnv-v-r luau—orpnv—nw'v r40 —ool‘\nmt~ono

    

l-O‘l
u
. 111' [-02
.11441 1-121
.3756) 1-ez
121 1
1

L
.I 1131;}:

‘I'LI OIOIIIOICI OOanoou‘oauao-nouuo-o.

Il‘ll'l‘ll‘l

.00
. L

    

'8

 

.0

a
ooOl‘cf-ol'c :.
oooooococ (1 nae.

00

  

u
a
o

2500
.300

value
Winte
of K1

For t

desc:

and
Th1

thi

 

147

value for K1002. Figure 5.3 shows the same plot for K1002 from the
Winter, validation data. For the Fall data about 65% of the variance
of K1002 was accounted for by the variables used in the regression.
For the Winter term the same variables accounted for about 23%.
5.4 Limitations
There are a number of important limitations on the processes
iescribed in sections 5.0 through 5.3:
--The use of number of independent variables much larger
than the number of cases;
—-The arbitrariness in the procedure for selecting the
variables to be entered in each group and the arbitrariness
of running such groups against the dependent variables
through multiple regression in "waves";
--The effects of substituting mean values for missing
values; and
-—The problems inherent in using regression analysis for pre—
dicting dichotomous variables.
The number of independent variables
The use of more independent than dependent variables is an obvious
1nd intentional violation of standard multiple regression requirements.
his breach of procedure may be defended by recalling that the intent of
:his process was to locate and identify variables that may prove useful
.n Predicting performance. This use of the multiple regression routine
.8 similar to using a very large guage mess screen to sort gravel; only
:he largest chunks of stone are identified. In our case only the vari—
.b1es which stand out as potential predictors are identified. 'Subtle
.nteractions and properties of the variables are not picked up in this

1rocess. Likewise, among groups of variables that are intercorrelated,

148

FIGURE 5.2
SAMPLE PHASE THREE REGRESSION RESIDUALS

FOR FALL TERM DATA BASE

+.270

 

  

 

.
o
. .
D
I n n n
O _ _ .
o .
.o . . .
a
. . .
"I _ o c n
O c o
. . . .
.o _ .
. .
o . . .
. . . . . . . . .
n . . . . . .
. . .
o . . . .
.
I u
. .
I O u
D . c
o . . .
a a . . n .
. . . . o . . .
o . ..
t n g u u
. . . . .
o .
. . .
‘
. . .
. . .
I . .
. .
o . . .
.
vl I
‘
H n U u
T 7
I
.
I 2 .
I .
6 t o .
' I
I . _
.
111 1111111111111111111111111111111111 111 11111 1121111111
I we o «H on o 03.2.1.5 o o» n. o .. oaooonoann d c o a. 5.0 a an a a no m on c a 0 ocean o 006::
. cu. -— _-.-——- - - . _-——.—-.-— p - -.- _ -. .u - _ p — -.-. o —.--
L (I I 1... (It .1 1.1.11. 1 .1! I t. 1 slittirz... .- t. I 1. It... 1 t... I a. II I l... l I 1 111...! 1 11......
L §E1gpsv45n37019915121561.73196511.3.2200751715089011 51313116012084.95425251823
' I .6‘9L",‘Vb§9l(au$lzl9glcggflwlo’zn.B57‘1z,°712\‘7‘;ru}£‘;.z7‘. Bil.9’73‘1f3“0¢ (7479560344,
l £SL§IC.II7'CII1“:(6NVU5C‘ FIF f7Vn..-lvv OELILL ‘I.’ 1.1772 A. JO 1:: 0.1: 1. .' LILL I... ll 1.3....l‘nl.‘ a $Y7|.1¢.5.Jl.§\‘16 EL 57.11/47‘F'N
l 221:112-51‘5653111711181777012211103...81111018121e21‘1027-126
11$... 14131431111111.731111.55111135196119151111‘71;
1 53.21.. a. 1 .117717112517 oi:1:2)¢7171r72.\.12~3.371a143731
17171531112111.11511111116181112111;
L ....... .................2...:.......................
u - - . v - u - - u - v - - o — - o - - — -
_
n )7) ) ) 1 )1 l 3) )2 ) .
. . o a s n n o n a o o o o ,
(.2. ( 1 4 . (t .1 S. a... I _
_ _

97192131110008.25281739 «1914;514:21497090.sn2t 57.9211 11911111291861.731‘667 87 .4 5.127
oesLAIVSEIOVAIsvlcoIZOCZTEL 6.1251401175495263...51671112112151.110558111611 71 1386756.... v21...“
QLC‘ETIQ‘I0:.SDSQZDOGL‘77—JJOOD‘U54719‘I.I.7.V.A >45!’.‘L.b7|VIA.I-1P$azlwlo Alzgglrag‘ith 301725 42 e753- vol-V.
71532158L11IS767511915701217764 .01557611 8459...! v1.11.- 1467. 11.51. 31281571731891.473191417. 1247711761111
6.. 61474141498349.47111167194117995155.2901}... 5124411....» .. .. 45.3145974121.664813612561.19576v35yotlrsa7
57.1..r74117113..Cc£4!b7.2§71(56484602.... 283720:19510717~£r446111.11.04.1154176614007121 5171zo2rtru<1614117 ,
1gzzi‘o.t’ EUEVV319~6HP§L$FV1°7LVA£DB—I .65V ‘95:7.fl9|‘ut“J-l"t::°l9l' filo-Z; nglfiVS'V .
...................................................l...l................ .. .................n...t......... .

ll'lll'l

K10

_

.

attach0000900069290000000000oocbooaéocoooooooooo09800L00D0to
oabouccooownvcoootoothaccoobn OOOJCODIODODOODQOODDOLLOO vuocoooccaoaoccgi 0
030603000: Connect... .00: vocoOKctbéobtbo:657.0300 u . 7.002.759»: (CL.....L..GO s... OOOKDOZCLDELSOL .1. 6.109.574; v
oco0000006CDC-00:DcoaocoocooLoootfl.u(o.u.l 005000000:.ucrzncoooaataoooocevo“36006695..no&.~00:..co..50coc
c.4026 900011.. €5.79: 3....0 u. 72.6.uCDLN5LPDA ..C: 3001390100300916 009.71.001.05 free. .CCSOOQ....L:...C..O\: .ceto
11559.3..(1‘5733100559 .5..913°:(...:¢f..c!.v$....7:._£112.65. .17.: gbfcy§.ronzc Shoe-SL3..572£[>..<:51.93515.
1.755.371),’.’17.’7’§lrr7.&’;l§’76..’711.27.51. ..975‘7’77’5’77’ .K...
1..l....o....“1..611..s....-....l-l.l..‘.-ooll..lv1|...11‘11l.l-.I io.'.'...1.1.1..:‘.lulcl.‘8

couccoobfihu LMDOLOODODOOOODQ vaccoooooco..¢.agoc
6054000009050... .0... LL 6091.. ucrngww

    

Actual

Ill“!

 
 

E

....................................................................~.......2..........................

K1002

n 111114

'0...-
0051111110-

 

 

U“
' “"“I .2112;

""00an

+25D
+.27O

 

149

FIGURE 5.3
SAMPLE PHASE THREE REGRESSION RESIDUALS

FOR VALIDATION (WINTER TERM) DATA BASE
-25D

K
10
K10 Value Estimate Residual —.270

Actual

_ “ 11
I

I

l

I

.
I
.

I

I

I

I

I

I

I

I

I

l

I

I
.000

I1 11 1‘ 11 19.5.12111 111311

0 0 0° ° 00°C 306000
mam .... 4. .. .4..:_.~
.. E

3!

1 3
0 30 G
. . .
I. I E... l E I. I. E "E... r. E 9 “on E" EIDVEMSMGUIN0039N .
03,"? g55,z£76‘l§5‘££l§0!69‘1 05727I£ 3 39 6.7. ‘
NHN“I9§L1"1C?Z¢Y>L<’1OEI 9500251523 05!).0L9Zr 21056620516737 39.2316557h145757

57

2513611106352316552033.21153566232527.3161%....16‘2321Lg;590:6;1322010‘3‘151 A.
.....-..o..c......................o....-.........-...........-.....-.....-
.. . .. ......... . . . . . . ... .. . ... ...... . .. .. ._. .

:

A
310611.076’022275 ..6555572‘6072330 §§§3059750L39560201715L405.01700
$922202:L17107$09£917t92607£1745527 09119001171056}... 0f1571,7911$560164!£202
9.7-5. .9 EL311552.».20L257571575603i5621 06 OOISGOC 375928.. £7.05. 5515.1;176916‘3761
5131.56712L 855.751 . _L.~5.121230505:01 05652 6251735751671572.27.22.59...£«BLIflIfiSSEA13
, . Z?» 03‘... 01.722.c671 NJZG CI} I733}!

:1 #:1021557 (L11?€e!€2!1721.2n £57150 173LK§GI6LOPIEAAOI7 L6é$2666£gt 3‘ 07‘09
77H576 .l .9917570.» $k¢2177955>§17u3777 7137H§755I781<I7L957997§9I7§g .‘
.....l..1............................ ....................................1.

75 (ISIS
.LEOBS

MLTI'L! l

000003000000000000000000070000080.000000000090 00 0 00
100000000 0005026.»..«500 0000 00000 00 000000500 0 00 000000000

000000000
0000000 9000 0 ,
00r€2§vOC 0&6 UCCQ 0000 ODOODCDODO

 

111.161.0005 0600...! 0651.50 L335 DOGCO 0
05003000 000005901050 0600 00550

0
° Otlvoco‘U-CCOOUIVDO‘COOO
030 50535000.) 00:690.) 009v . ,
240073000 0050:... 'ZvODLC 0006 707L0 CID TOLOICCOL 0060007 0737WNMWUNWMMWMMMWW
...n.€10600 00.: 3. 70?..13- 11.0.) 606.50 (50 105512.406 51.60361 3661 0(9n.179. 90160. C A ,
$6.51.)... £6.53. ..5. I556. SI. l.6.1.(:>.6 $55.51. .ISI....6.)..I:2>..’M5 l
.11..“1 ..u...11.1lu 11!- ...u‘ .1- '.1..‘o ..lu..1. 1...]‘1..“.o'-o A .l
D Z
I
l 3
E Z

only th‘

dependa
"signif
As
be regr
restric
Under r
indeper
such c:
ticall
the ph
ableS‘
possib
Sent a
howeve
ables
large]
Althm
and n
that 1
inter
the v
sien
"Sig:
fabrj
more

deVe

150

only those few variables accounting for the largest share of the
dependent variable's variance are identified and sorted into the
"significant" set.
Arbitrary group selection and regression runs

As mentioned earlier the largest group of variables which could
be regressed against a dependent variable was about 70. This is a
restriction imposed by the size and capabilities of the computer used.
Under normal circumstances with an N of about one hundred, seventy
independent variables would be a reasonable and adequate number. Under
such circumstances the investigator. would be searching for a statis—
tically significant set of variables which best describe and reflect
the phenomenon measured in the dependent variable. This set of vari—
ables would have the smallest intercorrelation within itself of all the
possible groups of variables, thereby allowing each variable to repre—
sent as much independent information as possible. In this study,
Iowever, it was not the intention to find such an optimal set of vari—
lbles but rather to identify a useful set of variables within a much
.arger set of variables potentially related to the learning process.
Jthough background variables were grouped together in sets of seventy,
nd instructional variables were grouped the same, it is recognized
hat many variables in different groups were interrelated, and those
nterrelationships were never exposed by this method. Obviously, if
he variables were grouped differently and passed through the regres—
ion routine We would have gotten different "answers" in terms of the
aignificant" variables. However, the method used does allow us to
Lbricate a crude model which can be tested and further refined through
>re standard and statistically acceptable methods. If the variables

veloped for the model in this crude procedure allow predictions to

be made
that a
potenti

It
cases v
or depex
small :
missin

number

study'
larges
the va
more t
appare
indeps
more (
Varim
ues d
Ships
deter
the e
be mc

Ld

disc}
a Va

base

151

be made which approach reality (even if distantly), then we may assume
that a refined version will produce even more useful results, and the
potential usefulness of this type of modeling will have been demonstrated.
Substituting mean values for missing values.

It is standard practice in regression computations to delete any
cases where there is one or more missing value for any of the independent
»r dependent variables. In a large data set each case may have a
small fraction of the variable inventory missing, deleting cases with
missing values from the regression analysis would quickly shrink the

number of cases and limit the usefulness of such a regression. In this

study means were substituted for missing values in order to include the
largest possible N in each regression analysis. This slightly reduces
the variance of each independent or dependent variable and each appears
nore tightly clustered around its mean value. This tends to reduce the
apparent variation in the dependent variable accounted for through the
independent variables and thus the resultant analySis may be said to be
more conservative. Although missing data was not widespread in the
Variables used in this study, the use of means in place of missing val—
xes does not noticably affect our search for the "strong" relation—
hips among variables. Again, if it was the intention of this study to
etermine the statistical significance of the relations among variables,
he effect of substituting the mean for missing values would have to
2 more carefully considered.
rédicting dichotomous variables.

In section 5.2, the second phase of the regression analysis was
Lscussed. Here regression equations were essentially used to predict
value of 0 or 1 (incorrect or correct response, for each test item

sed on the independent variables respectively). The proper use of

the regres
continuous
variable.
would null
and relat
ever, in

statistic
possible

nous varj

relations

 

152

,e regression equation assumes that the variables involved are
>ntinuous, but here the equation was used to predict a dichotomous
.riable. This practice violating a basic regression assumption

luld nullify any conclusions concerning the statistical significance
.d relationships of the dependent and independent variables. How—
’er, in this study no conclusions were being drawn concerning the
atistical significance of the relationships between variables, only
ssible relationships were being sought. The prediction of dichoto—
us variables was used only as a crude tool to search for variable

lationships.

 

ure de
in the
and u
ground
knowle
fit 01

tion ‘

Appen
coeff
iable
Varia
in pa
"beta
here
the

H ”dard
is a

the

ated

CHAPTER 6
RESULTS AND ANALYSIS

u.0 Introduction

The results of the regression modeling and validation proced—
lre described previously are presented in this chapter. For each element
.n the K matrix generated during the weekly testing routine the 8 , 1 ,
.nd u coefficients (and the a constants) relating the K1 to the back-
.round variables ( B), the instruction variables ( I), and previous
nowledge ( K), are presented. Statistics describing the goodness of
it of the model to both the base (Fall '77) term data and the valida—
ion (Winter '78) term data are also provided.

The coefficients 8, A,11 and a are tabulated in the tables in
ppendix E in two forms. Each is presented first as an untransformed
oefficient which is to be multiplied directly by the appropriate var—
able in the model. Secondly, the coefficient computed on standardized
ariable values rather than on original unstandardized values is given
1 parenthesis. The second coefficient is usually referred to as a
teta weight" (Nie et al., 1975) but this convention will be dropped
:re in favor of the term 'coefficient weight' to avoid confusion with
e 8 coefficients. Since the coefficient weights are based on stan—
rdized variable values, the relative size of each coefficient weight
a measure of the relative effect of each independent variable on

a dependent variable (Nie et a1, 1975). The constants ( a's) associ-

ed with the coefficient weights are all equal to zero.

153

T:
viousl
table
an ind
the co

6.1 W

were i
readir
ing tr
tion

and K

model

is nu
Whicl
digit
to t1
tWO
for

m

the

Eco:

Prm

det:

 

154

Table 6.1 summarizes the tested ecosystem concept coverage pre—
viously presented in Table 3.10. An inspection of this coverage
table and the coefficient weights of each of the variables provides
an indication of the relative correlational importance of each of
the course components modeled.
6.1 Week One

During the first week of class the basic concepts of Ecosystems

were introduced in the lecture and reinforced in the SAS and textbook
readings. The practice quiz also contained a number of items pertain—
ing to Ecosystems concepts. The first quiz contained enough informa—
tion to construct the mastery or performance variables Kl through K6,

and K8 through K The coefficients relating these indices to other

12'
model components are contained in Table E.l.

In Table E.l each coefficient is referenced by a B , A , or u
(or a for the constants), the acronym of the independent variable it
is multiplied by in the model, and the dependent or Ki variable to
which it contributes. The Ki variables are referenced with a four
digit code, K030l for example, where the first two digits (03) refer
to the element of K matrix and C matrix represented, and the second
two digits (01) to the week represented (for the example K0301, K3
for week 1 is represented).

Week One Instructional Variables

From Table E.l it appears that student attendance at lecture and
the second SAS are major contributors to the successful mastery of the
Ecosystem concepts Cl through C6’ and C8 through C12. Use of the
practice quiz did not generally improve performance and may have

detracted.

 

 

 

155

 

 

H5 mags

 

 

 

EmuammcwEEoo. 50:33:60 .mfimOHe—Sw .Emgfimmumm ”95:0:qu coaumuznom

 

156

 

.ucoo . H5 Hugh

ace

also
sequ
Fres
and

ment

they

take

cla:
The
imp

the

tm

th

 

157
BACKGROUND AND PRIOR KNOWLEDGE

A prior understanding of the concept of population (C32) may
also have benefited students, as well as the knowledge of food chain
sequence (B19), and the definition of an ecosystem (328). Those
Freshmen and Sophomore students (B5) with a few transfer credits (B3)
and good previous course performance (B6) did better on these K ele-
ments than did others in the course. The students that performed well
on these K indices also tended to do poorly in High School Biology if
they took it, tended to take more College Chemistry, were required to
take MSU remedial Math course 081 but not 082, were not repeating
BS 202, and reported to find lab work helpful but not the studying of
class notes. (B123, B135, B139, B156’ B161’ and B171, respectively).
The successful student also reported a tendency not to "freeze" on
important exams (B63), a confidence that it is not necessary to know
the type of exam questions beforehand (B80), that it was not necessary
to work with others on course work (B100), that it is generally unwise
to follow the majority (B107, B108) and that it best to answer a ques—
tion based on how you feel it should be answered (B94). These students
may be characterized as prepared, methodical, unimaginative, conserva—
tive and conscious of the opinions of others (B116).
GOODNESS OF FIT AND EXTRAPOLATION

Table 6.2 contains a Summary describing the goodness of fit of
the model's regression—based equations to the base term (Fall '77) and
the validation term data. The following parameters are included to
prOvide an indication of the goodness of fit:

Multiple R — the multiple regression coefficient between the

set of independent variables and the dependent

variable.

“”1

to ex:
model
term'
,t.nific
fit x.-
we‘Eks
tfirm.

c°mm

 

158

R2 - proportion of the total variation in the depend—
ent variable explained by the combined linear
influence of the independent variables.

Sig. — an approximate significance level for the resulting
multiple regression equation.

Adjust. R2 - adjusted R2, a more conservative estimate of the
fraction of variance of the dependent variable
explained; based on the equation
Adjust. R2 = R2 —-§E% (I—Rz) where N = number
of cases and, K = number of coefficients + constants
in the regression equation.

R2* & Sig. - will be explained under week 2.

Number of
Variables — number of independent variables in the regression

equation, not including the constant.

Std. Dev.
Resid.

Standard Deviation of the residual, where the
residual is equal to the difference between the
actual value of the dependent variable and the re—
gression estimate.

For the K elements generated within the first quiz it was possible
to explain 51 to 64 percent of the variance using 18 to 29 independent
model variables. When these equations were applied to the validation
term's data only one dependent variable, K1’ was estimated with a sig—
_nificance level equal to or less than 0.10. The reason for this poor
fit was the lack of any close similarity during the first few class
weeks between the validation (Winter '78) term and the base (Fall '77)
term. However, the two versions of the course converge toward a

common ground as the weeks proceed.

N.W Mud—”QB

 

 

159

mHnmoHHmmm uoz I <2
22222222222 202 I 22

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

22 <2 <2 <2 <2 <2 <2 <2 222. 222. 222. 222. 222. 22222
22 <2 <2 <2 <2 <2 <2 <2 222. 222. 222. 222. 222. 22222
22 <2 <2 22 22 22 <2 <2 222. 222. 222. 222. 222. 22222
22 <2 <2 22 22 22 <2 <2 222. 222. 222. 222. 222. 22222
22 <2 <2 22 22 22 <2 <2 222. 222. 222. 222. 222. 22222
22 <2 <2 22 22 22 <2 <2 222. 222. 222. 222. 222. 22222
22 <2 <2 22 22 22 <2 <2 222. 22<. 222. 222. 222. 22222

III2m11IIIIAmw1llnmmw|11nwmnz1lmmmlllll 22 <2 <2 222. 222. 222. 222. 222. 22222
22 <2 <2 22 22 22 <2 <2 222. 222. 222. 222. 222. 22222
22 <2 <2 22 22 22 <2 <2 222. 222. 222. 222. 222. 22222
22 <2 <2 22. 222. 222. <2 <2 222. 222. 222. 222. 222. 22222
mo .222

222222 22 u 2 222 n 2
2222 222222 . 2222<222<2222 2222 22<2

 

ZOHH<AOM<Mme QZ<
HHm ho mmmznoou mZOHH¢Dom ZOHmmmmumm H MmmB

N.© mqm<H

trat
inte
cept
phye

effl

ist

Eco

anc
flo
the

ct}

6.2 Week Two
During the second week of class the course content was concen—

trated on the basic chemical and physical processes underlying biological
interactions in ecology. In the lecture some of the basic ecosystem con-
cepts were reviewed and the concepts of energy storage (both chemical and
physical), photosynthesis and respiration processes, and equilibrium and
efficiency were introduced. The SAS, Tape Lab, and textbook readings
expanded these topics and introduced some of the basic concpts of chem—
istry. The Practice Quiz contained items covering only several of the
Ecosystem concepts.

The second weekly quiz contained items which allowed K8, K9, and
K10 to be updated and a new value to be computed for K14. Thus, perform—
ances on the concepts of organic and inorganic molecules (08, C10) energy
flow (C9) and energy storage (C14) were measurable. Table E.2 contains
the coefficients relating these indices (K8, K9, K10, and K14) to the
other model components.

WEEK TWO INSTRUCTIONAL VARIABLES AND PRIOR KNOWLEDGE

 

Of the instructional sessions of the second week, only the time in
the third tape lab (154) appears to significantly correlate with perform-
ance on the ecosystem concepts. None of the other instructional sessions
correlate significantly with the performance indices. Those students
that did not do well on ecology items on the Pretest (318, B29) tended to
be.more successful than those who did on the second week's K elements.
The students whose K values indicated a high degree of mastery after the
'Second week had done well at recognizing a definition of behavior on the

Pretest (B but had scored poorly on several Pretest items requiring

21) 9
a sophisticated interpretation of experimental data (B24, B33). Like—

wise previous mastery of the concepts of inorganic molecule and

nutri‘
secon
was n

mic

that
mathe
314'
a ho
pref
stud
long
resc

dim

did
wor

ten

161

nutrient (K1001 and K1101) contributed to successful performance the
second week, while previous mastery of the concept of metabolism (K1201)
was negatively correlated with success on the second week's K elements.
BACKGROUND

Correlations with items from the Background Questionnaire indicate
that the successful students (on week two K elements) took more advanced
mathematics in high school (B136), had a positive attitude toward science

( ), had generally a higher self—reported GPA (B ), reported having

B149 153

a hobby involving plants or animals (B ), and indicated that they

158

preferred a general, broad view of biology in the course (B ). These

183
students were generally weak in writing skills, did not expect to spend
long hours studying outside class, found textbooks but not films or other
resource materials helpful in science courses, and did not prefer to
discuss course materials with friends (B198).

In the affective sphere, the successful students reported that they
did not "freeze" (B63) or feel excessively nervous on exams (367), or
worry about how well they did after the exam was over (B69). They also
tended to prefer taking exams over writing papers (B70), but dislike

"pop" or surprise quizzes (B Overall, they may be characterized as

79)'
calm and confident in their ability to perform on tests (B83, B87),

testwise and independent of peer pressure in academic matters.

'GOODNESS OF FIT AND EXTRAPOLATION

 

Table 6.3 contains goodness of fit and extrapolation parameters for
’the week two regression equations. For the three K elements updated from
week one (K0802, K0902, and K1002), 65 to 84 percent of the variance was
explainable with 15 to 21 independent model variables. The R2* and the
associated "Sig" (significance level) indicate the approximate propor—

tion of the variance in the dependent variable accounted for by the

m . W MmflfldfiH

 

162

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

<2 <2 <2 <2 <2 <2 <2 <2 222. 222. 222. 222. 222. 2222.2
I I I I I I I I I I I I I 22222
I I I I I I I I I I I I I 22222
22 22. 222. 222. 222. 222. 222. 222. 222. 222. 222. 222. 222. 22222
22 22 22 222. 222. 222. 222. 22.2. 222. .222. 222. 222. 222. 22222
22 22. 222. 222. 222. 222. 222. <22. 222. 222. 222. 222. 222. 22222
I I I I I I I I I I I I I 22222
I I I I I I I I I I I I I 22222
I I I I I I I I I I I I I 22222
I I I I I I I I I I I I I 22222
I I I I I I I I I I I I I 22222
I I I I I I I I I I I I I 22222
222222.222 .222 22 .222 2 2 .222 2 22222 2 .222 22 2
mo 2 2 N onfluasz 2N .>on umMn 0222225:
222252 .32 2<
2222 222.223 I 2222422222222 2222. 22222

 

ZOHHEOEHMAH Qz<
HHM ho mmmznooo monH<DUm onmmmmwmm N Mmmz

m . o MAE; ,

 

inde

from

and

abor

inc]

K08(

var:

tim

Sim

l'eS‘

tio

Thi

 

163

independent variables excluding the previous value of Ki' For example,

from Table 5.7, K0802 is equal to K0801 plus Quiz items Two 1, 2, 4,

and 5. Thus, K0802 is highly correlated with K0801. From Table 6.3,

about 84% of the variance of K—802 is explained by the 14 variables

including K0801, in the regression equation shown in Table E.2. If

K0801 is excluded from this regression equation, about 68% (R2*) the
variance of K0802 is explained by the variables remaining in the equav
tion. The significance level, 0.001, remains relatively unchanged.
Similarly 41 and 58% of the variance is explained for K0902 and K1002,

respectively by unrelated variables. Note also that the standard devia—

tions of the residuals have decreased from the previous week's values.
This indicates an improvement of the "fit" of the equations to the data.
For the Winter term extrapolation data about 32, 59, and 23 percent

of the variation in the K0802, K0902, and K1002 variables, respectively.

No data was available to calculate a K1402 for this term. For the

:three variables, most of the variance is explained by the previously

calculated variables, K0801, K0901, and K1001, with 10 to 20 percent

explained by the remaining variables.

ecos
The
and
cycI
its
the
and
the
and
sic

Tat

 

164
6.3 Week Three

Energy, its production, capture and movement through the
ecosystem were the main topics of the third week‘s instructional sessions.
The basics of the Ecosystem concepts were again reviewed (C1 through Clo)
and the concepts of food chain or web (C23), habitat (C18) and life
cycle (C24) were introduced. The practice. quiz covered most of these
items. The quiz given at the end of week three contained items allowing
to be updated (K0303, K0803, K0903,

the K elements K3, K K9, and K1

8’ 4
and K1403, respectively). Additionally, K elements measuring mastery on
the concepts of photosynthesis (K1503), the physical environment (K1603)
and habitat (K1803) were also generated from the test data. The regres-
sion generated coefficients for these elements of K are presented in
Table E.3.
WEEK THREE INSTRUCTIONAL VARIABLES

The students who performed successfully on the week three K measures
were characterized as those who did not spend much time in Tape Lab 4

‘(155) but had done well on the previous quiz (I ). Attendances during

101
the first lecture (Il) and the second SAS (113) are positively correlated
with success on some of the K elements and negatively correlated with

others.

‘PRIOR KNOWLEDGE

 

In the area of previous knowledge the successful students were those
who knew the basics of ecology on the pretest (318’ B30, 341) and had
_expanded that knowledge during the first three weeks of the course
”(K0101, K0201, K0401, K0802, K0902, and K1402). A negative correlation
awas found with the mastery of the concept of metabolism (K1201). These

students were generally those who began the course with a good grasp of
the concepts of biology and ecology and an ability to interpret graphs

and experiments (B57).

BAC]

.——-

C0!"

few

rea

tiv

one

W63

sid

he]

ia]

 

' ——2._;;W

165

BACKGROUND
In background, only a few characteristics proved to be significant
correlationally. The successful students tended to have taken no or

few college physics courses (B were not required to take the remedial

131)’

reading course ATL 101B (B ), and indicated that they prefer quantita—

148
tive discussions, as with mathematical derivations, over more qualitative
ones. As in the previous week's discussion these students were generally
weak in writing skills, did not expect to spend long hours studying out—
side of class, found textbooks but not other resource materials or film
helpful in science courses, and did not prefer to discuss course mater—
ials with friends (3198

Significant correlations with Affective Questionnaire items were
not found in any pattern. Variables B61’ B67, B81, and 399 are positively
correlated with some of the K elements, and negatively correlated with
others. The only straight forward result here is that the successful
students for week 3 tended to feel that they should not answer test
questions based on the way they feel.

.GOODNESS 0F FIT AND EXTRAPOLATION

 

The goodness of fit parameters for the equations developed with the
Ithird week's data are contained in Table 6.4. About 72 to 95 percent of
the variation of those K elements with information stretching back into

the first and second weeks of the course was explained using 15 to 19
independent variables. Removing this prior information (K0301, K0802,
K0902 and K1402), about 29 to 70 percent of the variation was explain-
able with the remaining variables. Forty—two to forty—six percent of the
variation in the K elements appearing for the first time (K15, K16’ and
K18) was explained using 14 to 16 independent variables. Extrapolation

of these equations to the winter term generally produced a better fit

ELITF. r... c (run-.rllrtfx‘

2V I W MHQQAVH.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

22 <2 <2 <2 <2 <2 <2 <2 222. 222. 222. 222. 222. . 22222

22 <2 <2 <2 <2 <2 <2 <2 222 . 222. 222 . 222 . 222 . 22222

22 <2 <2 <2 <2 <2 <2 <2 222. 222. 222. 222. 222. 22222

22 I <2 <2 <2 <2 <2 22. 222. 222. 222. 222. 222. 222. 22222

I I I I I I I I I I I I I 22222

I I I I I I I I I I I I I 22222

I . I I I I I I I I I I I I 22222

22 222. 222. 222. 222. 222. 222. 222. 222. 222. 222. 222. 222. 22222

22 22 22 222. 222. 222. 222. 222. 222. 222. 222. 222. 222. 22222

6 I I I I . I I I I I I I I I I 22222

6

1 I I I I I I I I I I I I I 22222

I I I I I I I I I I I I I 22222

22 22. 222. 222. 222. 222. 222. 222. 222. 222. 222. 222. 222. 22222

I I I I I I I I I I I I I 22222

I I I I I I I I I I I I I 22222

222an22225 «.222 «22 .222 22 2222M252 .222 2.22 62.2w” “2%“? .222 22 2222M2=2

2222.252 3. n 2 .22m mg. n z
2222 22.2222 I 222232222222 22222 2.22

 

ZOHH¢AOM<MHNM DZ<
HHm .mo mmmznoou mZOHH<Dom ZOHmmmMumm m MmmB

+2. . 0 “24:22.2.

than
to ni
measr
able
and I
cant
able:
prob
and

the

 

167

than seen in the week two extrapolation parameters. Sixty-eight

to ninety-seven percent of the variation in the dependent variables

measured (K0303, K0803, and K0903) was possible using all the avail—

able information. However, when the antecedent K values (K030l, K0802,

and K0902) are removed from the equations only K0903 shows a signifi—
cant amount of the variance (41%) accounted for by the remaining vari—
The poor extrapolation for this week and the fourth week is

ables.
probably due to a problem during the winter term resulting in the third
\

and fourth weekly quizzes being combined into a single shorter quiz given

the fourth week. This undoubtedly confounds the data.

 

on

em

pm

111

M W.

c}

168

6.4 Week Four

Instruction during the fourth week of class was concentrated
on the internal structure and function of consumer organisms. Several
ecosystem concepts were reviewed, but very little new added to the
previous instructional delineation of ecosystem. The concept of equi—
librium was reintroduced within the concepts of structure and function
of the living organism and the parallels drawn between the organism
and the ecosystem. The practice quiz contained a number of ecosystem
concepts.

Table E.4 contains the model coefficients for the K elements
measured within the fourth quiz. K elements K3, K4, K5, K9, and K16
were updated with the new data (to K0304, K0404, K0504, K0904, and
K1064, respectively.) No new elements were added.

WEEK FOUR INSTRUCTIONAL VARIABLES

 

Some distinct trends begin to emerge during the fourth week which
characterize the students showing a high level of mastery of the eco—
system concepts measured. Such students tended to spend less time than
others on Tape Lab 8 (159) and more time and effort carefully reading
the assigned textbook readings (180). These students also reported
spending more time outside class studying course materials (I72, 181),
and reported that they found resource books and magazines very helpful
in science courses.

BACKGROUND AND PRIOR KNOWLEDGE

The successful students of week 4 also tended to know something
I. about ecology before entering the course (B15), but performed poorly
on the pretest on several items testing general biological knowledge

(816’ 323, and B These students generally were not required to

37)‘

take a remedial math (B ) course and described themselves as

138

COD

sch

edr

prc

re;

int

the

169
conservative in some measures (B98). They generally had taken high
school biology and received a high grade, were minoring in elementary
education science, and reported having an activity-oriented science
program in their own elementary school experience. Although they
reported having a positive attitude toward science, they had little
interest in it and were not intending to devote much time to it in

).

their own elementary classrooms (B191

GOODNESS OF FIT AND EXTRAPOLATION

 

Table 6.5 contains the goodness of fit and extrapolation para—
meters for the equations developed from the week four data. The vari—
ation accounted for in the five independent variables (K0304, K0404,
K0504, K0904, K1604) ranges from 73 to 93 percent using 13 to 16
dependent variables. Eliminating K0303, K0401, KOSOl, and K1603 from
the respective calculations, the amount of variance accounted for by
the remaining variables ranges from 29 to 63 percent. Note again that
the standard deviations of the residuals have decreased over the pre—
vious weeks indicating an increasingly better fit to the equations to
the data.

When applied to the winter term data, 77 to 97 percent of the
dependent variable's variation is accounted for by the independent
variables in the equations, with the exception of the equation for
K1604 which did not produce a significant result. After removing
prior K measures (K0303, K0401, K0501, and K0903), very little of the
variation is accounted for by the dependent variables. This may again
>1bé the result of the permutation in weeks three and four of the winter

term.

m It, M22230“

"I

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

I I I I I I I 22222
22 22 22 22 22 22 . . . . .
I 222 222 222 222 222 222. 222. 22222
I I I I I I I I I 22222
I I I I I I I 22222
I I I I I I I I 22222
I I I I I I I I 22222
I I I I I I I I I 22222
22 22 22 222. 222. 222. 222. 222. 222. 222. 222. 222. 222. 22222
I I I I I I I I I I I I I 22222
w I I I I . .I I I I I I I I I 22222
1 22 22. 222. 222. 222. 222. 222. 222. 222. 222. 222. 222. 222. 22222
22 22 22 222. 222. 222. 222. 222. 222. 222. 222. 222. 222. 22222
22 22 22 222. 222. 222. 222. 222. 222. 222. 222. 222. 222. 22222
I I I I I I I I I I I I I 22222
I I I I I I I I I I I I I 22222
$2222 .222 2 .222 2 2 .222 2 .2282 2 .222 2 2
20 y. «2 2 22222222 22 <62 2222223 2 22222222
2022252 .mum .
22 u 2 222 I 2
2222 22.2222 I 2222322222222 2222 222.2

 

 

ZOHH<AOAH<Mme Q72
HHm ho mmmznoou mZOHH<Dom onmmmmomm .2 Mmmz

\I ‘3 2.1—.3...

CO]

is:

V2

IE

171

6.5 Week Five
During the fifth week of class, instruction in BS 202

continued to be concentrated on the structure and function of organ-
isms, with the emphasis on reSpiration in animals. Although no new
Ecosystem concepts were introduced, the emphasis on respiration ex—
panded the general concepts associated with the flow of energy in the
ecosystem. In addition to this coverage in the lecture and SASs, a
number of ecosystem concepts were reviewed or expanded in the Tape
Labs and textbook readings. The Practice Quiz contained items cover—
ing nine of the Ecosystem conceptsy

The fifth weekly quiz contained items which allowed K8, K9, K10,

K14, and K to be updated (to K0805, K0905, K1005, K1405, and K1505,

15
respectively). Two new performance measures K19 and K20 (K1905 and
K2005) were also generated from the data. Table E.5 contains the
coefficients relating these K elements to the model's independent
variables. ‘
WEEK FIVE INSTRUCTIONAL VARIABLES

The pattern which began to emerge from the week four data continv
ues to become clearer in the week five results. The students who
demonstrated mastery of a number of the week five K elements tended to
be those who attended lecture regularly, especially the third and fifth
lectures (I3 and IS)’ attended SAS9 (120) and scored well on the pre—
vious week's quiz (I103). These students also reported that they spend
a large amount of time studying outside of class (I72 and I81) and that
A they carefully read and reread the textbook assignment (1802.
PRIOR KNOWLEDGE

In terms of prior knowledge, the Successful students tended not to

know the definition of a population on the Pretest (314), but did know

SOI

tht

 

172

something about energy relationships in ecology (B Success on

41*
the week five elements was correlated positively with previous mastery
measures on the concepts of matter cycle (K0304), consumers (K0504),
organic molecule (K0803), and negatively with mastery of the concepts
of ecosystem (K0201), decomposer (K0601), and especially negatively
with photosynthesis (K1503). Both positive and negative correlations
were found with the concepts of energy flow (K0904), inorganic molecule
(K1002) and energy storage (K1403).
BACKGROUND

The successful students on week five K elements tended to report
that they do not let emotion interfere with performance during a test
(B66), do not become confused with increased study or during a test

(B68), do not feel uneasy before a test is returned (B do not feel

78) 9
that changing an answer during a test is to their disadvantage (B92),
and do not View themselves as conservative in certain areas (398).

IThese students also indicated that they would only be satisfied with

‘a high grade in the course (B ). For some K elements, the Successful

152
students reported that they did not find the memorization of concepts

helpful, but for other K elements the students reported that they

preferred memorization (B ). These students also indicated that

175
they had a positive attitude toward science but low achievement in
math, found textbooks helpful in studying but not reviewing exams, and
intended to devote a large amount of their own elementary claSsroom's
ti '

me to sc1ence (B188).
GOODNESS OF FIT AND EXTRAPOLATION

 

The parameters describing the goodness of fit and extrapolation
:for the week five regression equations are presented in Table 6.6.
The proportion of variance explained for the dependent variables with

prior K meaSurements (K0805, K0905, K1005, K1405, and K1505) ranged

 

 

0 .w Nflndfifl

 

173

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

22 <2 <2 <2 22 <2 <2 22 222. 222. 222. 222. 222 . 22222
22 <2 <2 22 $21 <2 <2 22 222. 222. 222 . 222 . 222. 22222
I I I I I I I I I I I I I 22222
I I I I I I I I I I l l l rcwwv—
22 22 22 22 22 22 222. 222. 222. 222. 222 . 222. 222. 22222
22 22. 222. 22. 222. 222.1 222. 222. 222. 222. 222. 222. 222. 22222
I I I I I I I I I I I I I I 22222
I I I I I I I I I I I I I 22222
22 222. 222. 222. 222. 222. 222. 222. 222. 222. 222. 222. 222. 22222
22 222. 222. 222. $2. 222. 222. 222. 222. 222. 222. 222. 222. 22222
22 222. 222. 222. 222. 222. 222. 222. 222. 222. 222. 222. 222. 22222
I I I I I I I I I I I I I 22222
I I I I I I I I I I I I I 22222
I I I I I I I I I I I I I 22222
I I I I I I I I I I I I I 22222
I I I I I I I I I I I I I 22222
I I I I I I I I I I I I I 22222
2222“,; “.222 222 .222 22 m 2 2 a .2222 222 22.822 22 .222 22 2
.2352 2 222 2 .me 22:23.. 22222222
22 I 2 222 .. 2
222.2 222.222 I 2222222222222 2222. 2222

 

onH<Aom<xme Qz<
HHh ho mmmznooo monH<§om onmmmmumx n mez

0.0 mqm<H

frc

[(11
re:

 

 

174

from 62 to 99 percent. Removing this prior data (K0803, K0904, K1002,
K1403, and K1503) reduces the amount of variation explained by the
remaining independent variables to between 38 and 80 percent. Ten to
fifteen independent variables were used in the full equations. For
the newly calculated K elements (K1905 and K2005),51 to 56 percent of
the variance was accounted for by 18 to 20 independent variables. Ex-
trapolating the equations into the validation term's data resulted in
48 to 87 percent of the variation being explained for those variables
with a lengthy prior history (K0805; K0905, and K1005). Removing this
prior information reduced the amount of variance accounted for by the
independent variables to between 28 and 46 percent. Extrapolation of
the equations for the remaining week five variables produced few

significant reSults.

 

«In! .1

 

175

6.6 Week Six

The major instructional thrust for the sixth week of the
course was in reproduction. Most ecosystem concepts were only touched
on briefly in the lecture and SAS. Here some emphasis was placed on
metabolism and the flow of energy at the molecular level. A number of
ecosystem concepts were briefly reviewed or expanded upon in the Tape
Labs and textbook readings. Thirteen ecosystem concepts were covered
on the practice quiz.

The quiz given at the end of the sixth week contained items
relevant to only four ecosystem conCepts. Data was available to update
elements K1, K4,, and K6 (to K0106, K0406, and K0606) and generate on
initial value for K22 (K2206). Table E.6 contains the model coefficients
for these four K elements.

WEEK SIX INSTRUCTIONAL VARIABLES AND PRIOR KNOWLEDGE

 

This week's data provided little information on the students‘
1mastery of the ecosystem concepts contained in C , or correlational
information on relevant model variables. About all that can be gleaned
from Table E.6 is that the students who demonstrated a mastery of the

week six K elements were more likely to have attended SAS 11 (I but

22)’
not to have used the Practice Quiz for week five (147). These students

had demonstrated a prior understanding or mastery of the concepts of

metabolism (K1201) and photosynthesis (K1505), but success on the week

six K elements were negatively correlated with mastery of the concepts
of energy storage (K1405) and, particularly strong with physical
environment (K1604).
IBACKGROUND

From the Background Questionnaire, these successful students

tended to report that they had or intended to take a course in geology,

 

176

earth science, astronomy, or space science (B were not in elementary

144)’

education with a science major (B ), and were not required to take the

145

remedial reading course ATL 101 (B These students tended to have

148)°
had no previous ecology courses, but several math courses beyond the
remedial, had a positive attitude towards science and expected a high
grade in the course. They reported that they found textbooks and films
helpful in learning but not lectures or lab work (B189)‘

Similar to previous weeks they also reported that they did not
become confused when intensely studying for or taking an exam (B68),
did not think about unanswered questions during an exam (B84)’ and felt
that it was adequate to go through an exam once, then quickly check
their answers (B87). Generally, they are described as calm and controlled
when prepared, testwise, conservative and somewhat independent of friends

or peers (Bll ).

9
GOODNESS 0F FIT AND EXTRAPOLATION

 

Table 6.7 contains the parameters describing the goodness of fit
Iof the regression equations developed using week six data and the extrap—
olation of these equations to the winter term data. Ninety—three to
ninety—eight percent of the variation in the dependent variables with
prior history (K0106, K0406, and K0606) was accounted for using 10 to
14 independent model variables, but only 14 to 39 percent of the same
variance was accounted for by the same sets of variables when the prior
‘data (K0101, K0404, and K0601) was removed from the equations. About
-70 percent of the variation in the newly computed K element (K2206)
was accounted for by 13 variables. Little significance was found when
the prior data was removed from the equations for K0101, K0404, and

K0601.

, 2‘ a”

 

177

MH

0H

NH

 

 

42 <2

No. awe.

OH. m2o.

<2

<2

mom.

2mm.

m2

Nmn.

ohm.

¢Z

Hoo.

ooo.

2mm.

mom.

<z mam.

now.

mam.

mam.

H00.

H00.

H00.

qu.

nmm.

2mm.

mac.

mom.

Hoo.

22222
22222
22mmmII
22222
22222
22222
22222
22222
22222
22222
22222
22222
22222
22222
22222
22222
22222

 

 

 

2H
manmfium>
mo
Honaaz

mz mz

.wfim m
« 22

wow.
N

mm I 2
Emma MMHZH3 I ZOHH<AOA<Mme

cam.
m

oanfiuasz

22.
.222

22H.

mqo.
m .vfimwm

«N .>on

.wuw

wqm.

m
N

22:222.

200.
.222

0mm.

mm

MOH I 2
Emma AA<m

mum.

2
«22222::

@0202

 

ZOHH<AOM<Mme Q24

HHm mo mmmznooo monH<Dom onmmMMUmme Mmmz

mic mqm<H

 

plac
sper
cefii
text

cep1

the
com
inf
The

are

Th6
um

tn

 

178

6.7 Week Seven

During the seventh week of the course the main emphasis was
placed on animal reproduction and development. Very little time was
spent in lecture or SAS on Ecosystem concepts. Any mention of the con-
cepts was in the nature of a short, passing review. The Tape Labs and
textbook readings did, however, review and elaborate on ecosystem con—
cepts, and the Practice Quiz did include a number of related items.

The quiz given at the end of the seventh week contained data for
the update of two K elements, K0407 and K1607, and for the initial
computation of elements K2307 and K2407. This did not provide much
information on student progress in the mastery of ecosystem concepts.
The regression equations developed for these four independent variables
are contained in Table E.7.

WEEK SEVEN INSTRUCTIONAL VARIABLES PRIOR KNOWLEDGE, AND'BACKGROQEQ

An examination of Table E.7 produces several generalizations.

The successful students on the week seven K elements tended not to have
used the lecture tape for the seventh lecture (I38) and not spent much

time on Tape Lab 13 (I They did demonstrate some prior knowledge

64)'
of animal behavior on the Pretest (BZO) but had few or no previous col—
lege biology courses. Previous to the seventh week they had demonstrated
a mastery of the concepts of ecosystem (K0201) and decomposer (K0606)

but not efficiency (K1905). They also tended to agree that it is best
not to always let the majority make a decision (B108). Both positive

and negative correlations occurred with the item concerning the advant—
age of thinking about the manner in which exam questions are answered
(B83) and the previous mastery of the concept of inorganic molecule

(K1006).

179

GOODNESS OF FIT AND EXTRAPOLATION

 

Table 6.8 contains the parameters measuring the goodness of fit
of the regression equations generated using Fall term data and the
extrapolation of those equations to winter data. For the two K ele—
ments with previous history, K0407 and K1607, 95 to 98 percent of the
variation was accounted for with 10 to 13 independent model variables.
Removing that previous history reduced the percentage variation accounted
for to 26 to 44 percent. About 68 percent of the variation in the new
variables K2307 and K2407 was accounted for by 12 to 13 independent
variables. Extrapolating the relevant equations to the winter data re-
sulted in an accounting of 73 to 99 percent of K0407 and K1607. The
results after removal of the prior information on each variable were
not significant.
6.8 Week Eight

The general topics covered during the next week of class

were concerned primarily with animal nervous systems and behavior.
Several ecosystem concepts were reviewed and the ecological ramifica—
tions of behavior and territoriality introduced. The week was

abbreviated by the Thanksgiving holiday and no quiz was given.

 

180

 

 

 

 

 

 

 

NOONM
noaax
nowHM

.nocHM
homHM

nO¢HM
NONHM
ROHHM
ROOHM
momoM

 

Nowox
mooom
nomoM

 

m2nm2um> .222 2 .222
22 «
203532

ZMWH “MHZHZ

2 2
02222252

mm I z
I ZOHH<AOA<MHxM

.222 2 .22222 2 .222
«2 .>22 2 22

.32
mo2 u z
2222 22<2

ZOHH<AOM<¢HNM Qz<

HHm mo mmmznoou monH<Dom onmmmmum¢_n Mmm3

w.o mqde

2
~222u2=2

noqoM
nomoM
NONoM
noaoM

.0

W ANQH.

 

181

 

 

 

 

 

 

u o c o NOQNVH
22 <2 <2 <2 <2 <2 <2 <2 222. 222 222 222':
22 <2 <2 <2 <2 <2 <2 <2 222. 222. 222. 222. 222 22
I I I I I I I I I I I I I 3222
22222232 .222 2 .222 2 2 .2122 2 22222 22 .222 22 2
No « *N N wHaHuHDZ «N .>oQ . afimfiuHSZ
22.232
Honaaz .Vum
22 u z 222 I z
2222 22.222: I 2222522322222 2222. 2.22

 

ZOHH<AOA<MHNM Dz<

HHh ho mmmznooc monH<Dom onmmmmomx n xmmz

w.o wqm<fi

 

 

182

6.9 Week Nine

Ecology was the main topic of the instructional sessions
conducted during the ninth week of the course. In the lecture emphasis
was placed on the concepts related to the cycling of matter and the flow
of energy in the ecosystem. The concepts of producer, consumer, decom—
poser, energy source, organic and inorganic molecules, energy, photosyn—
thesis, efficiency, food web or chain, symbiosis and succession were all
expanded upon and tied together under the rubric of the internal func—
tioning of the ecosystem. This expansion and unifying theme was carried
through the SASS, Tape Labs, and textbook readings. The Tape Labs and
textbook readings also introduced and expanded upon the concepts of bio—
mass, mutualism, competition, predation, saprophyte, biome, evolution,
trophic level, climax, diversity, and eutrophication. The Practice Quiz
contained items covering twenty-eight ecosystem concepts.

The eighth quiz, given at the end of the ninth week contained in—
formation which allowed eight of the K elements to be updated and initial
values for nine new K elements to be computed (K0308, K0508, K0808, K0908,
K1508, K1608, K1908, K2308, and K2508, K2608, K2708, K2808, K2908, K3008,
K3108, K3208, K3308, respectively). Table E.8 contains the model coef—
ficients developed for these seventeen variables.

PRIOR KNOWLEDGE

An inspection of Table E.8 indicates that most of the relevant
independent variables in the regression equations are related to prior
knowledge of the ecosystems concepts. The students successful on the
‘K elements developed from the eighth quiz tended not to be familiar
with some elements of ecology on the pretest (B25), but in succeeding

weeks had mastered the concepts of ecosystem (K0201), matter cycle

 

 

 

183
(K0304), organic molecule (K0805), photosynthesis (K1505), physical
environment (K1607), and mutualism (K2206). They tended to perform
poorly on the concepts of habitat (K1803) and energy storage (K1405). ;
Negative correlations with performance on the latter concept were
particularly pervasive. Both positive and negative correlations were i
found with the previous mastery of the concepts of consumer (K0504),
energy flow (K0905), nutrient (K1101), metabolism (K1201), efficiency
(K1905), and respiration (K2005).

WEEK NINE INSTRUCTIONAL AND BACKGROUND VARIABLES

 

The successful students also tended to make use of the tape of
40). Both positive and negative correlations were found 2
I I

between the independent K elements and attendance at lecture 6 (I6)

lecture 9 (I

AND SAS 17 (128). These students also reported that they expected to
spend many hours studying outside class (B154), did not use the ideas
from one exam question to answer another (B93), believed that it was not
always best to follow the majority (B107), and found that their families
approved of their chosen careers (B106)’ Both positive and negative 2.x
correlations were found with a reported belief that thinking about the
grade a person will receive in a course interferes with studying (B72)
for that course. These students also tended to report a high biology
grade in high school, were required to take a remedial writing course,
and a high college GPA, had a negative attitude toward science and
seldom read about biology or any science topics (3197).
GOODNESS OF FIT AND EXTRAPOLATION

Table 6.9 contains the parameters describing the goodness of fit
of the equations in Table E.8 to the Fall term data, and to the Winter
term extrapolation data. For the dependent variables with prior infor—

mation (K0308, K0508, K0808, K0908, K1508, K1608, K1908, and K2308)

 

 

 

 

 

 

 

184

 

 

 

 

woomm

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2 22 22 22 22. 222. $2. 222. 222. 23. 222. 22222
I I I .I I I I I I I I I I 22222
J‘Afillfllj. 222. 222. 222. 222. 222. 222. 222. 222. 22222
IIII22IIIII222||II 22 222. 222. <22. 222. 23. 222. 222. 222. 222. 222. 22222
I I I I I I I I I I I I I 22222
I I I I I I . I I I I I I I 22222
I I I I I I I I I I I I I 22222
I I I I I I I I I I I I I 22222
22 , 222. 222. 222. 222. <22. 222. 222. $2. 222. 222. 222. 222. 22222
.2 222. 222. 222. 222. 222. 222. 222. 222. 222. 222. $2. <22. 22222
I I I I I I I I I I I 22222
.22 22 22 222. 222., . . . .
Ifdl‘Ill :2 222 222 222 222. 222. .22. 222. 22222
222. 2122. 222. 22222
I I I 22222
I I I 22222
2 . .
$2252 2 m22222.32 «2 emmwm 22 222 22 2
. 22.22;. . m2222222
22222 222.223 I 2222322222222 222 I 2
2222 222.2

  

ZOHH<AOM<MHKM 92¢

HHh mo wmmznoou monH<Dom onmmmmumm m MmmB

m.o mqm¢H

 

 

 

 

. ..

 

 

 

 

 

 

 

 

 

 

 

 

 

 
 

 

 

 

 

 

 

 

 

 

 

 

 

v o a o - mMVH
0H <2 <2 <2 <2 <2 <2 <2 N<N qu Hoo qu mmm mo
MH <2 <2 m2 m2 m2 <2 <2 me. qmq. Hoo. 0mm. th. wONmM
«H <2 <2 <2 <2 <2 <2 <2 moq. HmN. Hoo. mom. Noe. mOHmM
m2 <2 <2 <2 <2 <2 <2 <2 mmm. com. 20o. ohm. own. woomM
2N <2 <2 22 m2 mz <2 <2 222. nmq. Hoo. 22m. emu. momwx
02 <2 <2 <2 <2 <2 <2 <2 2mm. 22m. 200. oqm. mwm. mowNM
ma <2 <z m2 m2 m2 <2 <2 omH. qu. Hoo. qu. 0mm. wONNM
m2 <2 <2 man. wooNM
mmw. womNM
I quNM
Now. wommm
I wONNM
- N m
.>mn umsnw< waafiuHDX
mu n z vum
2222 222222 I 2222<222<22 222 u 2
NM Emma 22<m

HHh ho mmmzno

  

onH<Aom<Mme Dz<
oo wonH<Dom onmmmMumM m Mmmz

22.22002 2.2 222<2

186
80 to 90 percent of the variation was explainable with 11 to 21 independ—
ent model variables. Removing the prior K variables (K0304, K0504, K0805,
K0905, K1505, K1607, K1905 and K2307) from the regression equations re—
. For

rults in a reduction of the explained variance to 32 to 62 percent.
the new K variables (K2508 to K3308) 35 to 54 percent of the variation
is explainable using 13 to 21 independent model variables.

Extrapolating the quiz eight equations to the Winter term data
A range of about

resulted in a reduction of the explained variance.

6 to 82 percent of the variance within the dependent K variables is

explainable using the full sets of independent variables mentioned in
the previous paragraph. Removing the prior K variables as discussed
above, reduces these percentages. Note, however, that 26 to 52 percent
0f the variation in K0308, K0808, and K0908 is still accounted for by
This indicates that much of the

the remaining independent variables.

model is transferable from term to term.

 

6.10 Week Ten 4

The main emphasis of the instructional sessions of the final
week of class was on the flow of energy through the ecosystem and popu—
lation ecology. This served to unify most of the ecosystem concepts
presented previously in the term. The lecture was devoted mainly to the
introduction of the few remaining uncovered ecosystem concepts and to a
complete and thorough review of the total ecosystem concept. This review
was accomplished by interrelating all of the ecosystem concepts through
the concepts of the flow of energy and the cycle of matter. The SASS,
Tape Labs, and textbook readings all contributed to this final summary
and review. Twenty-two items relating to ecosystems concepts were
included on the Practice Quiz.

The ninth quiz given at the end of the tenth week contained items
which allowed for the update of six K elements and the computation of
initial K elements for eleven concepts (K0209, K0909, K2309, K2509,
K3109, K3209 and K3409 to K4409, respectively). Table E.9 contains the
regression equations for these K elements developed from the Fall term ‘Wd
data.

WEEK TEN INSTRUCTIONAL VARIABLES

A pattern similar to that observed in the previous weeks emerges
from the inspection of Table E.9. Students performing well on the K
elements tended to have a lower attendance at lectures 7 and 10 (I7,

110) but higher in SAS 19. They also tended to use practice quiz
nine but not ten (ISO, 151), Spent more time on Tape Lab 18 (169) and
scored higher on the previous quiz (1107).

PRIOR KNOWLEDGE

 

In terms of prior knowledge, the students demonstrating some

nastery of the K elements for this week tended to understand the

 

 

 

fi~e "' *' '—*—*—‘‘—_‘-'‘‘'‘_‘‘''''''''''''''''''''''''""""""-------II!!!

188

rudiments of biological organization (B11) on the pretest, as well as
something about the definition of a niche (B39), but not a practical
example of the concept (B32). Neither did they tend to understand the
concept of food chain (B42) or the relationship between respiration

and photosynthesis in green plants (B43). Positive correlations were
found between the previous mastery of the concepts of ecosystem (K0201),
niche (K3008) and community (K3108) and performance on the week 10

K elements. A negative correlation was found between the previous per—
formance on the concept of biotic environment (K2608) and these K ele-
ments. Both positive and negative correlations were found between the

week ten K variables and prior knowledge of the concepts of energy flow

 

1n the biosphere (B35), metabolism (K1201), energy storage (K1405), life

cycle (K2407), behavior (K2508) and population).
BACKGROUND
In terms of the Background and Affective questionnaire items, the

students demonstrating some mastery on the week ten K elements tended

to report that they had not taken the Physical Science Course for ele— ~~= ,
lmentary education majors (B130), did find resource books and magazines
‘ helpful in learning for science courses (B168) but not films (B163).

[They also reported that thoughts of doing poorly interfered with their

rtest performance (B that they did not count on friends for help in

74)’
‘preparing for an exam (B81) and that it was unwise to Skip around on
~an exam to answer all the easy questions first (B88).
Table 6.10 contains a summary of the parameters describing the
goodness of fit and extrapolation for the regression equations developed
:from the week 10 data. For those K elements with prior information it

’was possible to account for between 83 and 98 percent of the variance

lusing 12 to 22 independent model variables. Removing this prior

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1!!!
4| I I I I I I
I I I I I I I I I 88.x
I'll!!! I I I I
I I I I I I I 822
[fly I I I I
I I I I I I I I I 832
I I I I I I I I I I _ 83M
IIIIIIIW I I I I
r I I I I I I I I 8mg
1’4 I I I I
I I I I I I I I 85
IIIIIIIIIII I I I I
I I I I I I I I 832
{III P In I .
I I . I I I I I 822
[1% I I I I
S soc. s2. . I I I I I 8an
L . . . . .
I I aw a; :5 mos 80 as. So. ems. m8. 882
III}! I I I I
I I , I I I I I I I 892
{IL I I I I
I I _ I I I I I I 8.882
{ILL I I I I I I I I
w {I I 822
1 I I I I I I I I I 8.22
{I II- .I II III
I I I I I I I 88s
ON WC. HOH. mo. . .
I!!! o . . . .
I I m: lam 8o 5 ooo «mm. So. as. is. 822
3e35, .3 I I I I I I I I I 822
m .wwm 2
NO * {N N m .WHW Mm .VHWU ow
uwnasz 33:52 «N :62 N2 Hm . N2 2
a o a z
.25 8%? 2 EH 2
mm u 2
IllfllllllllllI 2228 MMHZHS I 20HH<Aom<mem 2mm“ “gab

 

20H9<Aom<myxm 22<
HHh mo wwmznooo monH<Dom onmmmmumM QHMWMS

DH . o max—an...

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

<2 <2 <2 22m. 222. <00. <22. 220. 202m2

<2 <2 <2 com. <22. Hoo. 2mm. ooo. 20022

<2 <2 <2 222. 22m. 200. wsq. Hoe. somm2

22 <2 <2 mma. mom. Hoo. 2o<. mme. mc<m2

I I I I I I I I momm2

22 Hoo. <22. <20. sew. 200. 222. mmw. momm2

<2 20. 222. 222. mwe. Hoo. 222. com. mo<m2

I I I I I I I I moom2

I I I I I I I I 20222

I I I I I I I I 20222

I I I I I I I I 20222

I I I I I I I I 20022

<2 Hoo. 22m. e02. mme. 200. 222. w<2. momm2

I I I I I I I I mo<~2

222. Hoo. maq. mmo. N22. 200. 2mm. mom. 20222

I I I I I I I I momm2

2 .222 2 .22222 2I .22w 2. 2. 20222

«222u222 «N .>ma sum“ N 22228222
m2 I 2 .eum 2<
2222 222222 I ona<aom<2axm 2mm“ mama

 

If!

ZOHHEOSE 92¢.

8H2 mo mmmznoou m20H8<DOm ZOHmmmmumM 0H Mum:

A.u.coov 0H6 2129:.

 

191

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

3 <2 <2 <2
mm <2
HN <2
ma <2
23322, Him «N a N N2 2
mo ma .3122 Son 2232:
.3252 .285 umnnm< 2
mm u z moa u 2
Emma MMHZHS I 20HH<Aom<MHNm 222B AA<2

 

 

2825922222 ;
22 2o 2222282 22822502 282222222 82222

A.u_coov OH.© mAm<H

i]

a!

VI

 

192

Iformation (K0201, K0908, K2308, K2508, K3108, K3208) reduced the
mount of the variance accounted for by the remaining independent
ariables to between 26 and 63 percent. Extrapolation of these equa—
Lons onto the winter term data produced mixed results; from almost 0
o 89 percent of the variation accounted for with all the prior infor-
ation, to O to 12 percent with the prior information removed. For

he newly generated K elements (K3409 to K4409) between 37 and 64 per—
ent of the variance was accounted for by between 10 and 28 variables.
one of these equations were successfully extrapolated into the winter

erm data.

193

v.11 Final
The last set of model coefficients were based on those eco—

system concepts covered by items contained on the Final Exam (see
Table 3.9). These test items contained the information necessary for
the computational update of twenty—three ecosystem concept performance
indices: K0310, K0610, K0810, K0910, K1010, K1410, K1510, K1610,
K1910, K2010, K2210, K2310, K2710, K2910, K3210, K3410, K3610, K3710,
K3910, K4010, K4110, K4210 and K4310. The coefficients relating these
K elements to the model variables are shown in Table E.lO.
PRIOR KNOWLEDGE

From Table E.10, the student successful on the final K elements
may be characterized as having a prior knowledge of the (macro) flow of

energy in the ecosystem (B B41), the definition of ecology (B18),

35’
and relationships in the physical environment (B37), but did not under—
stand the flow of energy at the molecular level (B13) or the concept of
plant respiration (B42). Both positive and negative correlations were
found between the K elements and a pretest item testing the interpreta—
tion of an experiment (B23), as well as general understanding of ecol—
>gy (B51). Throughout the term these students also demonstrated a mas-
:ery of the concepts of organism (K0106), matter cycle (K0308), organic
mlecule (K0808), inorganic molecule (K1005), photosynthesis (K1508),
ood chain or web (K2309), predation (K2808) and population growth/
tability (K4009).
lCKGROUND

In terms of background the successful student may have been a
ansfer student (B155) but transferred few credits to MSU (B3), tended

have a positive attitude toward science (B149) and, had little or no

 

194

previous college biology courses (B125). These students also indicated
that they felt that the study of class notes was very helpful in a

science course (B ) but that studying and discussing course material

171
was not helpful. Both positive and negative correlations were found
between K elements and the expectation of a high grade in the course.
From the Affective Questionnaire, the successful students tended
to report that they become confused on tests or with intense study (B68),
that students should think how they are answering questions on a test
(B85) and look for helpful ideas for answering some questions on a test
in other questions (B93), that every word in a test question is import—

ant and, that it was not necessary to look over the entire test before

starting (B89). These students also reported that they enjoyed working

with students who get good grades in class (B99), but didn't need the
encouragement of friends when they met with failure (B101). These
students may be characterized overall as fearing exams, somewhat depend—
ent on friends and family for support, with a tendency to work well with.

).

others and very conservative (3117

INSTRUCTIONAL VARIABLES

It appears that the most successful of these students tended to
attend the eighth lecture (18) (a week when attendance was low because
of a holiday), and the second and ninth SASS (113, I20). They used the
fifth practice quiz (I47) but not the first lecture tape (I32), and
tended to have retaken each of the nine practice quizzes in preparation
for the final (197). These students also scored high on the second

quiz (I ).

101
GOODNESS OF FIT AND EXTRAPOLATION
Table 6.11 contains the parameters describing the goodness of fit

and extrapolation of the model equations developed using the data from

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

02 22 22 200. 022. 022. 200. 0<2. 020. 222. 200. 220. 002. 02022
22 20. 2<2. 200. 002. 222. 200. 00<. N20. 202. 200. 200. 0<0. 02022
I I I I I I I I I I I I I 02222
<2 22 22 200. 2<0. 202. 200. 002. 0<0. 202. 200. 0<2. 022. 02022
02 22 22 200. 0<<. 200. 200. 002. 220. 222. 200. 020. 220. 02222
_ 22 N. <<0. 200. 000. 022. 200. 0<0. N00. 002. 200. 220. 202. 02<22
I I I I I I I I I I I I I 02222
I I I I I I I I I I I I I 02222
22 200. 022. 200. 222. 202. 200. 022. 2<0. 002. 200. 022. 202. 02022
02 <0. 202. 200. 022. 022. 200. 220. 220. 222. 200. 220. 222. 02002
02 200. N2<. 200. 222. 022. 200. 2<2. 220. 222. 200. 200. <22. 02202
22 H0. 222. 200. 220. 022. 200. 22<. 220. 220. 200. <<2. ~20. 02002
I I I I I I I I I I I I I 02202
I I I I I I I I I I I I I 02<02
22 22. 220. 200. 022. 022. 200. 22<. 220. 022. 200. 220. 220. 02202
i I I I I I I I I I I I I I 02202
I I I I I I I I I I I I I 02202
, 2222222> .222 2 .222 2 2 wwww 2 .02222 2 .222 2 2
2 2o 2 IN N 02220202 22 .>22 22m20< N 02222222
1 Monsaz .mum .
l 02 I 2 202 I 2
, 2222 222222 I 2022<202<2222 2222 22<2

 

ZOHH<Aom<282m 22<
8H2 ho mmmznoou monH<Dom onmmMMUmm A<2Hh

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

I I I I I I I I I I I I I 02222
22 <2 <2 <2 <2 <2 20. 222. 022. 022. 202. 002. 222. 02222
22 <2 <2 <2 <2 <2 20. 022. 220. <22. 202. 222. 222. 02022

I I I I I I I I I I I I I _ 22222
22 22 22 220. 2<2. 222. 202. 222. 020. 222. 202. 222. 2<2. 02<22

. I I I I I I I I I I I I I 22222
22 02. 0<0. 202. 2<2. 202. 222. 022. 2<0. 222. 202. 222. 222. 02222

I I I I I I I I ; I I I I I 22222

I I I I I I I I I I I I I 02222
<2 22 22 200. 222. 222. 220. 222. 202. 202. 222. 202. 222. 02222

I _ I I I I I I I I I I I I 22222
22 22 22 200. 222. 222. 202. 222. 000. 202. 222. 022. <22. 22222

I I I I I . I I I I I I I I 02022

I I I I I I I I I I I I I 22222

I I I I I I I I I I I I I 02<22
02 222. <22. 220. 220. <22. 220. 222. 2<0. 002. 200. 222. 222. 02222
22 <2 <2 <2 <2 <2 202. 222. 020. 202. 202. 0<2. 022. 22222

02222222 .222 2 .222 2 2 «222 2 .02222 2 .222 2 2

2o 2 <2 2 022222:: «2 .>22 2 2 22222222

umpezz .vum umsflu<
22 I 2 202 I 2
Emma MMHZHB t ZOHH<Aom<mem EMMH A4<m

 

ZOHH<AOA<MHxH Qz<
HHm ho mmmznoow wonH<Dom onmmmmumm A<ZHM

~.u.fiouu 44.0 flJnfl2 .

 

 

 

 

 

 

 

 

 

 

 

 

 

 

oaqu

 

 

 

 

 

 

mN <2 <2 <2 <2 . <2 Ho. mam. «mo. hqw. Hoo. mww. qu. OHm¢M
NN <2 <2 <2 <2 <2 Hoo. wmq. mmH. wmo. Hoo. wmn. mmw. OHN¢M
mN <2 <2 <2 <2 <2 Hoo. dmm. mOH. mmn. Hoo. NNw. 00¢. OHHQM
mN mo. «we. Hoo. mum. 5mm. Hoo. woo. mqo. mmm. Hoo. OHm. «mm. OHOQM
wN m2 m2 Hoo. hem. NHN. Hoo. mom. omo. NMN. Hoo. HNw. 00m. OHmmM
NHamfiHm> .wfim Nm .me m m .me m .vwwmm m .me m m
we 2 2 N wamfiuasz «N .>wQ mmh N waafiuaaz
229892 .vum u v<
mu u z MOH u z
2mmh MMHZHZ l ZOHH<AOM<¢HXM ZmMH AA<W

 

 

ZOHH<AOA<Mme Dz<
HHM mo wmmznooo mZOHH<DOM ZOHmmmmomm A<ZHM

2.2.2000 22.0 2222.2

 

 

198

the fall term final exam. The twenty-three K elements calculated from
the final exam items are all updates of previously developed K elements.
When all the previous information is included, 74 to 98 percent of the
variation in these elemtns is accounted for using 18 to 35 dependent
model variables. Removing the previous information (K0308, K0606,
K0808, K0909, K1005, K1405, K1508, K1608, K1908, K2005, K2206, K2309,
K2708, K2908, K3209, K3409, K3609, K3709, K3909, K4009, K4109, K4209,
and K4309) it is possible to account for 32 to 85 percent of the vari—
ation in the independent variables using the remaining dependent vari—
ables in each regression equation.‘ This tends to be an improvement over
the fit of previous week's equations. Note also that the standard devi—
ations of the residuals have grown significantly smaller for the final
version than they were in the early weeks of the course.

When the final equations were extrapolated to the winter term data,
a fairly good fit was obtained using all the prior information in most
cases. Thirty—one to eighty—five percent of the variation in the inde—
pendent K elements is accounted for by the extrapolated equations where
data was available to apply the extrapolation. Removing the prior

information on each K element, reduces the goodness of fit considerably.

 

 

 

6.12 Summary .

A summary of the goodness of fit of the final version of the course
model to the Fall term data and extrapolation to the Winter data is pro—
vided in Table 6.12 For each K element the parameters describing the
fit to the base and extrapolation term data are given. The number of
times the equation was updated based on new weekly data throughout the
course, and the last time the element was updated are also provided.
This latter parameter is equal to the last two digits of the four digit

"K" code used throughout this chapter (if the equation was updated based

 

on week 3 this parameter is equal to 3; if based on the final it is equal
to 10, etc.).

For eighteen of the forty K elements it was possible to account for
over 90 percent of the variation using an average of 26 independent vari—
ables. For twenty—eight of the dependent K elements it was possible to
account for over 70 percent of the variation using about the same number
of independent model variables. Those K variables with the greatest
amount of variation accounted for were also those for which there was
sufficient information available from the weekly tests to provide a number
of updatings throughout the term. For those variables which were based on
information contained in only one weekly test, the amount of variation
accounted for by the independent variables was proportionately less;
about thirty—five to sixty percent of the dependent variable's variation.

The large proportion of accountable variance is slightly deceptive
because in those instances where the K element was updated from one week
-to the next, the value of K element is based on a running sum. The K ele—
ment for one week is equal to the sum of the previous week's K element

adjusted for the number of contributing test items plus the values of

 

200

.wvou nom<¥ may no Anuv mu«m«v 03¢ ude can Cu Hmavw

 

.umouwum wsu mauvaHuxm .ucoEUHw

"acquuH50Hmuwu xwaw: on» mo wuumuu=uuo uan

0
EN

2 osu mo m:0HumH=UHmuwu thows mo noses: wLHH

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

nH <2 <2 1 ;<2 <2 <2 <2 <2 Nmn. wan. HOO. wow. ewe. A H §Nx

On HHH. <mO. Hoo. mnw. «Nu. Hoo. Hnn. Nco. cow. Hoo. HHa. mmm. OH q MN:

mH <2 <2 <2 <2 <2 HOO. mum. wwo. mom. HOO. new. ONO. OH N NNM

Om m2 m2 Hoo. ONN. mHm. HOO. QQm. who. mum. Hoo. HNO. com. OH N 0N2

nN HO. an. HOO. mom. MHN. Hoo. oae. Nmo. mow. Hoo. Hom. own. OH m me

0H <2 <2 <2 <2 <2 <2 <2 mHn. an. Hoo. Nme. an. m H wHM

¢m wz wz HOO. m<o. now. moo. own. 0<0. NOo. Hoo. qu. Ohm. OH m mHM

on m2 m2 Hoo. owa. new. Hoo. awn. nmo. wmm. HOO. 0mm. mum. OH Q MHM

an N. cao. Hoo. owe. OHQ. HOO. 0<0. «no. 000. Hoo. wnm. mew. OH c #62

mm <2 <2 <2 <2 <2 <2 <2 MON. . NHQ. HOO. HHw. Han. H H NHM

mN <2 <2 <2 <2 <2 <2 <2 mnH. Hmn. HOO. #00. «mm. H H HHM

HN HOO. OmN. HOO. Hon. awn. Hoo. Omn. Hao. ooo. HOO. Ono. mam. OH a OHM

mn co. NOH. HOO. man. can. HOD. umo. nno. 5N0. HOO. mma. hum. OH 2 m2

0N HOO. Nxa. Hoo. nnn. onw. Hoo. mam. uno. nmo. HOO. mom. cam. OH O ax

NN Ho. QMH. HOO. nae. can. moo. mmc. Hmo. mum. HOO. vvm. Nsm. OH n 02

aH m2 m2 Hoo. nmn. .nnn. Hoo. Onm. Nmo. NNm. HOO. «MO. wcm. m m 2

OH m2 m2 Hoo. «no. ham. HOO. mmc. ONO. new. HOO. mwo. cam. h c «M

HN HH. HnO. HOO. emu. Ohm. HOO. Que. Nmo. Ofim. H0O. uno. nwm. OH n m2

ON mO. HOH. nOO. mHH. «an. HOO. Nn¢. moo. «mm. Hoo. mam. H¢m. m N N2

«H m2 m2 Hoo. «om. wan. NN. an. mac. mam. Hoo. 0mm. . Ono. o N 1%“

nmmmwwmdwuumww « .me «Nu {3w «a «Rug: .mam «um . HAHN mm .www Nu m . NunwsmHm Hmuumvna HM

IHnu ummH ecu ... a. .num umwh mHnauHs: vuumaswwmw mo ”fixes:
:« moHamHum> @552

no anneaz mu I z mOH I z
2229 22H2H3I20HH<AOM<MHXH 2229 AH<h

 

 

~2<222m 2c

HF<2CA<¢FRE az< HHm mo mmmznooo

~2.e uAm<H

 

 

201

.wvou non<¥ wSu u

o 290v mufiwav 03u ummH wsu cu Hosts

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

uncaumH=UHmuwu >onw3 02u mo wucouusouo ume och

 

 

 

 

 

 

 

 

 

 

 

 

 

.umwuoum wsu wcuuaHoxw .udeun 2 may we wcowumHDUHmuwu Aquu: we 2055:: u:H~

2 <2 . <2. 2. .. <2 <2 <2 <2 <2 oS. 22. So. mom. 9:. o 2 .32

2 <2 <2 <2 <2 <2 S. mom. go. So. So. now. So. 2 N 32

NN <2 <2 <2 <2 <2 So. 23. 2:. one. So. 22. So. . S N No“

mN <2 <2 <2 <2 <2 So. <9... 22. 22. So. NS. ooo. o2 N 32

S mo. ooo. So. Rm. 2:. So. So. So. So. So. oS. <2. 8 N of.

2N 22 22 So. Non. N2. So. mom. ooo. N2. So. So. ooo. S N 222

3 <2 <2 <2 <2 <2 <2 <2 «No. o3. So. Non. o2. o 2 of.

NN <2 <2 <2 <2 <2 S. 222. ooo. 22. So. ooo. Nmo. o2 N R2

2N <2 <2 <2 <2 <2 2o. oNo. moo. <5. So. ooo. omo . S N 82

<2 <2 <2 <2 <2 <2 <2 <2 22. So. So. 23. as. o 2 was.

2N 22 22 So. 2<2. 2N2... So. Nom. o8. So. So. N22. 9:... o2 N <22

2 <2 <2 <2 <2 <2 <2 <2 N<N. 23. So. 32. 22. o 2 22

. RN 2. So. So. 9:. Nos. So. 82. go. 82. So. 2S. 9...... S 2 N22

2 <2 <2 <2 <2 <2 oo. «.2. $2. ooo. So. oi. ooo. o N So.

2 <2 <2 <2 <2 <2 <2 <2 mom. 82. So. £2. 32. o 2 ooo.

2N 22 2 oo. $2. to. moo. 22. 82. So. So. So. 2N2. oH N 2N2

o2 <2 <2 <2. <2 <2 <2 <2 SN. 2:. So. 32. ooo. o 2 of

No 22 22 So. NS. 222. So. one ooo. Now. So. 32. <2. o2 N :2

2 <2 <2 <2 <2 <2 <2 <2 22 . nNo. So. :2. 2:. o 2 2N2

NN <2 <2 <2 <2 <2 So. <3. 82. 28. So. o2. 2<2. o N 2N2

-mmmwwwowwumflm «.28 «N2 .32 N2 9 2 .222 2N2 .332 N2 .32 N2 2 2 = . vowwmmmmww ammomwmn 22

[Han ume wzu .ze:. HQHUHsz .>on umsn 0H HuH 2 ummq mo Mm£E32
5 3322:; .23 :3.

we nuns—.2 m“ I z mofi n z
2222 22223-2oifiofi25 2222 22:

 

22<=22w ZOHH<AOA<¢HXM Oz< HHh ho wmmznooo

3.28. N25 “525

 

 

202

new test items divided by the total number of test items involved to
normalize the variable. The K element is directly related computationally
to the previous week's K element and thus correlated. The R2* column in
Table 6.12 contains the proportion of variance accounted for by the inde—
pendent variables in each regression equation excluding the previous K
element for each entry. Here,for the majority of K elements, the account—
able variation falls into the 40 to 60 percent range. Since it is not
realistic to entirely eliminate the effects of specific previous know-
ledge any more than it is to color the interpretation of the results by a
deliberate computational bias, the amount of variation in the K elements
accounted for by the independent model variables probably lies somewhere
above the R2* values and below the R2 values for Fall term. The mean val—
ue for R2 + R2*/2 is .6541, indicating that for Fall term on the average
about 65 percent of the variation in concept mastery is accounted for by
variables representing cognitive and affective background and instruc—
tional behavior. Recall that Bloom estimated that up to 65 percent of
school achievement may be accounted for by cognitive and affective entry
characteristics.

Looking at the parameters describing the extrapolation of the model
to the Winter term data in Table 6.12, the same pattern as described
above is again apparent. Of the twenty—one K elements which the model
could be applied to, 70 percent or more of the variance was accounted for

by,the independent model variables for eight K elements; and 50 percent

 

l I *
)for those values or R2 listed as NA no previous K elements were
available to drop from the regression equations; for the mean calcu-
lated above R2* was set equal to R . .

 

203

or more for 16. Removing the previous week's K elements from the equa-
tions decreases the accountable variation precipitously. Only two entries
in the R2* column represent accountable variations over 20 percent. The
mean value for R2 + R2*/2 for the extrapolation term is .337, indicating
that on the average the model developed on Fall data accounted for about
34 percent of the variation in the relevant mastery variables collected
Winter term.

BS 202 was not taught by the same instructor Winter term as Fall term,
nor was the course taught and organized in exactly the same manner. Pre-
Sumably, if the course had been taught in precisely the same manner both
terms, the model equations developed from the Fall term data would have
fit the Winter data much better than demonstrated in this study. However,
the approximately ”34% fit" of the model to the Winter data does provide
evidence supporting the contention that the routine within a course and
its effect on learning may be modeled, and that modeling has some valid-

ity across terms.

‘6.13 Synopsis

Tables 6.13, 6.14 and 6.15 contain a summary of the major relation—
ships between model variables. Table 6.13 contains the major relation—
rships between the background variables (B) and (K). A "1+” in the table
indicates that for one of the weeks a positive model relationship was in—
'dicated between a B_Element and a K element (K a +B). Likewise a "1—"
indicates a negative relationship. A "If” or other combinations indicates
that both positive and negative relationships occurred during differnt
‘weeks (only those relationships with coefficient weights equal to or
greater than 0.050 are shown in Tables 6.13, 6.14 and 6.15) An inspection

of Table 6.13 shows that most of the first 209 background variables were

 

 

204

related to concept item performance2—that is, item performance was re—
lated to previous performance in college (Bl — 510), prior knowledge
(Bll - B60) (although the pretest factors contributed little), affective
characteristics (B61 - B116)’ and many items of miscellaneous background
(B

B ). College entry level tests, such as the ACT and SAT (B

117 _ 209 210'
B236) did not play a useful role in the model.

Table 6.14 contains the relationships between the instructional
variables (K) and the concept knowledge indices (I) in the same format

as Table 6.13. Here it is obvious that some instructional variables

play a larger role than others. For instance, attendance during the

 

first lecture (11) and the second small assembly session (113) appear to
have a significant influence on many of the fist ten ecosystem concepts
presented (K1 - K10). Other I elements such as the use of practice quiz
9 (I50) appear to have a greater effect on the mastery of latter concepts.
Table 6.15 contains the relationships between concept performance
(K') and prior concept performance (K) in the same fashion as used in
Tables 6.13 and 6.14. Note here that the strong relationships shown along »,
the diagonal (top left to bottom right) indicates the expected relation-
ships between current learning and previous knowledge. The many off—dia—
gonal positive and negative correlational occurances indicates the re—

lationsips between inter—connected concepts.

205

I2

+2

12

+2
Ii

.7.“

+2

+~

+~

12

I2

[2

4...”

IN

 

I—

+2|2+2

+2 12::
..2 .1
I2
:2
IN
:2

+2 +2+2+2

it -2
.-2

la

+~
+~

IH

+2

I2 ..2
..2
+2
..2 +2
I212
..2
+2
..2 ..2
+2
+2 .
-2222 271.2
..2 ..2
+2: +2

 

Snag—:SmmemafimmohnmfificmmNoNthNW§n~N~ NONE £223 ..— m—N— :3 m a

CC wmofivcH omnodsocx unwocoo .m>

QC 6.235.252 vcaopmxuom
”muucwuusouo «Hm—202228.200 mo 225269222

bananwn

 

 

206

 

+2+2 +2+2

 

 

W“
i

luv

+2 -2 +2 +2
I222
I2 I2 I2I2 I2 +2 I2
+2 +2 +2 -2 I2 -2
+2 +2 +2 -2 I2
I2 +2
_ +2 __.+2 I2
-2 +2 +2 I2 +2 I2
+2 +2 +2 I2
-2 -2
+2 +2+2-2 +2 I2 +2
I2 -2 -2 +2 +2
+2 +2 +2 +2+2
+2 +2 +2
+2 -2 .2 3+2
I2 I2 I2 I2
I2
-2 -2 +2 +2 I2I2
I2 I2I2I2I2 I2I2I2
+2 +2 I2
-2 +2 -2 +2
I2 I2 I2 I2 I2
+2 -2
-2 +2 +2+2
+2
+2 +2 I2 -2
I2 +2 +2 I2 I2+2
I2I2 I2 I2
+2 +2 I2
+2 I2
+2
+2 +2
+2 +2
+2 +2
+2 -2 22 I2 -.2

 

.399533338R5nnfiSommuwmhwwwnwflmwfi2N8...— 23222 :— 22 :92 m m h w m .— n N2

.. .11...

x

2.u=00v m2.o m2m<8

 

207

 

+2
H2 I2 +2
-2 +2 I2 I2 +2 Ii
-2 -2 -2
_ ._. -2-2 I2
+2 +2
+2
+2 +2 +2
I2 I2 +2 +2+2
+2 +2 +2-2 +3 -2-2 +2-2 +2
+2 +2+2
I2 I2 +2 I2
I2
+2 I2
-2
+2 , +2 I2 I2 -2
.2 +2 I2
I2 +72 +2+2I2
+2-2-2 I2 +2
+ +2+2+2
I2
+2 I2 I2 I2-2 +2
I2 I2 . +2 +2 +2
I2I2 I2 I2 +2 +2 +2
+2
+2+2 I2 I2 I2 I2
+2
+2 I2
+2 +2+2 +2
I2 I2I2 I2 +2 +2 . +2
+2 +2
-2 +2 I2
I2
+2 +2 +2

 

5992:3mnanbmwnmninm“2nonmucwr~w~ns~fluuflown-.2 252925522222 m c \- w m : m N 2

.2
2.u=oov o2.o m22<2

 

 

 

208

.2 I2 22 -I2
+2 +2+2
+2 +2 +2 +2
I.— IH IT.—
. I2
_ _ I2 -2
+2
I2+2 2
+2 I2+2 [2 +2 IHIAIH l2...—
+2 +~ +2 +2 +2 +2
I2|2|2
I2 I2I2 I2 M2 +2
I2
I2 +2
I2 I2 I2 I2 I
2
l.— +
2 +2 +2 +2+N
I." IH
I2 +2 +2 +2
+2 +2
I2 I2 +2 I2 I2 I2 +2
IH . .n +.— If“
I I2 I .
+2 +2+2 +2 2 +2 +2 +2
+
-2 +2 +2
+2 HM
+2 :3 ii iii +2
+2 2 I2-2
. I2 +2 +N +2
IT..— +~ +2
I2+2 +2 +2+2 .+2

 

imagggmmamhmgmnfimm“2momwuouh~owa§2NN~ NONE 23222.22 22 222: a w

. 1...:

x

2.22.82 2 .o 2:22:

nwm:mN2

 

 

 

209

+2 I2
+2 I2 +2 I2 -2
I2 I2 I2 I2 I2 +2
. +2. +2 +2
I2
I2 +2 I2
I2 +2
+2
+2I2+2
+2
+2 +2
+2
+2
I2 I2 I2

+2

H2

+2
+2

I2
+2+2

+2

+2

 

I2
+2

+2

+2
I2

I2

+2

+2 I2
+2 +2+2+2

 

5n:§2:3wmamhn$mmfinm“SommwowstNquNnNNNNONE 922-2225222292 m m h w m a m ~—

v.

2.22282 2 .o 222252.

 

 

 

 

210

+2
I2
+2 +2 +2
+2
I2
+2 +2 +2 +2
+2 +2 .
I2 I2 I2I2 I2
I2
I2
I.— I2
+2
+2+2 +2 +2
I2 I2 +2 +2
+2 -2 I2 +2 I2
+2 I2
+2
+2 I2 I2 +2
. +2 +2
+2
+2+2
+2 +2 +2 +2+2+2+2 +2
+2
IN IN I2 I2 I2
+2 I2-2
+2
+22 I2 I2 I2
I2I2 I2 I2I2
+2 +2 +2
. I2I2 I2 I2
-2
+2 +2 +2+2+2 +.2

 

52.236223233858963“ Bonmumunwfimwawflfi N82 222$ :2 2222222 m o 2. m m .2 m N2
v2
32v $202252 wwuw23052 uawuaou m> 22v 322223-252 22220302223222
umwocuuuauuo 2w=02umawpuou mo hucmsuwum
<2 .9 2.22322.

 

 

 

211

+2
+2+2 +2 +2+2
+22 +2 +2 +2 +2 22 +2
+2__,_.+2L +2I2 I2 +2 +2 +2 +2 +2
+2 +2
+2
+2
I2 +2
I2 +2
I2
I2 I2
+2 +2
I2
+2
+2
+2
+N
+2 I2 +2 I2
+2 +2 +2 -2 I2 I2 I2
I2
+2 .
I2 I2
I2 I2
I2
I2 I2
+2 I2
I2
+2 I2 I2
+2 +2 I2 .
. +2
I2I2 I2
+2 +22: +2 I2+2 I2

 

5m:~:2:3@memhmgmninmfi2momw~ewh~wNmN§mNNmNONE m2n2w2m2:2m2~222c2 m m 2. w m .2 m N 2

v2

2.2:o02 22.0 m2m<e

 

 

212

. +2
+2 +2+2 +2

+2

Snagggamwnhmgmfimnfi2ncmm~m~hww~mufinNNNNoww2m2h2m2m2im2w222c2 m m 2. w m : n N 2

+2

2
2.2aoov +2.o m2m<e

 

wo2
~02

mo.2

 

 

 

IN
I2
+2
+2 +2 +2
+2
+2 +N I2+2 +2 I2
+2 +2 +2 +2
2 +2
I2
+2 +2 +2
I2 I2 +N +2
I2 +2 -2 : -2
+2 IN I2 +2 +2
+2I2 I2 -2 . . +2 +2
+2 +2+2 +2 +2 +2
+2 ++ +2 +m
+2 +2 H2+2+2 +N I2 +2
I2 2 I2+2+2 I2 +m I2
-2 I2 I2 +2 +2 +2+2
I2 . +2 I.— +2 +42 +2 +2
I2 +2 +2+2+2 I2 +2+2 +2
+2 -2 +2I2 I2 I2+2 I2 IN+m +2+2
I2 +2I2 +2 +2 2: I2 +2 -2 I2I2 +2
+2 I2 +2+N +2+2
+2 I2+2 +2 +2 +m+~
II— lfi I— +®
+2+2 +2 +2 +2 +2+2 +2 +2 +2
+2
+2 +2 I2 +N I2
+2 I2+2 +2 I2I2I2 +2 I2
+2 +2 +2 +_ +m .
. I +2 I2 +2 +2 +22 +2 +2+N+2 +2+2 +2
+2 +2 -2I2 +2+N+2 +2 I2 I22 I2 +2+2+2
I2+2 +2 +2 +2 +2 +2

 

$992.23mmomnmcnmnfimmfi Semmwmunwwumuanawu N82 2222 :2 2N2 2292 a m 2. w m : m N2
.v.
CC mummEnowywm 202.222 m> ADC wag-“2222022292 unwocoo
"muocwuusuoo Hacc2um2uuuoo mo 232202225222
2 ..w 22.-22222.2.

 

2N'memxov\ooc\

rm

 

214

+2 . . .
+N +2
+2
I2+2...... I2 +2

 

snag—:3wnwnhmgmmfimn“fianmwathNfifinNNn2N8202h2w2m2 .22 22222.2 m m 2. w m a m N 2
x
-

2.22282. 2.0 2.2.2225

 

 

 

CHAPTER 7

APPLICATIONS
-AN EXAMPLE

There are a number of situations in which a course model of the
type developed in this study might be applied. If a course was offered
and taught regularly by the same instructor in a manner and with a syl—
labus unvarying from term to term a model of this type could be developed
and implemented as a quality control monitor and feedback mechanism.
Regular processing of the information collected on the students through
the model would produce predictions on probable student performance in
the course, much in the same manner as the model "predicted" student per-
formance in the Winter term for the current study{- The instructor on a
regular basis throughout the term could suggest possible corrective remed-
iation for those students which were predicted to have difficulty with
certain aspects of the course. The model in this situation would serve
as an instrument to improve course efficiency and productivity.

Another possible use of a model of this type is in the formulation
and investigation of research questions. The model provides a concept?
ual structure which will fit any type of complex, sequential instruction.
Given that a large number of variables are involved in the processes and
phenomena in question, the modeling methodology provides a method for
’isolating those variables strongly related to the dependent variables
under study. Once these "strong" components are isolated, the more
subtle interactions may be investigated and isolated with further exper—
imentation and standard statistical techniques.

215

 

216

To demonstrate the usefulness of the modeling scheme, a combina—
tion of the two previous suggestions was undertaken using the BS 202
course model developed in this study. This demonstration employs the
model both in a variation of the "quality control" feedback mechanism
and as a "first stage" investigatory tool.

As mentioned earlier, one of the perennial problems in BS 202 is
the large number of students in the course who do not achieve the level
of mastery of biological science desirable for elementary teachers,
although they do receive a passing grade. Many components have been
added to the course over the years it has been taught to improve stu—
dent performance. Still there are students that perform at a level inad—
equate for their intended profession.

A possible new approach to this problem would be to divide the in—
coming student class into groups based on specific individual academic
inadequacies or instructional behaviors and modify or supplement the
instruction given each group to improve student performance. It might
be possible to isolate members of the class that are poor readers, for
instance, and provide the group with instruction which minimizes the
effect of reading on performance in the course. Another group might be
found which has poor study skills. These individuals might be provided
with supplemental instruction to improve their study of biological sci—
ence. Unfortunately, previous attempts at the identification of such
groups have met with little success.

The BS 202 course model is based on a large amount of information on
,student behavior and background. If definite and meaningful student
groups exist within the class, it is probable that this information con—
tains parameters useful in identifying those groups. It is also possible

that those variables which consistently appear as the "strong" correlates

 

217

in the regression model detailed in Chapter 6 and Appendix E are
related to the grouping parameters. If such was the case, then by
using these variables groups within the class could be identified
and characterized. Furthermore, if these variables were all meaSured
within the first week or two of the term, then the groups could be
identified in time to apply modified, supplemental, or remedial instruc—
tion for the remainder of the term and perhaps improve student performance.
What was done here to illustrate an application of the model was to
search through those variables which appeared as strong correlates with
the dependent performance variables and choose from among them those which
were collected early in the time schedule of the course. Using this sub—
set of variables strongly related to performance students were sorted into
several groups based on similarities in the values of the variables used.
The students assigned to each group were then very similar to one another
on many of these characteristics measured by variables available early
in the term. Dissimilarities on these characteristics were apparent be—
tween groups. At that point, the complete variable profiles of the stu—
dents in each group were analyzed for similarities. While the variables
used to initially group students reflected characteristics measured early
in the course, this analysis of the entire profile provided more informa-
tion on and insight into the nature of each group. Thus students were
first groupedaccording to characteristics measurable early in the learn-
ing process, then student information reflecting characteristics meaSured
throughout the entire course process were examined and analyzed. This
analysis of the total information available on each student produced
broad group profiles and suggestions or hypotheses on how the learning

performance of each group could be improved or supplemented.

218

An inspection of the tables containing the model coefficients in
Appendix E confirms that there are indeed a number of variables which
appear as strong correlates with the performance indices throughout the
term. There are at least forty—two such strong variables which are
available within the first two weeks of the term. These forty-two vari—
ables are listed in Table 7.1 with a description of the general grouping
parameters to which each contributes. These variables reflect previous
academic performance, prior knowledge of biology, testwiseness, test
anxiety, attitudes, expectations, and learning preferences. The last
three variables indicate instructional behavior and performance.

To locate the meaningful student groups the forty—two variables
were used as grouping variables in the Veldman (1967) Hierarchial
Grouping Analysis computer program (HGROUP) located on the University
of Michigan computer and available through the MERIT Network. This pro-
gram groups N cases in N-l to 2 groups through an algorithm which assigns
cases to groups in an attempt to maximize the average intergroup distance
while minimizing the average intragroup distance. The HGROUP program
deals with profile similarity and groups cases based on a minimal increase .1
in within—group variation at each step. Table 7.2 contains the HGROUP
clustering of 94 students from the Fall term, beginning with the forma—
tion of twelve groups from thirteen. Eleven students were not included
in the grouping analysis because of the unusually large amount of missing
data in these students' profiles, thereby decreasing N from 105 to 94.

Student cases in Table 7.2 are represented by a one to three digit code
«followed by a "C". Table 7.2 does not provide any information on the

first eighty grouping steps; from 93 groups to 13 groups. Starting with

the twelfth grouping, the most similar groups are combined forming ll,

10, 9, etc., groups. Each absorption of one group by another increases

TABLE 7.1

GROUPING VARIABLES

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

VARIABLE ACRONYM GENERAL GROUPING PARAMETER
B3 TRCR Transfer Credits 1
B6 MSUPTS Previous Academic Performance 1
B18 PRE008 General Knowledge, Ecology
B19 PREOO9 4§Lecific Knowledge, Ecolflgy i
BZ3 PRE013 Knowledge and Application, Biology 1
B28 PRE018 General Knowledgg, Ecology I
B35 PREOZS Specific Knowledge, Ecology
B41 PRE031 General Knowledge, Ecology
B42 PRE032 General KnowledgeJ Biology
:63 AFFOO3 Test Anxiety I.
67 AFFOO7 Test Anxiety
B68 AFFOO8 Test Anxiety
B71 AFFOll Test Anxiety
B74 AFF014 Test Anxiety
B81 AFFOZl Test Wiseness
B85 AFF025 Test Wiseness ‘
B87 AFF027 Test Wiseness W”
B89 AFF029 Test Wiseness
B9O AFFO3O Test Wiseness
B92 AFFO32 Test Wiseness
B93 AFFO33 Test Wiseness
B94 AFFO34 Test Wiseness
B98 AFFO38 Peer Orientation
B107 AFFO47 Peer Orientation
Bl08 AFFO48 Peer Orientation -
3123 BKGOO4 Biology Background
B125 BKGOO6 Biology Background
B126 BKGOO7 Attitude Toward Biolggy
B131 BKGOlZ Science Background
:135 BKG016 Science Background

138 BKGOlQ Mathematics Background

 

TABLE 7.1 (Continued)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

VARIABLE ACRONYM GENERAL GROUPING PARAMETER
B149 BKGO30 Attitude Toward Science
B151 BKG032 Egpectations in Course
B152 BKGO33 Egpectations in Course
B153 BKGO34 Previous Academic Performance 2
B161 BKGO42 Learning Profile h
B168 BKG049 Learning Profile
B171 BKGOSZ Learniflg Profile
B175 BKG056 Learnincr Profile 1
Il LECOOl Lecture Attendance
I70 0NE03l Practice Quiz Usage
I101 Q2 Performance on Quiz Two

 

 

 

 

 

221
TABLE 7.2

HIERARCHICAL GROUPING ANALYSIS OUTPUT
(VELDMAN'S H GROUP)

12 GFfIUPS AVYER C HBINING S 6 (“I 1‘) AND S ‘2 (“I 3). Elflflk I 75.5000
1 (I A) 1 53‘ ‘76 NH

 

 

1
c 2 («u 25) 2 '1 c 11¢ 15c 30¢ 11¢ 13¢ 11c 15¢ 60¢ 62¢
66 71¢ 76¢ 77¢ 76¢ l‘t 91¢ 91c 1uoc 102¢ 1¢3¢ 1o1¢
c 1 (u: 3) 5 27¢ 96¢
c 6 (u- 17) 7 16¢ 23¢ 21¢ 25¢ 31¢ 36¢ SEC 1¢c 11¢ 50¢ 52c 55¢
71 92¢ 93¢ 1¢5¢
1 7 11: 16) E zzc 31¢ 12¢ 59¢ 61¢ 6!: 66¢ 91¢ 1o1c
c e (NI 5) 9 Inc 29¢ 56¢ 96¢
1 9 1h: 11) 1o 11¢ 26¢ 27¢ 26¢ 69¢ 75¢ 03¢ 61¢ 97¢ 99¢ (SE
1 11 (1- 1) 12 37¢ 57¢ 70¢ »
c 16 (u: 1) 19 0?: 65¢ 91¢
1 17 (u- 5) 20 19¢ 73¢ 79¢ 32¢
c 16 1". 1) 21 32¢ 61¢ 65¢
c 31 o.- 2) 35 56c 6.6
11 caouvs 117¢n c nexuxuc c 9 (n- 11) nun c 17 (I- 5). 59901 - 62.3716
1 1 1 - 1) 1 53¢ 6
1 2 1»: 25) 2 3¢ 6c 11¢ 15¢ 50¢ 11¢ 13¢ ‘11c 15¢ 16¢ 60¢ 62¢
66 71c 76¢ 77¢ 76¢ 61¢ 90¢ 91¢ 100¢ 1021 105¢ 101¢
1 1 (n- 5) 5 67¢ 96¢
¢ 6 (n: 17) 7 16¢ 23¢ 21¢ 25c 51¢ . 36c 36¢ 1¢¢ 16¢ 50¢ 52c 55¢
71 92¢ 93¢ .105¢
1 7 (~- 16) 1 22¢ 31¢ 1:: 59¢ 61c 66¢ 06c ‘91¢ 1o1¢
1 6 (u- 9 16¢ 79¢ 56¢ 60¢ . '
1 9 (u- 16) 10 11¢ 20¢ 26¢ :7: 26¢ 19¢ 69¢ 73¢ 75¢ . 79¢ 62¢ 03¢
61 97¢ 99¢ ‘
c 11 (n: 1) 12 37¢ 57c 70¢ a
1 16 (u- 1) 19 63¢ 65¢ sec
5 ;! (u- 1) :1 37¢ 61¢ 65¢ .
1 1 (n- 2) -5 56¢
2.0 ‘.
10 690095 1112: c nuxuxus 1 2 tue 25) AND 1 7 (a: 10). Elana - 61.3652 '
1 (N: 1) 1 5}: 67¢ a
c z (u- 35) 3c 6c 11¢ 15¢ 22¢ 30¢ 31¢ 11¢ 12¢ 13¢ 11¢ V
5 16c 59c 60¢ 62¢ 61¢ 66c 66¢ 71¢ 76c 77¢ 70¢ 61¢
9c¢ 91¢ 91¢ 1009 101c 10?: 105¢ 101:
c 1 (u- 3) 5 67¢ 96¢
c 6 (v- 17) 7 16¢ 23¢ 21¢ 25¢ 31¢ 36¢ 35¢ 10¢ 16¢ 50¢ 52¢ 55¢
71 92¢ 93¢ 105¢
0 r (n- 5) 9 16¢ 29c 56¢ 60¢ -
c 9 (u- 16) 10 11¢ 20¢ 26¢ 27¢ 26¢ 19¢ 69¢ 73¢ 75¢ 79¢ 62¢ 03¢
1 97¢ 99 _
c 11 (NI 1) 12 37¢ 57¢ 70¢
c 16 (n: 1) 19 63¢ 65¢ 61c
, 1 15 (u- 1) 21 32¢ 61¢ 65¢
c 31 19- 2) 35 56¢ 0'6
9 110095 111:: COHFXNING 1 9 (u: 16) auo c 11 (us 1). 59909 - 65.0161
1 (u: 1) 1 53¢ 67¢ 09¢
1 2 (u: 35) 2 3c 6: 6c 11¢ 15¢ 22¢ 30¢ 31¢ 11¢ 12¢ 13¢ 11¢
15 16¢ 59¢ 60¢ 62¢ 61¢ 66¢ 61¢ 71¢ 76¢ 77c 76¢ 61¢
66 96¢ 91¢ 91¢ 1001 101c 102c 103c 101¢
1 1 (n- 3) 5 67¢ 96¢
1 6 (n- 17) . 7 16¢ 23¢ 21¢ 25¢ 31¢ 36¢ 33¢ 10c 16¢ 50¢ 52¢ 55¢
71 92¢ 93¢ 105c *~
0 r (u- 5) 9 15¢ 79¢ 56¢ 6¢¢ I
1 9 (n- 20) 1: 11¢ 12¢ 20c 26¢ 27¢ 22¢ 37¢ 19¢ 57¢ 69¢ 70¢ 73¢ .s'
75 79¢ 72¢ 13¢ 61¢ 97¢ 99¢ '
5 16 (n- 1) 19 63¢ 65: 62¢ ,
‘ 1' (N- 1) 21¢ 32c 61 ,
C 31 (N: 2) 35C 5“ C 65C 8 3
.
c 3:\nuurs 111:9 (Dnnlnluc 6 1 (~- 1) AND 1 1 (u: 3). sauna - 93.3100
‘ 2 ::' 1;; 5: 5c 53¢ 67¢ 67¢ 59c 96
- . c 3c 6c 6c 11¢ 1
.5. I... 5.. m .2. .35 if: 22$ #5 9:: $2 "3‘ “t
‘ a ‘N_ ‘7) 6;: ::: 91c 91¢ 100¢ 1o1¢ 102¢ 103¢ 1o1¢ 75‘ "“
7“ 92¢ 3g: 1::: 25¢ 31¢ 56¢ 33¢ 10¢ 16c 50¢ 52¢ 55¢
: 9 :3: 2;; 12¢ 16¢ 79¢ 56¢ 90¢
c 11¢ 12¢. 20¢ 2
75¢ 79¢ 12¢ 83¢ 9:: 5;: 2" 57¢ ‘9‘ 57‘ °°c 7°‘ 73‘
g ,6 ( _ 99¢
‘ 10 1: 2; :3: 63¢ 15¢ 23¢
' 32¢ 61
c 31 (n. 2, ”c s" 5 65¢ 14.6
7 100093 111:9 (OlBlNlu‘ c-“z 19- 35) 110 1 6 (u- 5 .
‘ A ::: ‘3; g: 3‘ 53: ‘7‘ '7‘ )i9gnnaa 9. 107.6966 .
¢ 6c 6c 9c
.1, ,3. 1“ :2: :2: 2:: s: :2: :2: 2:: :1:
‘ 6 10“ c a: so: 01¢ 66c 90¢ 91¢ 91¢ 100¢ 101¢ 162; 103;
(N' 17) 7c 16¢ 2 1
‘ 6 (9. 20) ¢ 92‘ 9;: 15;: 25¢ .1¢ 36¢ 36¢ 10¢ 16¢ 50: 52¢ 55¢
10¢ 11c 12¢ 20¢
- 75c 79c 32¢ 13c :2: 2;: 3;: 37‘ ‘9‘ 57¢ °°t 70¢ 73‘
‘ 16 ‘~- 1) 19¢ 63¢ 05¢ 63¢
‘ 15 (~- 1) 21¢ 32¢ ‘1: 65¢
‘ 3“ 0'- 2) 35¢ 56¢ 8 5
.

   

 

6 SROUPS AVIEI COMBININ- 6 2 (N-
7 1¢ 51¢

6

G 2 1:: 57) 2¢ 5 6C
25¢ 23 3¢¢
46¢ A 50¢
71¢ 7 77¢
101¢ 10 103¢

C 9 (k- 20) 10¢ 1 12¢
75¢ 7 IZC

C 16 (N- 6) 19¢ 6 85¢

6 18 (“I 6) 21¢ 3 61¢

6 31 (N- 2) 35¢ 5 C

S 6|0UPS AFTER ¢OIBXIXN S 9 (N-
6 1 (NO 7) 1C 53¢

 

 

6 2 (ll 57) 2¢ ¢¢
25¢ 2’ 30¢
66¢ 6 50¢

7 77¢
101¢ 10 1¢3¢

6 9 ("I 2‘) 1°C 1 12¢
65¢ 6 70¢

6 16 (H- A) 19¢ 61¢ 85¢

6 31 (N- 2) 35¢ 5

A GROUPS A'IER ¢DIBIN1NS G 2 (N.
G 1 (N 7) 1C S¢ 53¢

G 2 (N: 61) 2¢ 3¢ 6
26¢ 25¢ 29¢
l5¢ 60¢ 63¢
68¢ 71¢ 75C
92¢ 93¢ 94¢

G 9 (ll 2‘) 10¢ 11¢ 12¢
65¢ 69¢ 73¢

G 31 (“I 2) 35¢ 52¢

31GRDUPS AV!!! ¢0HBINXN6 6 2 (N-
7 1¢ .¢ 53¢

6 2 1:: 63) ?¢ ¢
2A¢ 25¢ 29¢

LAC 55¢ 66¢

65¢ 66¢ 65¢

91¢ 92¢

9 9 (l- 2‘) 10¢ 11¢ 12¢
69¢ 70¢

2 SROUPS AFYEI ¢OIBINING 6 1 (NB
6 1 (MI 73) 1C 2

22¢ 23¢ 24¢

62¢ 63¢ ALC

"60¢ 62¢ 63¢

81¢ 55¢ I6¢

101C 102C 163¢

6 9 (II 2‘) 10¢ 11¢ 12¢
" ' 65¢ 69¢ 70¢

 

222

TABLE 7.2 cont'd.

‘0) AND G 6 (N: 17). EDRUR I 116.5689
67¢ 57¢ 9

89¢
A 9¢ 15¢
31¢ NC “t Ht
52¢ 55¢ 56¢ 59¢
78¢ 80¢ 81¢ 86¢
1¢4¢ 1b5¢
20¢ 26¢ 27¢ 2P¢
83¢ 86¢ 97¢ 99C
28¢
65¢

'20) AND 6 18 (NI 5). ERROR I 122.3687
67¢ 87¢ 89¢ 96
7C

15¢
63¢
60¢
90¢

37¢

6c 9¢ 11¢ 15¢
31¢ 31¢ 36¢ _ 36¢ 10¢
52¢ 55¢ 56¢ 59¢ 60¢
, 70¢ 10¢ Ht 66c 90¢
um um
20¢ 21¢ . 26¢ ' 27¢ 21c
73¢ 75¢ _ 79¢ 22c 63¢
66¢

57) AND 6 16 (N- A). 9ERR09 I 126.3512
67¢ 27¢ 96¢

7C ¢ E9¢ 1£¢
30¢ 31¢ 35¢ 36¢
50¢ 52¢ 55¢ 56¢
6¢ 77¢ 78¢ Ft
13°C 131¢ 1’2¢ 163C
20¢ 21¢ 26¢ 27¢
73¢ 75¢ 79¢ E2¢

61) AND G 31 (N! 2). ERROR I 130.9125
67¢ 87¢ 89¢ 96

7¢ 8C 9¢ 15¢
33¢ 31¢ 34¢ 35¢
58¢ 50¢ 52¢ 55¢
71¢ 75¢ 76¢ 77c
93¢ 95¢ 1¢0¢ 101¢
20¢ 21¢ 26¢ 27¢
73¢ 75¢ 79¢ 82¢

7) AND G 2 (NF 63). ERPOR I 111.9151
5¢ Rt 9C

25¢ 29¢ 30¢ 31¢
A5¢ 66¢ 65¢ 50¢
66¢ 66¢ 67¢ 68¢
87¢ 88¢ 89¢ 90¢
10£¢ 1”S¢

20¢ 21¢ 26¢ 27¢
73¢ 75¢ 79¢ 82¢

END 0' 91L! 0" IISUUR¢EI CAUSES A IEIUIN 70 ITS.

EIECUIIDN TERMINAYED

8516

21: 17: 04 II1. 623 Ic-o ¢.97

36¢
52¢
71¢
91¢

ZF¢
83¢

11.0

 

 

 

 

 

223

the total within—group error. Notice that the largest absolute in~
crease in error (A E) for these grouping steps occurs when 8 groups
are combined to form 7 groups. For the purposes here, it is assumed
that the 8 groups represent an "optimal” aggregation of individuals
on the forty-two grouping variables. Thus, the grouping analysis has
prodiced eitht groups of students with maximized intragroup similarities.
Tables 7.3 and 7.4 contain the group profiles across the forty—two
grouping variables.

After the development of the eight distinct groups the entire
array of background, instructional and performance data contained within
the B, I, and K matrices was examined for within—group similarities and
between—group differences. A number of similarities within each group
was found as well as many between—group differences. The fact that
students within each group are similar not only on the grouping variables
but also on many other variables as well indicates that the groupings
are in some sense legitimate, and may provide a clue to improved instruc—
tion in the course. The next section contains a profile of each group
based on the examination of the entire store of model information. The
group profiles are presented as general descriptors or characteristics
of the group as a whole which are based on the tendencies of the students
in the group on a large number of variables.

The suggestions for group performance improvement presented at the
end of each profile discussion are based both on group tendencies and a
comparison of the more successful students in each group with the less

‘successful ones. Such suggestions are of course hypotheses in need 0f

testing through future studies.

 

i‘x

 

TABLE 7.3
VALUES OF GROUPING VARIABLES B18 THROUGH I7O

 

BY GROUB NUMBERS

GROUP
G1

0002010

1111111

9’ I1 v9

G2

 

G6

 

 

 

'0".

0231!!-

,3

332002

.5

lellb

 

1!. 0 ~13 5203321..th

72¢

 

 

a a s

 

Viv v v v V} v} VIQ}

E

.0
b m

m

\JZBZZZh
v

 

«V.

. .
\Q%
N. 0000110
$1 .
8
0100011
I .
A
l°vu.naw1°'
3
goon-at“
I .
2
I’LCAU-Uo
.3! I
0
l9J100101
.
”.010011
I .
7
030.... 0100
I .
u
I [-0- ".5000
1
81110111
I .
‘
071111.110
.
l n
I
I .
3629.111...
.

7
51130000
I .

010000 _

--u-c-nco

 

 

 

 

 

 

a. . 111..

n.- .C 1 50001101100 . .

L I. .

.5..C1I.11L..11C1 \111

 

13°1-

 

 

 

 

5.016701001001235
|703050
11111

15 3 2316006025 07. 39
12223335 A 555799“

9
G

gww002011zwa20001
11111011111111.1111 11
ZhlbflZJZZzthUJng-c
£13W33233332Z§3332
2212422 1332352332223
302%33323331231‘302
511W§22z12222111°1
02142221322229.2233
000W0220200200¢0201
:10333222211333 2’.—

-

. 000000100 100000000

00000011010001.0110.

. 31.310333112223333)!
,.

21110010102301.1112.
.

1§°°1°"&o%:'1
“11111111111111.1111!
4.0001041000000000
11.101001111111100:
1.0000000000000000
\- DDDDDDDDDDDDDD 9009010
1ncnua‘naooxu-:
100000 1001000100000
1100001010-00333 1‘
11.000011000000000

01111111111113.1111 .

.

”1.1111110010010111
«.0 L100m-10010111r7-1100
“‘01. 7.111111141311011
r0010 0010100001100
an \111011101011900101
00011000000011
10101011000100.1011
00000 10101111000011
0; .1100111 110110120 01
9.0031000010011110
11111111010114.1111...
11111111111114.1111...

012367 07 97 901. 592507.,
11122223.- 56777700099

G31

fits?bltflaiti‘uélSIBltllEJlilillliliifiszil

 

225

TABLE 7.4

VALUES 0F GROUPING VARIABLES B

3’ 36’ I101

 

 

‘
l
l
i
TABLE 7 .4 (Continued) 1

 

Group Profiles and Hypotheses for Instructional Improvement

Group G1

Group G1 was composed of seven individuals Fall term 1977.

They appear to have entered the University with the intention of pursuing
a scientific or technical major, but for some reason, probably the lack
of quantitative skills, changed major to a curriculum requiring BS 202.
Generally, they had a neutral or positive attitude toward science,
aimed at a high grade in the course but expected to be satisfied with
an average grade. They had taken some college physics or chemistry,
received an A or B in high school biology, found lab work and class
notes helpful in their studies and considered memorization important.
Initially most knew some superficial aspects of biology, but little of
the details. They tended to be anxious about test taking and having
enough time to complete the tests, and generally not testwise or clever
at playing the testing game. The individuals in this group tended to
be somewhat conscious of and dependent on peer opinions and decisions.
The students with the higher GPAs in the group were more conservative
than those students in the group with low GPAs. They attended nearly
all the SAS sessions, and usually used the lecture tapes to make up
for missed lectures. On the continuum of dull to bright students,
this group as a whole tends toward the dull end.

There are a number of possibilities for improving the performance
of those students characteristic of this group:

1. Encourage the use of the lecture tapes to make up for

missed lectures.
2. Encourage the use of the practice quiz in preparation"

for the weekly quiz.

 

 

3. Attention and remediation needs to be directed toward

the poor study and critical reading skills character-
istic of this group.
4. Some effort should be expended to Correct the tendency
of individuals in this group to try to memorize every—
thing taught in the course.
5. These individuals need to be actively kept involved in
the course and encouraged to spend considerable time
and effort in preparation.
6. Some of the individuals in this group do not benefit much
from anything they try to improve their performance.
Perhaps those individuals in this group with low GPAs
need individual tutoring.
(It should be remembered that each of the above suggestions is
a hypothesis in need of testing. Each hypothesis is suggested through
the data collected and analyzed, but not proven. Additional studies
are needed to pursue these and all of the following suggestions or
hypotheses.)
Groups G2 and G31
Group G31 appears to be an irregular artifact, and so was merged
with Group G2 for the purpose of analysis. Group G2 was composed of
thirty-seven individuals, roughly one—third of the students in the course
Fall term.
Into this group falls the conscientious and the bored, the easily
discouraged and the undaunted. It is a group with little regularity
Ibeyond a tendency to be irregular in study habits and instructional be—
havior. In general, the group was rather deficient in background know-

ledge of biology although most took High School Biology with a grade of

 

 

La

 

229

A, B, or C. They tended to be non-science oriented with almost no
background of college biology, physics, or chemistry. Although most
reported a neutral or mildly positive attitude toward biology, many
indicated a negative attitude toward science in general. The group
was generally not test anxious, but was moderately testwise and fairly
independent of group pressure; although the worst performace was given
by the most conservative of the group. The group has a GPA range of
mid 2.03 to mid 3.05, with the grade in the course related proportion-
ately to GPA. Most aimed for a high_course grade but indicated they
would be satisfied with a 3.0 to 4.0. The poorer students in the group
tended to be the worst judges of their own potential. Resource maga—
zines and books were viewed as helpful in a science course, but not
as much as lab, classnotes and memorization. The score these students
received on the problem solving test (PRBSOL) may indicate the potential
performance in the course. The Myers—Briggs test scores for this group
indicated that most of the GZ students had profiles characteristic of
elementary teachers, and would be expected to have little interest in
technical things or abstract thinking.
Several areas for performance improvement are suggested (or hypothe-
sized) by the information collected on these students:
1. The one, single area of major improvement for this

group may be each individual's need to develop a

regular, consistent, disciplined mode or habit of

study. They need to spend time each week regularly

reading, studying course materials, and using the

practice quiz. They should avoid any tendency toward

irregular study and then try to catch up before the

quiz.

 

 

 

230

2. This group would also benefit from instruction in
critical reading and test taking skills.
Group G6
This group was composed of seventeen individuals Fall term,
mostly freshmen, sophomores, and transfer students. They tend to be a
self—confident, although not overachieving, group of hardworkers who
quickly adapted to the course. With one exception all the members of
this group received a course grade in the 3.0 to 4.0 range. The group
had a somewhat mixed background in biology, most having taken high
school biology and received an A, B, or C, and over half having taken
one term or more of college biology. Many may have transferred biology
credit to Michigan State. The group had a generally positive attitude
toward all science, including biology and intended to aim for a 4.0
in the course, but would be satisfied with a 3.0. The group had a
good attitude toward lab, registering nearly perfect attendance in the
small assembly sessions. They tended to use the lecture tapes to make
up for missed labs, used the practice quizzes from midterm to final,
spent considerable time in the tape labs and spent between 4 and 6 hours
in outside study regularly each week.
The impression from this group is that they need little in the way
of extra help in the course. Some possible areas of improvement are:
l. Lectures. Missed lectures tend to lower final grade.
Lecture tapes should be used to make up for any
lectures missed.
2. These students should try to use the practice quiz
each week regularly throughout the term. They should _
take the practice quiz like a real test, then correct

their mistakes.

 

 

231

3. Some of these students, especially the younger ones, need

some coaching to improve their testwiseness, and confi—

dence in their ability to take exams.
Group G8
The five individuals in this group tended to have an average
GPA and little or no background in college chemistry or physics. They
received an A or B in high school biology and reported a neutral or
mildly positive attitude toward both biology and science in general.
They tended to initially know something about the basics of biology
and ecology but little about specific biological or ecological proces—
ses. They aimed high in the course, but indicated that they would be
satisfied with an average grade. The group was generally mixed on test
anxiety and self-confidence, and can only be considered partially test—
wise. They generally reported themselves to be independent and not
affected by peer pressures, but the results of their Myers-Briggs
assessment is contradictory, indicating that the need for encouragement
and praise. They generally registered a preference for memorizing facts
over other modes of study. There is also some evidence that these indi-
viduals become discouraged, and perhaps self destructively defiant, very
easily and quickly.
The following are areas where an increased instructor effort might
result in increased performance and better attitude in the part of
these individuals:
1. These students need continual attention, encouragement,
and praise throughout the term.
2. These students should be encouraged to attend the
lecture and SAS regularly.
3. They should use the practice quiz each week, looking up

the answer to each question as they go through it.

kg 4.14.2 in;

fish’L-A$-_Q:. ‘

.5

4. Use of the tape labs should be enc0uraged. Perhaps the j
instructor could provide more attention to these students C
in the individualized sessions.

5. These students need help in critical reading and with the
improvement of their study skills. Many of these students
try very hard, but the lack of study skills causes them
to waste a great deal of time, and eventually they become
discouraged and give up. .

Group G9 .

Group G9 was composed of twenty students Fall term. This may
be the group that the BS 202 instructors most clearly perceive as fail—
ures in preparation for biology teaching. This group is composed of
mainly transfer students (75%) and freshmen. Although they come to the 8
course with a GPA between 2.5 and 3.5, about one—half receive a grade
of 2.0 or less; one—quarter of the group receiving a 1.5 or 1.0. No
member of this group received a course grade above a 3.0. The group
had a mixed background in biology. Those that took biology in high
school reported a grade of B, C, or D. Although some reported taking W
another college biology course, performance on the pretest was mixed;
most knew simple definitions, but missed the more sophisticated questions.

The group reported mixed apprehension about tests, and most of the stu-

dents were not particularly testwise. Many lacked self confidence, but

did not tend to rely on their peers for support. The group had little

science background and indicated negative to slightly positive attitudes «
toward biology. Over half were neutral or negative towards science in

general. They all indicated that they intended to aim for a 3.0 or 4.0

in the course and would be satisfied with nothing less than a 3.0. Many

were disappointed.

233

This might be a group to separate into a separate section where
extra attention and encouragement could be provided. In addition,
the following suggestions for performance improvement are provided:

1. Lecture attendance and/or use of the lecture tapes

should be mandatory.

. 2. Use of the practice quiz should be encouraged. Perhaps
the practice quiz could be administered in a "dry run"‘
fashion in SAS, followed by discussion.

3. Minimum amounts of time spent in the tape lab should

be set and mandatory.
4. Instruction in the improvement of study efficiency
and critical reading should be provided to reduce
wasted student effort and prevent discouragement.
Included here should be the effective use of memorization.
Many of the G9 students appear to have potential, but unfortunately
most of it goes unfulfilled because of poor study habits and attitude.

Group G16

There were four individuals in this group Fall term. These
individuals tend to be intelligent, self confident, testwise, and inde—
pendent. They had some high school biology, but little or no college
science. They tended to have a neutral or positive attitude toward
biology and science in general, and had high expectations for their own
performance in the course. Although they had little previous knowledge
of biology they were confident in their ability, and their self assess—
ment was essentially accurate. They were clever thinkers and reasoners;

INFJ or INTJs in the Myers-Briggs classification.

l “.414.

“:32”

234

$5.1..—

Although this is a high performing group, the following suggestions

as a;

could possibly improve their performance: 1
1. These individuals should be given the opportunity to
improve their test taking skills and reduce their

test anxiety.
2. Increased usage of the practice quiz should be encouraged.
3. These students should be given some help to improve their
use of the tape lab and prevent wasted effort.
4. The use of the straight memorization of fact should be
discouraged.
5. These areas should be corrected early in the course to
prevent discouragement.
Group G18 1
This last group was composed of four students Fall term. These
individuals tended to have about an average GPA on entering the course.
They took high school biology, no college biology, but took several
college physics and chemistry courses. With this science background
they generally had a neutral or positive attitude toward biology and W
science in general. They tended to know something about ecology, were
able to generalize about the relationships of matter and energy, but
apparently had forgotten many biology facts. They aimed for a high
grade in the course but were willing to settle for an average grade.
They were moderately confident in their abilities to take tests and
were generally independent. ¢
Concentration in the following areas for these students would prob—
ably improve their performance in the course:
1. They should attempt to attend all of the lectures.
2. Use of the practice quiz as a "dry run" before the real

quiz may improve their performance.

235

3. They should study six or more hours outside class
regularly each week.
4. Some effort should be made to help.decrease these
students' test anxiety, improve their study skills,
including the deemphasis of memorization, and improve
their test taking skills. This will help decrease
their frustration with the course.
Summary
This is as far as the grouping analysis can be taken with the infor—
mation and procedures of this study. The next step is to administer the
instruments containing the forty—two grouping variables to a new, fresh
BS 202 class and divide the class into tentative groups based on the
responses. Table 7.5 contains the operational1 variable values on which
to base such groupings. The experimenter may also wish to add additional
items or instruments, such as the Myers Briggs or the Cognitive Style
Mapping, to further aid in student differentiation and group assignment.
One possible research design would then have the groups each split
randomly into experimental and control groups. The control groups would
then be merged and received the standard BS 202 instructional package.
The experimental groups would receive various treatments based on the
hypotheses proposed earlier in this chapter to improve instructional
performance. Such instructional treatments would include, but not

necessarily be limited to; instruction in the improvement of study skills,

 

1These are the operational values of variable; for the pretest items an
"0" indicates an incorrect response, a "l" a correct response. For the
Affective and Background Questionnaire items (B 3 to B 7 ) the responses
are shown as a number from 0 through 4, indicating a sfudent response of
1 through 5. The 0—4 is an artifact of machine scoring.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

o o «meg qmm

o o o H o o o mmomg m mm

o o mmomg Nam

o o o o o o omomg com

o o mmomrflw mwm

o o ANDRE» nmm

o o o o o o mmomaw mwm

H H H H H H H HNOEE Hmm

H H «HOE: qmm

HHOE Hum

H H woof? mom

noomg new

moon“?. mom

0 o o Nmommm qu

HmomMm Ham

0 o o mNomMm mmm

o wHomMm mum

o o o MHommm mum

o H H H H @0055 mHm

o o H H H H H woommm me

ooqmlo oomqlo ooowlo oowmlo ooomlo OOHmloom ooquOON 9?sz mm

o o OOHIO mnlo mwlo oHlo oqlo MOMS. mm

wHU 0H0 aw mu $0 No Ho Ehfiouo< anmHHm>
mmbomo

mK MHQH

 

 

omINH nNIHN MNIO mNInH wNImH NNIHH omlom NO HOH

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

H.o Hmomzo . cam

o H H H H H H HooomH HH

«.m.N.H s.m.~ m.N.H s.m.~ omouxm msHm

s.m.~.H s.m.~.H m.~ s.m.~ s.m.m «.m.N m.~ Hmoomm H:MH

s.m.~.H s.m.~.H m.N.H «.mHN.H m.N.H.o mqoosm onm

s.m.~.H m.~.H s.m.~ s.m.m.H s.m.~ Nqoumm SHm

~.H H.o N.H N.H N.H.o N.H.o m.N.H smooxm mmHm

m.~ N.H.o m.N m.~ N.H s.m.N mmooMm NmHm

N.H.o N.H.o N.H.o H.o H.o N.H.o H.o Nmousm HmHm

m.N .m.N.H m.~.H s.m.~ s.m.~.H omooem msHm

H.on H H.o- H.o- H.o- H.o- H.o mHouMm mMHm

H.o o o o o H.o oHouMm mMHm

H.o o o H.o o o NHouMm HMHm

m.~ m.N.H m.~ s.m m.~ s.m.~ sooomm GNHH

o o H.o o H.o sooumm mNHm

N.H.o m.N.H.ou H.o N.H.o N.H.o H.o accuse mNHm

o o o o o o wqomfi mOHm

H o H H H H ssosm< sOHm

o o wmomm< mam

wHo oHo so we so we Ho asaouo< mHanums
mesomu

mesaHucouv m.H mHm<a

238

critical reading in the sciences, improvement of test taking skills,
individual tutoring, development of consistent, disciplined study,
additional course instructional sessions and mandatory attendance.
Additional instructional treatments may also be instituted as the
investigation uncovers further intragroup problems and shortcomings.

In summary, then, what has been demonstrated is the possible
development of a tool for improving student performance in the course.
When perfected, the grouping mechanism would channel students through
different "tracts" of the course designed to remediate or minimize
student weaknesses, and capitalize on student strengths. This grouping
mechanism would be based on the results of the experimentation and
investigations following the leads presented in the model study. These
leads and suggestions for grouping presented in the current study were
a direct result of viewing and modeling the course as a system of sequen—
tial events and processes. Such a view establishes some order to the

chaos of the reality.

 

 

CHAPTER 8

SUMMARY, EVALUATION, RECOMMENDATIONS
AND CONCLUSIONS

In this study a very simple application of systems approach and
modeling has been applied to the instructional system of a college bio—
logical science course. In this abstract conceptualization students
were viewed as entities passing through the instructional processes of
the course, and the changes in their knowledge of a subset of the con—
tent taught in the course, measured through their test performance,
was considered to be the product of the course. Ecology, a main com-
ponent of modern biology was used as the subset of content on which stu—
dent performance was monitored. Each component of the course was viewed

as an instructional session contributing to the students' knowledge of

ecology. The effects of the instructional sessions were aggregated on
a weekly basis and related through the model to student performance on
ecological concepts based on items from the weekly quiz. Student con—
cept performance was also related through the model to a large array of
background information collected at the beginning of the course. The
model was successively applied and updated weekly throughout the ten
week schedule of the course.

At the beginning of the study it was necessary to establish a
reference frame for the content. To do this a content analysis of the
‘concept of ”Ecosystem" was conducted to the depth necessary and apprOv
priate for an Ecology course for college biology majors. An analysis

of the Ecosystem content taught in BS 202 was then conducted. When

 

 

240
comparing the two analyses it was apparent that although the version
of "Ecosystem" taught in BS 202 is less sophisticated than the refer-
ence version, it is comprehensive, and probably represents all that is
digestible in ten weeks by non-science, elementary education majors.
From an analysis of the content of the weekly tests and final exam it
appears that not all of the concepts were tested for and that very few
were tested for repeatedly throughout the term. This lack of compre—
hensive and repeated testing of key concepts reduced the efficacy and
usefulness of the modeling process. _The scarcity of repeated measures
on performance indices also makes it difficult to generalize the model
for other terms.

One of the basic intents of the model building process is that the
final product will organize information in such a manner to make prev 1
viously unrecognized relationships recognizable. For this study, a
large amount of information was collected on students' backgrounds and
instructional behaviors. The approximately 900 variables reflecting
this information were then processed through a series of multiple re-
gression prbcedures from which the final model equations emerged. The
relatively small number of students involved in the study (N = 105)
and the regression "filtering" process identified variables with strong
correlational relationships to the performance indices. Those variables
in the final version may be assumed to be important background para-
meters or components of the course. Conversely, however, if a variable
did not turn out to be a strong correlate, it cannot be dismissed as
tunimportant or irrelevant to the course. More study needs to be given
to these variables. Perhaps another "layer" of modeling may produce
additional information about the course. .

The modeling process employed in this study required very close

attention to the details of the initial data processing. The process

 

241

detailed in Chapter 5 for removing errors and developing clean, consistent
data sets is absolutely necessary anytime a modeling process of this sort
is undertaken. Without this very close attention to details the results
of this study, as modest as they may be, would never have emerged.

For the Fall term, 1977, it was found that the variables represent-
ing student background and instructional behavior (from 13 and I ,
respectively) accounted for about 65 percent, on the average, of the
variation in the performance or mastery variables. Extrapolating the
model to the Winter 1978 term, these same variables accounted for about
34 percent of the variation in the dependent, performance variables.

The latter poor fit for the Winter term is probably due to a number of

changes in the course, including instructors, between the two terms.

It is probalby reasonable to state that, under the assumptions and con-
structions of the model, about 65 percent of the variation in perform—

ance is accounted for by student background and instructional behavior

(Tamir's (I976) antecedents and transactions).

Bloom, as discussed in Chapter 2, stated that up to 90 percent of
the variation in school learning may be accounted for by cognitive and
affective entry characteristics and quality of instruction (1977).
Together, he estimated that cognitive entry behaviors and affective
entry characteristics may account for up to 65 percent of the variation.
This study includes cognitive and affective entry characteristics in
the background variables. These variables when included with instruc—
tional behaviors, which measure something different from quality of
Vinstruction, account for about 65 percent on the average, of the vari-
ance in performance indices, a measure which is very similar to Bloom's
school learning parameter. Thus, this study is somewhat in agreement

with Bloom's estimates.

 

242

The model developed in this study provides a different view of a
standard academic course. It also appears to be transferable across
terms with some limitations. However, the largest contribution of the
methodology demonstrated is not in the academic exercise of model build—
ing, but rather in the uses of such modeling as a research tool. In the
present case, the combination of the modeling methodology with grouping
analysis produced results which may prove useful in improving the in—
structional quality of the course. Further study of the legitimacy of
the groupings and hypotheses suggested for performance improvement in
the Chapter 7 is needed to support this contention. However, the

grouping analysis based on the results of the model building process may

be an important link between a research tool and classroom implementation.
The systems model organized the process of teaching into a heretofore
undeveloped schema. From the vast chaos of information describing this
particular learning process, the model pointed to certain definite rela—
tionships which appear on first examination to be potentially very useful
in restructuring the course and improving learning. If this is the case,
then the concept of course modeling, hopefully using more sophisticated
techniques than employed in this study, may be of some value in educa—
tional research.

In retrospect the methods employed in this study to develop the
model coefficients were very cumbersome and time consuming considering
the results of the effort. That is, a great deal of effort was put into
the determination of the strongly correlated variables but the method of
Ithat determination, the use of multiple correlation on "waves" of vari—
ables, did not insure that all of the relationships were examined. If
this study is replicated it is recommended that the number of independent

variables used be significantly reduced, that the number of students

 

 

 

243

in the study group be significantly increased and that multivariate

linear regression analysis replace multiple regression. The use of

multivariate regression is more appropriate for use with the vectors
used in the systems model than multiple regression.

This study has explored, albeit imperfectly, the application of
the systems approach to instructional research. It has shown that such
an application is possible and pointed to some of the advantages of
viewing instruction using the conceptualization of this abstract organ—
ization. Although the model developed in this study was based on some
very simple assumptions, the grouping analysis using variables identified
by the model illustrated a realistic and practical utilization. It is
hoped that more research efforts will develop the possibilities of this

approach further.

 

 

APPENDICES

 

 

APPENDIX A

Course Description

 

 

 

 

y ’9 244

APPENDIX A

Biological Science 202 Fall l977

Course Syllabus

Staff: Dr. N. Jean Enochs
Mr. Charles Elzinga
Mr. John Fogl

Ms. Elaine Rybak

 

Texts: Keeton, William T. Elements of Biological Science, 2nd Edition;
New York: Norton l973. 312‘ '
Enochs, N. Jean. Biological Science 202 Study Guigg. Unpublished.

Burnett, Fisher, Zim. Zoology. New York: Golden Press. l973.

Burnett, Alexander, Zim. Botany. New York: Golden Press. 1970.

There are copies of these textbooks on reserve in the Materials Center,
Room l03C McDonel Hall, which also has other Biology books and materials.

Introduction

This course is designed for the preservice elementary school teacher;
however, it is open to any student who wants an introductory Biological Science
course. Its purpOSes are to help you l) to learn basic biological facts,
concepts, and skills; 2) to think scientifically, and 3) to develop confidence

in your ability to teach Biology as a science in elementary schools.
conviction that you, the learner,4mpst actively learn, that teachers can only

help you learn, and secondly, that your gpility to ask questions and find answers,

0 take responsibility for yourself is most

 

important.
self as you learn Biblogical Science with as much help or as little help as you
need. As instructors we propose to help you by l) suggesting what you should
learn by presenting objectives; 2) designating one or more learning activities
by which you may learn to fulfill the objectives; and 3) evaluating and docu-
menting your success at learning and performing in the Biological Sciences.
Your role is to learn actively, to take responsibility for your learning to the
You will be able to pace yourself through an individ-

limits of your capacity.
If

ual study session and decide to what degree you wish to pursue the subject.
you have little background in Biology, you may ask questions and repeat tech-
niques without slowing others who may already be familiar with some of the
material.
over the designated learning experiences at your discretion to ask questions,
investigate and go beyond the minimal levels of mastery. The individual study
session is designed to give you as much individual help and time as ypp wish.

Course‘Methodology
The procedures for this quarter have evolved over a seven year period as

Students have tried and responded to this approach to learning. We have made
Slgnificant changes in the course on the basis of student suggestions; and we

It is our

to think and to make decisions, t
Therefore, we have designed the course to allow you to deVelop your-

If you already know the material specified to be learned you may skip

 

 

anticl;
experii
G1
SI
l

E

 

V“
‘ ' 245
1 APPENDIX A
2

anticipate continuing the development of the course on the basis of your
experience this quarter. The general description of the procedure follows:

General Assembly Session (GAS): Monday 9:l0 . . . . . . . . 1 hour
Smail Assembly Sessions (SAS): M, H or T,T . ... . . . . . 2 hours
Individual Learning Session (lLS): M, w or T, T

(or as long as you need). . . . . . . . . . . . . . 2 hours

Evaluation Session (ES): Friday, 9:l0 . . . . . . . . . . . l hour
Minimum . . 6 hours/week.

You are scheduled to attend a figneral Assembly Session (GAS) on Monday in
which information suited to lecture rather than laboratory or discussion session
is presented. The context for the weeks work will be set in this session and it
wiH be heavily content oriented and coordinated with the assigned reading
concerning the biological facts and concepts of the week. It may be used for
guest lecturers on biological topics or movies, and it is the only occasion
where all the students meet with the senior instructor at the same time.

The Small Assembly Session (SAS) meets in lO6C HcDonel Hall and brings
together 22 students for group interaction with the aspects of the course that
may best be learned in this manner. This session is oriented toward elementary
school ”sciencing” and techniques for maintaining and investigating animals,
plants, and microorganisms in the classroom. In it you will become acquainted
with the instructor who is personally concerned with you and your progress through
the course.

From there you will move into the lndividggl Learning Session (lLS). You
should have read over the objectives for the week and assigned readings before
coming to the Learning Center to begin the learning activities which can best
be done individually. You will be able to follow the programs at your own rate,
with or without further explanation from the instructor and receive help on a
One-to-one basis. You may spend as much or as little time in this session as
you need with as much or as little assistance as you need in learning to fulfill
the week's objectives and going beyond the minimal requirements to do independent

activities. You should also use this time to ask the instructors questions

about the lecturesz assigned reading or any aspect of the course which is unCIear
to you. Since we anticipate the students with an average background (l year of
himischool biology) will spend about 2 hours in the lLS, we strongly urge you

to begin the weekly program early and to finish it another day rather than doing
it all in one sitting. There will be a tally of how many students are in the
Learning Center at each hour so that you may know when the slack times are and
try to come then for any extra time you need. You will also find on the bulletin
bOard a schedule showing when your SAS instructor will be on duty in the lLS,

You areralso scheduled for an Evaluation Session (E5) on Friday. During
flfis hour you will be given a quiz over the week's objectives using pictures,
Siagrams, objects or specimens from the GAS, SAS, ILS and assigned reading. It
IS anticipated that this session will be both a testing and learning situation
f°r YOU.' There is no mid-term examination requirement.

Attendance

Students are expected to attend all sessions: absence is considered to be
Serious. Absences due to ill health or death in the family are excusable and
car‘be made up by contacting your instructor on your return. The privilege of

rieup worl
lr the E5
rrates co
rrris‘rte
lrr it has
garter wi
hr can I

lailrr

Your
rs'rgnrrrer
list; wl l
learning
are of r
rrrategi
l'rsrrrss
Ir. Error

rrslgr
Urrr'rr

irfrr
rd r

 

————‘

, 246 Appendix a

makeup work is not guaranteed under any other circumstance. The makeup work

for the ES will require the absentee to write a 2-3 page report which demon-
strates comprehension of all the objectives for the unit and/or completing
requisite quizzes. The makeup work must be done within one week after permission
for it has been granted. Students who miss more than two week's work during the
quarter will be required to obtain written permission from Dr. Enochs before

they can make Up the quizzes or continue in the course. '

Grading

Your grade on the course will be determined by your performance on class
assignments and your score on the written tests and final examination. §_e_l_f_
tests will be provided at intervals to allow you to see how successful your
learning Strategies are in relation to the stated objectives and the perform-
ance of other students in the course, in order that you may modify your
strategies if necessary for you to reach your goals. You should feel free to
discuss your grades with your instructor and/or with the senior instructor,
Dr. Enochs. The grading scale follows:

Quizzes l-9 (30 each) 270 points 333-370 points (90%) = Ll.0
314-332 points (35%) = 3.5

Final Exam 60 points 296-3l3 points (80%) = 3.0
277-295 points (79%) = 2.5

Participation and ' 259'276 points (70%) = 2°C
Assignments Jig points 240-258 points (65%) = l.5
222-239 points (60%) = l.0

Total Points 370 points

Your grade in the course will be determined by your performance on class

assignments, participation, and your scores on the written tests and final
examination .

The tests will be based on the instructional objectives :and may use
information, pictures, diagrams, objects and specimens from the GAS, SAS, ILS
and assigned readings.

Logrse Content

Elementary teachers l) must be familiar with the basic facts, concepts,
and principles of biology but that is not enough. Teachers also 2) need to
be proficient in the processes of science such as observing, measuring, class-
ifying, using numbers, predicting, inferring, communicating, hypothesizing,
experimenting, and interpreting data. Teachers 3) must be skilled in handling
living organisms and laboratory apparatus. Even that is not enough, teachers
Li) Should be curious and investigative, and have a positive attitude toward
learning. This seems like an impossibly big order, but it is with these ideas
In mind that we have designed this course. It is finally enough that you are

fully responsible and creatively free in helping youngsters learn to be, in
the let century.

r It is our belief that you will benefit from your college career in propor-

1 tion to the degree to which you are directly involved with your learning. We
feel that this method will allow you direct involvement with the subject matter
and at the same time give you individual attention from the'instructors and
encouragement to go beyond the minimal requirements. We will be interested in
Your reaction to this methodology, and we will be responsive to your observations
and suggestions as we try to make the time spent in this course profitable to
YOU. I encourage you to come in and talk with me when I am in the lab, during
my office hours, or call and make an appointment. '

Sincerely,

41 t m» facet“;
#3" Enochs

 

 

 

 

247

APPENDIX A
COURSE OUTLINE

 

 

 

 

 

 

 

 

 

 

 

 

 

:d< Ecological Thread Organismal Thread ReadiggsK
; introduction Invertebrates ’Kzl-h; 66-69
Oct GAS: Organizing Biology SAS:Classifying/Pond 2:24-39
)4 SAS:Models of Biology Teaching Water le8-25
. ILS:Microscope ' Handout: Levels of
SAS:Nature of Life Organization
1 GAS:Chemical Basis of Life [ILS:Mollusks, Anne- K:13-3l; 35-46
pct SAS:Physical Basis of Life lids, and 46-56; 62-66
1043 ILS:Cells Echinoderms 2:40-44
SAS:Preld Trip/Trees Handout:Chemical
Basis of Life
? Producers
Pct GAS:Energy and Life SAS;Arthropods Kz57-62; 82-84;
ibil SAS:Photosynthesis 95-lOl; ll8-ll9;
3 ILS:Producers ' 127-l37
- SAS:Insects 2:45-64
0. HandoutzPhotosynthesis
Consumers Vertebrates Kz3l-3k; lO9-ll5;
" GAS:Homeostasis 1 SAS:Pield Trip/ 119-126’ 137-‘6‘
9C3 SAS:Consumer Anatomy I museum
r'e28 lLS:Consumers l fiSh, amphib- 2:65-81
SAS:Consumer Anatomy 2 lens: HandoutzLiver
reptiles .
Functions
GAS:Respiration/Control SAS:Birds K2200-208; 226-23];
P°t3l- SAS:Consumers 2 SAS:Mammals 234-239
”mu 3 ILS:Skeletal Systems 2132*96
i Handout:Respiration
i Continuity of Life K:279'289; 322-329;
th GAS:Molecular Reproduction SAS:Decomposers 326-34l; 165-177
74‘ SAS:Genetic Disorders
5 lLS:Reproduction l 3228'hl
SAS:Plant Development
‘ GAS:Homeostasis 2 SAS:Mosses, Ferns, K:l86-l96;341a3h6
”mu SAS:Animal Development and Gymnosperms
thB ILS:Reproduction 2 HandoutzMenstrual
j SAS:Human Development Cycle
' ' . 8:45-55; lOA-lOS
' lgteractions of Organisms ‘“*_
"mu GAS:Behavior/Nervous System Thanksgiving K:2h8-274
ihqz 'SAS:Invertebrate Behavior Holiday
” .SAS:Vertebrate Behavior ~

 

248

APPENDIX A

2

 

 

..cek Ecological Thread Organismal Thread Reading;

r

i GAS:Biogeochemical Cycling SAS:Flowering Plants Kz392-402; 4l8-h25

Nov.28- SAS‘1Water Quality

Dec. 2 lLSiEcology l ‘Handout: N+C cycles

; SAS:Air Quality 8:56-63; lO6-ll5
GAS:Energy Flow/POpulation SAS:Taxonomy thoz-AlS; 388-390

Dec. SAS:Pood and POpulation leZ-ls

5'9 ILS:Ecology II 2:4-7

i SAS:Planet Management Handoutz‘l'axonomy

 

final Exam: Friday, December l6, l977, l2zlr5~2zl+5 p.m. in McDonel Kiva.

'k

\

rBooks for reading designated: K

= Keeton, Z = Zoology, B = Botany

 

 

APPENDIX B

Background Variables

 

 

 

 

 

249

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

han 2mm“. H455

 

.0333 $5 00 tote .3203 Mme—4mm h.0 30> o :05 9.0... .m
.omtaou mwcu cu towed HmmHmwm ome..ou $0 cmu> a .a
o . . . .>m0_o_n ome—.00 .0 33330 0.5 .m
Mm. 600 O HNM O OOH GMN 0 00¢ O $3.033? corona 0:0 .~ .
609500 260.032? .>m0_00~ {€303 30—03 Maui—lam 0c .—
3033 05; . .w . OOOGMQ
.
.3020“. cm 09500 >mopoou c< .n
. . . . ..oofim :3: c_ 09500 30.03 c< .N
..Vm OQN. 0 CNN 0 OOH OHN 0 0mm O .3500 >mo.00u oz .—
ncuxou 2,0... . .m mOOUMm
a“ om
.o :4
cu 0M 1
O O O O on 0N
He «on o mom H Hm 005 0 0mm 0 .< ;
“>mo_o_m c_ uc0E0>o_£0o wo _0>0_ c30> moAHEUmou anon uosu
000cm 05 38:2,: ..oocom :3: c_ 30.03 5 773300 0.03 30> m. ..H .VOOUMM
.AomtaoU ucooum mummy mucoemconxu new «not. 50 cowuuotouc_ .m
. .033 33 .4.
.302“; name .m
.0095 33 .N
Om mflfiofl OON.O $0 ©NHoH 0&0 O .>m0_0_n Eovo: .—
.Apoma xoofixou one. 05 00003:. 53:00 0:0 :22 9.06 «.80 30> u: MOOUVHQ
”mm: pom: xoonuxuo or» ..oozum Lam: c_ omtsou >mo.0_m m x000 30> u. .m
.HOOLOm Law; a. >mo_o_n $0 mtoo> 03a cozy 0.05 .m
._0050m 3mm; c_ >m0_oma e0 meno> 030 .a
. . 00 on m. c. >mo 0. 0 cm0> 0 .
. . r H s c .... . H .a a. co m
.Vm mom O 500 H OOH mON 0 06m H ..0030m 5mm: 5 30.03 mo 50> 0:0 :05 mau— .u
.Hoozum cm”; c_ A>m0_0.m>za .>m0.00n .>couoav >m0_0_n 0c ..
"coo—3 05... . .N NOOGMM
.cozuo .m
.comuouavo _o_00am .4
. . . _ 55.30.32. 220 .m
an is H 8m 0 8H 3H H Sea 2 .23. .N
£033.60 33550—0 ..
um. 50.3... >: .— :HOOUMM
z .>ma.nfim z<mz z .>mQ.nHm 24m: ZHHH Ehdouo<
OHDQfiHfl>
man awn. “EH3

 

 

. ..a
I

» BZHZDMHmZH

 

mmH<zonemm00 02Domomo<m

 

250

 

 

 

 

 

 

 

 

 

 

 

 

 

 

.u .m
.c .3
.0 .m
H mHH.H owNH HH 8nd HomH 2.. ”N.
3:050>0E00 $0 _0>0_ L30> .
300:2... .93 m1.” :05 L050 33300 mu_m>:n_ 000200 coxmu 0>mz 30> w. .m. . MHOUMm
. . .mON m1; :9: .050 000.500 3320 0m0200 030 :05 0.5:. .m _
m w¢m O OOO O OH O O .mON wt... :05 00:00 33300 35:0 0m0200 030 .N
.mON £3 :05 ..050 00.500 mu_m>:a 030200 < .—
3033 02...; _ .N. NHOUMM
.mON 00:03.0. .0032; E 002020 30:97:30 .m
50230233“ 03300 >n mow 00:0_0m .0035; 00303 0>mz .:
Hm HON .H Noooo GO «NooH Nm¢.O 505055003 >0 mow 00:0.0m 30.0%.:— 00302 0>mz .m
.moa 00:33 .0033: c0030 03: .~
.mo~ 00:0_0m .0032: 9.00.00 00: ._ HHOUMm
H0>o£ _ an: ad .: ,
.o.~ 30—09 .m
. . .m.~ ..0 o.~ .:
HH 3m H 2N H Hm SNH OHNH “MUM “M
o J _ 080va
. “mm: 003000.. _ umsu 003m 05 50> w. .0—
.02 .N
.00» .—
mm HON O @fiw O OOH OO.‘ O OmN O 503?: 30:00 :3: :_ 00.500 0 :00?» 30> 0>0= .m OOOUMm
.03300 00:0_0m .3300: 0: .m
00:03.03 00:0..0m .3302 05 mo Acomusfiuwnam 03300 >3 L030; 0 .J
. . . . 0053000 00:03”... .5302 05 90 Acoszfimé >5 L030: 0 .m
mm mom H oom H OOH wmm H own H .355 .23.: 3...; 25 .H
.00:0_0w .3302 00 ..m0> 0:0 :05 0.00. .—
95
2:00.00 02.0: _ :m: u< .m WOOU
030300 >L0> .m
031000 ..4
#0530: .m
03030: .N
. o . . .0>.ummo > 0 .
mm N80 $HN 8H ommo 8...... _ .._
~00:0_0m 20030—03
0:033 0030.30 50> 003.5000 3000:3000 009.. 053028 05 mo :02: .n OOGMm
z .>mn.nam E z. .595...“ g ENHH E90003
0HA¢HH¢>
whmH Emma «Hm—Hz; wan Emma HASH .. ,

 

 

 

 

 

 

.ufiH 1523440.“)? .::.:.:5: .IC 0453.43.43.05...

\4‘
““2220 Hub mah— QZDOMQMUAVM. n pf!- .IVII|I|.| lww \

 

 

251

 

om 000.0 omo.o

Hm

owq.o

wqo.o

dz A
.3> 4
Amvum:=ou _u>o_ :w;m_; :o __ .mo. .90. :0: .-

NNO©Mm

 

Hm oom.o Nom.o

>m

HmN.o

mom.o

.02 .N
.00) ..

No. :h: .—N

HwooMm

 

Hm moq.o owo.o

mm

Hmm.o

o>m.o

~00 2%: .ON

omome

 

Hm wqm.o mow.o

mm

Nmm.o

q0m.o

.02 .N
.m0> .—

—wo :h: .m

.—

mHQOMm

 

Nm wom.o oqw.o

Na

Hom.o

wqw.o

.02 .N
.m0> .—
Am030_:500k 0>_umu_u:uadv :m. m. .m.
“mom:300 :00: mcmzo__ou 0;: 0x00 0: 00:_300: E0 :0 :0xou 0>m: _ an: u<

wHooMm

 

Hm Hmo.H mmm.H

mm

owo.fi

.owq.H

.m3_=0_mu .m
.mu_0050:ume 0m0__ou >:ouuano:u:_ :o n0_um20;ums .oocum zmw: vou:0>vm .:
.>:uweo:0m_:u .00500 :m_: .m
.>:00Eo0m .oozum gm”: .~
.m:n0m_a .oocom ;m_; .—
umm 003_:0003 um0n m_
00: 0>mz _ 30:0 .oozum ;o_z :_ m0.ums0zums :_ 00:300 vouco>vm umoe 0:» .s.

 

 

 

>Houxm

 

n wNm.H ooo.H

mm>.o

mwm.o

.m0m:3ou >:um.50;0 0m0__ou 00:1» coca 0:05 .3
.m0m:300 >:um_E0;0 emu—.00 00:;u .m

.m0m:300 >:um_Eozu 0m0__00 010 .N

.0m:300 >:um_E0;0 ome—.00 0:0 .—

ucuxou o>os _ .0—

oHome

 

Hm 00H.H mmo.N

«mo.H

ooo.~

.o.~ 30—0o .m
.m.~ :o o.~ .
.o.n .
.m.m .
.o.a .
"max vo>_000: _ 00:0 000:9 0:0 .mo> w

- a—NM—‘I

.m—

mHOUMm

 

Nm mmq.o 000.0

 

 

mm¢.o

 

 

«Hq.o

.02 .N
.m0> .—

w>:um_Eo;0 _oozum ;m_; :m 00:300 0 coxau 30> 0>mz .:

«Hoax:

 

 

 

z .>mn.nHm z<mz

whma Emma MMHZHB

 

.>HQ.QHm

z<mz

>>¢H Emma AA<m

 

EMHH

Shuouu<
0H90Hu0>

 

 

 

“MHAVZZOHHWHWM

 

 

252

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

630.02. >:0> .m
030.00.. .3
.00 30.0 NNN.N 8 0006 «med gummy. .M
.0>.0mm0: >:0> ..
20:0:0m 3. 005.00
0:033 0030.000 :3o> 000.:0000 3000:3000 000:. 9:36:00 0:0 00 £0.52 .00.. OMOOMm
6: .N
N0 306 30.0 00 $0.0 £05 .3 ._ .
3.0. 0.5. 0:00 00 00:.300: 30> 0:0 :0 00.00 30> Bo .mu . ONOUVHM
6: .N
mm :06 mum... mm med nmwd $2. ._
2.0. .....< 0.00 00 00:.300: 30> 0:0 :0 00.00 30> 0.9 .m~ wNOOMm
>250 00: 000: .m
.02 .N
.00 .
mm mood 0.43.0 3 mid mood $2.538” n
00:0.00 0.. 0000:3200 3.0.00: 300550... :0 0:260 m:.v.:03 0:0.3o> 0. .2 , NOOMfl
, >730 00: 000; .m
mm 30.0 30H .3 «0.0.0 HmoH .mwn .N.
.3 :3o> 00:0.00
0. 60:01.0:00 9:500... 33:020.... :0 v:mxoupmc§:oz 0:0 30> 0. .oN ONOOMM
. .oz .0
.00> ..
cm mqu 03.0 mm mend oomd .35.: 82: :0 £58...» .3530 5:8 mmooxm
nn>mo.00m 50.3 $2003 00.500 03:00 0 00.00 00 :0... :0 300.00 0;... _ .mu
.0 .m
.c t...
0.. 30.0 804 3 205 SN; ..M ..M
.. ._ . 380.0
“mm 003.5000 000a. 0. 00.006059: 00 >030 >E :. 0:0E0>0....00 00 .05. 0..» :N
3.9.0.: :o .2. .02 .02 E... .0
.3. F5. .0.
.3223 2:... .m
0H :0... N9; mm 804 RTN 35%....” H.
a: 0. 0.. : :0 >
30> 00:300 :00... .0>0. 000cm... 0:0 0. 00...) .0:MW3000:0H0:HWM o:w:”03n.0M0H:. .m~ MNOOMm.
z .>mn.PH.m E z .5986 zfiE ZMHH shack“;
. OHSNHHMNV
mum.” Emma. ME;

 

han Emma. Adm

 

 

 

 

 

“NNHAVZZQ H“ “a: u

 

 

253

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

6: .N
an 08.0 oooé ww ~36 0mm... .3» ..
.50: 0...: ~o~ 00:0.0m .00.mo.o.o 9.30000: 20 . .nm NMOUMM
6: .N ..
«m «3.0 306 mm 306 m8... .3» ._
300:00 :0:uo:0 so: 3m... 3 00:03:05 . A? H OMOUMQ
.xou: :00 0:30: o. :0>o .m -
3.003 :00 A0:30... o. u m 3..
3.003 :00 0:30: o u m .m
3.003 :00 «:30: m n m .N
.x0oz :00 0:30: N :0:0 000. ..
mm mow... «004 cm qmwé 0:4 . "3.3 :o
.00|.m|u|3m. 00:33 0......0 :0... m:.>03um 0.003 :00 0:30: 0:000 00 000008 . .mm mmaMm
.o.~ 30.0: .m
“an.“ n o..~ Hm
«m 03.0 ~24 mm 02.0 mm... .Mfi H w.“ .M
. no...- .I “on .—
umcm: 0:0 55.: a. 0m0:0>0 2.00-0005 0m0..00 20:03 >.. .Jm ..VMOOMQ
.o.~ .m
. . ”MN a.
qm mHO.H :oq.~ mm «No.o Hmo.N .MM .M
.o.: ..
..0. 03300 0.:0 :. 0:. 30.000 0.303 00:0 0005 00.030. 0:... .mm
mmowmm
.QN fi
H...
.3 8o; 02.0 3 3:5 3.3.0 .3 .M
.0... ..
"0. 00.500 0.5 c. :00 5.0 2.2 _ 0003 0:... .~m NMOUMQ
#:0503300: 0 00: a. 3.30.. 0:03 . 003000: 30.50 .m
.>03>:0 u. 9.3 00 0:03 0.30) 03: 0:050:30: 0 2.0.3: o» .9.
.0>o:0 00:03:06 :0: 0:23.300: 350:0 2.0.30 00 N.|:m .m
Qm mmO.H mHm.H mm me.H NN©.H £0?e>&:9~t$§t:69_0.203000Nfla .N
.0:0£0:.300: 00:0.0m 0m0..00 :0 :0.u3:.:um.0 0 ...:.3: o: Nflmm ..
00:33 2:» 9.0.3 Ea . ..m :HMOGMm
z £55.95 24...: 2 .59.5m a EH 3000004
_ OHOQHHNP

who... Emma. ME;

 

:6.“ Emma. Adah

 

 

 

0 O o

o

"HZHZDMHmZH

 

0032233000 0200000640

E

 

 

254

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Nm 65H H mwm N @m NNm 0 wow N .0505.» m:.30.>0: 0:0 6:33 [0» m:.>taum .Om omnuUMm
mm 000 c 0.0 F 00 m «o 0mm.a .Bo.3c:&2:.323upso$¢ a; mqoomm
mom-o Nqa.a. mm Hmm.o ome.” .nwmefler: a: wqoomm
N0 084 80.0 mm ommd 20..” $8.23: .2 300%:
am $06 mad am 984 Now. w .2253: .3 , $0005
00 urc w <c< o 00 000 c «cm . .083300 a; _ mmdwmn
m 000 c mun a 00 cro c «mo 0 05:: .i «woumm
20.0 30.0 3 8.0.0 3.: 0522:..23 .2 3000.:
Nm 000.0 wmm.~ am moo.o omo.m i:i::zfo%3 0: Ncoozm
.3030: 300.0: 1.00.0: .3030: .3020...
>.0Ew:...xw >:0> 00:30:60 >.u:m..m 002
m J m N .
”0.000 9.30200 0:0 00...
000.500 0000;700:200 :0 00:0.00 0000 :.
0:000:00 m:_::r.0. :. 30> 00 03_0> 00. 0: 050.8000 m:.20..o~ 0:0 00 :000 000..
3.001 0 00:0 u3OA< .m
«m mq>.o 000.0 00 om>.© wm>.o .529=.025033< 4
. .E00.00 .02 ..
..000 .40030302 .0E.» .00.0.u:0 :0000030: 05030:... 005.0 HQOUMM
00:: :30> :_ 00.000 0000.0:-00:o.00 :0 0.:.0:0.00 :050 030:0 .000: 30> 00 .3
.xoux 0 00:0 239.2 .m
.:u:0=. 0 00:0 0302 .w
«m Hwo.o omo.o 00 000.0 >wo.o 48030.3 ._
7000 .0023... .9520: 50:30.;
.:00.:0E< 0.02:0.00. .0:o_0000 0000.00 0.0030302 .06..» 00.0.0.5 :0000030:
9:03.05. «05... 00: :30> :. 00.000 .00.m0.0.: 30:0 .00: 30> 0.. .o... UQOQMM
.0: .N
«0 N35 20.0 mm 80.0 0N0... .2; .. .
«0.05:0 :0 3:30 5.: 0:..000 >000: 0 02?. 30> 0.. .mm UMOUMM
.00:0.:00x0 00:0.00 33:95.0 >E :. 0:030 >..0.u:0000 00: 00:0.00 .m
mm mmn.o «00.0 00 ooN.o won.o 62 A
00> ..
30:07.0 3.3000 0:00:00 >.000:.Eoc0:0
.0050 >:00:0E0.0 :. 30> 00 «.3300 00: :02} 005.00 05 00: .mm MOUMM
z .>mn.nfim z<mz z .>mn.oaw z<mz EMHH a>=0u0<
QHDNHHQ>

mhmH ZMHB muHZHB

 

000a zany AA<m

 

 

 

 

 

 

255

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

0:200:03. >:0> .m
0:300:02. ...
.0:.u00:00:. >.0:0..0 .m
mm 000.0 >N0.N 00 owmé 0H0.N 05.2 .N
.0:.:on >:0> ..
.0: ...3 00:50 0.5 00:0 0000.0.u:0 . N0 NOOMVMm
53.00:. to) .0
.1303»... .0.
. . . . .000:0>0 03000 .M
mm H00 0 0H0 m 00 0mm 0 00> N .33 .N
>000 >:o> ..
5.30.3.0 0:0 0000.035... _ £00.00 0>0: . 000:=om0mumw00UMHMuvwwquww ..w . HSQMMH
...0x >..0:o.0000x0 E:o.+:o0 cu co.0:0u 0:0 >0. 030.2500 .m
4:20:00 >0 000000.05. 0:0 v9.0.0: ._.
60:.005. >..0:0.00000
:0\0:0 >.0:0..0 0. 00:05:83.0 >E 00:0 :030 030?.0: >.u:m..0 .m
Nm Om¢oo OOO.N wm NQN..O m¢®.H 00:00:... :0000 0:0 00.0...00 >E 00:”. 03o>:0: >.u:0.u.t30 .N
. . .15.: 0000 0:.5 >5 :o\0:0 0.0.0 .000 :0000 _ 00:0 00:00 00 ..
>..0:0:00 Em _ 06003 0:53 0:0 0:.000.0.u:0 :05. .00 OQOWMQ
Nm mww.o Om>.N 00 0.0.0 Hwo.m .3505 3 0.03 :o 0000.50 98 0:20.00: .00 mmowva
0m. och .. odd _. 00 CCA H and F .0000: :3o> 00 00:00 :0 ..0 0:.u.:30¢ .3
NM mmfié wow. ~ 00 >m0.H 0004 .0338: mm 3232. 0.: :o 6:... mm 05.2.95... ..0 >m00
Nm mOH.H mgaN mm Nam-O mqm.N 00.000 :0 0:000:00 0:00:09... 000:. 0:0 m:.~.:oE0.._ .wm omOOMm
Nm NNNAV HwQ.N mm m¢w.0 0mm.N .0E0_oo:0 00>.00 :0 0000300.... >.03o.>0:0 0:.20.>0¢ .mm mmOO
th _. «Rm 0. GO mom C $.03. 0. .00:0.:. 5.3 .0.:.000.: 00:300 0:0 05003003 0:0 0:.>.03um 5m QMCU
m 0004 oomJ 00 000.0 000...” .38: $20 0.22:: m 9:330 .00 mmowxm
Nm mmwd wand 00 000.0 0m0.N .039. $20 :6» 052.30 .0 Nmoomm
Nn 9.0.0 mmqé 00 000.0 00m.~ .xooexfi 2.0 05:53. ..0 HmQuMm
.330: .330: .330: 3:20: .330:
>.0E.0:uxm >x0> 00:30:60 >.u:0._m 002
m a m N _ .
z .Emn.n....m zfig 2 (55.30 23.... 2mm... Each“;
UHQWHHQ>
000." 2mm... MEHS >00H Emma .55. ..

 

 

 

 

u “2%!“ EH

 

 

256

 

 

.NON :0:0 0:0: .m
.Nou a m. .0
.00. u 0. .m
o o o o ..VoO- I m .N
wq mmo H “OH N 00 MNw O qqq N ...:\om CMLV mmO— .— ..

”0. 00:0.00 m:.:0000

00 0:39.00 00030.0:0 . :u.:3 05.0 509000? >5 .0 0000:0300. 0:.P .50 N00 am
.:035 >:0> .m
.LUfiE .£
.0500 .m
.0.00.. .N
.0.00.. >:0> ..
. . . . . ”00 000.:0000 000: 0. cu:v..:u .oo:u0 >:00:050.0 o0

wq me O mwm N Nm Ow“ O NwO m 00:0.00 056000 00 0:0 0:05>o.n:0 m:.>.:o.0 00030.0:0 _ :u.:3 00 03mm“. 0:... .00 OOOQMM

.0000...0:00 m:.:0000 0 0:0300 m:.x:03 0:0 30> .. x.co 0:0.0003: m:.30..00 0:0 :030c<

 

Nm q>q.0 >N0.0

00

000.0 000.0

.0moucou .0:o:um 000:: 0 5:00 00 00:.0500 co:0

0:0 :u.:3 .00aoucoUn30 0covcvamuc. .o :0053: 0 .o :uno m:.c:000:00:3 co
mc.0m:0coucou 00:.» >0 00:0.0>00 0:0 00aoucou 0:0 u- .0:0:00 00 0.0.0000
...0000

0:0 .:0000:: .:0amv 0o .0>0. 00:.000 0 00 00000:0v:3 0:0 00:00:00030 00
000 :0.3u.0:0q 0 ..0:: co 0000:o:0.0 :0:0 0:0 0:0 .00awucou 0000.0:
:0:0o :0.3 co.00.00000 :.0:0 00 ouco:uuw: :0.3 >0..0:ocom mo .0>o.

:m.: 0 00 wouano:0:. 00:.» 0:0 00auucou 0:0 n- u...uuam 00 .0:0c00

«00aoucou 00 000 :0.3o.0:0a 0 .o m:.n:000:0nc3 000: 0:0 c.0000
00 30> 0.00:0 0.303 :o.0u::00:. 00 000>0 0:.30..00 0:0 00 0:0 :u.:2

0000Mm

 

000.0 0HH.H

00

000.0 HON.H

.:0000 0:0 ..0000 :0000:m :. 00.000 .0:0>00 00 :o.00:o.ax0 0:0 :o.00:0cou:ou
.00:0 0u0m030 0.0:: 0:0 00 :o.00.o: :.0:0 00

:o.00300.0 0500 :0.3 03: .00.:00 30. 0 :o :o.00:0:uu:ou :0aouuc. :4

.00.:00 030 :0 0:0 :. :0000 0500 :0.3 03: .00.:00 >:05 .o 30.>:0>o :<

..0>0. >u>::0 0 00 00.:00 >:05 mo 30.>:0>o 000:: <

H.

—NN.

“>03 m:.30..0» 0:0 :. 00000.00 00::00 0:0 .0 0:00:00 0:0 0>0: 00 :0.0:a 0.303 _

0000Mm

 

Hm m>q.0 00>.H

 

 

00

NNm.o Hmm.H

..:005 0:. 0:0.0030.0 50.00::

00 0:0.000..aam we 00.050x0 >c05 0:0 .000:0.:0ax0 :o5500 50:0 :30:0
0:0.00:003... 0>.000..036 00 .000 000:0 0 :0.3 0000:00.0 >.:mao:o:h
.0:o.03.ucou 0:0 00.0.u:.:: .0:0:00 0:0 .0 0:0.000..a5. 0:0 000:003... 00
:0>.m 00.n50x0 00:0000: ..03 300 0 :0.3 03: .0>.000.0:03a 0:0 .0u.mO0
.0woo:a :o 0:0.00>.:ov .00.0050:005 .o.:.00oa 00

:o.00:un.u .0co.0.uv0 0:0 :o.0a.:u000 0.00.. 00 :0.3 0>.000.0c030

“0. :ooa0x00 0 0o :o.0000 :o 0:3000. 0 :. 000:000:a 0.000 0 mo m:.v:000:0v:3
>5 m:.v.0 :. 000:00.u 0:0 0: 00 v:.. _ :u.:3 :o.003u0.v mo oa>0 0:»

0 0000M0.

 

:

 

 

z .>mn.nfim 24m:

050..” Emma. ME;

 

 

 

.>mn.nfim z<mz

~50... Emma. Aflfim

ZMHH

ahaouu<
manuauab

 

 

 

 

KI...“
§H<E°HHWBHO EhchN-k. < H « I‘vll lw‘

 

 

257

 

 

 

 

 

 

.0.00=.0> >.050:0x0 .0 .0.00:.0> >.0:0..0 .N

 

 

 

 

hum." 2MB 4.2m

 

.0.00:.0> .m .05.0 00 00003 0 ..
3 0.00.0 30.0 .3 000.0 000.0 .2 a... a a... 2...... 3.
.NON m0 00:0 00:00:00 :0:00 50::— 0:00: 0>0: . ”00:00:00 30:00 50:0 NON m0 WOOUMM
0:000 :0.005:00:. 00:0>00 0500 0>0: 30> m. >.:0 :0.000:a m:.30..00 0:0 :030:< .00 f
0
2 .20980 200: z. .5053 :00: 20.0.0 E0800
. , 0003.50
0:: 0000.... 000.203

 

 

PUFF—tinny.

 

258

 

m>

000.0

mNm.0

00

000.0 000.0

I I‘ll. Vlt‘l'll

._mc=u 0:0.00000000 .
.0000! 00003 9000. .
.000:_.oe 0:0 .00000cm u_0o:aa .030c:_e .
.0.¢E_cm 00.:—_00.c: 003:06 .

.00cm_a :0000 0.000000002 .

0:0 000\0_ 0:0; 00003n£0000 .
.0395 050 E 0000 000... 00 00.500 000.03.: 05 9533.0. 2.0 00 .m MOommm

 

mm

mmq.0

000.0

00

<>q.0 >00.0

.00_uoa0 0500 0:0 00 0.0300>_0c_
.02000>0000 030 00 0.0:0w>_0c_
.00_0_::c&ou .00o>00 .m
.00_uoan .000>00 .~
.00_uoa0 0:00:00_0 00 0.0:0_>_0c_ .—

00 0.0300330 002.000” 0.. 0w notation < .00 GOOMMW

 

mm

mH0.0

w>0.0

00

000.0 H00.0

.E00mc.x .0E_c0 00_0c0 000 >_0030 cum>x0 0:0 00 000:0_00:00 .m
.090000>£00000 0~_0oz0c>0 00 >000 950 0o—nuc0 .4
.0_09;0c>0 c_00000 000 0_:_0u015 :00 0:0 090msoga .
.>_..._0c0 00000—9. my
.0000 000 a:_>nuu 0.>000 0:0 00.000000 ..

0_ 00:0 0_ 09003 0cm 00mxo_0
c0003 00 00003.0 00 5.500.003 4:0 .0 >000 .950... 0.00 00 0.0.0.; 0020 0;... .m moommm

 

mm

0N0.0

0mm.0

00

000.0 wm0.0

.0__03 0.00 AHU

.0ocosaeos ..ou
.00_0:uu>
.00E000500su
.mo.u:c

DNM:

000
>050 0. 0..ou .mewcm 00: 0:0 00.00 0c0_a an 00_0m0co0. on >_00.c_000
0.000 >050 50000003... 0 £00005 0:00 0600 00 920.00. 0.33 00> m. .N NOOWMM

 

mm

 

0N0.0

 

>00.0

0m

 

>00.0 mmm.0

 

.5000>0 c0000 .
.u_:uo_oe .
.0_.0ccago .
.__ou .

..00 £095 A. V

~0_ c050”)
000500 000 00 ..0 000:.uc_ :0_00~_c0000 00 0.0>0_ 0c_30._00 0:0 00 sums) .—

m
a
m
N

000000

 

:

 

 

z .>mG.QHm

z<mz

0>0szmma MNHZHB

 

.>mn.0am z<mz

nus” Emma 4.3m

 

ZMHH ahaouu<
0H000u0>

 

 

 

HMMHNMN ..HZESMHMZH

 

 

259

 

mm qmq.0 mm0.0

0m

Nom.0 HNm.0

_..nfioao.:..0r_a ...
.u..u 03:00 .m
6......0005. .N

.2... :2... u 00 00:00000 m G

 

 

.0 3.2.2.0.... :u 0. 00.22.02. 0.0;

.30.... 00mm 050 00 00.5.:

>_0:000_0:00 .00m. .0 0300.....aci0 0 00 3:0 0»; .00: 00 0.5.0.5930 m

00 0:0 .003. 00 33.0.0950 0 00 0:00 .0000... 00...: 30.000. 000:... 0.5.”... <

.0.

 

 
 
  

 

 

 

 

 

.0cuE_._yaxJ m:.3:..o.. 0:0 :5 00...... 0.0 ._ .0....r. 0. 0:0.00030 OHOWMW
.00:0.au00..0>5.9.0602500 00.2-0353500 ..000
00:03:00.0 ..menun. _o>.:..r.u 0min......:..._.n.nmo..e>..n...us
. . . . .00:20:03.;.n:v._nmo.o>_c.mu 20.00.3400; 0.000 00.0.
mm NNm 0 000 0 0a 000 0 Now 0 ..aczczu.:27r:o .202
.09..0>.n..uz-00..o>_::c0 ..0E0:00..C>.0 . v1.0.0.
0. £05 000. .32: 0 E. 3:05.50 3:. .0 @OOmmm
.u>o.._c 0:0 00 0:0: .m
.0:0=.:0._.>:0 .50.... 00 0.0.0.5”. 0:0 00:0... 00 0.5.00.0. 1:: h.0 >050 0:0 .0
. 02000.. ..0 >030 00.0 .
00 0.00 0 .00 0 00 000 0 . 000.0 22; .32.. .5 .N
£033.95 ..0 >030 0:0 ._
0. >mo.000 .0 woommm
.3052”. 0:0 ..0 000.0..30. >.....:..00.0..00 00...; .m
0:00.400. 9.0. 00:0.0 >.:0 :0. . .0.
50.000000; 0 c. 2.50 00.... 0:: m. .m
. . . . .350 00 0.0.0. .C 30.9.... 000:0:0 .N
mn NMfi 0 0m.» O HO Nom 0 MNQ O 000:0}? 000:...0 :0 0:0E:0.._>c0 05 .303 000.0000. 9
c0.0000020 0.003 c .0 3020 10:...0 0.: .n NOOEP
0000.0 ..0 ..0 .m
00.5.0. .553: ..r. ..0 0000.. 00.0 ..0
100:0... .3000 ..c 002...... .m
WK HNM.O MMH.O N® mmN.O $00.0 00:0... 0:056:00. 00 0000.. 0:: :. 03> . 0.1.30.5 0:00.30
.0305... c 0:0. 00.: :0 c.5300; 0:50.... .9. 0.00.06: 0 ..
>0 000.020.5063 0. .5031: 0.. 40.0.0050... ..o cozmx: .0 OOOMHMW

 

 

 

 

 

 

 

 

z .>mn.nem z¢mz

whaH ZMHH MHHZHS

 

z

.>NQ.QHm z<m2

nnaa Emma AA<h

 

ZHHH

E>=000<
wanwaum>

 

 

 

HmflHNMN ..HZEMHWZH

 

 

260

 

mm maq.o

mam...

om

010.0

mam...

.0>onm osu .0 0:02 .m
.0:_.0mm> .0 fi.0.00:0 0:.. >:mE 50.3 00L0>ou mm: x :05 .4
.0::050 :. 00:0:0.._0 J.ocuu_.o: o: :0.3 0m_oe >00> m0: 00m £00m .n
.. .> 0:5 cos. 0::um_oe mmo. nozocm x :03 .m
.> ..c... :05 0.53.08 0.5.: 0030...... x :2. G
smLma ..03 0:. .c 000.:3m 0t.m:. 0:0 .0 :o.u.v:ou 0.000000
0:0 mm: 00:3 .35.. x.:. .0 2:. 0:0 0< .mLDCL w :0. 0:0..cam uzm.:n
:. 0000.0 :02. ;:03 s.c_ ..0: 03. 05h .00.:00 .5: 0:03 > 00m 000::
0:0.0 0:0 .0 0mocu 0._:3 .mguc.::x 0430. 2:: .000: 5.0: :0 0:..0m0) 30.3
000000 0.03 x :05 :00:: .:0_0 9:. .0 m0>00. 0gp .> 0:6 x .m:mm ..0: >00

03. :00:3 0000.0 0.43 .m000 :0.c0m :. .mu:¢.0 53.:0009 00.0003-..03 03h

 

 

 

 

.m. maommm
. . u:.» o 0:.» o 02.. o o
x . H
M . M .n .1
3 / H. n... ..I
\>.\ m“ //, .. n4 0. mw
r. m. ,_ m. 1.1.! . .
m. oNN.o ooa.o om Hom.o wmm.o \\\ .. .. 2
.momccgu 050.03 m.: 30: mzo:m ms0o:m m:_3o..o. 0:0 .0 £0.52
5:303 3.0m 0: 0:0 0900.0 :00 >502 0:0 005 0550590 20:... .3000 or... NHOMMW
.x..E m.:0cuoe m.: undm_s “occmu 0: 0aaouon “£0.03 momo. >00: :.ou.00 < .N.

 

mm

2 .RmQAFw

mqmé maaé

 

 

2.4m:

om

m2...

 

 

000.0

..cno...u.c. .4
.00:::0. .
.ouocc.
..m_00m
29..ou as any: :00
:o.>ozoa n.5h .Jua.0 uOsm 0:0 :0 va>oe >.u:0.m.n:ou m2:u~.. m::o> 0gb
.mco.u.v:ou >:ouo:ona. 000:: 0030.0: 0003 m0:0~.. 0m::. 60:. muoo >coz

:aaommm

 

 

 

chm... am... MEN—.3

 

.PHQ.PHm

>>0H 2mm... Aqua

5

 

2mm...

anuud
mangang

 

 

 

.H MHHHMN .. HZMNDNHMZH

 

 

 

261

 

mm

13.0

000.0

00

00.0.0

wwod

.0.0:0000 0.00.:
.000000000 00:00m.mo0 .00:02:00.>:0
.m0nc00000 00.>0:0:

.m0m000uc. :0.0.000600 @
.. .z.;uoa. m_ .:
.m
.~
.m0mn0_0:. >0.0c000 m:.>0000 ..

 

 

 

 

 

 

33000:. 3.200 5.00.0000 m< .0. OHOWMAH
.00.000 :0>.m 0 .0 0%.. 0:0.0 0:h .m
.:0.00.0000 00 0.:: .000. < .
50.00.50. 0:0.0 x0e..0 < mv
mm. mwNoc @HH.O N¢ meoc -.VM0.0 .5300: ..0 000:. 00 £00.. 0.00.0000000:0 <
.EJ0m>3000 0:0 :. :o.0.000 00 0.00 .0:0.00:0. 0:h .—
“nc 0.5.00000 000: 0: >06 050.: < .m. MHCWMW
.0:00 0:0 :.
0900» 00 00:60: .0000 0:0 000:0 m:.:0>:0 m: ..00 0.:m000 0000 m.:h .
.0:00 0:0 :. 0000. 00m 00>0 0003 000:» m
.0:00 0:0 :. mm000 m.. 0003 000:»
.0:00 0:0 :. mmo00 RN. 0002 000:» ..
o o o 0
mm 0H0 O Nam 0 mm.» C NmN o A20:00 0:0 .0.
0000» 00 00:60: 0:0 000:0 003.0:00 >.:c:00000 00.00.000 0:0 0.000 00::
.mo. 0:0 :0
«0:0: 00: 50:0 00 ~. 0:0 .mm000 Nu 0:0:00 :0:0 0: .0:00 0:0 0:0:m000:0 >_:0>0
00>.0060:0 0000.00m.0 00 00:0:0 0 mmc0. 000:0: 0:0 0>.m 00 x00: 0 000.03 0:
.m00» :000 00 «mo. 0:. .0 0:0 :0 0:0: 0 000 0:0 m=00u mm 0:m:00 0: >00
0m0.. 0:» .E0:0 0:300 0:0 mm000 0:0 00 ..0 :0000 00: 0.000 0: .0:00 0
c. 00).. .300» >50: 30: 000 0:.0 00 0:06.000x0 :0 000000:00 0030.000 50 .00. :QHomM—m

 

 

 

z {wan—Arum

Em:

who... Emma. NEH—.3

 

z.

Jan—.95

shaa Emma

Adm

 

Sufi"

3000.005
0H00fi00>

 

 

,. 0

rl‘ll‘l“
.
u EEMH WZH

HWMHHNMHAH

 

 

262

 

mm @006 ommd

om amio mac...

.00000 ..0 00. .0000 m. 0. .0
.... .m
... .~

{0

(
\Iovs..0fi0.00..0>0 00 >m00c0 0005 0:0 0.003 0:0.0030.m m:.30..00 0:0 *0 :0.:3 :. .m.

_.__ _.
:0: cc: \flw:

7 7

 

 

 

 

 

 

_ 5 _ 0 _ _ 0.3.. _

 

..
.

 

m0:0.0 c0000

_ 00000:. _.n- -. 0200m oo.

 

 

 

.m0:0.0 :005 0:. >0 00025.0 >323 000 .0. 90030000 0.0.300 00:... GHOm-Hmm
.msm.:0o00000.s 0:0 .m.0s.:0 .m0ca.0 .m
.0.003 0:0 :. 00.0000 0:0 ..< .0
o . . . .>:0.000 .0 >000m 0:0 000 000000 0000 >.:0 0:p .m
WK ..VNQ O Mum-m.- O mm omfi O HNG O .0000 00:...00 0 ..0 3:0:00500 0.00.00 0:0 0.00.0 0:... am
. . .>0.:06800 0 .0 00:0:00500 0.00.0 00h »
waommm

“00 000.0:00 800m>000u :< .n.

 

 

 

mm ,oom.o mmq.ot

 

 

.>.0.000 0006 m:.00000000 0003 0.000.000 0:0000 .
.0000m 000 0000.000E00 .nummouuzm 0005 000 00.0000 “WU
.00.0000 000E 0:.000 0003 0000>00 0:0 .~
.m:.0000000 003 :o.00_:000 0000.0) 0:0 .—

”00:0 m. com. 0:0 wmm. :003000
:o.00.:000 .000.acm 0:300m 0:0 :. 03:0:0 0:0 00. :0.00:0.0x0 0000 00h .m.

 

 

S a 0m :m 3 3m.
$055.; ...-. . . -- .11: I}: u
3880--1-11 as
.I ..u I’m...-
mdésg I-.. -. mm.
3.285 . ...\A--- . )1. m...“
$.85. ..-l- -.z-1-1-‘\\ - r - Hw
.8. ..

.50000.0 m.:0 00 «00.00 m. :0.0m0:d

 

:maommm

 

 

z .En.nm.m ENE

whmH Emma. mEHS

 

z .BE—Amhm z<mE

hum-n Emma 4.90m

 

EEHH

0H00H00>

5.900.004

 

 

Hll‘lmMH-Hanfl u .HZMWSMH-HWZH

 

 

263

 

.0>onm 0:0 .0 0:02 .

a.quo .0000. ..0... 00.0000 50.. >0..o0c 00.; .00 30. 0:.00c00 .
.0000: .0000 .000.0 00;

.000. 0:0 .0 0mn.0 .0:0.0000000 0.0. 0 c.

.000. 0:0 .0 050.0 .mco.0000000 >.0m0 :0 c.

0/

.20 .
-1~'~0: m

 

 

 

 

. o .
00 000 o 000 o 00 000.0 000.0 .2-_-:ae.a.& .JN «Nommm
-
. .
.0>cnm 0:0 .0 0:0: .m
.0.~E.:0 000 20.03 .mo~000: EOum:.x
0;. .0 0003EJE 000 000000E0000 00.0.0 0000000 000.0000 000 0.mE.:< .:
. .0.:0.0 mc.0.0000 0:0 000000E0000 >0
000.:V00 Aaov :0m>x0 0;. ..0 0000000 :00. c. £0.53 0.00.0 c0000
>0 000.0000 New 0:. ..c 0000000 0.00.0 0:.0.0000 0:0 000000E000: AMWV
. .0uzm.0 c000m -a
>0 000.0000 00:000500 :00000 X0.0600 0:0 ..0 01.>000 00000050000
0..£3 00000050000 >3 00000: cua>xc 0:. ..0 0030000 00:0.0 c0009 .N
.0.0>0 c 00: .ccn.:0 .0 30.. >03 0:: c ac.0000 00;. ~00 ,
00 000 0.05.:0 0..:3 New >.cc .000000 00000050001 0cm 0.00.0 :0000 ..
n o o c . .
MN m-Hm O dO-H O N@ Nfifi O HON 0 ”000000“. 0_0>0 con-.00 05 c. 00...: 00 00... 000 0.0.55. .m~ MNOMMAH
.00000 .m
.ouuomc. .:
. .cvo0o0z.00 .
.nca.0000m
.000>.n00;
mm O>N.o ooa.o cm nqm.o mma.o NNOMMm

~0000050000m 0 .0 0:0.: 0:0 0. m:.20..0. 0:0 .0 30.:: .NN

 

mm ome-O nam.o

mm

wwq.o awe-o

.00c0c>.C000 000 Am

.c.0£0 0;. c. 0030. 000:0 :01. 0000050: 0005 00m .
.c.0:0 0;. z. 0020. 000;. :05. 00..060 000 .N
.c.0:0 000. 0:0 c. 0030. 000:0 :05. 00000. 00003 ..

0 :.:zu 0:3. 1 .0 0c. 0;. .c 0.cE.C< ..N

.unu

Hmommm

 

mm mwN.o >HH.O

 

 

 

m>~.o mwo.o

 

.00050 ..0 c. 0500 0:. m. ._ .: .._ .N
._._ .m .. ..
..0000000 0:. >0 v0..0000 >m00:0

.c:.m.0o 0;. .0 xb. .0030 >.:: 0;.0000 :cE 0.003 Amv:o_.0:u.0 £0.52 0. .ON

wNo000

 

 

z .>mn.nam z¢m2

w>¢H EMMB MMHZHB

z

.>mn.nHm z<mz

unma EMMH AA<M

ZHHH

mapwfiuw>

E>0000<

 

 

 

 

 

 

 

263

 

.0>on~ as» .o 0:02

A.000 .000.. ..0... 00.000m :m.. >0..000 so.; .03 30. mc.muc00
..0003 .0000 ..00.0 mm:

.010. 0:. .0 amoum .0:0.mmouu:m cum. 0 :.

.0x0. 0:. .0 060.0 .mco.00000:m >..00 :0 c.

9" o o
—1~‘m: m

 

”0m0000n 0.0>0 :03.00 on. c. 00..: 0. 00: 0.0 m.mE.:< .mw

‘ L . .
. .
.u>oax 0:. .0 0:0: .m
.m.ms.:o 0.0 £0.33 .mo~ouoz 500m:.x
0:0 .0 0.00505 0.0 0.0m0080000 .0..um 0m0000n 00..000. 0.0 m.ms.c< .:
. .m.:n.a mc...amu. 2:0 m.0mcaeouon >3
00..:00. Ame. com>x0 0:. ..c 00:00.0 :.0. :. 50.;3 m.cm.g :00.m
>a no..000. N09 0:. ..c 00:00.0 mucc.a m:...am0. new 0.0m005000c .mv
. .mu:0.0 coo.m \
>0 00..300. 00:300500 :02.00 x0.aE:0 0:. ..0 00.>0.Q n.000080000
0..;3 0.000050000 >3 00000: cao>x0 0:. ..0 00:00.0 mucm.0 :00.o .N
.0.0>0 c 00: .con.c0 .0 30.. >03 0:0 : 00.0000 was. New ,
a: 0m: m.ae.:c o..:3 Nou >.cc 00:00.0 m.0merouuv can mu:0.a :00.o .—
0 O 0 O
m. mam o .03 0 mm N...» 0 SN 0 mmommn.

 

m.

oNN.o

code

.3

0.0m... wmaé

.mmm.m .m
.muuomc.

.nd
.cvoao.:u.m .
.0:0.mmo.m
.0.o>.a.0;

..0aao:nnc.m c .0 050.: 0;. m. mc.20..o. 0:0 .0 30.:3 .

NN

NNommn.

 

mm

omqé

mam...

ma

wwqd awed

‘ ‘ .

.:.030 0;. z. .030. 000;. :03. .o..05m 0.0

.m:0.0>.c.00 0.0 .
.:.1£0 0:. c. .030. unozu cog. m00.0E:: 0.05 0.0 .
.~
.:.020 000. can c. .030. 000:. can“ .0umm. 000.n ..

 

 

 

 

 

 

~ 35.0 .000. 0 .00”: 0;. a... m.cE.:< ..N HNommm.
.00.:u ..0 c. chm 0;. m. a. .: .._ .~
. .... .m .. ..
0 A o 0
mm 33 o 0.: 0 cm mum o 3.0 o .-_‘625... 2: E 03.33 .685
.c:.m..o 0;. .0 .xb. .009. >.:: 03000. :0... 0.303 3.0.0.300... £0.53 0. .ON ONOWMQ

 

 

z ..EQAHHm

24m...

mum... 2MB NEH...

 

.3543... 24m:

26H 2mm... 13¢...

 

ZMHH

Buy—0.30.

0.30.33,

 

 

.HWWr—‘Enna - Erinlunllllwl

 

 

 

 

264

 

mm

wqm.o

me.o

«a

wmq.o mmm.o

.>..:35500 0 0. .0.m:o.. >00000 .0 >000.0...0 0:. ..m
.>..:05_:00 0 0. 5a.:0m00 0:. .0 0.00 0:. AJV
.>..005500 c 0. 5A.:cc00 0:. .0 0~.m :0..m.3000 0:. .n
.5:.00a.0 .c:. 00. x00. 0. 00 0.003 000 0.0:: .~
... :0.00300.0¢ .00500..>:0 .0 0:.: 0:. ..
o. :.0.00 .25.00 .0 .00.: m .0 0:0.0 .mu.mo.000 0:h .mN

mmommm

 

mm

mH¢.o

mam.o

mm

000.0 Nq¢.o

.>..:35500 .m
.50.m>m 00000 .0

.000:0m0.: .m

.50.m>m000 WU

.00..m.3000 v

«53.00300 00.00m 0 m0:.00n00 .m0: m0.30..0. 0:. .0 0:0 :0.:3

.wu

wmommm

 

mm

om¢.o

mam.o

ma

mm0.o 000.0

.0m0:. .0 000: .
.:.:.:305 0:. .0 00.m 000.003 0:. :0 Am
.m

.:.c.:305 0:. .0 000.m 0.0.»00 0:. :0
.:000: 0:. .0>0 .N
.00000 0:. 00>0 ..

0.3.m.05 .r05 0:. 0m0. 0.303 0.0.0005 m mm0.0n 000 00000 :0
00>0 50.. m:.>05 ..0 .m00 0. .r03 50.. «00.3 a:...c>0.a :..3 00.0 00 0.

..N

nmommm

 

m.

0mm.o

mmw.o

mm

NNm.o qu.o

.m:.o:0>00m .m
.m.m0.:5>m .0
.Em...m00ca .m .
3.0200000 new .
.:0....00500 ¢.\

“.0 0.050x0 :0 0: 0.303 00:00:mmm0m m m:..00 000. <

.o~

omommm

 

mm

 

om0.o

 

mum.o

00

000.0 wmm.o

. .m.0050.0.m 0>0:m 0:. .0 ..0 AMW.

.00.m.00 0:. 500. >00000 .0 .300.
.0o.m:00 0 .30:..3 o:.00..0:3. 03:..000 .0:000 50.m>m000 00 .:
.:.c:0 000. 0 .0 mx:.. 0:. m:0.0
00mm00 m. .. m0 50.m>m000 0:. 50.. 000.000 >..0m0.m m. >o.000 .m
.0.0>0 >00000 :0 m0 0:.:. :03m 00 m. 0.0:. .~
.m50.n>»000 ..0 .0 0..m.00.00.0:0 o m. >00000 .0 30.. 0 ..

~0.0:0m0.: 0:. 0. >0.0:0 00:..0m00 m.:050.0.m m:.30..0. 0:. .0 :0.::

.mN

 

mNommm

 

 

z .>NQ.QHm

20%:

mum... 2mm... ..MHZHB

 

 

 

.>mn.on z<mz

snaa zflma AAfim

 

ZMHH

5500004
manwfiumb

 

 

 

 

 

265

 

 

mm

0mm.0

n0H.0

m0

mmq.0

mmm.0

.:o..000.m:0..
.0mo.3..0u .0 0.3.00.3:05 .
.co..0..0m0.
.m.m0:.:>mo.0:0

0
Ln

O-fi
Nlm

 

 

 

 

 

 

0:. o. n.0u300.0 60.. 00¢n00 m. >o.0:0 000. :u.:3 :..3 xu:0.u...0 0:»

 

.:0..0..0m:0.. ..
”.0 ..3m0. 0 00 0033.0 :0:.00 ..0 030 A3:0... :00... .«n NMOWMHAH
.m.00300.a 0.0 Wm
.m30.0>.:.0u 0.0 Am
.:.0:0 0:. :. .030. 0mo:. :0:. m30.0&3: 0.05 0.0
o . . . . .:.0:0 0:. :. .030. 0mo:. :0:. .0..0Em 0.0 .N
mm mmq O ROG O #0 hm.» O .th 0 .:.0:0 000. 0:. c. .030. 03:. :0:. .030. 000.: ..
3.2.0 .0 0.50..»0 0 .0 00. 0:. .0 m.0E.:< ..n Hmommm
.m:.0000.a 0:. .0 0:0: .m
.:.0:0 000. 0:. :. 30: .o .363: 0:. m:.¢.00.0:. ..xl
.m.m0:.:>m0.0:a .0 0.0. 0:. o:.mm0.0:. .m
.:.0:0 000. 0:. :. Am.0>0. 0.:00... «00.0 .0 .0:E3: 0:. 0:.m00.u00 AHV
m8 wmqoo HMQ.O ¢¢ quoo wmo.o 0.0:. .00 0. .0>0. 3:00.. ..o. 0:. :. 0.55:0 0:. 0:...00 ..
">0 00n00.u:. 0: :00 :.0:0 000. 0 :. .0>0. 0.:ao.. 00.
an omommm

 

 

z .>mn.nam

z<mz

Quad ZMNB KMHZHz

 

z

.>Nn.nHm

whoa 5mm...

AA<H

 

SHHH

abacuud
manowum>

 

 

 

 

 

 

 

 

 

..coE..00x0 0:. 60.. ..0. ..:00 .m

Nm0.0 5N0.0 Hm 000.0 0N0.0 62 ~.

.m0>

.0..0.00: 0:....4 :. 0>..u0..0 :3> .00.E0:0 0:. m. .mm

 

266

 

x3> 0.0.0). 43> .00Em .005w
a 0 Q Q o
0.0.0>m .006m 03.0 10> 03.0

 
 

.30.0: Em.m0.0
0:. :. :30:m 0. 00000 0.02 n.0u.E0:u 0:. .0..0 0.30: 0>.03. .:0E..0ax0 0:. .0
. . m..:mu. 0:» .0.0 0.30:m 0.3..30 .0:. :. 0..0.00: 0:. .0>..00..0 0.03 0.3..00 0
0. 00000 m.00.Eo:u 0:. .0 :.c: .0 0:0 .. .0:. 00:0000. 0: ..0:.0 :000 :..3
.000. .0: 0.0 m.0u.60:u 0:. .0:. m.:0E..00x0 m30.>0.0 60.. 30:: .0000.0.0 0:»
.0.:..30 :.... 0:. 0. :3) 0:0 0.0.0). .0 0.:3060 .0300 0:0 .o.3..30 :..30. 0:.
0. 0.0.0>u 0:0 .005m .0 n.:3OE0 .0300 .0.:..30 0..:. 0:. o. x3> 0:0 03.. .0
0.:3050 .0300 .0.3..3u 0:000m 0:. 0. .00Em 0:0 x3> .0 m.:30£0 .0300 .0.3..3u
.a... 0:. 0. .005m 0:0 03.. .0 0.:3050 .0300 00000 :0:. .0m00.0.e 0:»

 

3 P. . ..Ouupwa M N —

 

.30.0: E0.00.0 0:. :. :30:m m0 00:..0
. . m .0mm0.o.a 0:» .0..0.00: 0.0:0m30:
...00 :. m0.3..3u 0..0.u0: .00..:00. 0).. 30. . .
9...... :. 0>..00..0 0.0 3.0.0... 0:0 .:3> .005... 0:.. 100.505 30: .30. .059.) AmmomM—km
0:.E.0.00 o. .coE..0ax. :0 00.030cou ..a.mo_0.: 0 .0..0. .. .Ommu.o.. .

 

 

 

 

 

 

 

ah0000<

z .3505 2.02 z .5905 as... 0.... 3.3.5.

 

£3 a... .0..sz A E... 5.0.. 0.2. . .

 

 

 

 

 

 

 

 

 

 

267

 

 

5:95.093 2.0 .00.. ..Ou 0.009 .n
.0: ON

:0... who... .3 «N06 mend .8» .. Twmommm

«2.302. «:2... c. 03000.... 0.0.0.6 .00....05 a... a. .._n 4.

.5 8500.004
2 .595... 240: 2 53.80 2<0.: :0 3.3.;

 

 

 

 

 

 

 

 

mum... 2mm... MEHS . hnmfi Emma. Aim

 

 

 

 

 

n HZMWSMH. MZH

 

 

A

 

 

 

268

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

mm 30.0 28.0 mm 30.0 owmd 2 2 22 . 2 2 :2 22 .2 22 2 222. . “qua...“ u...” .2 80.5.
mm NoN.o .35 3 3...... mg... 22.22 .0 2.. .2... 22 2 22 .2 22 22...”...ghwmw ”we... .2 80.0..
..m 02.0 50.6 am mam... $10 .222. 22.. 2:2 .0 .22 2. .2 2.2.2.. .2 22... .222 22. _ .2 3.00.2
mm mw¢.O dom.o mm NEVA. MQN.O .402. 3%.. 030 a 9:30.. 0.0.2. .2... >325 30> 0:200. to: . .n. wHOha
mm 3.; 80.0 8 $2.0 mid 2 222 .2222... .2 22 .2 .2. .222 _ .._ SEE.
mm 8.; «on... .3 8.; 2m... .2 :2. 222 2.2 .2. . .22 . .2 2.22.. :2 2. .2. 22 .2 Some.
3 3.0 0:5. mm and 28.0 .22 2 2 2. 2 22 .2: .2 222 2. .22 2.2 _ .2 285.
mm Hom.o 00m.o mm 590.0 RONA. .3375 00:05.33. >5 5.: 80.30:. 3.02. 9:00 .0 3330..» J... «dong
mm 2.3.0 30.0 8 and 3...... 2.2 2 22.2 ..2 2 22.. {.2 . 2.. .22.... ”Lanna... ”mm...“ .2. 38.2
mm 890. 3.; mo 8.2.5 m2... 222... 2 22 22.2.2 . 2 .2 2...“... “.._..fiwfi.m....fi .2 N805.
0m o~¢2o NNN.o mo 090.0 0mm.o .9. Ba. 2.8.0 205 3:3 3:8 _ t 330 co.wwnwwnouwwEoowavmwwwmwoumtum!" .2 HHORE
0m ome... mom... mm mmqé. .moo4o .338 a E 2...; E .8 522.222 5. 9.3 :2. .8... a 3.... .90.: 28.. . .2 c—cnflq
mm 23.0 08.0 mm 8.; 9.0.0 ..22. 2.... _ .2 .2 22. 22:2 .2. 2 .2 . 22 2 .2... 2 2 .2. 2 a. 88.0..
mm. 23.0 coma mm .35 3.6 ..2 . .2222 22 .2 .2 22.2. 2 ..2 . 222 2 22 . 222 2. 2 88.0.
mm .35 $00 3 m3... 03.0 222 _ 22 2 .2 .2222. _ ..222H..........M....uwu 2 Boa...
mm «00.0 cow... mm «N0... @050 .85583. .2 5.3 0.8.3:. 09. on 3:23. .2302.» E :3 o 9.33 as.) .0 000.95..
mm 8nd 3.; ma .35 3...... .22 .2: . 22.. 22...... 2:2. 22.. .222 2 :2 . 2 38.0..
mm we; 3.; ma 83.0 22.0 .2. .2222 . 22 222 22 22 22... . .2. 2...... . :2 . 222 .2: 2 8%
0m dom.o mHm.O mm mHm.O 0mm... .33... .9... 0:0 330 00:03:02.. 9... «9:5 :0 a: 300.. . .m moomfi
mm oN<.O ©m~.O mm 500.0 mmN.O .588 3.3.2.. o 9.3 3 «>2. _ 5:: 3.0.5.. 30> .oo. 3 you _ .u Noon:
mm mom... 2m... 3 8.; 50.0 .2222 .22. .o 2222 :2... .._..wMfiuhmhw an...“ a...” ._- .88.:
z .>mn.nam 24m: z .>mn.nHm z<mz SMHH ah0000<
0H00a00>

2.0..“ 2mm... mg;

nnaa 2mm... A44...

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

   

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

mm SN... m8... 3 wmm... So... .._... .._... s 2...... 2. 2... 2.... 2 2...... .2 .8. .... .2 vqommfl
..m 0.3... NNN.o 3 $3. .21... .2... 2.2 .2 2.. 2...... 2... 2...... E... . .2 58......
.mm N000 0mN.0 mm $3.0 mmN.0 .3... a... v.._... ..ofio $5 3...... :6 >5 8 :3 m.2 an wmomg
Gm. «CM 0 QHM 0 mm qu O OOOWQ .3930 so.» .._—9.. .0» xmo Ga 30206... . .nm “mom:
..m .3... E0... 8 02... So... 2 2...... .._. .2. .. .2 .mmm.......... HUMEWWHLMWBMPWK .2 08...:
mm mHm.O mOfioo mm QNN.O Hwouc 3.00. 0; 0.0.00 c0333. 0 ca .0330 05 .0 x55 00 >5 vmuwuhouwowuwnufl .mm mMOhm—Ra
..Nm N®¢.O OBM.O mm m®¢.0 qwm.o 330;.“ >05 300.. m... 30; Cu 9.20.0000 9.03350 9.3 .0393 BDOLWUHHMNMMW“ .am QMOWQ
. {.r . :0. 0:0 cu..:u .03 c
«m HMN.O ©m0.0 Gm OQNoo HQ0.0 0. $3 05 c. mcozmuaa .050 so.» v.00... 02.0.3 My 3.0.0.... 3003” U .mn mmOhg
mm mhmoc ONH.O mm ¢O<.O NON.O .0mmucc>0mm_0 30:0 3 :300 0.0... m. .0320 cm @5955 .05 3:... 3.0.05.6 oco .~m NMOm:
Gm Ohfioo mmm.O @@ ¢N¢.O mmm.0 5.0» m c. 0. mm: 0; 2...: 003330 05 5.3 >20 m>03—a.uu_wnzwowmvmmw.uou ..n HMOnH:
mm OmN.O H®0.0 mm NNH.O OM0.0 0:00.09... m. .3232. a c. 0.03 .303 .om OMOWMAN
mm HOW.O ©m¢.O mm ¢m¢.0 .mmm.0 £03330 .6... 3.3m 0; 0.0.30 :3 0.3:» 05 .95 0.00— 0.305 ucwvaun < .3 ®NOanH<
mm. ¢N¢.O NNM.O @m mw¢.0 qwm.0 03m 00 mm: 0... .I :96 0...... 302300 03.30 05 .ozmpwmwrwuw ”Mann.“ .3 wNOE
.319: .30» xoonu 3 we: 0:000...
mm 0.3.0 mmN.0 mm mmqé mwN.0 3.. m S... .2... 3:0 .3. a .08.... o... B «53...... a. u. 5.22.3 .2. nmomg
.093 05 9......0 £05 5.3 mfg...“ to
0m 0n¢.0 500.0 mm 0.3.0 mm~.0 .8... 2. 2.3 2. .32... c. 22...... m... 5.... 3.. 3.2.... 22.2.... a t .on omen:
.umuu a :0 mco.umu:u 0:“
mm QON.O «No.0 mm O0N.O NmO.O 3:033: m. 0... 30: 0.55 3 >3 0.00;... a... 7.0» 0 3x3 0:00.50 0 :9.) .3 MNOhhdfl
. . . c 0013....
mm. mom O mmfi 0 mm Hom.O mmm.o no; 0... 005 30:30.. 00.03320 «3000 0.5;. 0» unfucounvwwwhm ”cows: < .aw «Nonmg
. . . o . :0 30.7.2.0
mm 3.. o 3.. 0 mm SN 0 8.. o 2. 2:2... .. 2 ..2 .2... 2.... 2 22...... ......H........M 2. 2 .. .. .2 28......
mm NON.O MN0.0 mm me..O HMOaO 42.30:. >03» >5 0. 30.3... ..o 915053 0:... 0a 0.00... 3:05... 3:003; .un. *NNOWE
. . w. 35.304
2 Ema PH. . . zmHH
m 23:... 2 BE Pam 24m: manuuumb
23 Emma. ME; :3 Emma Afla

 

 

 

 

 

 

 

 

 

\‘j
MHNNHduZZOHHWmND EHHUMNHHHHG‘ .LL llllllllll

 

 

J

270

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1.. o H: o 22...... .2 2...... 2:2... 2.. 2,... 2 2... 2.... .2 2 .. .2 $8.2 ,
C C O O 0 o O . h
Wm WWW w wa+m WM NMKiG hmmw4c 00.0030 30> :03: :96 320.05 05 5.3 0:0—0 0m 0» 00.03 m. 3 0.00.0 :. 3
em .26 :36 mm wmmd 265 2 .2 .22. 2... . .2. 2: .2 22 :22 2 .2 88.2
mm OGN.O Hmooo mm HNN.o HM0.0 .0:. 00 03009.0 3 00;: 00 00 0:0 30303.35 30:00 0» 0x: . .m: MQOME
.>m! C30 . €¢omrfl<
Qm Qom.O HwQ.O ma Nw¢.o ®NM.O >5 0.6: 00 9.3.0 :05 05630.0 :0 .o Em: 0 mezzo: .0000... .000 . .3 N¢Ohh<
m. mHm.O mwév mm $3.0 OO¢.O .m.m 0 00 0003:. 3.300 0 E m.~ 0 00m _ 0. 0:. .0500 0.5.000 u. .m: mm
mm mNm.O oquo mm Nmm. «C hmwiu .05. 00 00mm: 0 2 «.0: 030.850 .3 «QCR m
H . .3 00:. _ c013 ucgflmm.aouco 0.20:0...» >5 .000: 3:00 . ..J AHQOWE
mm mmm..0 omw.0 mm NDMJV. 5590 0.3:... 5. 0 0
abacuud
z $098.... 2%.: z .5933 240:, 20.5 023.2,

053” Emma MEHB

human an“. Add.“

 

 

 

 

 

 

 

 

 

 

 

 

Name _

of pH
grade
you d.

sheet
on th

can \

271

ANALYTIC ABILITY

BIOLOGICAL SCIENCE 202:PROBLEM SOLVING

nme Student Number

 

 

The objective of this exercise is to determine student aptitude on types
i problems not often encountered. Your performance will not effect your
'ade. Please do the best you can, but DO NOT GUESS. Leave an item black if
DU do not know the correct answer.

Please fill in your name and student number on this sheet and on the IBM
leet. narken in the appr0priate squares under your name and student number
I the IBM sheet. '

The questions are all multiple choice. Answer each question that you
in without guessing by marking the appropriate box on the IBM sheet.

You may write or draw on the test pages if you wish.

 

1 PART I E

 

 

 

ART I

272

Directions: In this part, you are to choose from five diagrams the
one that illustrates the relationship among three given
classes better than any of the other diagrams offered.

There are three possible relationships between any
two different classes:

. indicates that one class is completely contained
in the other, but not vice versa.
indicates that neither class is completely
' contained in the other, but the

two do have members in common.

(:::) (:::> indicates that there are no members in common.

Note: The size of the circles does not indicate relative
size of the classes.

 

Example:

Birds, pets, trees.

(15.0 (2) O O O

The correct answer, (b), shows that one of the classes (trees) has
no members in common with the other two. (No trees are either birds
or pets, and no birds or pets are trees). (Q) also shows that the
other two classes have some members in common, but neither is
completely included in the other (some birds are pets and some pets
are birds, but there are birds that are not pets and there are pets

that are not birds).

T l‘ I
T
m

 

 

273
2

The flve possible choices for questions i through 8 are given below:

0) <2) (3) @ O
(1:) QED (s)

l. Nuts, pecans, forks.

2. Adult women, infants, black-haired people.
. Fish, minnows, things that live in the water.
. Governments, democracies, dictatorships.

Adult men, government officials, congrégsflen

3
l;
5
6. Students, children, parents.
7 Lizards, cats, reptiles.

8

Frozen desserts, milk products, ice cream.

II. (In answering the questions below it may be useful to draw a
rough diagram.)

Questions 9 and lo.

(A) It is assumed that a half tone is the smallest possible
interval between notes.

(B) Note T is a half tone higher than note V.

(C) Note V is a whole tone higher than note H.

(0) Note w is a half tone lower than note x.

(E) Note x is a whole tone lower than note T.

(F) Note Y is a whole tone lower than note w.

{hich of the following represents the relative order of the notes from the
lowest to the highest?

(I) wivr.

(2)Yuxv1.
(3) vvrvx.
(It) vuvrx.

(S) ‘YXVVT.
ihich of the following statements about an additional note, 2, could
NOT be true?

Z is higher than T.
2 is lower than Y.
(3) I Is lower than w.
2 is between V and Y.
Z is between H and X.

 

 

274
3

Questions ii and i2

(A)
(B)

(C)
(D)
(E)

(F)

A red ticket is required for entrance.

You can get a red ticket if you have your blue form

signed by the director.

The director will only sign the blue form if you surrender
yOur yellow pass to him. '

If you have a green slip, you can exchange it for a yellow
pass, but only if the director has signed your blue form.
if you have a valid driver's license, you do not have to
have your blue form signed by the director in order to

get a red ticket.

You can get a yellow pass on request, but only if you do
not already have a green slip.

The above procedures fail to specify

(1) whether anything besides a red ticket is required for entrance.
whether you can exchange a green slip for a yellow pass.

(3) the condition under which the director will sign the blue form.

(A) how to get a red ticket if you have a yellow pass.

(5) how to bypass the requirement of having the blue form signed. ‘
i

Having which of the following makes it impossible for someone without

a driver's license to enter?

(A) A signed blue form.
(8) An unsigned blue form.
(C) A yellow pass.

(D) A red ticket.

(E) A green slip.

Questions 13 and lh

This problem is about Fish, Snail, Bean, and Bird islands. People
have been traveling among these islands by boat
for many years, but recently an airplane
started a business. Carefully read the clues J
about possible plane trips. The trips may be . ',.
direct or include stops and plane changes on
an island. When a trip is possible it can be

made in either direction between the islands.

.00
(B)

 

People can go by plane between Bean and Fish Islands. '
People cannot go by plane between Bird and Snail Islands.

(C) Pe0ple can go by plane between Bean and Bird Islands.

. -Can people go by plane between Fish and Bird islands?

(I) Yes.
(2) No.

(3) Can't tell from the clues.

Can people go by plane between Snail and Fish Islands?

(l) Yes.

(2) No. (3) Can't tell from the clues.

DIREETIONS:

1. Please

 

N

llrite y

w

Answer

.=-

Check l

.' Read ti
ll coml

m

0‘

Be sun

N

- Return

Answers I-
” Spring
2) Sprini
3) Cand

i) Band

 

 

 

 

275

CONTROLLING VARIABLES
TIONS:
lease read and examine the first page carefully.
rite your name and student number at the top of page 2.
iswer questions i, 2, 3 and A, then
week your answers with those given below.

sad the t0p of page 3 carefully, then answer questions 5 through
I completely.

a sure to write your name and student number at the top of page 2.

:turn your ”test” during your SAS.

'5 l-h

>ring K: Brass, Few coils, Thin wire, Big coils .

>ring N is made of THIN, STEEL wire and has MANY, SMALL coils.

and l are different: They have different sized coils. imw

and P are the same: Same coil size, number of coils and metal.

 

red

 

276

('5
it:
an
'H
iGD
=
)h
a:
ir-
El:
)2:
(:3
'U

 

There are i6 springs and three weights shown above.

Some of the springs have big coils and some have small coils. The springs
with BIG coils are: A, B, C, D, F, G, K, P.

Some of the springs are made of thick wire and some are made of thin wire.
The ones made of THIN wire are: D, F, G, H, J, K, N.

Some of the springs have many coils and some have only a few. The springs
with MANY coils are: A, E, F, G, H, L, N, P. '

Some of the springs are made of steel and some are made of brass. The STEEL
springs are colored black and the BRASS springs are colored red. The brass
(red) springs are: A, C, E, F, H, I, K, 0.

 

The steel (black) springs are: B, D, G, J, L, M, N, P.

Weights number i and number 2 weigh the same amount. They are both heavy.
Weight number 3 is lighter.

(If you have any questions please raise your hand).

on P.‘

on

2)

3)

1!)

 

 

Please answer the following questions. Be sure to use ALL of the information
Page i.

Circle the words below which describe spring K:

Brass or Steel.

Many coils or Few coils.

Thin wire or Thick wire.

’ Small coils or Big coils.

Which of the following is TRUE about spring N? (Circle the correct choice).

a) Spring N is made of THIN, BRASS wire and has MANY, LARGE coils.
b) Spring N is made of THICK, STEEL wire and has a FEW, SMALL coils.
c) Spring N is made of THIN, STEEL wire and has MANY, SMALL coils.
d) Spring N is made of THIN, STEEL wire and has a FEW, SMALL coils.

How are springs C and l DIFFERENT? (Circle the correct choice).

a) They are made of different kinds of metal.

b) They are made of wire of different thicknesses.
c) They have different numbers of coils.

d) They have different sized coils.

How are springs G and P the SAME? (Circle the correct choice).

6) Same coll size, number of coils and wire thickness.
b) Same coil size, number of coils and metal.

0) Same metal. number of coils, and wire thickness.

d) Same metal, coll size and wire thickness.

Suppose
a diffe
IIe coul

wei
and

we

and

278
3
Suppose we wanted to see whether or not the number of coils a spring has makes
a difference on how far down it goes when a weight is hung on the spring.
We could test this by hanging
weight number i on spring A
and
weight number 2 on spring E,
and seeing which spring goes d0wn farther:
or
we could test the same thing by hanging
weight number 1 on spring N
and
weight number i on spring i,
and
seeing which spring goes down farther.
Here are some tests for you to make. (Please raise your hand if you have any questions)
5%. Can yourmakeeafitest to see if the size of the coils makes any difference in how

far a spring will go down? /Yes/ /No /

 

If so, the test could be made by hanging
weight number __ on spring __.
and

weight number on spring __.

6) Which spring could you compare to spring B to show whether the size of the

coils changes the amount the springs go down?

'iSpring __. N0 spring / /

279
i,
7) Can you make a test to see if the thickness of the wire that the springs are
made of makes any difference in how far a spring will go down? LE LE7
If so, the test could be made by hanging
weight number _ on spring _.
and

weight number _ on spring _.

8) Which spring could you compare to Spring 9 to show whether the thickness of the

wire that the springs are made of changes the amount the spring goes down?

 

Spring _, No spring I

 

.7. F
.

 

 

280

9) Can you make a test to see if the kind of mess} a spring is made of changes
the amount a spring goes down? [EEE7 [E§:7
If so, the test could be made by hanging
weight number __ on spring __

and

weight number ___on spring.__.

l0) If you put weight number‘l on spring §.and weight number g on spring A, would
this be a good test to see if the metal the springs are made of affects the
amount they go down?

/No /

 

/Yes/

ll) Can you make a test to see if the weight hung on the spring changes the amount it

will go down?

/Yes/ lNo /

 

if so, the test could be made by hanging
weight nunber _ on spring __
and

weight nunber __ on spring _.

APPENDIX C

Instructional Variables

I

Matrix Variable

APPENDIX C

281

Instructional Variables ‘

 

FALL TERM 1977

WINTER TERM 1978

 

 

 

 

 

 

 

 

 

Element Acronym Mean Std.Dev. N. Mean Std.Dev. N.
I1 LECOOl 0.914 0.281 105 0.767 0.381 75
I2 LECOOZ 0.876 0.331 105 0.800 0.360 75
I3 LECOO3 0.848 0.361 105 0.717 0.406 75
I4 LECOO4 0.733 0.444 105 0.733 0.398 75
I5 LECOOS 0.752 0.434 105 0.667 0.424 75
I6 LECOO6 0.733 0.444 105 0.717 0.406 75
I7 LECOO7 0.702 0.460 104 0.617 0.438 75
I8 LECOO8 0.562 0.499 105 0.683 0.284 75
I9 LECOO9 0.562 0.499 105 0.567 0.446 75
I10 LECOlO 0.648 0.480 105 0.583 0.444 75
I11 LECATD 7.356 2.516 104 6.167 2.182 75
I12 SASOOl 0.990 0.098 105 0.883 0.289 75
I13 SASOOZ 0.990 0.098 105 0.900 0.270 75
I14 SASOO3 0.933 0.251 105 0.900 0.270 75
I15 SASOO4 0.962 0.192 105 0.917 x 0.249 75
I16 SASOOS 0.962 0.192 105 0.833 0.336 75
I17 SASOO6 0.952 0.214 105 0.550 0.448 75
I18 SASOO7 0.924 0.267 105 0.800 0.360 75
I19 SASOOB 0.933 0.252 104 0.667 0.424 75
I20 SASOO9 0.893 0.310 103 0.650 0.429 75
I21 SASOlO 0.932 0.253 103 0.717 0.406 75
I22 SASOll 0.921 0.271 101 0.650 0.429 75
I23 SASOlZ 0.960 0.196 101 0.533 0.449 75
I24 SASOl3 0.950 0.218 101 0.567 0.446 75
I25 SASOl4 0.941 0.238 101 0.650 0.429 75
I26 SASOlS 0.839 0.374 31 0.600 0.282 75
I-27 SASOl6 0.891 0.313 101 0.533 0.449 75

“ I28 SASOl7 0.921 0.271 101 0.750 0.390 75
I29 SASOl8 0.901 0.300 101 0.800 0.360 75.
I30 SASOl9 0.931 0.255 101 0.617 0.438 75
I31 SASATD )6.990 2.166 101 12.917 3.227 75

 

 

282

APPENDIX C

 

 

 

 

 

 

 

 

 

 

Matrix Variable FALL TERM 1977 WINTER TERM 1978

Element Acronym» Mean Std.Dev. N. Mean Stdrgev. N.
I32 LTPOOl 0.010 0.098 105 0. 0. 75
I33 LTPOOZ 0.010 0.098 105 0.013 0.115 75
I34 LTPOO3 0.010 0.098 105 0.067 0.251 75
I35 LTPOO4 0.029 0.167 105 0.040 0.197 75
I36 LTPOOS 0.010 0.098 105 0.040 0.197 75
I37 LTPOO6 0.019 0.137 105 0.040 0.197 75
I38 LTPOO7 0.029 0.167 105 0.013 0.115 75
I39 LTPOOB 0.029 0.167 105 0.033 0.103 75
I40 LTPOO9 0.019 0.137 105 0.040 0.197 75
I41 LTPOlO 0.010 0.098 105 0.027 0.162 75
I42 LTPTOT 0.171 0.545 105 0.280 0.831 75
I43 p02001 0.019 0.195 105 0.160 0.369 75
I44 pgzooz 0.019 0.195 105 0.227 0.421 75
I45 PQZOO3 0 0 105 0.187 0.392 75
I46 902004 0.457 0.501 105 0.227 I 0.421 75
I47 902005 0.457 0.501 105 0.293 0.458 75
I48 PQZOO6 0.410 0.494 105 0.240 0.430 75
I49 902007 0.390 0.490 105 0.320 0.470 75
I50 PQZOOB 0.371 0.486 105 0.293 0.458 75
I51 902009 0.333 0.474 105 0.347 0.479 75
I52 PQZTOT 2.457 2.442 105 2.293 2.958 75
I53 T01 1.003 0.271 76 0.836 0.357 75
I54 TC3 0.901 0.273 89 0.807 0.501 75
I55 T04 0.833 0.363 75 1.286 0.968 75
I56 T05 0.752 0.429 83 1.189 0.497 75
I57 T06 1.371 1.018 89 0.900 0.709 75
I58 T07 0.916 0.642 85 1.190 0.510 75

"I59 T08 1.353 0.642 88 0.811 0.418 75
I60 T09 0.613 0.302 81 1.222 0.465 75.
I61 T010 1.320 0.445 89 0.632 0.315 75
I62 T011 0.930 0.516 89 1.275 0.915 75

 

 

283

APPENDIX C

 

 

 

 

 

 

 

 

 

 

I
Matrix Variable FALL TERM 1977 WINTER TERM 1978
Element Acronym Mean Std.Dev. N. Mean Std. Dev. N
I63 T012 1.238 0.567 86 0.841 0.472 75
I64 T013 0.942 0.491 82 0.945 0.366 75
I65 T014 1.008 0.413 82 0.907 0.407 75
I66 T015 0.841 0.431 82 1.128 0.357 75
I67 T016 1.220 0.516 81 0.805 0.429 75
I68 T017 0.460 0.382 .68 1.276 0.584 75
I69 T018 1.395 0.657 '77 0.319 0.158 75
I70 ONE031 .527 .701 93 0.474 0.660 75
I71 ONEO32 2.362 .788 94 2.333 0.577 75
I72 ONEO33 2.064 1.086 94 2.474 0.872 75
I73 Tw0031 0.805 0.689 77 0.483 0.642 75
I74 TWOO32 2.208 0.848 77 2.207 0.712 75
I75 TWOO33 2.803 1.083 76 2.509 0.932 75
I76 TREO3l 0.671 0.602 73 0.671 0.0 75
I77 TREO32 2.164 0.834 73 2.164 0.0 75
I78 TREO33 2.800 1.044 70 2.800 0.0 75
I79 FORO3l 0.701 0.631 87 0.536 0.735 75
I80 FORO32 2.000 0.812 86 1.786 0.672 75
I81 FOR033 2.741 1.014 85 2.607 1.061 75
I82 FVEO3l 0.704 0.697 81 0.553 0.654 75
I83 FVEO32 2.198 0.900 81 1.894 0.598 75
I84 FVEO33 2.617 1.056 81 2.277 0.911 75
I85 81x031 0.789 0.754 71 0.611 0.706 75
I86 51x032 1.914 0.775 70 1.759 0.677 75
I87 51x033 2.690 0.994 71 2.151 0.860 75
I88 svu031 0.623 0.708 77 0.709 0.710 75
I89 SVNO32 1.974 0.843 77 1.907 0.758 75

1190 svm033 2.558 1.219 77 2.345 0.948 75
I91 NINO3l 0.760 0.768 75 0.706 0.703 75‘
I92 NINO32 1.716 0.973 74 1.784 0.794 75
I93 NINO33 2.493 1.132 75 2.240 0.966 75

 

 

I

Matrix Variable

284

 

APPENDIX C

 

FALL TERM 1977

WINTER TERM 1978

 

 

 

 

 

 

 

 

 

Element Acronym Mean Std. Dev. N. Mean Std. Dev. N.
I94 FINO61 0.472 0.641 89 0.556 0.585 75
I95 FIN062 1.697 0.946 89 1.574 0.967 75
I96 FIN063 2.135 1.140 89 1.741 1.029 75
I97 FINO64 0.500 0.678 88 0.528 0.584 75
‘ I98 FIN065 1.307 0.975 88 1.907 0.826 75
I99 FIN066 2.591 1.301 '88 2.745 0.919 75
I100 Q1 20.892 3.333 102 24.323 3.821 75
I101 Q2 21.224 3.798 98 26.317 3.101 75
I102 Q3 21.202 4.542 99 25.320 3.049 75
I103 Q4 22.042 3.202 95 27.397 4.206 75
I104 Q5 24.516 3.091 93 28.000 3.437 75
I105 Q6 23.056 4.060 89 22.820 4.391 75
I106 Q7 23.387 3.962 93 27.276 3.872 75
I107 Q9 22.087 3.015 92 26.283 3.852 75
I108 Q10 22.363 3.237 91 23.667 3.676 75

 

 

APPENDIX D

FACTOR ANALYSES

285

TABLE D.l
PRETEST FACTOR ANALYSIS

 

 

 

 

 

 

 

 

 

B Matrix Fac- Eigen- PCTof.

Elements Variable Communality tor* value VAR CUM PCT
Bll = PRE001 .30445 1 2.50708 14.3 14.3
B12 = PREOOZ .74161 2 1.93106 11.0 25.3
B13. = PRE003 .42990 3 1.57459 9.0 34.3
B14 = PRE004 .34633 4 1.52678 8.7 43.0
B15 = PREOOS .49032 5 1.45112 8.3 51.3
B16 = PRE006 .38673 6 1.20621 6.9 58.1
B17 = PREOO7 .31443 ~7 1.15199 6.6 64.7
B18 = PRE008 .25685 .8 1.08548 6.2 70.9
B19 = PREOO9 .24983 9 .94324 5.4 76.3
B20 = PRE010 .65.78 10 .86626 4.9 81.2
B21 = PREOll .32335 11 .82853 4.7 85.9
B22 = PRE012 1.00227 12 .70668 4.0 90.0
B23 = PRE013 .17973 13 .63201 3.6 93.6
823 = PRE014 .61759 14 .59487 3.4 97.0
B25 = PRE015 .44553 15 .53464 3.0 100.0
B26 = PRE016 .33886

B27 = PRBOI7 '58876 Analysis: Principal Factoring**
B23 = PRE018 '76886 with.33 Iterations and Varimax
B29 = PRE019 “68459 Orthogonal Rotation.

B30 = PRE020 .30051

B31 = PRE021 .66861

B32 = PRE022 .33987

B33 = PRE023 .38825

B34 = PRE024 .89144

B35 = PREOZS .36339

B36 = PRE026 .90180

B37 = PRE027 .89388

B38. = PRE028 .29826

 

 

 

 

286

TABLE 9.1 CONTINUED
PRETEST FACTOR ANALYSIS

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

as elements B46 through B60.

B Matrix
Elements Variable , Communality
B39 = PRE029 .47304
840 = PRE030 .59189
B415 = PRE031 .59372
B42 = PRE032 .29020
B43 = PRE033 .58242
B44 = PRE034 .84169
Major Components Standard
Factor ("Pre" test items) Mean Range Deviation
1 21,23,24,26,31,33,34 0.0 -2.16 to 1.19 0.899
2 1,2,3,5,17,18,30 0.0 -1.40 to 1.61 0.897
3 1,3,5,7,14,21,32 0.0 -1.85 to 1.37 0.827
4 1,7,9,12,21,24,26,27, ,
30,33 0.0 -5.07 to 0.97 1.014
5 2,19,20,21,24,30,32 0.0 -1.70 to 1.41 0.863
3,17,18,24,26,31,34 0.0 -1.72 to 1.84 0.911
7 3,5,12,23,24,26,29,30,
34 0.0 -3.25 to 1.64 0.923
8 4,5,6,8,11,16,18,21,
22,23,24,30 0.0 —2.33 to 1.20 0.806
9 2,12,17,26,27,31,34 0.0 -1-85 to 1-63 0-864
10 698917919921926927 0.0 “1.66 to 1.51 0.933
11 2,3,6,14,18,21,27,28,
33 0-0 -l.46 to 1.76 0.820
12 2,9,15,17,18,23:24 0.0 —1.16 to 3.34 0.782
13 2.5.6.10»14,22,25 0.0 -1.61 to 1.17 0.821
14' 12,18,21,22,25,29,3l 0.0 _1.19 to 2.30 0.774
'15 5,6,7,10,11,17,18,
23,24,25,30’31,32 0.0 -2.28 to 1.40 0.795‘
*Pretest Factors are included in B **SPSS PA2 Option

 

 

287

TABLE D.2

AFFECTIVE QUESTIONNAIRE FACTOR ANALYSIS

 

 

VARIABLE COMMUNALITY
851 AFFOOl .33224
B62 AFF002 .58348
B63’ AFF003 .62353
864 AFF004 .33870
865 AFF005 .49620
B66 AFF006 .29129
B67 AFF007 .33136
B68 AFF008 .27740
B69 AFF009 .42686
70 AFFOlO .34254
71 AFFOll .26843
72 AFF012 .37906
73 AFF013 .35248
74 AFF014 .54894
75 AFF015 .28740
76 AFF016 .42324
77 AFF017 .21490
78 AFF018 .51831
79 AFF019 .39920
80 AFF020 .31958
82 AFF022 .54067
83 AFF023 .58784
84 AFF024 .24068
85 AFF025 1.00699
86 AFF026 .11840
87. AFF027 .11573
88 AFF028 .44775
89 AFF029 .29973
90 AFF030 .12821
91 AFF031 .27303
AFF032 .29683

www-wwwwwwwwwwwwwwwwwww

\D
N

 

 

FACT- EIGEN- PCT 0F

0R* VALUE VAR CUM PCT
1 3.98067 22.7 22.7
2 2.22991 12.7 35.5
3 1.83957 10.5 46.0
4 1.62837 9.3 55.3
5 1.46467 8.4 63.6
6 1.34250 7.7 71.3
7~ 1.24444 7.1 78.4
8- 1.10681 6.3 84.7
9 1.03649 5.9 90.7
10 .83154 4.7 95.4
11 .80332 4.6 100.0

 

 

 

 

 

 

ANALYSIS: PRINCIPAL FACTORING**
WITH 10 ITERATIONS AND VARIMAX
ORTHOGONAL ROTATION

 

288

TABLE D.2 (Continued)

 

 

 

 

 

 

 

VARIABLE COMMUNALITY
393 = AFF033 .48614
B94 = AFF034 .29880
B95 = AFF035 .22451
B961: AFF036 .38471
397 = AFF037 ' .33337
B98 = AFF038 .44918
B99 = AFF039 .44896
B100= AFF04O .60531
B101= AFF041 .28223
3102= AFF042 .44650
8103= AFF043 .27658
B104= AFF044 .25514
B105= AFF045 .28700
B106: AFF046 .42460
B107= AFFO47 .25481
B108: AFFO48 .23745
(Affective Questionnaire STANDARD
FACTOR MAJOR COMPONENTS Items, "AFF") MEAN RANGE DEVIATION
1 2,3,5,6,7,25,30,32,33,41 0.0 -1.61—2.08 0.907
2 10,20,22,25,31,33,36,42,45,46 0.0 -1.94-3.61 0.971
3 2,4,9,16,18,25,32,33,41,43,44 0.0 -1.75-1.63 0.838
4 1,4,9,25,28,34,37,39,40,42 0.0 -2.79-1.48 0.850
5 1,5,10,13,14,34,37,44,48 0.0 -2.42-1.42 0.821
6 2,7,9,24,33,34,36,39,40,43 0.0 -l.24-4.65 0.847
7 2,3,8,16,20,23,25,29,33,34,37,45,47 0.0 -2.33-l.84 0.829
8 2,3,10,14,22,23,24,25,28,31,33,35,37,40,
42,44,45 0.0 -1.74-2.01 0.840
9 3,5,22,23,25,40,41,42,45,46 0.0 -1.44-3.94 0.836
10 3,8,15,16,17,18,19,23,25,33,34,46 0.0 -1.97-2.45 0.818
11 2,5,12,14,19,33,38,40,42,46 0.0 -1.38-1.83 0.805

 

 

 

 

 

*Affective Factors are included
'_,**SPSS PA2 option in B as Elements B109 to Blll

 

TABLE D.3

BACKGROUND QUESTIONNAIRE FACTOR ANALYSIS

 

156

 

 

B Matrix

Elements Variable Communality
B121== BKGOOZ .54719
3123 = BKG004 .50201
3124 = BKGOOS .42654
B125 = BKGOO6 .37496
3126 = BKGOO6 . 75568
B1281: BKG009 .36752
B130 = BKGOll .28720
B131:= BKG012 .39482
B133== BKGOl4 .45750
B135 = BKG016 .25327
B136 = BKG017 . 34421
3137.= BKGOlS .39120
3138:: BKG019 .30725
B139 = BKG020 .27691
B140 = BKG021 .44576
B141 = BKGOZZ .40536
B143 = BKGOZ4 .59727
3144_= BKGOZ4 .30524
3145 = BKGOZ6 . 34726
B146 = BKGOZ7 .57897
B147.= BKGOZS .81401
B148 = 131(0029 .60365
B149 = BKG030 . 76220
B150 = BKG031 .69106
B151 = BKGO32 .56750
B152 = BKG033 .46199
B153 = BKG034 .55654
”B154== BKGO35 .51077
B155== BKGO36 .57872
B = BKGO37 .67004

 

 

 

 

 

 

 

 

 

 

Fac- Eigen- PCT of

torfr value VAR CUM PCT
1 4.61321 15.3 15.3

2 3.66936 12.2 27.5

3 3.01920 10.0 37.5

4 2.63990 8.8 46.3

5 2.26269 7.5 53.8

6 1.97362 6.5 60.3
'7 1.69051 5.6 65.9
’ 8 1.59578 5.3 71.2

9 1.57649 5.2 76.4
10 1.55107 5.1 81.6
11 1.34883 4.5 86.1
12 1.23278 4.1 90.2
13 1.18913 3.9 94.1
14 .92674 3.1 97.2
15 .84963 ,2.8 100.0
Analvsis: Principal Factoring**

with 21 Iterations and Varimax
Orthogonal Rotation

 

290

TABLE '9 . 3 CONTINUED

 

wwwwmwwwwwwwwwwwwwwwmwwwwwww

B Matrix
Elements Variable Communalitx
157 = BKGOB8 .36526
B158 BKGO39 .45860
159 = BKG040 .79317
160 = BKGO41 .67308
161 = BKG042 .44745
162 BKG043 .39290
163 = BKG044 .53208
164 BKG045 .59895
165 BKG046 .46947
166 = BKGO47 .78492
167 = BKG048 .64009
168 = BKGO49 .34698
169 = BKG050 .50186
170 BKGOSl .82095
171 BKGOSZ .45371
172 = BKG053 .31704
173 = BKG054 .59783
174 = BKGOSS .71381
175 BKG056 .65008
176 BKG057 .65604
177 = BKG058 .41004
178 = BKG059 .49227
179 = BKG060 .28790
180 = BKG061 .47901
181 = BKG062 .44370
182 BK0063 .63778
183 = BKGO64 .42908
184 = BKG065 .14452
185 = BKGO66 .55022
186 = BKG067 .46653

 

 

 

 

291

TABLE D.3 CONTINUED

 

 

 

 

 

 

 

 

Major Components .
(Background Question— ' Standard
Factor naire Items ”BKG") Mean Range Deviation
1 7,22,24,30,3l,32,47, 0.0 —2.31 to 2.03 0.923
50,51,56,57,67
2 5,7,22,26,33,38,41,44 0.0 -2.67 to 1.93 0.916
45,47,51,67
3 6,28,29,35,36,47,48,I 0.0 —2.38 to 1.71 0.863
51,55,56,61,67
4 4,27,30,31,38,40,45, 0.0'-2.48t61.80 0.865
46,47,48,50,51,52,67 ‘
5 4,25,31,32,33,34,35, 0.0 —l.77 to 2.32 0.858
44,47,51,53,63
6 2,7,28,30,33,34,39, 0.0 —2.26 to 2.63 0.879
51,55,56,57,58
7 7,19,24,28,34,35,44, 0.0 -2.12 to 2.40 0.870
48,55,56,59,60
8 4,7,9,12,22,24,27,31, 0.0 -2.43 to 1.85 0.843
34,40,41,43,45,47,51,
55,60
9 5,6,17,19,21,24,25,28 0.0 -2.70 to 1.69 0.919
29,32,33,36,40,44,45
55,57,58,60,6
10 4,28,29,30,31,34,40, 0.0 -1.68 to 2.43 0.896
41,42,48,52
ll 22,28,29,3l,34,44,47 0.0 —2.69 to 1.62 0.853
49,54,63
12 9,24,25,28,3l,34,40, 0.0 -5.23 to 1.26 0.862
43,47,48,51,54,55,57,
63,67
13 2,4,5,24,31,34,36,39, 0.0 —2.36 to 2.27 0.861
47,48,50,51,54,55,57,
61,63,67
14 5,7,17,24,26,27,32,34 0.0 -1.88 to 2.69 0.824
35,39,44,47,48,51,54,
55,56,57,58,66
15 5,7,12,19,22,24,30,34 0.0 —2.19 to 2.05 0.810
46,55,63,64
1_
*Background Factors are included **SPSS PA2 Option
in B as Elements B188 through 3202.

 

 

 

 

APPENDIX E

 

Model Coefficients

 

 

292
TABLE E.1

MODEL COEFFICIENTS FOR WEEK 1

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DEPENDENT VARIABLE, K{1_ Independent
- ’ Variable
Coefficients K0101 K0201 K0301 P K0401 K0501 Acronym
B .062 .046 .025
-31 (.203) (.172) (.127) CLASS
B .001 —.002
‘33 (-.193) (-.284) TRCR
B -.005 —.003 —.003 «.002
-35 (-.993) (—.885) (-.359) (—.430) CRERN
B .0002 .0002 .0001 70001
-36 (1.091) (1.129) (.995) (.608) MSUPTS
3. 13 .002
’ (.067) PRE003
B_,19 .035
’ (.059) PRE009
8 .166
"321 (.189) PRE011
8_ 22 -.23V
’ (—.196) PRE012
B .099
-324 (.189) PRE014
.083
(.268) PRE018
PRE020
PRE021
-.072
(-.159) PRE022
.039
(.117) PRE025
PRE026
—.052
(—.159) PRE028
PRF005
-.025
(-.149) PRF009
.045
(.093) AFFOOl
AFFOO3
AFF004
AFF008

 

 

 

 

 

 

 

 

 

 

 

293

TABLE E.1

MODEL COEFFICIENTS FOR WEEK 1

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DEPENDENT VARIABLE, K'- Independent
’3L Variable
C°effiCientS K0101 K0201 K0301 ’K0401 ’K0501 Acronym
&_ .111 .119
’69 .(.232) (.309) AFF009
B -.068
_;f’73 (-.184) AFF013
B__ .056
’77 (-.134) AFF017
B__ -.098 -.067
’78 (—.204) (—.218) AFF018
B .086
B -.033 —.203 -.145
7.80 (-.043) (-.241) (-.237) AFF020
B .112
".83 (.145) AFF023
3_ .054
.86 (.163) AFF026
B__ .064
.87 (.127) 435027
B -.033
‘391 (-.078) AFF031
8_ .059 0.072 —.043
’94 (.127) (-.174) (-.143) AFF034
B -.089
".100 (—.157) AFF040
d. .049
:107 (.118) AFF047
B__ .072 .086
.108 (.123) (.134) AFF048
8__ .040
,109 (.181) AFROOl
d. .017 .059
.116 (.099) (.220) AFR008
B__ .039 .047
__g .123 (.187) (.147) BKGOO4
d_ .023
...126 (.120) BKG007
d. .070 .052 .059
__g .135 (.174) (.146) (.230) BKG016
d. .123 .
.137 (.193) BKG018
B -.090 -;166
-;138 (-.142) (-.171) BKGOl9
g_. .149 .089 .197
.139 (.267) (.200) (.291) BKGOZO

 

294
TABLE E.1

MODEL COEFFICIENTS FOR WEEK 1

 

DEPENDENT VARIABLE K' Independent
Variable
Coefficients K0101 K0201 K0301 K0401 K0501 Acronym

8

BKGO42

.1 fag-$341 .

ONEO32

295

TABLE E.1

MODEL COEFFICIENTS FOR WEEK 1

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

__1.
DEPENDENT VARIABLE, Ki; Independent
' Variable
Coefficients K0601 K0801 K0901 K1001 K1101 Acronym
8_ -.001
.3 (-.138) TRCR
B__ -. 004
.5 (—.752) CRERN
6_ .0002 .0001
.6 (.869) (.172) ‘MSUPTS
B__ .096 -
.15 (.195) PRE005
3 -.112
".16 (-.133) PRE006
d_ .097 .137
119 (.152) (.239) PRE009
8.. .179 .567
.21 (.120) (.241) PRE011
6. -.440
..22 (-.215) PRE012
8_ —.218
.26 (-.257) PRE016
8.. .071 .168
.28 (.136) (.210) PRE018
E. -.509 “T120
.30 (—.344) (.142) PREOZO
B —.112
".32 (—.094) PRE022
Q. -.133
.34 (—.160) PRE024
5_ .100
___ ,35 (.184) PRE025
8_ -.088
.36 (—.114) PRE026
8_ :i116
.38 (-.146) PRE028
8_ :023
__f .45 (.232) PRETOT
B_ .044
- ¥;47 (.177) PRFOOZ
d_ -.041
__. .48 (-.152) PRF003
'B__ -.143’ -7034 .
.50 (-.314) (-.130) PRFOOS
B — . 041T
".51 (-.163) PRF006
B;_ 1 .551
.57 (.192) PRF012

 

 

296

TABLE E.1

MODEL COEFFICIENTS FOR WEEK 1

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

V
DEPENDENT VARIABLE. K'. Independent
4L Variable
Coefficients K0601 K0801 K0901 ' K1001 K1101 Acronym
3_. .122 "57035
.61 (.151) (-.110) AFFOOI
8__ -:040*
.63 (-.092) AFFOO3
E; .078
.64 (.168) AFF004
E. —.078
.65 (-.150) AFFOOS
3 .060 .090
".67 (.127) (.179) AFF007
8_ .074
.68 (.130) AFF008
8__ -.090
.78 (-.109) AFF018
8 -.155
‘.80 (—.165) AFF020
8_ .048
.91 (.102) AFF031
Q. .108 .079
-92 (.185) (.129) AFF032
B__ —.105 -.107 ,
.94 (-.207) (-.232) AFF034
B_ —.119
.95 (-.146) AFFO35
8. .179 .288
.96 (.159) (.162) AFF036
d. .100
.98 (.195) AFF038
6_ —.112 -.338 -.133
.100 (-.116) (-.222) (-.153) AFF04O
5.. -.102 -.074
.107 (-.144) (-.115) AFFO47
E. .139 .088
_1— .108 .(.116) _(.123) AFF048
B__ .106
.116 (.236) AFR008
4. .053
_g. .117 (.188) AFR009
d_ .030 .
.121 (.103) BKG002
&_ .066 .066 .070
.123 (.198) (.187)_ (.130) BKGOO4
8_ J .080 -.040
.126 (.256) (-.136) BKG007

 

 

 

297

TABLE E.1

MODEL COEFFICIENTS FOR WEEK 1

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DEPENDENT VARIABLE, K'. Independent
7:i— Variable
Coefficients K0601 K0801 K0901 K1001 K1101 Acronym
Q. .098
.128 (—.169) BKGOO9
8_ -.028
.134 (-.120) BKG015
B__ .128 .061
.135 (.193) (.154) BKG016
B -.143 -.262
‘;138 (-.141) (4.161) BKGOl9
B__ .194 .218 .267 .118
.139 (.273) (.290) (.234) (.174) BKG020
3.. -.054
.144 (-.116) BKGOZS
E. —.052
.150 (.234) BKGO31
B_ -.082
_.152 (-.135) BKG033
E. .449 .744
.156 (.257) (.269) _ BKG037
73_ .031 .037
.161 (.125) (.094) BKGO42
&_ -.053 -.075
.166 (—.209) (-.280) BKG047
B .065
".169 (.152) BKGOSO
6_ -.083 -.027
.171 (-.178) (-.102) BKG052
Q. -.048
.183 (:.194) BKGO64
6_ —.O66
.188 (-.251) BKFOOI
d_ .033 .076
.191 (.123) (.175) BKFOO4
B__ -.392
.196 (-.092) BKF009
8. —.104 .075 -.143
.198 (-.358) (.170) (-.311) BKFOll
'B_ -.064
__ .205 (-.174) PRBSBZ
B_ .001
.208 , (.136) - E1
4. .002
.209 (.129) NS
8.. .003
.217 (.077) MSUR

 

298
TABLE E.1

MODEL COEFFICIENTS FOR WEEK 1

 

1

 

 

 

 

 

 

 

 

 

 

 

 

DEPENDENT VARIABLE. K'. Independent
’ 4' Variable
Coefficients K0601 K0801 K0901 P K1001 ¥ K1101 Acronym
I__ .242 .389
. 1 (. 289) (. 289) LECOOl
I__ .255 .663
.13 (.106) (.171) 543002
I__ .152
.32 (.063) LTP001
1_ -.041 -.107
.70 (—.115) (-.190) ONEO31
IL_ . .040
.71 (.139) ONEO32
n_ .086
.8 (.096) K0800
u__ .199
.9 (.108) K1000
1L_ .027
.23 (.020) K2300
It. .166
.27 (.163) K2700
*II_ .184 -.204
.32 (.115) (—.122) K3200

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

TABLE E.1

MODEL COEFFICIENTS FOR WEEK 1

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

i:_ _—
' DEPENDENT VARIABLE, K'. Independent
4' Variable
Coefficients K1201 I Acronym
__—
fl. .0004
,8 (.160) GPA
B_ . 108
.19 (.158) PRE009
B .285
‘321 (.178) PRE011
B__ .121
.28 (.213) PRE018
B___ -o 047
.49 (-.178) PRF004
Q. -.063
.51 {-.212) PRFOOS,
E. .036
.57 (.104) PRF012
Q. .123
.71 (.224) AFF011
B . 093
‘173 (.175) AFF014
AFF029
AFF038
AFF047
AFR008
BKGOl6
BKGOZO
BKGO34
BKGO42
_EK0052
BKG053
BKG064
BKFOll
BKF012

 

 

 

 

 

 

 

 

 

 

 

300

TABLE E.1

MODEL COEFFICIENTS FOR WEEK 1

DEPENDENT VARIABLE. K'. Independent

0 ‘5'— Variable
Coefficients K1201 F ’ Acronym
8_ .035

 

 

 

 

 

 

 

 

.202 (.107) BKFOIS
d. -.020

..205 (-.077) PRBSBZ
B__ -.001

.209 (-.130) ' NS
6. .001 .

.210 (.084) TF
I__ .120

,1 (.126) LECOOl
u_ . 068

-8 (.1121 K0800

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

QQQQ‘Q
\OCDNO‘Ul-L‘UJN‘H

Q

010

Q
g...
.._-

012
013
014
015
016
017
018
019
020
021
022
023
024
025
026

u 027

-028
4 029
030
031
032
033

-0.
.0095
.0331
.2110
.5500
.3237

2697

.1059
.3810

0.4617
0.2390

301

TABLE E.1

Model Constants
Week-1

034
035
036
037
038
039
040
041
042
043
044

 

 

 

 

302

TABLE E.2

MODEL COEFFICIENTS FOR WEEK 2

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DEFENDENT VARIABLE. K'. Independent
L Variable
Coefficients K0802 K0902 K1002 K1402 Acronym
8_ -.137 -.256
.18 (-.093) (—.116) PRE008
9_ .093 .194 .366
.21 (.089) (.115) (.145) pRE011
fi_ -.120 —.239
774 (-.185) -.246) PRE014
8.. -.O80
.25 (-.099) PRE015
5— .040
.27 (.109) PRE017
B_, -.035
.33 (-.083) PRE023
B_ . 022
, 34 (. 057) PRE024
8_ -.o35 -.041 .
.39 (—.085) (-.082) PRE029
R. -.042 —.030
.43 (-.111) (-.067) PRE033
-.121 -.117
(-.306) (-.143) AFF003
.221
(.257) AFF007
.053
(.124) AFF009
.156
(.175). AFF010
—.104
(-.196) AFF019
.193
(.132) AFF021
.344
(.195) AFF023
-.062
(-.087) AFF025
.084
(.185) AFF027
—.037
(—.088 AFF028
-.034
(-..O81) ' AFF044
AFFO45
.045
(.197) AFROOl

 

 

 

 

 

 

 

 

 

 

 

303

TABLE E.2

MODEL COEFFICIENTS FOR WEEK 2

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

-r
DEPENDENT VARIABLE, K'. Independent
‘11 Variable
Coefficients K0802 K0902 Acronym
B .029
-.136 (.111) BKG017
R. ~ .056
.137 (.098) BKGOl8
8_ .072 .140
.149 (.213) (.275) BKG030
&_ -.043 —.O63
.153 (-.179) (-.221) BKG034
B_. -.027 -.046
.158 (-.079) (-.113) BKG039
B__ —.035 -.068
.183 _1-.110) (—.141) BKGO64
8_ -.029 -.067
.198 (-.087) (-.136) BKFOll
,1__ .141
.15 (.156) SASOO4
. 1_ -.053 -.135
__‘ .54 (-.083) (-.179) TC3
u__ .136
.1 (.152) K0101
u__ .118
.4 (.100) K0401
u__ .481
~ .8 .(.689) K0801
11- . 452
-9 (.604) _K0901
“_. .079 .257 .159
.10 (.109) (.4921._ (.148) K1001
B. .051 .219 .416
.11 .(.946) (.175) (.221) K1101
u__ -.064 -.097 -.219
.12 {-.098) {—.092) {—.138) K1201

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

{2

p a a a Q Q. Q 9'

0.3068

0.0122
0.6047

-0.0236

304

TABLE E.2

Model Constants
Week-2

034
035
036
037
038
039
040
041
042
043
044

305
TABLE E.3

MODEL COEFFICIENTS FOR WEEK 3

 

DEPENDENT VARIABLE , K' Independent

I Variable
Coefficients K0303 K0803 K0903 K1403 K1503 Acronym

18 —.043 . PRE008

PREOIO

-.034 -.080 —.115
~ — 1 — 130 PRE013

PRE012

.119

PRFOIS

AFF001

AFF007

 

306

TABLE E.3

MODEL COEFFICIENTS FOR WEEK 3

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DEPENDENT VARIABLE. K'. Independent
4L.1 Variable
Coefficients K0303 K0803 I K0903 ’ K1403 ‘ K1503 Acronym
B__ .351.
.105 (.222). AFF045
d_ 6.117:
__ .117 (6.280) AFR009
8— «.099
-123 (4.2001_, BKGOO4
B__ -.039 ‘
.124 {-.129) BKGOOS
'75. «.055
.130 (6.170) BKG011
B__ -.034 -.076 -.120
.131 {-.057) (-.100) (-.101) BKGOIZ
B__ .110
__ .139 (.104) BKGOZO
B__ .022 .093 .093
.148 (.045) (.149) (.096) BKG029
B__ .036
.155 (.080) BKGO36
B__ —.014
.177 (—.073) BK0058
s -.033
”.178 (—.121) BKGOS9
8_ —.011 -.038
.182 (-.033) (-.088) BKGO63
8. -i043
.195 (—.167) BKF008
6_ .010 .050
___ .197 (.050) (.127) BKFOlO
B —.024 -.016
__;_,198 (—.096) (-.060) BKF011
B__ -.029
.200 (:.115) BKF013
1 -.064 -.219
_g“.13 (:.036) (-.061) SASOOZ
_1 -.053 -.193
7.55 (-.086) (-.200) TC4
- )__ —.104
_ .70 (-. 208) ONE031
1__ .016 .
____.77 (.061) TRE032
—.067
.1:.199) TRE033
.020
(.215) 02_

 

 

 

 

 

 

 

307

TABLE E . 3

MODEL COEFFICIENTS FOR WEEK 3

DEFENDENT VARIABLE.

K'-

 

 

I Independent

 

 

 

 

 

 

 

 

 

 

‘45 Variable
Coefficients K0303 K0803 K0903 I K1403 K1503 Acronym
__
u__ .126 .085
.1 (.133) (.086) K0102
n. .253
___..2 (.216) K0202
R. .592 .062 .272
.3 (.551) (.055) (.157) K0302
u__ . -.158
.5 (-.102) K0502
u__ .945 .426
.8 (.938) (.211) K0802
u_. .045 .403
.9 (.058) (.505) K0902
u__ -.173
.12 (—.133) .K1202
vi. .019 .109 .615
.14 (.046) (.204) (.743) K1402
u__ .086
.27 (.092) K2702
u__ —.132
1,29 (—.130) K2902

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

308.
TABLE E.3

MODEL COEFFICIENTS FOR WEEK13

DEPENDENT VARIABLE, K1;

 

 

Independent

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Variable
Coefficients K1603 K1803 Acronym
3 -. 099
".20 (—.126) PRE010
e - 314
".21 (-.134) PRE011
B .126
‘.61 (.150) AFF001
8_ -.158
, 64 (-. 196) AFF004
3. I227
.69 (. 150) AFF009
B_ .258
.76 ( 308) AFF016
8_ .288 - 315
.81 (.108) (-.114) AFF021
8__ 110
.86 (.125) AFF026
B__ .153 '
.97 (.192) AFF037
‘71_ -.139
.102 (- 125) AFF042
R. -.151
.104 (-.196) AFF044
3. .090
.151 (.179) BK0032
B_ —.044
.163 (-.110) BKGO44
6_ .105
___ .174 (.237) BKGOSS
9_ -.127
BKF005
LECOOl
LE0002
SASOOZ
LTP003
T05
ONEOBZ

 

 

 

 

 

 

 

TW0031

 

 

 

 

 

 

 

309 -
TABLE E.3

MODEL COEFFICIENTS FOR WEEK 3

 

 

 

 
 

 

 

 

 

 

 

 

-:
DEPENDENT VARIABLE, K' Independent
Variable
Coefficients K1603 K1803 Acronym
1_ .130 _i—
.76 (.199) TRE031
1__ .246
.100 (.218) Q1
1__ .018
.101 (.181) Q2
14.. -. 544
.2 (-.257) K0202
E. .908
.4 (.340) K0402
“—- -.198
.12 (-.136) K1202

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

-0.0776

0.1471

0.5022

0.6422
0.7196
-0.9258

1.1521

310

TABLE E.3

Model Constants
Week-3

034
035
036
037
038
039
040
041
042
043
044

 

 

 

311

TABLE E.4

MODEL COEFFICIENTS FOR WEEK 4

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

_.
DEPENDENT VARIABLE. K'. Independent
344* Variable
Coefficients K0304 K0404 K0504 K0904 K1604 Acronym
8_ .6277 .035 .037
.15 (.090) (.083) (.086) PREOOS
B__ -.O68 -.067
____.16 (-.100) (-.091) PRE006
3:. -.045 -.041 —.Q67 -.058 -.143
.23 (-.093) (7.110) .(—.133) (7.111) (-.212) PRE013
B__ -.116
.24 (—.253) PRE014
B_. —.087
.28 (-.154) PRE018
IR. —.070
.29 (3.123) PRE019
d. -.035 -.035
.37 (-.113) (—.780) PRE027
d. .035
.46 (.118) PRFOOl
8.. -.038
.50 (-.164) PRF005
5_ —.020
1 .52 (-.092) PRF007
8_ -.161
.90 (-.103) AFF030
B__ -.050
.94 (-.091) AFF034
d. .023 .020 .030 .075
____.98 (.053). (.059) (.064) (.122) AFF038
_. -.029 ~
___..123 (—.076) BKGOO4
8.. .127
__. .128 (.261) BKGOO9
d. .081 .072 .193 .097
.138 (.098) (.113) (.224) (.109) BKGOl9
@— .058
A .147 (-082) BKGOZS
R. .009 .013 .024 .010
« .168 .I.048) (.085) (.120) (.050). ,EKGO49
3.. .018 .018 .040 .022
_ 4191 L081) 1. 104) (.174) (.093) BKFooz.
4__ .036 .
.35 (.032) LTP004
I_ -.038
-46 (2.094 p020041
. I. -.017 -.027 -.026 -.026
.59 ‘ (-.056) (-.116)_ (-.085) (7.082) T08

 

 

 

 

 

 

 

 

 

312

TABLE E.4

MODEL COEFFICIENTS FOR WEEK 4

 

DEPENDENT VARIABLE, K11—

Independent

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Variable
Coefficients K0304 K0404 ’ K0504 ’ K0904 ’ K1604 . Acronym

1__ .007 .007 .023

.72 (.040) (.051) (.125) 0NE033
1__ .012 .023

.80 (.046) ' (.071) FOR032
1__ .009 .012 .016 .012

.81 (.049) (.080). 11.084). (.057) FOR033
u_ .793 .

.3 (.898) K0303
u__ .027 .805

34 (. 046) (. 804) K0403
u__ .450

.5 (.5011 K0503
n__ -.074
' ,6 (-.065) K0603
11.. . 784

.9 .8561 K0903
It. .101

.12 (.101) K1203
“—- .074

.15 (-096) K1503
“—- .027 .028 .133 .034 .520

.16 (.053) (.071) (.250) (.061) .730) K1603
u__ .050 .040 .042 .047

.18 (.103) (.106) (.083) (.089) K1803
IL .025

 

K2303

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

' 9

99999999
komNOUI-bWN-H

010
011
012
013
014
015
016
017
018
019
020
021

 

-0.0471
-0.0043
-0.0735

-0.0201

0.3653

313

TABLE E.4

Model Constants
Week-4

 

 

034
035
036
037
038
039
640
041
042
043
044

 

 

 

 

314

 

TABLE E.5

MODEL COEFFICIENTS FOR WEEK 5

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DEPENDENT VARIABLE, Kt; ‘ Independent
Variable
Coefficients K0805 K0905 K1005 K1405 K1505 Acronym
B__ -.016 -.052
.14 (-.045) (—.114) PRE004
E. -.004
.28 _(e.011) PRE018
d. -.005
.34 (:.013) PRE024
B__ -.088
.39 (-.151) PRE029
B__ .041 .101 .094
.41 _.(.094) (.198) (.157) PRE031
B__ -.015 -.031
-66 (-.039) .(+.063) AFF006
B__ .041 .094
.68 (.088) (.145) AFF008
d. .006
.69 (.016) AFF009
d. -.041
.76 (-.081): AFF016
9.. -.085
.77 (—.133) AFF017
R. .027 .062
.78 (.063) (.105) AFF018
8_ .076
.91 (.128) AFFO31
3.. .020
__ ..92 (.050) AFF032
8.. .033
.93 (.048) AFF033
R. -.056 —.080
.98 (-.118) (-.124) AFF038
__ .028
.103 (.034) AFF043
B_ -.039
_., .116 (—.136) AFR008
8_. .008
.130 11.055) BKGOll
BKG012
-.113
(-.144) ‘BK0018
-.028
(-.084) BKGO33
BK0056

 

 

 

 

 

 

 

 

315

TABLE E.5

MODEL COEFFICIENTS FOR WEEK 5

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

-:
DEFENDENT VARIABLE. Kf1___ Independent
Variable
Coefficients K0805 K0905 ,K1005 .Kl405 K1505. Acronym
B —.001 1""""'4"""
73177 {—.009) BK0058
d_ .094
.181 (.194) BKGO62
d; .004
.188 (.021) BKFOOl
B__ -.003
.194 (-.016) BKF007
3.. .018
.199 (.063) BKF012
.I__ .011
.3 (.024) LEC003
1__ .029 .055
.5 (.061) (.085) . LE0005
"1__ .028 .059 .054
.20 (.054) (.089) (.069) SASOO9
_1__ -.075
‘ .60 (—.O80) T09
I__ —.001
.103 (—.013) 04
—.134 -.284
(r.121) (-.186)_ K0204
.144 .465
(.134) (.314) K0304
K0504
K0604
K0804
-.185
(e.135) K0904
.789
(.787) K1004
.144
(.135) K1104
-.045 .498 -.090
(-.076) (.727) (-.111) K1404
—.047 -.054 .456 .
(-.080) (:.079) (.564) K1504
.130
(.145) K1604
K1804

 

 

 

 

 

 

 

 

 

 

 

 

 

 

316

TABLE E.5

MODEL COEFFICIENTS FOR WEEK 5

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

-;
DEPENDENT VARIABLE. K'- Independent
. 5* Variable
CoeffiCIents K1905 K2005 Acronym
5. -.117
1.14 (7.218) PRE004
5. .101
.19 (.133) pREoog
8.. .138
.31 (.215) PRE021
8_ —.166
.36 {-.171) PRE026
8.. .071
.38 (.113) PRE028
R_ -.105
.62 (-.154) AFF002
8.. -.075
.66 (-.132) AFF006
73— .062
.68 (.111) AFF008
III- .066
.78 (.129) AFF018
8.. .124
-89 (.198) Appozg
8.. —.198
.90 (-.115) AFF030
6_ .070
.92 (.116) AFF032
B_. -.088
.98 (-.159) AFFO38
8_ .131
__4 .100 (.113) AFF040
B__ .062
.111 (.173) AFR003
B__ .056
.125 (.140) BKGOO6
B__ .043
._; 3130 (.192) BKG011
8_ -.070
- .152 (-.180) BK0033
R. .122
.1, .153 (.295) BK0034
d_ -.031 .
.175 (-.127) BK0056
‘73.. .081
.188 (.250) BKFOOl
d. .056
.189 (.174) BKF002

 

 

 

 

 

MODEL COEFFICIENTS FOR WEEK 5

317

TABLE E . 5

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

‘?
DEPENDENT VARIABLE, K'. Independent
E; Variable
Coefficients K1905 K2005 Acronym
8_ —.0§8—‘
,192 (-.109) BKFOOS
A_, .064
,3 (.095) LEC003
L .056
L5 (.101) LECOOS
A_. .126
,20 (.162) SASOO9
7k. -.111
.21 (-.094) SASOlO
A_, -.O67
.58 (-.145) $07
A_, .016 .012
,103 (.175) (.152) q4
u .213
~L3 (.161) K0304
u_ .266
__ 95 (.178) K0504
H. -.239
,6 (-.232) K0604
“_. .276
28 (~199) K0804
H— -.251
9 9 (- . 214) K0904
R— -.113 -.O68
1315 {-.133)_ (-.02§21 K1504
I“; -.179
,19 (-.147) K1904

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

{2

0.1129

0.1644
0.1326

0.1593
0.1483

0.2497
0.5722

318

TABLE E.5

Model Constants
Week-5

 

034
035
G36
037
038
a39
040
041
G42
G43
G44

 

 

 

 

 

 

 

TABLE E.6

MODEL COEFFICIENTS FOR WEEK 6

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

F
DEPENDENT VARIABLE, K'. Independent
%—— Variable
Coefficients K0106 K0406 K0606 P K2206 Acronym
B_ -. 047
.33 (-.101) PRE023
B -.035
__;—,64 (-.088) AFF004
6_ .017 .086
168 (.039) (.135) AFF008
B_ -. 031
,75 (-.064) AFF015
6_ -.016 -.077
,84 (-.041) (-.138) AFF024
5_ —.023 -.110
.87 (-.055) (-.179) AFF027
0_ —.024
,91 (-.077) AFF031
B__ -.021
,95 (-.039) AFF035
B_ -. 017
,110 {-.086) AFROOZ
TL. .013 .064
,119 (.055) (.185) AFR011
*B_ .019
,121 (.098) BKG002
B__ .068
,138 (.108) BKG019
B__ —.022 -.110
,144 (—.058) (-.198) BKGOZS
6_ .019 .098
,145 (.046) (.163) BKG026
a. .030 .147
,148 (.056) . (.190) BK0029
3.. .008
_:165 (.061) BKGO46
8.. .018 .089
.4 .189 (.087) (.293) BKFOOZ
8_, -.020
- .201 (-.087) BKF014
A__ .023 .115
__ ,22 (.033) (.112) SASOll
11 .081
,23 (.110) SASOlZ
1. -.016 -.077
.47 (-.043) (-.139) szoos
A__ .013
,81 (. 069) FOR033

 

 

 

 

 

 

 

 

320

TABLE E.6

MODEL COEFFICIENTS FOR WEEK 6

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DEPENDENT VARIABLE, K'. Independent
iL— Variable
Coefficients K0106 K0406 K0606 K2206 Acronym
7E; .005
,101 (.138) Q2
1__ .003
,104 (.055) 05
H. .810
.1_ (.9271, K0105
u_. .896
,4 (.907) K0405
u_. .786
16 _1,962) K0605
u__ .022 .110
.12 (.032) (.105) K1205
u__ -.050 .057
_,14 (-.083) (.071) K1405
u__ .060 .299
,15 (.089) (.304) K1505
u_, -.025 -.037 -.025 —.115
,16 (—.035) (-.069) (-.035) (-.110) K1605
.019
(.039) K1805
.023
(.048) K1905

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

' 9

99999999
CWNO‘UI-l-‘wND-e

0.4817

321

TABLE E.6

Model Constants
Week-6

034
035
036
037
038
039
040
041
042
043
044

 

 

 

 

322

TABLE E.7

MODEL COEFFICIENTS FOR WEEK 7

 

DEPENDENT VARIABLE,

K'o

 

 

I Independent

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

‘7i— Variable
Coefficients K0407 K1607 K2307 K2407 Acronym
B —.
‘317 (-.198) PRE007
B__ .020 .163
,20 (.069) (.313) PRE010
iii .044
,24 (.102) . PRE014
B__ .136
,31 (.253) PRE021
d. -.192
,42 (-.188) PRE032
Q. .092
_,46 (.195) PRFOOl
B__ -.027
,60 (—.112) PRF015
5. -.022
.64 (-.056) AFF004
6_, -.093
,67 (-.184) AFF007
E .029
__",76 (.071) AFF016
Q. —.056 .260
,83 (-.071) (.147) AFF023
B__ -.030
,86 (-.071) AFF026
8_ .206
,93 (.198) AFF033
&_ 5 -.025 . —.240
,108 (-.059) (-.303) AFFO48
&_ -.007 - 058
,125 (—.039) (-.173) BKG006
8_, .009
,167 (.046) BKGO48
__ -.167
__1 .184 (-.250) BKGO65
fi_ —.015
.189 (-.073) BKF002
‘B__ —.093
__ .197 (—.196) BKFOlO
8__ -.108
,_,201 (-.209) - BKF014
L -.114
,6 (-.119) LECOO6
X_. .015
.23 (.021) SA8012

 

323

TABLE E.7

MODEL COEFFICIENTS FOR WEEK 7

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DEPENDENT VARIABLE, K‘- Independent
+ 1 Variable
Coefficients K0407 K1607 K2307 K2407 Acronym

_. .095

_,24 (.109) SASOI3
__ -.041 -.294 -.454

,38 (-.050) (-.197). (-.179) LTP007
__ * .066
__ -.008 -.O73

,65 (-.025) (-.121) TC14
__ .020

.102 (.215)_ 03
__ .040 .200

.2 51-055) (.148) K0206
_. -.058

,3 (-.058) K0306
__ .880

,4 (.932) K0406
._ .026 .351

.6 (.036) (.271) K0606
_ . 407

,8 (.156) K0806
._ .102

,9 (.095) K0906
__ .110 -.474

,10 (.120) (-.230) K1006
_ . 433

,15 (.289) K1506
_. .628

,16 (.887) K1606
_. —.012 -.173

,19 (-.026) (-.206) K1906

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

QQ
\DmNChU-L‘UJN‘H

Q Q Q Q Q

O10
O11
O12
O13
O14
O15
O16
O17
O18
O19
O20
O21
O22
O23
O24
O25
O26
-.O27
. AO28
T'O29
O30
O31
O32
O33

0.0836

0.1164

0.7302
0.2944

324

TABLE E.7

Model Constants
Week—7

O34
O35
O36
O37
O38
O39
O40
O41
O42
O43
O44

 

 

 

325

TABLE E.8

MODEL COEFFICIENTS FOR WEEK 9

L

DEPENDENT VARIABLE, Kl;

 

 

I

ndependent

Variable

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Coefficients K0308 K0508 K0808 K0908 K1 508 Acronym
5. -.025
-20 (-.O65 PRE010
3.. —.O63 -.052 —.178
,25 (-.069) (-.O76) (-.16O) PRE015
B :018
_,27 (.054) PRE017
8_ -.024 .
,66 (-.O55) AFF006
5_ .022
,69 (.072) AFFOO9
B__ -.070
,90 (-.O65) AFF030
8.. .054 .102
.93 (.067)_ (.103) AFF033
—7{_ .052 .050 .114
.106 41.069) (.088) (.124) AFFO46
8.. .050 .043 .078
,107 (.091) (.102) . (.115) AFFO47
B__ .042
.146 (.096) BKG027
B__ —.033
,148 (-.074) BKG029
8.. -.012
,151 (-.056) BKG032
3_. .029
__ ,154 (.100) BKG035
8.. .019
,161 (.100) BKGO42
771— —.020
,167 (-.101) BKGO48
'7{; —.010
.175 (:.059) BKG056
B_. .008
__g .177 (.057) 8K0058
d. .037
~ .179 (.117) BKGO6O
, d. -.008
__ _,183 (-.052) BKGO64
B__ .015 .027 .
,197 (.089) (.102) BKFOlO
A__ —.077
.24 (—.O71) SAS013
1.. .037
___ ,28 (.068) SASOl7

 

 

 

 

326

TABLE E.8

MODEL COEFFICIENTS FOR. WEEK 9

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DEPENDENT VARIABLE, K'. Independent
‘1 Variable
Coefficients K0308 K0508 K0808 K0908 K1508 Acronym
4_ .255
.40 (.148) LTP009
I_ .028
,49 (.075) PQZ007
&_ -.OO4
. 101 L. 0710 42
A__ -.009
,102 (-.169) Q3
u__ .055 .252
,2 (.055) (.196) K0207
H. .993 .137 .349
.3 (.984) (,178) (.281) K0307
u__ —.O95 .827 -.O76 -.331
,5 (-.O98) (.891) (—.102) (—.278) K0507
1{_ .080 .889 .077
_,8 _(.067) (.895) (.085) K0807
.715
(.859) K0907
-.O37 .I41
(—.056) (.133) K1107
.062
(.070) K1207
-.045 —.O36 -.114
(-.058) (-.059) (-.115) K1407
.037 .583
(.065) (.694) K1507
.064
(.066) K1607
—.O46
(-.122) K1807
.055
111089) K1907

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

327

TABLE E.8

MODEL COEFFICIENTS FOR WEEK 9

 

 

 

 

 
  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DEPENDENT VARIABLE, K{1_ Independent
Variable
Coefficients K1608 K1908 K2308 K2508 K2608 Acronym
B__ -.023
,15 (—.050) PREOOS
B_ .088
4,18 (.077) PRE008
8_ .117
,19 (. 1g) PRE009
8_ -.115
,25 (-.136) PREOIS
B_, -.134
,34 (-.137) PRE024
B -.070
",35 ¥(-.258) pRE025
8 .037
",43 (.143) PRE033
8__ .031
,44 (.055) PRE034
8_ .019
,48 (.135) PRF003
B_ -.096
,66 (-.154) AFF006
B__ -.024 .111
,71 (-.101) (.121) AFFOll
B__ .033 -.037
,72 (.076) (—.158) AFF012
8_ .069
,74 (.267) AFF014
B__ —.121
___,,85 (-.133) AFF025
B_ - . O48 -. 105
,92 (-.165) (-.160) AFF032
8_ .099 .416
,93 (.131) (.224) AFF033
B__ .098 .321
‘;7 ,106 (.139) (.186) AFF046
B_ . 086 . 285
, ,107 (.167) (.226) AFFO47
B__ .015 .084
.1. ,111 (.069) (.159) AFR003
B_ -. 022
,113 (-.155) AFROOS
8_ —.041
,116 (-.292) AFROO8
&_ .019
,119 (.070) AFROll

 

 

 

 

 

 

 

 

 

 

 

328

TABLE E.8

MODEL COEFFICIENTS FOR WEEK 9

DEPENDENT VARIABLE, K'o

_;

Independent

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Variable
Coefficients K1608 K1908 K2308 K2508 K2608 Acronym
E .046
",126 (.297) BKGOO7
8.. .079
___ -138, (.155) BKG019
8_ .074
-154 (.138) BKG035
B__ -.016
,183 (-.O64) BKCO64
B__ .075
,193 (.250) BKFOO6
3_ .016
.195 (.061) BKFOO8
B__ .015 .067
,197 (.075) (.136) BKFOIO
8.. .024
-200 (.174) BKF013
x -.080
‘,6 (-.134) LECOO6
1__ -.044
.9 (—.185) LECOO9
1_- —.077
.25 (-.083) SASOl4
A__ -.204
.27 (-.241) SAS016
0.. .085 .178
__ -28 (.105) (.182) SASOl7
1.. .227
.40 (.118) LTPOO9
A_ -.027 —.095
,67 (-.O76) (-.110) TC16
4.. -.013 —.015
,762 ,(.-069) 1,-140) NINO33
*_. .012
__1 ,106 (-181) .99
n_ .237 .133 .970
.3 ,(.251) (.217) (.418) K0307
' n__ —.177 .245 «.649
__ 1.5 (2.195) 11.184) 114.291) K0507
n. .114 .556
.8 ,(.103) (.203) K0807
“_. —.355
,9 (:1237) _K09O7
“_. -.O63 ‘ -.O63 .295
.11 41:.064 11:.120) (.250) K1107

 

 

 

 

329
TABLE E.8

MODEL COEFFICIENTS FOR WEEK 9

DEPENDENT VARIABLE, K'i

Independent

 

 

Variable

 

 

 

 

 

 

 

 

 

Coefficients K1608 K1908 K2308 K2508 K2608 Acronym
u__ -.237
.12 (-.143) K1207
n__ —.066 -.128 -.280
___1114 (-.087) (-.261) (—.151) K1407
u__ .074 .184
.15 (.178) (,197) K1507
1L. .745
.16 (.782) K1607
“_. -.O71 —.030 .046 —.301
.18 (—.154) (—.053) (.152) (-.266) K1807
n_. .683
.19 (.925) K1907
n_ .048
,20 (.053) K2007
u__ .094
,22 (.099) K2207
u_. .081
.23 (.171) K2307

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

330

TABLE E.8

MODEL COEFFICIENTS FOR WEEK 9

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DEPENDENT VARIABLE, K'. Independent
. *;—— Variable
Coefficients K2708 K2808 K2908 I K3008 k K3108 Acronym
3.. .057
__.hlL (.104) PREOOZ
d. .152
,13 (.252) PRE003
A. .135 .212
.19 (.252) _(.313) PRE009
B__ .076
,27 (.138) PRE017
IL_ -.124
,30 (-.157) pRE020
d. -.064
.33 (-.109) PRE023
d. -.143
,61 (-.142) AFF001
8.. -.241
.68 (-.224) AFFOO8
A. -.116
,72 (-.2O8) AFFO12
.271
(.255) AFF017
.249
(.259) AFF028
.094
(.097) AFFO34
.247
(.136) AFF040
—.176
(-.140) AFF041
-.073
(—.087) AFFO48
BKG014
.042
(.090) BKG016
-.100
(-.159) BKG032
-.161
(:.214) .BKCO33
-.O65 .
(—.O97) BKG038
.110
.175) BKG060
BKGO65

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

331

TABLE E.8

MODEL COEFFICIENTS FOR WEEK 9

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DEPENDENT VARIABLE, K'. Independent
71. Variable
Coefficients K2708 K2808 K2908 K3008 K3108 Acronym
"7{_ .042 -.038 .087
,194 (.132) (—.126) (.160) BKF007
B__ .053
,196 (.232) BKF009
C. -.055
.202 (:.212) BKF015
A_ -.152 —.114
,6 (-.144) («.108) LEC006
'I_ «.105
.7 (-.183)' LEC007
A -.419
“,25 (-.212) SASOl4
A .064 .112
",27 (.096) (.133) SASOl6
1__ -.169
____,28 (—.O98) SASOl7
A__ -.204 -.540
,38 (-.163) (—.343) LTPOO7
A .448 .360
‘;40 (.294) (.188) LTP009
A -.094 .382 -.219
(.395) (-.227) PQZOO9
-.065
(-.121) T013
-.219
(-.193) TC14
-.015
(—.O70) SVNO33
NIN032
-.020
(- 160) 02
.014
(.134) 03
.012
(.140) 05
.022
(.189) 96
K0107
-.406
(-.165) K0307
.578
(.169) K0407

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

332
TABLE E.8

MODEL COEFFICIENTS FOR WEEK 9

DEPENDENT VARIABLE, K' Independent

 

 

 

 

 

 

 

 

 

 

 

 

 

. ‘i ' Variable
R. .634
.5 (.269) K0507
n.. .130
.9 (.087) K0907
“_. .204 -.172 .318
.12 (.196)_ _(—.175) (.182) K1207
u__ -.334
,14 (-.287) ' K1407
“_. -.281
..16 (-.191) K1607
III; .190 -.162
.18 .(.267) _(:.241) K1807
1(_ -.147 -.211
,19 (—.166) (-.134) K1907
n.. .356 .231 .205 .311
.20 (.412) (.212) (.105) (.161) K2007
n_. .089 .281
(.297) , K2207

 

K2707

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

333
TABLE E.8

MODEL COEFFICIENTS FOR WEEK 9

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

_..
” , . DEPENDENT VARIABLE, K'i Independent
' Variable
Coefficients K3208 K3308 Acronym
C. -.135
,20 (-.198) PRE010
C. .063
,49 (.300) PRF004
3:. -.112
,66 (—.146) - AFF006
C. —.101 z
..71 (-.231) AFFOII
6.. -.336
,90 (—.177) ‘ AFF030
C. -.146
.100 (- 176) AFFO40
B__ .033
.143 (.128) BKG024
8.. .100
.144 (.235) BKG025
B__ .093
.161 (.274) .- BKG042
8.. -.O44
- . 165 (-. 222) 131(9045
C. -.085
,167 (-.243) BKGO48
3.. -.O49
.198 (-.195) BKFOll
A .216
..".25 (.157)_ SASOl4
‘ I. -.339
.28 (r.281) SAS017
A. .165
.49 _(.247) p02007
.A., .049
-89 (.192) SVN032
.K__ -.023
... .101 (-.261 02
.)__ .020
.103 (.193) Q4
,. ‘.I_. -.009
‘ ..._.104 (3.134) 05
F__ .298 .327
.2 (.259) (.185) K0207
n_ .287
.5 (.268) K0507
u__ -.172
.._ .9 (-.143) K0907

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

MODEL COEFFICIENTS FOR WEEK 9

DEPENDENT VARIABLE, K

TABLE E.8

 

 
 

Variable

 

 

 

 

 

 

 

Coefficients K3208 K3308 Acronym
E_ .122
.11 (.129) 31107
H. .121
,12 (.099) K1207
“— -.259
. 14 (—.190) K1407
“_. .117
,15 (.101) K1507
u.. .237
.16 (. 138) K1607
“.— .254
419 (.231) K1907

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Independent

 

 

 

 

 

335
TABLEiEB

Model Constants

Week-9
O 1 O34
o 2 o35
‘0 3 0.0082 O36
a 4 O37
O 5 0.1277 O38
O 6 O39
O 7 _ O40
a 8 0.0910 : o41
o 9 0.0604 ‘ o42
O10 O43
all O44
O12
O13
O14
O15 0.0625
O16 0.0235
O17
O18
O19 0.1921
O20
O21
O22
O23 0.5670
O24
O25 0.5010
926 0.2058
’ O27 0.0794

.-O28 0.8656

.1!” o29 0.0569

O30 0.4013
O31 0.5087
O32 0.9271
O33 0.5583

 

 

 

336

TABLE E.9

MODEL COEFFICIENTS FOR WEEK 10

DEPENDENT VARIABLE, K' Independent
Variable

Coefficients K0209 K0909 K2309 K2509 K3109 Acronym

 

—

-.119 PRE006

PRE018

PRE029

 

 

 

337

TABLE E.9

MODEL COEFFICIENTS FOR WEEK 10

 

 
  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DEPENDENT VARIABLE, K'. Independent
7*; Variable
Coefficients K0209 K0909 K2309 K2509 K3109 Acronym
B .—
",130 (-.148) BKG011
C. .009
.143 (.055) BKG024
B_ . 054
.145 (. 144) BKG026
C. -.023
.152 (—.083) BKG033
C. .008
,161 (.069) BKGO42
8 —.031
‘.163 (—.163) BKG044
s —.032
‘,164 (-.088) BKG045
B_ . 040
___ -168 (.223) BKG049
B_ -. 032
,178 (—.090) BKG059
8.. .011
.._ .188 (.086) BKF001
C. .027
.191 (.135) BKF004
C. .040
,195 (.192) BKF008
C. .021 -
,196 (.112) BKF009
8.. .070
.199 (.345) BKF012
8.. .060
__. ,201 (.173) BKF014
. I. -.024 —.027
-7 (:.054) (:.104) LEC007
A__ -.053
.. .10 (-.089) LEC010
A__ .048 .033
. .28 (.075) (.075) SASOl7
A__ -.031
.. ..29 (r.053) SAS018
A__ .021
..30 (.041) SASOl9
A__ .108
.40 (.086)_ LTP009
I. .215
___ ,41 (.073) LTPOlO

 

 

 

 

 

TABLE E.9

MODEL COEFFICIENTS FOR WEEK 10

DEPENDENT VARIABLE, K'.

‘JL

Independent

 

 

Variable

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Coefficients K0209 K0909 K2309 I K2509 ’ K3109 Acronym
. A. .023
.150 (.093)’ 202009
A .016
._;T.66 (.060) T015
A -.040
".94 (-.090) FINO61
X.. .009
.103 L. 157) 94
I. .010
.nM (J13 05
A__ -.005
.107 0.095) Q9_
n_. .776 .188
.2 (.829) (.121) K0208
n. .064
..3 (.105) K0308
n_. .870
,9 (.949) : K0908
“_. -.055
__.12 (-.084) K1208
n.. .030 .084
.14 (.054) (.115) K1408
-II. .065
,22 (.104) K2208
n. .876
.23 (.876) K2308
C. .069 .024 —.052
____.24 (.170) (.086) (-.077) K2408
n.. .325 .130
___ ,25 (.493) (.120) K2508
“—- -.039 -.067
.26 (-.101) (-.104) K2608
n__ -.152
... .27 (-.111) K2708
n. .069
.28 (.110) K2808
u__ .050
.30 (.135) K3008
U.. .522
.31 (.854) K3108
n_. -.051
.32 (-.063) K3208

 

 

 

 

339

TABLE E.9

MODEL COEFFICIENTS FOR WEEK 10

 

DEPENDENT K' Independent
. ' Variable
Coefficients K3209 K3409 K3509 K3609 K3709 Acronym

._ .065

—.070 ‘ -.115

AFF030

 

 

 

 

340

TABLE E.9

MODEL COEFFICIENTS FOR WEEK 10

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

J V
DEPENDENT VARIABLE, K'- Independent
‘9 Variable
Coefficients K3209 K3409 K3509 K3609 K3709 Acronym
8_ -.092
.145 (-.222) BKG026
B__ -.093
___..162 (-.179) BKGO43
3.. -.020 -.035
.163 (-.129) (:.176) BKG044
8_ .112
,164 (.198) BKG045
8.. -.061 -.049
.167 (-.304) (-.238) BKGO48
C. .054
.168 _(.285) BKG049
_. —.131
,171 (-.248) BKG052
B__ .051
,179 (.265) BKCO60
B__ -.082
.182 (-.223) BKG063
8_ .049
.191 (.227) BKF004
C. -.015
,195 (—.090) BKF008
8_ .020 .048
,199 (.121) (.223) BKF012
B_ '-.-. 039
,200 (—.237) BKF013
A. -.056
_._ .7 (-.134) LEC007
A —.054
SASOl6
SAS019
.263
(.286) PQZ009
-.366
(—.388) PQZ010
TC18
.047
(.159) FINO61
-.036
(-.180) FIN062
Q4

 

 

 

 

 

 

 

 

 

 

 

 

 

 

341.

TABLE E.9

MODEL COEFFICIENTS FOR WEEK 10

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

W
DEPENDENT VARIABLE, K'i Independent
’ Variable
Coefficients K3209 K3409 K3509 K3609 K3709 Acronym
A__ .008 .031 .044
,107 (.178) (.203) (.298) Q9
u_. .180
,2 (.234) K0208
u__ -.099
,5 (-.129) K0508
H. .496
,8 (.172) K0808
u_. .244
,11 (.123) K1108
u__ .128 -.509
.12 (.240) {-.305) K1208
u__ .135
,14 (.174) K1408
“_. -.174 -.514
,15 (—.291) (-.263) K1508
“_. —.345
,16 (—.140) K1608
“_. .068
__ ,19 (.104) K1908
u__ .100 .374
,20 (.169) (.195) K2008
n. —.088
,25 (—.125) _._» K2508
u__ .067
._ ,26 (.155) K2608
u__ -.070
___ ,30 (-.171) K3008
H. .037
__. ,31 {—.171) K3108
V_. .126
,32 (.189) K3208
u~_ .118
,_. ,33 (.202) K3308

 

 

 

 

342.
TABLE E.9

MODEL COEFFICIENTS FOR WEEK 10

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

W
DEPENDENT VARIABLE, K‘- Independent
% Variable
Coefficients K3809 K3909 K4009 K4109 K4209 Acronym
C. .039 -.
.11 (.136) (-.224) PRE001
C. .037
..14 (.121) FRE004
B__ .164
.19 (.144). PRE009
C. -.059 .167
,35 (-.118) (.184) PRE025
5— .146
.38 (.176) PRE028
B—- .072 ‘
-39 (.137) PRE029
C. .076
.43 (.158) PRE033
C. .164
J 44 ( . 164) PRE034
d_ -.113
,57 (-.316) PRF012
771— .061
.59 (.169) PRF014
8.. -.019
_. .60 (-.111) PRF015
B_. .087
.62 (.174) AFF002
B -.203
AFF010
.181
(.210) AFF014
.045
(.132) AFF015
.102
_(.175) AFF018
-.131
-.182) AFF019
.215 .548
(.222), (.196) AFF021
.062
(.220) AFF028
AFF029
AFF030
—.032 -.067
(-.116) {-.118) AFF037

 

 

 

 

 

 

 

 

 

 

 

 

 

 

343

TABLE E . 9

MODEL COEFFICIENTS FOR WEEK 10

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DEPENDENT VARIABLE. 3:; Independent
Variable
Coefficients K3809 K3909 K4009 * K4109 ! K4209 Acronym
3.. -.124 '
...104 (-.137) AFF044
3.. -.152
.108 (-.220) AFFO48
CE; -.127
-III (-.269) AFR009
.. .026
.118 (.096) AFROlO
8— .030
.127 (.133) BKG008
8— .053
.128 (.159) BKCOO9
B__ —.060
.134 (-.144) BKG015
B .020
'_.136 (.101) BKGOI7
.025
(.155) BKGO30
BKGO44
.009 ~
(.062) BKG049
-.037
(-.229) BKG052
—.042
(-.145) BKCOS3
BKG059
BKG060
-.092
(-.148) BKCO65
.133
(.312) BKFOOI
.022
(.140) BKF004 ,__
-.019
(—.129) BKFOO9 g.
BKF013
-.021
(-.125) CKEDI4
.069
(.200) BKF015

 

 

 

 

 

 

 

 

 

 

 

 

 

 

. 344

TABLE E.9

MODEL COEFFICIENTS FOR WEEK 10

 

DEPENDENT VARIABLE, K1i__

I

ndependent

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Variable
Coefficients K3809 K3909 K4009 ! K4109 1 K4209 Acronym
3.. :,078 -.O89
.7 (-.165 (:.146) LECOO7
A._ —.093
___,9 (-.165) LEC009
A. .112 —.065 -.105
.10 (.121) (-.144) (-.128) LECOlO
I. -.247 .094
.27 (-.175) (.105) SAS016
I. .056 .
.29 (.121) SASOI8
A__ .347 .195
JO (. 200) ( . 228) SASOl9
A__ .208 .089 .196
.50 (.462) (.315) (.241) p02009
A__ -.198 -.072
.51 (-.429) (-.247) PQZOlO
.A__ .151
,66 (.165) TC15
A__ .059 .058
,69 (.088) ( 174) TC18
I. .053
.89 (.204) SVN032
_A__ .021
.100 (.253) 01
I. .029 .011
_.102 (.298) (.366) 03
A_ .005
,107 (.107) Q9
H__ -.108
.1 (—.149) K0108
n.. .151
-2 (.128) K0208
n.. .276
__._.3 (.135) K0308
n.. .304
- .5 (.202) K0508
u__ -.236
,6 (-.163) K0608
n. .278 -.181
..12 (.168) (-.353) K1208
n__ -.092
.14 .(:.102)_ K1408
n.. -.219
,18 (-.194) K1808

 

 

 

 

 

 

 

 

 

 

345

TABLE E.'9

MODEL COEFFICIENTS FOR WEEK 10

DEPENDENT VARIABLE , K'1i Independent

' Variable
Coefficients K3809 K3909 K4009 K4109 K4209 Acronym
u__ .178

 

 

 

 

 

 

 

 

 

 

 

.19 (.140) K1908
u__ -.144 -.060 .
___ .24 (-.137) (-.184) K2408
n... -.064 .198
.25 .1-3124) (.187) K2508
n. .121
,28 ' (.086) K2808
n.. .090
.30 (.095) K3008
n__ .086 -.083
.31 (.292) (-.139) K3108
u__ .089 -.255
,32 (.138) (-.195) K3208
n. -.080
.39 (-.175) K3908
u .051
",41 (.081). K4108
u

 

_. .115
.42 (.138) K4208

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

346

TABLE E. 9

MODEL COEFFICIENTS FOR WEEK 10

1

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DEPENDENT VARIABLE. K‘. Independent
'497 Variable
Coefficients K4309 K4409 Acronym
C. -.141
.35 (-.156) FRE025
B__ .215 -.222
.39 (.226) (-.215) FRE029
3.. .088
.67 (.101) AFF007
C. .090
.69 (.101) AFF009
C. -.216
.74 (-.232) AFF014
C_. -.210
.79 (—.191) AFF019
s_ .670
,81 (.240) AFF021
B_ -.156
.87 (-.179) AFF027
B .100
’,88 (.114) AFF028
AFF048
BKGOZS
BKGO45
BKGOS9
BKFOO9
BKF014
LEC010
TC18
FINO61
02
05
K0208

 

 

 

 

 

 

 

K1408

 

 

 

 

 

 

 

347
TABLE E.9

MODEL COEFFICIENTS FOR WEEK 10

  

DEPENDENT VARIABLE, K'. Independent

 

 

 

 

 

 

 

 

 

Variable
Coefficients K4309 K4409 Acronym
n_ .193
.22 (.136) K2208
p__ -.132 .144
.24 (-.142) (.143) K2408
R_ .177
-25 (.119) K2508
u__ -.137 -.186
.26 {-.155) (-.193) K2608
E_ -.193
.27 (-.103) K2708
“_. .143
.30 (.152) K3008

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

348

TABLE E.9

Model Constants

Weekrlo

O34 0.9674

-0.0115 O35 1.1872

O36 -0.6598

O37 0.4990

O38 -O.1238

O39 0.3961

O40 0.2527

O41 0.3839

0.0938 O42 -0.4269

O43 0.4179

O44 0.3664
-0.1148
0.3219
0.3012
0.0425

 

 

 

349

TABLE E.10

MODEL COEFFICIENTS FOR WEEK - FINAL

 

r

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DEPENDENT VARIABLE. Ki: Independent
Variable
Coefficients K0310 K0610 K0810 K0910 K1010 Acronym
B__ .00009
.8 (.071) GPA
C. —.028
.13 (—.091) FRE003
8.. .024
.14 (.057) PRE004
Ii. .029
.19 (.084) PRE009
C. .049
.22 (.064) PRE012
B__ .020
.37 (.064) PRE017
B .019
'_.35 (.060) FRE025
C. .020 .029 .031
.41 (.051) (.073) (.107) FRE031
B__ -.009<
(-.058) PRF006
.022
(.081) AFF007
-.020
(-.063) AFF008
.014
(.051) AFF011
-.050
(-.089) AFF033
—.021
(-.077) AFF034
.018
(.053) AFF038
—.048
(-.071) AFF043
—.008
(-.050) AFR005
—.008
(-.052) AFR009
-.010
(:.058) BKG002
.012 .
(.060) BKG005
-.031
(—..68) BKG012
-.021
(-.085) BKG024

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

350

TABLE E.10

MODEL COEFFICIENTS FOR WEEK - FINAL

 

DEPENDENT VARIABLE, K'i

 

 

Independent

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Variable
Coefficients K0310 K0610 K0810 K0910 K1010 Acronym
C. .021 .035 -.013
.151 (.085) (.138) (:.070) BKGOBZ
B__ -.019 -.029 -.017
__..153 (-.O73) (—.110) (-.060) BKGO34
C. -.020 -.031 -.021
.155 (-.052) (-.080) (-.O51) BKG036
B .014 .022 .018
‘3167 (.071) (.109) (.084) 3K6048
B .012
7', 171 (.077) BKGOSZ
B -.016
".175 (-.107) BKG056
B .025
7.192 (.159) BKF005
VA__ .089
.12 (.064) SASOOl
A__ .090
.13 (.065): SASOOZ
A__ .022
.14 (.038) SASOO3
4.. .071 .102
.15 (.093) (.144) SAS004
.A-_ .030
.20 (.069) SASOO9
,A__ -.045 —.069 —.045 .
,25 (-.057) (—.087) (-.053) SASOl4
A__ .042 .057 -.042 .042
.28 (.060). (.082) (—.085) (.056) SASOI7
.I.. —.116 —.189
.32 (-.060) (-.098) LTP001
.A__ -.017
-47 (—.058) pozoos
A__ .010
..g .71 (.060). 0NE032
A__ .014
.76 (.059) TRE031
A. .003 .005 .003
__ .101 (.066) (.104) (.057) 02
A_ .003
,102 (.106) ' 03
n. .053 .091
.1 (.053). (.091) .1KHQ9
K0309

 

 

 

 

 

 

 

351

TABLE E.10

MODEL COEFFICIENTS FOR WEEK - FINAL

 

 

 

 

  
 

 

 

 

 

 

 

 

 

 

 

 

 

_.
DEPENDENT VARIABLE. K'- Independent
'ei Variable
Coefficients K0310 K0610 Acronym
n_. .830
-6 (.848) K0609 _. _
u__ .657
..__.8 _(.721) K0809
n..‘ .776
.9 ‘ LTD) Iggy
H_ .051 .867
, ,10 (.072) (.881) K1009
“_. .075
.14 .(.123) _K1409
u__ .063
. -15 (.102) ..K1509
u__ .024
.28 (.049) K2809
n._ -.038 -.059 —.045
.29 (-.053 (:.083) -.058) K2909
11.. .016
.36 (.052) K3609
n.. .076
,,40 (.077) K4009
n. -.027
.44 (-.O60) K4409

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

TABLE E.10

MODEL COEFFICIENTS FOR WEEK - FINAL

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DEPENDENT VARIABLE, K'- Independent
4 Variable
Coefficients K1410 K1510 K1610 K1910 K2010 Acronym
C. .008
.1 (.043) CLASS
B -.0003 -.0004 -.0007
‘;3 (-.051) (-.068) (-.116) TRCR
C. -.001
95 (-. 301) CRERN
C. .00007
,6 (.389) MSUPTS
B__ —.042
.13 («.083) PRE003
B__ —.064
.18 (-.058) PRE008
B_ .067
.19 (.118) PREOO9
C. -.052
.25 (—.051) PRE015
8.. .049
.27 (.119) PRE017
' 5.. -.029
.29 (:.O62) FRE019
B__ .049
.31 (.115) PRE021
C. .025
.33 (.072) PRE023
__ .041 .047
.35 (.090) (.094) PRE025
C. .024
__..37 (.076) FRE027
5.. .066
,38 ( 143) FRE028
C. -.035 .040
. 40 0.080) (. 086) pREmn
8__ -.028 -.032
’ .42 (-. 059) (—- 061) 321312032
C. -.003
. .45 {-.082) PRETOT
C_. -.031
.. .51 (3.128) PRF006
8__ .037 .
.55 (.158) FRF010
8.. .034
.62 (.068) AFF002
A__ .032
.64 (.106) AFFOO4

 

 

 

 

353

TABLE E.10

MODEL COEFFICIENTS FOR WEEK - FINAL

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DEPENDENT VARIABLE. K'. Independent
‘9 Variable
Coefficients K1410 K1510 K1610 1 K1910 1 K2010 Acronym
g -.038
“.66 (-.O74) AFF006
C. .025
.71 (.055) AFF011
s .045
‘373 - (.103) AFF013
9_ -.038 -.039
.74 (-.119) (-.082) AFF014
_Ii_ .026 .014 -.055
.76 (.056) (.044) (-.117) AFF016
B__ —.030
.77 (-.O63) AFFOIZ.
B_ -.041
.79 (-.074) AFF019
B__ -.032
.83 (-.053) AFF023
C. .036 -.039
.85 (.050) (-.051) AFF025
B .026
‘387 (.056) AFF027
B__ .021
.89 (.051) AFF029
C. -.095
.90 (-.073) AFF030
B__ -.048 -.072
...93 (e.079) (-.078) AFF033
B__ —.059
.94 (-.131) AFF034
C. .029
.98 (.064) AFF038
B__ .064
.106 (.077) AFF046._
C. .052
..108 (.073) _AFF048
C. —.032
.115 (-.133) .AFROO]
8__ .047
__ .116 (.182) AFR008
B__ -.017
.119 (-.094) 'AFROll
8.. .018
.124 (.064) BKGOOi.
C. .050
.128 (.092) BKG009

 

 

 

 

 

 

354
TABLE E.10

MODEL COEFFICIENTS FOR WEEK - FINAL

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

.1
DEPENDENT VARIABLE. K'. Independent
’7; Variable
Coefficients K1410 K1510 K1610 1 K1910 1 K2010 Acronym
B .072
".131 (.097) BKG012
B -.014
'_.135 (—.055)- BKG016
3. .062
-IAn (.085) BKG021
3.. .017
.149 (.072) BKG030
3.. -.052
.151 (-.174) BKG032
B .014
‘;154 (.054) BK0035
B -.026
73155 (- 060) BKG036
C. .008
-161 (.055) BKG042
3.. .012
..173 (.050) BKG054
8 -.010
(-.058) BKGOS9
BKF002
.008
(.050) BKF003
.019
(.075) BKF004
.040
(.156) BKF005
.009
(.056) BKF006
-.022
(v.089) BKFOO7
—.031
(«.120) BKF008
—.037
(—.078) LECOO7
.LECOO8
' LECATD
339.003
SASQO4

 

 

 

 

 

 

 

 

 

 

 

 

355

TABLE E.10

MODEL COEFFICIENTS FOR WEEK - FINAL

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DEPENDENT VARIABLE, Kt; Independent
' Variable
Coefficients K1410 K1510 K1610 K1910 K2010 Acronym
A .
"120 (.074) SASOO9
. A_ .088
.24 (.090) SA8013
A__ -.121
.32 ..(-.O81) LTPOOl
_A__ -.123
.33 (-.O83) LTp002
.A__ -.127 _
.38 (-.100) LTF007
A__ .122
.39 (.096) LTP008
A. -.225
.41 (-.103) LTPOlO
A .028 .017
' '142 (.077) (.064) LTFTOT
I. .040
.47 (.138) PQZ005
4.. .012
.52 (.138) PQZTOT
I.. -.012
.58 (n.051) TC7
7A. -.022
.62 (-.053) T011
3.. .032
,65 (.066) T014
A__ .024
.66 (-.O71) TC15
A. -.019
.68 (—.050) TC17
’A__ .0I2
.70 (.058) 0NE031
A -.014 .O3I
‘_.‘j71 (-.076) (.111) ONEO32
_A__ .015
.76 (.046) TRE031
'A__ -.011
_. .90 (-.060) SVN033
‘A__ -.013
.96 (-.075) -FIN063
. I. .021 .011 .047
.97 (.072) (.051) (.145) FINO64
.A__ .021
.98 1 (.106) FINO65

 

 

 

 

356

TABLE E.10

MODEL COEFFICIENTS FOR WEEK - FINAL '

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

C: _i
DEPENDENT VARIABLE. K'i Independent
’ Variable
Coefficients K1410 K1510 ' K1610 1 K1910 1 K2010 Acronym
*—. c.006
. 100 4:438?) (13
i— .006
.101 ~ (.111); 02
_. .003 ' ‘
,102 (.061) 03
4_ -.004
,106 (-.O89) Q7
11 --.ll3
'32 (—.l34) K0209
u_ .079
.11 (.081) K1109
u_ .647
,14 (.784) K1409
u__ .121 .789 .044
.15 (.146)_, (.884) (.072) K1509
n__ .080 .681
,16 (.073) (.847) K1609
3.. -.054
.18 {-.095) .K1809
3_ .056 .801
.19 (.084) (.793) K1909
n. .669
,20 (.743) K2009
u .149
.._;23 (.121) K2309
u__ -.056
.27 (:.053). K2709
u__ .088
.28 (.112) K2809
3.. .065
.31 (.129) K3109.
11.. .034
'.36 _(.110) K3609
u__ .138
,.40 (.086) K4009
“_ . 083
.. .42 (.157) K4209

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

357

TABLE E.10

MODEL COEFFICIENTS FOR WEEK - FINAL

 

DEPENDENT VARIABLE, K1;—

 

 

Independent

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

' Variable
C°effieiente K2210 K2310 K2710 1 K2910 1 K3210 Acronym
3.. -.0004 .
-3 (-. 115) TRCR
3.. .00001
,6 (.107) ‘MSUPTS
3_ .0002 .0001
.8 (.115) (.105) ~ GPA
B. -.029 —.034 -.057
".13 (-.110) (—.092) (-.104) PRE003
3_ -.035
.15 (-.070) FRE005
s .037
—L18 (. 063) PRE008
773. .047 .095 -.024
.23 (.115) (.157) (-.O67) PRE013
B__ .025
.29 (.072) FRE019
e -.072
‘,30 (-.O97) PRE020
e_ .040 .043
.33 (.147) (.126) PRE023
e_ .020
.35 (.076) PRE025
3_ .039
.36 (.061) PRE026
3_ .025
.41 (.103) PRE031
3_ -.003
.45 (—.O69) FRETOT
3. .012
.49 (.085) PRF004
3_. .026 .037
-51 (.147) (.138) FRF006_
3.. -.030 -.069
.. .59 (-.145). (-.222) PRF014
B__ -.097
__...66 (-.171) .AEFOO6
e_ .055 -.030 -.031
____.68 (.122) (-.116) (-.094) AFF008
B__ -.017
,69 (-.050) ‘AFF009
B__ .017
.73 (.073) AFF013
B__ ~.052 -.063
.74 (-.146) (—.120) AFF014

 

 

 

 

 

 

 

 

 

 

358

TABLE E.10

MODEL COEFFICIENTS FOR WEEK - FINAL

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DEPENDENT VARIABLE. K11 Independent
Variable
Coefficients K2210 K2310 K2710 1 K2910 1 K3210 Acronym
3_ .029 .022
.77 (.112) (.069) AFF017
e_ -.132
.90 (-.115) AFF030
C. -.044 -.053
.93 (:.093) (-.090). AFF033
B__ -.033 —.038
.99 (-.103) (-.095) AFFO39
3.. .030
,107 (.074) AFF047
3_. .014
.112 (.076). AFR004
3.. -.010
.123 (—.045) BKG004
3. -.017
-125 (-.O91) BKGOO6
C. .011
.126 (.070) BKGOO7
B . 024
'3128 (.087) BKGOO9
B__ -.037
.131 (—.067) BKG012
B_ .024
.139 (.055) BKGOZO
e_, .022 .014 .015
._.149 (.095) (.102) (.086) BKG030
B__ .050
.155 (.100) BKGO36
e_ .045
,161 (.180) BKGO42
e_ -.03I
.166 (-.148) BKG047
B__ .013 .020
__ 5.171 (.095)_ (.118) BKG052
e_ —.042
..173 (—.157)_ BK0054
e_ .011
__ ,179 (.057) BKGO6O
3. —.068 ,
..200 (-.244) BKF013
773.. -.031
.201 (-.108) BKF014
A_ .069
__ .1 (.121) LEC001

 

 

 

 

 

 

 

 

359

TABLE E.10

MODEL COEFFICIENTS FOR WEEK - FINAL

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DEPENDENT VARIABLE, K'. Independent
‘1 Variable
Coefficients K2210 K2310 K2710 1 K2910 1 K3210 Acronym
. A__ ~-. 039
.4 (-.121) LECOO4
.i.. —.038
,6 (n.104) LECOO6
‘71_ .041
.8 (.179) ~ LEC008
A__ -.063 .045
,9 (—.159) (.139) LECOO9
A -.063* -.054 I
",10 (-.185) (-.107)- LECOlO
A_ -.009
.31 (-.188) LECATD
A__ .176
.12 (.106) SASOOI
A__ .097
.13 (.083) SAsooz
A_ .035 .043
.17 (.066) (.057) SASOO6
A_ .027 .033
.20 (.075) (.073) SASOO9
.A.. -.097 -.190
.23 (7.117) (:.154) SASOI2
,A__ .092
.30 (.119) SASOl9
A__ .011
.31 (.102) SASATD
A__ -.O91 —.083
..35 (7.094) (-.058) LTF004
1.. .039
.38 (.058) LTP007
4.. .092
.39 (. 108) LTp008
1.. -.059
c.48 (—.147) .PQZOOfi
. A. .048
.53 (.092) .101
A__ -.114
__ .54 {—.157) 103
A__ .048
.59 (.156) 'TC8
A —.018
“361 .(g.053). T010
A - . 068
‘362 w (-.179) TC11

 

 

 

 

360

TABLE E.10

MODEL COEFFICIENTS FOR WEEK - FINAL

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

-:_
DEPENDENT VARIABLE, Kt; Independent
’ Variable
Coefficients K2210 K2310 K2710 1 K2910 1 K3210 Acronym
—- -.021
’65 (-.061) T014
.. .017
.70 (.073) 0NE031
_. .026
,74 (.111) TWOO32
._ —.012
.91 (—.066) NINO31
__ .003 .004
.101 (.109) (.096) 92
.002
(.053) 06
.007
(.084) .99
.012
(.164) 010
.083
(.121) K0109
K0809
-.159
{-.089) K0909
K1009
-.O64
(-.064) K1409
K1909
K2009
K2209
K2309.
K2709
K2809
.711
(.778) 'K2909
.787
(.794) K3209
~.O6O
(:.115) K3609

 

 

 

 

 

 

 

 

361

TABLE E.10

MODEL COEFFICIENTS FOR WEEK - FINAL

 
  

Independent
Variable
Acronym

   

DEPENDENT VARIABLE, K1;

Coefficients K2210 K2310 K2710 K2910 K3210

A__ .119. .132 ~.106
.40 (.101) (.075) (—.103) K4009

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

362

TABLE E.10

MODEL COEFFICIENTS FOR WEEK - FINAL

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DEPENDENT VARIABLE, K'- Independent
..i Variable
Ceeffieiente K3410 K3610 K3710 1 K3910 K4010 Acronym
3_ .0002
.3 (.181) GPA
B__ -.028
....,II (-.117) PRE001
e. .139 .122 .127 .064
.18 (.100). _(.086) (.149). (.308) PRE008
3_ -.035
.23 (—.076) PRE013
5 .052
‘,36 (.088) PRE026
B__ .085 .084 .021
.37 (.150) (.144) (.086) FRE027
A__ -.045
.42 (-.165) FRE032
3_ .024
.49 (.133) FRF004
8__ -.O2O
.51 (-.102), FRFOO6
B_. .029 .032
.54 (.094) (.102) pggggg
e_ .020 .018
.56 (-062) .(-054). .pnggi1
8__ -.039 -.043 '
.61 (-.103) (-.174) AFF001
3_ .024
.70 (.100) AFF010
B_ .047
__¥ .71 (.125) AFF011
e. -.022 “"
.72 (-.062) AFF012
B__ -.089 -.086
.85 (-.097) (-.091) AFF025
B__ .034
__...89 (.090) AFF029
e_ -.050 -.027
,91 (-.134) (-.113) AFF031
d. -.124
__ .93 (-.170) AFF033
e_ .022 .018
.94 (.062) (.078) 'AFFO34
B -.U28—
‘399 (—.054) AFF039
B .026 —.069 -.056
‘;101 (.053) (-.O97) (~.076) AFF041

 

 

 

 

 

 

 

 

 

 

 

363

TABLE E.10

MODEL COEFFICIENTS FOR WEEK - FINAL

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1“
DEPENDENT VARIABLE. K'. Independent
4L— Variable
Coefficients K3410 K3610 K3710 K3910 . K4010 Acronym
5 .113 .122
‘,105 (.094) (.098) AFF045
C_ .063
,107 (.126) AFF047
C_ .037
,108 (.065) .AFF048
B -.037 —.035
‘,113 (-.112) {-.104) AFFOOS
C. -.035
.115 (-.161) .AFR007
3_ -.O30 -.057 -.010
.125 {-.084) {-.243) (-.068) BKGOO6
3. .020 .024
.126 (.058) (.066) BKGOO7
B_ -. 042
.131 (—.109) BKGOIZ
3. -.016
.136 (-.157) BKG017
*e_ -.016
.141 (-.069) BKG022
3_ .053
.149 (.254) BKGO30
B_ -. 030
,151 (-.130) BKGO32
B -.024
‘3153 (—.097) BKG034
B .011
“3154 (.077) BKGO35
B —.081
‘;156 (~.101) BKCO37
3 —f019
‘3160 (-.079) BKG041
3. -.029 -.032
*_’,166 (—.103)_ .(7.108) BKCO47
e_ .026
..168 (.144) BKCO49
3 . 024
7.171 (.115) BKGO52
B .016 ,
‘3172 (.138) BKG053
3 —.024 -.021
‘3173 (-.080) (-.068) BKG054
6_, .023
.179 (.095) BKGO6O

 

 

 

 

 

 

 

 

 

 

 

 

364

TABLE E.10

MODEL COEFFICIENTS FOR WEEK - FINAL

 

-

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DEPENDENT VARIABLE. K1: 1 Independent
Variable
Coefficients K3410 K3610 K3710 K3910 K4010 Acronym
3. .062 .057 '.012
.181 (.137) (.122) _(.060) BKG062
3_ -.012
,192 (-.O93) BKF005
3. -.O43 -.013
,197 (-.219) (-.098) BKF01O
s_, -.022
.200 (-.106) BKF013
I. .056 .053
.1 (.059) (.055) LEC001
A__ -.056 -.028
.4 (¢.137) (-.110) LECOO4
A__ .061
.5 (.147) LEC005
A__ .042
,7 (.170) LECOO7
A .028
":8 (.081) LECOO8
A__ —.046
.10 (—.127) LEC010
A__ .168 -.157
.13 (.091) {—.088) SASOOZ
A__ .085
.17 .:‘. (.104) SASOO6
A__ .037
.20 (.064) SASOO9
A .064 .059
":23 (.071) (.102) SASOl2
I. -.O63 -.051
____,30 (-.O60) (-.048) SASOI9
I.. -.233
.32 (—- 130) LTpnm
1.. .118
. 35 .4 1 13) LTpnnA
I_ .084
.39 (. 123) LTFDOS
'“7&_ .049 .047
“ .47 (.092) (.086) PQZ005
A . 067
'253 (.100) 'TCl
A__ .065
.54 (.098) TC3
A__ -.041
.61 (-.102) TC10

 

 

 

 

 

365

TABLE E.10

MODEL COEFFICIENTS FOR WEEK - FINAL

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

7r
DEPENDENT VARIABLE. K'- Independent
‘ "; Variable
Coefficients K3410 K3610 K3710 K3910 K4010 Acronym
. I. .015
.81 (.084) FOR033
1 A_ ‘ .019 .018
.87 (.072) (.066) SIXO33
. A_ -.024
.91 (-.103) NINO31
_A__ .021 .022 .036
.97 (.052) (.054) (.140) FINO64
. I. .006
.101 (.117) 02
.003
73102 (.100) 03
.004 *
~“.105 (.091) Q6
n_ .100
.1 (.108) K0109
n. —.102 -.207
,2 (-.125) (-.131) K0209
K0309
.077 .117
(.060) (.140) K0409
-.081
(-.102) K1909
.131
(.136) K2309
-.068
(-.104) K2509
K3409
..K3609
K3709
.462
(.578) K3909
.540
(.651) K4009

 

 

 

 

 

 

 

 

 

 

 

 

 

 

366

TABLE E.10

MODEL COEFFICIENTS FOR WEEK - FINAL

 

DEPENDENT VARIABLE,

KL:

 

 

Independent

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Variable
Coefficients K4110 K4210 K4310 1 Acronym
3_, .136
.18 (.108) PRE008
B__ —.055
___,23 (-.104) PRE013
3. . 140
.33 (.188) PREO23
3_ .078
.36 (.114) PRE026
3_ .082
.37 (.160) PRE027
B__ .034
.59 ~ (.165) PRF004
3_ .030
3 54 (. 106) FRF009
3.. .018
.56 (.062) PRFOll
3_ —.108
.68 (-.153) AFF008
B o 067
AFF011
AFF012
AFF013
AFF015
AFF017
-.087
{-.105) AFF025
AFF029
AFF032
AFF033
AFF037
'AFF039
-.067
(-.104) AFF041
.121
(.111) AFF045

 

 

 

 

 

 

 

 

 

 

 

 

367
TABLE E.10

MODEL COEFFICIENTS FOR WEEK - FINAL

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DEPENDENT VARIABLE. K'i__fi Independent
Variable
Coefficients K4110 K4210 K4310 1 1 Acronym
B .088
‘3107 (.101) AFF047
3 .058
71108 (.087) AFF048
B__ -.033
.113 (-£112) AFR005
B__ -.052 .
9115 (‘0204) AFROO7
C. ‘.052
.117 (.142) AFROO9
B -.032 -.030
‘3125 (-.076) (-.093) BKGOO6
B .024
‘3126 (.076) BKGOO7
B -.209
_;143 (-.078) BKCO24
3_ .079
.149 (.216) BKG030
’“3. —.039
.161 (-.123) BKGO42
3. -.031
.166 (-.119) BKGO47
B__ .039
,168 (.184) BKGO49
B__ .082
.171 (.224) BKGOSZ
3 -.023
‘,173 (—.087) BKGOS4
B__ .036
,179 (.126) BKG06O
3_ .059
.181 (.144) BKG062
BKF005
BKF013
LEC001
LECOO4
LEC005
LECOOS

 

 

 

 

 

 

 

 

 

 

 

368

TABLE E.10

MODEL COEFFICIENTS FOR WEEK - FINAL

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DEPENDENT VARIABLE, K'. fl Independent
I Variable
Coefficients K4110 K4210 K4310 Acronym
,A__ .247
_.33 (.115) 335002
p I. .059
,20 (.087) SASOO9
. A. -.O49
.30 .(-.052) 5A5019
A__ .078 -
’42 (.139) LTPTOT
.I_, .050
.47 (.104) PQZOOS
.A.. .100
. 53 ( . 128) mm
.I_. .101
.54 (.131) TC3
‘ A. -.064
.61 (-.136) T010
, A. -.105
.65 (r.141) T0143?
'71.. .018
.87 (.075) SIXO33
III. __.037
.91 (-.134) NINO31
. A. .020
.97 (.056 FINO64
‘A__ .009
.101 (.156) Q2
I_. .007
.105 (.091) Q6
u -.182
_32 {—.130) K0209
3.. .081
’3 (.065) K0309
u .255
-.7310 (~171) K1009
u__ -.088
..38 (—.128) K3809
u__ -.189
__. .40 (-.123) K4009
u__ .486 _
.41 (.645) K4109
n. .491
.42 (.630) K4209
U_. .479
.43 (.781) K4309

 

 

 

 

 

 

 

 

 

 

 

9 9 o a Q Q
«5 O5 x: 0‘ 01 n~ O5 hJ-ha

Q

O10

9
p...
H

O12
O13
O14
O15
O16
O17
O18
O19
O20
O21
O22
O23
O24
O25
O26

' O27

-O28

,7 O29

O30
O31
O32
O33

0.0621

0.1040

0.0166

—0.3240
0.0930

-0.0851
0.0040
0.1385

-0.3150
0.0030

0.0692
-0.2232

-0.1424

-0.0518

0.0019

369

TABLE E.10

Model Constants
WeekeFinal

 

O34
O35
O36
O37
O38
O39
O40
O41
O42
O43
O44

-0.1661

0.2098
0.1810

-0.0158
0.1127
-0.2230
-0.5041
0.2203

 

 

 

LIST OF REFERENCES

 

370

REFERENCES

Abkin, M. H. Policy Making for Economic Development: A System

Simulation Model of the Agricultural Economy of Southern
Nigeria. Ph.D. Thesis. Michigan State University, 1965.

 

Alexander, T. R; Burnett, R. W,; Zim. H. S. Botany, Golden
Press, N.Y., 1970.

Anonymous. Computer Models and Application of the Sterile — Male
Technique. Proceedings of a panel organized by the Joint FAO/
IAEA Division of Atomic Energy in Food and Agriculture, Vienna,
December 1971, International Atomic Energy Agency, Vienna, 1973.

 

Arnold, G. W. and deWit, C. T. (Eds.). Critical Evaluation of Systems
Analysis in Ecosystems Research and Management, Wageningen, Center
for Agricultural Publishing and Documentation, 1976.

 

Bloom, B. S. Human Characteristics and School Learning, McGraw—Hill,
1976.

Brandenburg, R. A Task — Content Analysis of an Introductory Entomology

Curriculum. Presented at the National Association for Research in
Science Teaching, Annual Meeting, April 23-25, 1976, San Francisco,
California.

Burnett, R. W.; Fisher, H. I.; Zim. H.S. Zoology: An Introduction to
the Animal Kingdom, Golden Press, N.Y., l973.

Carrol, J. B. A Model of School Learning. Teachers College Record,
1963, 64, 723—733.

Ellis, Jack B. The Description and Analysis of Socio—Economic Systems

by Physical Systems Technigues. Ph.D. Thesis, Michigan State
University, 1965.

Enochs, N. J. "Biology Preparation for Prospective Elementary Teachers”.
Proceedings of the Conference Marking the Twentieth Anniversary of
the Science and Mathematics Teaching Center, SMTC, Michigan State
University, 1977. (R. Brandenburg and J. J. Gallagher, Eds.)

Forrester, J. W. Principles of Systems. Wright-Allen Press. Cambridge,
Massachusetts (a).

.Forrester, J. W. World Dynamics, Wright—Allen Press, Cambridge,
Massachusetts (b).

Haefele, D. Systems and Modeling: Self-Renewal Approaches to Teacher

Education. The American Association of Colleges for Teacher
Education, December 1971.

 

371

Keeton, William T. Elements of Biological Science. Second Edition.
W. W. Norton and Co., Inc., 1973.

Kuhn, Thomas S. The Structure of Scientific Revolutions. Second
Edition (International Encyclopedia of Unified Science, Vol. 2,
No. 2) University of Chicago, 1970.'

 

Laszlo, Ervin. Introduction to Systems Philosgphy Toward a N9!
Paradigm of Contemporary Thought. Harper and Row, 1972.

 

 

LeBaron, W. Systems Analysis and Learning Systems in the Development of
Elementary Teacher Education Models, October 1969, National Center

for Educational Research and Development, U.S. Department of Health,
Education, and Welfare, Office of Education, U.S. Government
Printing Office (Supt. of Documents) Catalog No. FS 5.258:58035.

 

Levin, Simon A. (Ed.) Ecosystem Analysis ppd‘Prediction. Proceedings
of a SIAMFSIMS Conference, 1974, Society for Industrial and
Applied Mathematics, 1975.

 

Manetsch, T. J., Park, G.L. Systems Analysis and Simulation with

Applications to Economic and Social Systems, Part I. MethOdology
and Linear Systems Fundamentals. Department of Electrical
Engineering and Systems Science, Michigan State University,
August 1974 (a).

Manetsch, T. J., Park, G.L. Systems Analysis and Simulation with
Applications to Economic and Social Systems, Part II. Department
of Electrical Engineering and Systems Science, Michigan State
University, November 1974 (b).

May, Robert M. Stability and Complexity in Model Ecosystems. Princeton,
University Press, 1974.

Meadows, D. L. The Limits to Growth. Universe Books, New York, 1972.
Nie, Norman H.; Hull, C. Hadlai; Jenkins, Jean 0.; Steinbrenner, Karin;

Bent, Dale H. SPSS Statistical Package for the Social Sciences,
Second Edition, 1975, McGraw—Hill.

 

Novick, M. R., Jackson, P.H. Statistical Methods for Educational and
Psychological Research. McGraw—Hill, 1974.

Odum, Eugene P. Fundamentals of Ecology, Third Edition. W. B.
Saunders Company, 1971.

Patten, B.C. Systems Analysis and Simulation in Ecology, Vol. II,

Academic Press.
Pugh, A. L. Dynamics User's Manual, 2nd Edition, The MIT Press, 1966.
Silvern, L. C. Systems Analysis and Synthesis Applied Quantitatively

to Create an Instructional System, Education and Training
Consultants Co., Los Angeles, California, 1965.

 

 

372

Silvern, L. C. Systems Analysis and Synthesis Applied to Occupational
Instruction in Secondary Schools, Education and Training
Consultants Co., Los Angeles, California, 1967.

 

Smith, Edward L. Techniques for Instructional Design, presented at
American Educational Research Association Annual Meeting,
April 16, 1974.

Smith, Edward L. Mathods for Content Analysis, Science and Mathematics
Teaching Center, Michigan State University, April 18, 1975.

Tamir, P. Private Communication, 1976.
Tamir, Pinchas; Gallagher, James J.; and Brandenburg, Richard. The
Use of Bloom's Theory of School Learning as a Framework for Case

Studies of Specific Courses. Unpublished, Science and Mathematics
Teaching Center, Michigan State University, 1977.

Tanner, C. K. Designs for Educational Planning. D. C. Heath and
Company, 1971. '

Thompson, R. B. A Systems Approach to Instrucpion. Linnet Books. 1971.

Veldman, Donald J. Fortran Programming for the BehaVioral Sciences.
Holt Rinehart and Winston, 1967.

Weber, W. H. Socioeconomic Methods in Educational Analysis. Teachers
College Press, 1975.