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ABSTRACT

THE EFFECT OF
STUDENT-CONTROLLED INSTRUCTION
ON LEARNING

BY

John Pray Fry

A number of areas of psychology have shown self-
direction to be a motivating, satisfying, and an effective
mode of interacting with one's environment. Despite the
enthusiasm of educators for progressive methods (e.g.,
Summerhill), there is only limited and questionable evi-
dence that self-direction is a motivating, satisfying, or
an effective mode of interacting within a learning environ-
ment. Moreover, the degree of effective learner self-
direction probably depends on the individual learning style
of the learner. The purpose of this investigation was to
test the hypothesis that inquisitive individuals can control
their learning or instructional strategies and thereby learn
more and be more satisfied than by conventional means.

In a manually simulated learner-computer environment,

192 55 were given degrees of control over the programming of

John Pray Fry

their own learning. The material learned was about "Com-
puters and How They Work," and was recorded on 52 one-
minute video tape segments. This information was presented

on a TV monitor under the following conditions:

a. "Student-Controlled Instruction" (SCI)--Ss were
given the opportunity to select the sequence of
presentation of the segments. After each seg-
ment 83 could ask questions about the material.

b. "Expert"--Ss viewed a fixed sequence of the
segments prepared by content experts. 55 were
not allowed to ask questions. Instead, each S
heard the questions asked by one of the "SCI"
Ss.

c. "Random"--Ss viewed a randomly ordered sequence
of the segments. Otherwise, 53 were treated the
same as "Expert" 85.

d. "Control"--Ss received no instruction.

It was hypothesized that only 53 high in "inquisi—
tiveness" would be able to take full advantage of the "SCI"
treatment. Therefore, all Ss were given a battery of six
tests to measure individual differences along this dimen-
sion. Eighteen measures of aptitude, interest, and learner
characteristics were also obtained for each S.

The relative effectiveness of the treatments was

determined by scores on a pretest, posttest, and retention

 

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John Pray Fry

test. In addition, ten measures-of attitude and motivation
were obtained for each S.

The predicted interaction between degree of
"inquisitiveness" and degree of control was confirmed, but
only for high aptitude Ss. High aptitude, high "inquiry"
85 learned more under the "Expert" treatment. Results for
low aptitude Ss were inconclusive.

Among the attitude measures, "SCI" 85 responded,
overall, with a more favorable attitude toward the method
of learning than other groups. Among the correlational
analyses, the more questions "SCI" 55 asked, the more they
learned and the more they liked the method of learning.

I The results support the hypothesis that different
types of students learn better (or worse) under different
degrees of student control. Failure of some "SCI" 85 to
perform better is attributed to their lack of familiarity

with the environment.

THE EFFECT OF
STUDENT-CONTROLLED INSTRUCTION

ON LEARNING

BY

John Pray Fry 3W:

A THESIS

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Department of Psychology

1970

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ACKNOWLEDGMENTS

The author wishes to express his appreciation to
Dr. Robert H. Davis for his inspiration and guidance in this
research and to Drs. L. T. Alexander, T. W. Forbes,

E. Jacobson, and F. R. Wickert whose suggestions were of
great value.

I am particularly indebted to Robert Goodman for
his suggestions and assistance during the critical phases
of data collection.

Jackie Koehler and Traute Reimer deserve recognition
for administering the "Attitude Survey" questionnaires and
for typing the many forms required.

I am also grateful to Stan Cohen, Gerald Gillmore,
Stuart Thomas, Dr. R. D. Hart, Dr. F. E. LeCureaux, and
Dr. F. B. Martin for their serving as judges.

Special thanks go to Robert Lewis for his enthu-
siastic performace as a computer science instructor, even
though he did not understand what he was saying.

I would also like to thank all of the students who
gave of their time and energy so freely and who appreciated
so much the goal of this research.

Finally, I am so obligated to my wife, Pam, that
she now feels free to do similar research.

ii

TABLE OF CONTENTS

LIST OF TABLES . . . . . . . . . . .
LIST OF FIGURES . . . . . . . . . .
Chapter
I. INTRODUCTION . . . . . . . .
II. REVIEW OF THE LITERATURE . .

III.

Research on Student-Controlled

Instruction . . . . . . . .
The Research of Mager . .

The Research of Grubb and Selfridge .

The Research of Campbell .

Research Findings and Theories from other

Areas of Psychology . . . .
Learning Theory . . . . .
Organizational Psychology
Engineering Psychology . .
Humanistic Theory . . . .
Individual Differences . .

The Learner/Instructor Control Dimension .

Instructional Functions .
Individual Differences and
Controlled Instruction .

An Environment Where Student
Can Function Effectively . .

METHOD AND PROCEDURES . . .

Experimental Design . . . .
Factor One . . . . . . . .
Factor Two . . . . . . . .
Factor Three . . . . . .

Student-

Control

Stratified Random Assignment . . . . . .

Pilot Experiment . . . . .

iii

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17
18
28
3O
31
32
43
44

48
49
53

53
54
59
59
61
62

 

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Chapter Page

Subjects . . . . . . . . . . . . . . . . . . 63
Pre-Experimental Measures (Independent
Variables) . . . . . . . . . . . . . . . . . 64
Shulman's Six Tests . . . . . . . . . . . 65
General Inventory . . . . . . . . . . . . 67
"Attitudes Toward Learning" . . . . . . . 69
College Aptitude Tests . . . . . . . . . . 70
Experimental Procedures . . . . . . . . . . 71
First Learning Session . . . . . . . . . . 71
Second Learning Session . . . . . . . . . 72
Subject Logs . . . . . . . . . . . . . . . 73
Retention Test Session . . . . . . . . . . 74
Control Subjects . . . . . . . . . . 75
Development of the Subject Matter . . . . . 76
Development of the Segments . . . . . . . 77
Taping of the Segments . . . . . . . . . . 78
Ordering of the Segments . . . . . . . . . 79
Measurement of the Dependent Variables . . . 81
Development of the Tests . . . . . . . . . 81
"Post-Attitude Survey" . . . . . . . . . . 85
"Terminal Interview" . . . . . . . . . . . 86
"Post-Interest in Computers" . . . . . . . 86
Card Sequencing . . . . . . . . . . . . . 87
IV. RESULTS . . . . . . . . . . . . . . . . . . 88
Analysis of Variance . . . . . . . . . . 88
Gain 1 (Posttest Minus Pretest) and
Gain 2 (Retention Minus Pretest) . . . . 91
Time Expended in Learning Sessions
by Ss . . . . . . . . . . . . . . 96
Number of Pages of Notes Taken by 85 . . . 99
Time Expended on Notes by Ss . . . . . . . 101
Perceived Learning as a Function
of the Environment . . . . . . . . . . 101
Attitude Toward Order of the Segments . . 104
Attitude Toward Method of Learning . . . . 106
Perceived Learning as a Function
of Task Difficulty . . . . . . . . . . . 108
Reviewing or Relating Material to Past
Experience . . . . . . . . . . . . . . . 110
Attitude Toward Computers . . . . . 112
Number of Questions Asked by "SCI" Ss . . 112

Number of Segments Repeated by "SCI" 53 . 114
Summary of AOV Results . . . . . . . . . . . ll4

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Chapter

Correlational Analysis . . . . . . . . . .
Attitude Toward Learning . . . . . . . .
Test Anxiety . . . . . . . . . . . .
Number of Questions Asked . . . . . . .
Time Expended in Learning Sessions . . .
Number of Notes Taken . . . . . . . . .
Time Expended on Notes . . . . . . . . .
Pre-Interest in Computers . . . . . . .
Miscellaneous Findings from the 3 x 4

Matrices . . . . . . . . . . . . . . .

Summary of Correlational Analysis Results

Comparison of Responses to the "Terminal

IntervieW" Questionnaire . . . . . . .

Agreement Among Rank- -Orderings of the

Segments . . . . . . . . . . . . . . . . .

DISCUSSION . . . . . . . . . . . . . . .

Interaction Between Treatments and
Subject-Types . . . . . . . . . . . . .
Performance of Ss in the "SCI" Treatment .
Performance of Ss in the "Random"
Treatment . . . . . . . . .
Performance of Low Aptitude/High Inquiry
"SCI" Subjects . . . . . . . . . . . . .
The Effect of Asking Questions . . . . . .
Learning Material . . . . . . . . . . . .
Concluding Remarks . . . . . . . . . . . .

LIST OF REFERENCES 0 O O O O O O O O O C O O C O O

APPENDICES . . . . . . . . . . . . . . . . . . . .

A. CODING OF THE CARDS . . . . . . . . .
B. "EXPERT" LIST OF QUESTIONS . . . . . .
C. "RANDOM" LIST OF QUESTIONS . . . . . .
D. INFORMATION SHEET . . . . . . . . . .
E. MAP TO LABORATORY . . . . . . . . .

F. ATTITUDE SURVEY TEST BATTERY . . . . .

General Inventory (biographical data).

Word Association . . . . . . . . . .
Political Position . . . . . . . . .
Attitudes Toward Learning. . . . . .
Inventory of Beliefs . . . . . . . .

Attitude Inventory (complexity scale)

Attitudes Toward Tests . . . . . . .

V

Page

118
122
125
125
129
131
133
135

137
138

140

148
151
151
154
156
157
158
159
160
162
170
170
173
175
178
180
181
183
185
187
188
192

199
201

Chapter

EXCERPT FROM MICHIGAN STATE UNIVERSITY
READING TEST . . . . . . . . . . . .

THREE EQUIVALENT FORM 60 ITEM TESTS. .
INSTRUCTIONS . . . . . . . . . . . . .
SUBJECT LOGS . . . . . . . . . . . . .
E'S CODE CONVERSION LIST . . . . . . .
SUBJECT MATTER SEGMENTS . . . . . .

POST-ATTITUDE SURVEY . . . . . . . . .
TERMINAL INTERVIEW . . . . . . . . . .

TABLE OF INTERCORRELATIONS BETWEEN
ALL VARIABLES . . . . . . . . . . .

RAW DATA FOR ALL SS . . . . . . . . .

vi

Page

204
206
239
247
250
251
309

316

320

325

Table

1.

10.

11.

LIST OF TABLES

Number of $5 for Each Cell of
Experimental Design . . . . . . . . . . .

Subject Characteristics . . . . . . . . . .

Proportion of Variance Accounted for by
Factors in "Attitudes Toward Learning"
Questionnaire . . . . . . . . . . . . . .

Rank-Order Correlations Between Rankings
Assigned to Segments by Pairs of Expert
Judges I O O O O O O O O O O O O O O O O

Coefficient of Concordance and Average
Intercorrelations Among Rankings Assigned
to Segements by Expert Judges . . . . . .

Change in Test Reliability After Deletion
Of Items 0 O O O O O O I O O I O O O O O

PrOportions of Variance Accounted for by
Factors in "Post-Attitude Survey" . . . .

Intercorrelations Between Gain Scores for
Instructional Treatment and Subject-
Type cells I O O O O O O O O O O O O O 0

Means and Standard Deviations, 2 x 2 x 4
Factorial AOV, and Four One-Way AOV of
Gain 1 Scores 0 O O O O O O O O I O O O I

Means and Standard Deviations, 2 x 2 x 4
Factorial AOV, and Four One-Way AOV of
Gain 2 Scores . . . . . . . . . . . . . .

Means and Standard Deviations, 2 x 2 x 4

Factorial AOV, and Four One-Way AOV of
Time Expended in Learning Sessions by Ss

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70

80

80

84

85

90

93

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Table

12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

22.

23.

Means and Standard Deviations, 2 x 2 x 4
Factorial AOV, and Four One-Way AOV of
Number of Pages of Notes Taken by Ss .

Means and Standard Deviations, 2 x 2 x 4
Factorial AOV, and Four One-Way AOV of
Time Expended on Notes by Ss . . . . .

Means and Standard Deviations, 2 x 2 x 4
Factorial AOV, and Four One-Way AOV of

Perceived Learning as a Function of the

Environment Scores . . . . . . . . . .

Means and Standard Deviations, 2 x 2 x 4
Factorial AOV, and Four One-Way AOV of
Attitude Toward Order of the Segments
Scores . . . . . . . . . . . . . . . .

Means and Standard Deviations, 2 x 2 x 4
Factorial AOV, and Four One-Way AOV of
Attitude Toward Method of Learning
Scores . . . . . . . . . . . . . . . .

Means and Standard Deviations, 2 x 2 x 4
Factorial AOV, and Four One-Way AOV of

Perceived Learning as a Function of Task

Difficulty Scores . . . . . . . . . .

Means and Standard Deviations, 2 x 2 x 4
Factorial AOV, and Four One-Way AOV of
Reviewing or Relating Material to Past
Experiences Scores . . . . . . . . . .

Means and Standard Deviations, 2 x 2 x 4
Factorial AOV, and Four One-Way AOV of
Attitude Toward Computers . . . . . .

Means, Standard Deviations, and Range of
the Number of Questions Asked by "SCI"
85 Within Each Subject-Type Group . .

Analysis of Variance of Number of Questions

Asked by "SCI" 55 . . . . . . . . . .

Means, Standard Deviation, and Range of

the Number of Segments Repeated by "SCI"

Ss Within Each Subject-Type Group . .

Analysis of Variance of Number of Segments

Repeated by "SCI" Ss . . . . . . . .

viii

Page

100

102

103

105

107

109

111

113

115

115

116

116

Table

24.

25.

26.

27.

28.

29.

30.

31.

32.

33.

Overall Correlations Between Individual
Difference Measures and Criterion-Test
Scores Within Each Treatment Group . . . .

Correlations Between Attitudes Toward
Learning and Criterion Scores Within
Treatment and Subject-Type Groups and
the Significance of Differences Between
These Correlations . . . . . . . . . . . .

Correlations Between Test Anxiety and
Criterion Scores Within Treatment and
Subject-Type Groups and the Significance
of Differences Between These Correlations.

Correlations Between Number of Questions

Asked and Criterion Scores Within Treatment
and Subject-Type Groups and the Significance

of Differences Between These Correlations.

Correlations Between Time Expended in
Learning Sessions and Criterion Scores
Within Treatment and Subject-Type Groups
and the Significance of Differences
Between These Correlations . . . . . . . .

Correlations Between Number of Notes Taken
and Criterion Scores Within Treatment
and Subject-Type Groups and the
Significance of Differences Between These
Correlations . . . . . . . . . . . . . . .

Correlations Between Time Expended on Notes
and Criterion Scores Within Treatment
and Subject-Type Groups and the
Significance of Differences Between These
Correlations . . . . . . . . . . . . . . .

Correlations Between Pre-Interest in
Computers and Criterion Scores Within
Treatment and Subject-Type Groups and
the Significance of Differences Between
These Correlations . . . . . . . . . . . .

Frequency and Percentage of 53' Response
to Items of "Terminal Interview"
Questionnaire Within Instructional
Treatment Groups . . . . . . . . . . . . .

Agreement Among Rank—Orderings of the
Lecture Segments . . . . . . . . . . . . .

ix

Page

120

123

126

127

130

132

134

136

144

149

Figure

1.

10.

11.

LIST OF FIGURES

The Degree of Learner/Instructor Control
for Selected Instructional Functions
Over Selected Referent Researchers . . .

Carrel Configuration: TV Monitor, Earphones,

and Microphone . . . . . . . . . . . . .

Control Room Configuration: Video Tape
Recorders, TV Monitors, Patch Panels, TV
Camera (Lens Only), Microphone, and
Earphones . . . . . . . . . . . . . . .

Scatterplot of Experimental Subjects in
Terms of Their Aptitude and "Inquiry"
scores 0 O O O O O O O O O O O O O O O 0

Mean Gain Scores for Instructional Treat-
ments Within Each of Four Subject-Type
Groups . . . . . . . . . . . . . . . . .

Mean Time Expended by 85 for Instructional
Treatments Over Four Subject-Type Groups

Mean Attitude Toward Order of Segments
Scores for Instructional Treatments Over
Four Subject-Type Groups . . . . . . . .

Mean Attitude Toward Method of Instruction
Scores for Instructional Treatments Over
Four Subject-Type Groups . . . . . . . .

Mean Perceived Learning as a Function of
Task Difficulty Scores for Instructional
Treatments Over Four Subject-Type Groups

Mean Gain 1 Scores for Subject-Types Over
Three Treatment Groups . . . . . . . .

Mean Gain 2 Scores for Subject-Types Over
Three Treatment Groups . . . . . . . .

X

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45

56

56

62

98

99

106

108

110

152

152

Figure Page

12. Example of Typical Card . . . . . . . . . . . 172

13. Example of "Overview" Card . . . . . . . . . 172

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CHAPTER I

INTRODUCTION

A number of areas of psychology have shown self-
direction to be a motivating, satisfying, and an effective
mode of interacting with one's environment. There is only
limited and questionable evidence that this is true for
learning environments. The purpose of this investigation
'was to test the hypothesis that certain types of individuals
can control their own learning or instructional strategies
and thereby learn more and be more satisfied than by
conventional means.

Many psychologists and educators have pointed out
the need to shape training programs to the wide range of
individual differences among students. Crawford (1962),
for example, has pointed out that because of limitations
in available manpower and because of imperfect selection
procedures, there is great heterogeneity of input into
training programs designed with rigid output specifications.
Along with many other specialists, he has emphasized that
the adaptation of training to the interests, aptitudes,
motivations, and background characteristics of trainees
constitutes a major research problem.

1

Today, individualized computer-assisted instruction
(CAI), adaptive to the individual needs of each learner,
appears to be the goal of instructional system designers
(Mitzel, 1970). However, in designing a CAI system, it is
necessary to decide if the learner should control the
programming of the system, if an instructor should control
the system for the learner, or if some mix of the two is
best.

A few studies dealing with student-controlled
instruction have been carried out. Mager (1961), Mager
& McCann (1961), and Grubb & Selfridge (1964) have produced
striking results and claim that student control increases
learning effectiveness. However, they either failed to use
«control groups or included other variables which may have
«accounted for the observed results. On the other hand,
‘the only investigator in this area who has done controlled
<experimentation, Campbell (1964), obtained equivocal re-
sults. In fact, the only experiment of his that produced
results in favor of student control was when he trained
$3 to use student control procedures.

As a result, student control remains a questionable
instructional technique, while being assumed otherwise.

There are three objectives of this dissertation:
first, to consider evidence from learning theory and
other areas of psychology that may be relevant to the

question of why student control of instruction should

facilitate learning; second, to define the variables in-
volved in the student/instructor control dimension; and
finally, to test in a controlled manner a series of
specific hypotheses regarding student control of instruc-

tion.

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CHAPTER II

REVIEW OF THE LITERATURE

In order to focus on the problem at hand, the
following review will be concerned only with attempts to
facilitate the acquisition of meaningful, verbal knowledge.
The review will begin with a look at the results of research
concerned with student-controlled instruction. Then, re-
search findings and theories from other areas of psychology
relevant to student-controlled instruction will be considered.
Finally, the variables involved in the student/instructor

control dimension will be defined.

Research on

 

Student-Controlled Instruction

 

Over the years, there has been little effort ex-
pended by educators in the direction of giving students a
say in their own education, much less any control. Re-
cently, however, a number of psychologists, especially
Rogers (1969), have advocated more student control of
instructional functions. Unfortunately, only a few inves-

tigations in this area have been carried out.

The Research of Mager

 

The earliest and most striking research done in
the area of student-controlled instruction was that of
Mager (1961), who has demonstrated that when a student is
given maximum student control interesting phenomena occur.

He describes his first exploratory study as

follows:

A single learner, who had expressed a desire to
learn something about the subject of electronics, was
taken to a small seminar-sized classroom equipped with
working tables and a blackboard. S was informed that
he would be given complete control over the curriculum.
It was explained that the instructor would try to
behave only as a responsive mechanism, that he would
offer information only in response to questions from
S, and that he would continue to offer information and
explanations until S asked him to stop or indicated
that he understood. S was informed that he would be
free to ask for information in any area of electronics,
that he would not be restricted to any particular
branch of the subject matter. It was explained that
S could ask for information, examples, demonstrations,
problems, reviews, or anecdotal material. The initial
instructions contained three different references to
the fact that the purpose of the experiment was to
assist S to learn as much about electronics as he
wanted to learn, that he was to be the sole determiner
of the topics covered, and that he should continue to
press the instructor for further explanations until he
was satisfied. Finally, 8 was told that an instruc-
tional session would continue until terminated by him
(p. 406).

Mager's six subjects initially did not believe the
instructions, but soon took control of their learning. They
demonstrated a strong tendency to associate new information

with old. "Meaningfulness," thus, seemed to be:

. . .not only a function of the number of asso-
ciations, but is also a function of the connectedness
of the instructional elements. When an ifiStructional
sequence contains gaps for the learner, he attempts to
forestall the collapse of the sequence, in terms of
its meaningfulness, by recruiting material from his
experience. Thus, if a learner is constantly making
correct or incorrect associations, such a phenomenon
is occurring during traditional instructional tech-
niques, but mostly without the knowledge of the
instructor. If, during a learning experience, the
learner attempts to increase the meaningfulness of
what he is learning by making associations and by
filling in the gaps, it would seem important to take
steps to answer the learner's questions at the time
they arise. This might be achieved by giving the
learner control over some aspects of curriculum con-
tent and sequence. It is likely that meaningfulness
of instructional material could be increased by
allowing the learner to indicate what gaps exist for
him at the time they appear, and then filling the gaps
with correct information (p. 409).

 

All of Mager's subjects displayed motivation and
enthusiasm in learning. They would initiate reviews or
attempt to recapitulate, not going further until they
could correctly restate previously learned material. Note-
‘worthy is the fact that the instructor was forced to
". . .play the role of a device which provided knowledge
of results, rather than a device which provided a review
(p. 411)." The subjects averaged 65 minutes per session
and said that they terminated their sessions because,

". . .they felt unable to assimilate more material at that
time, rather than because they were bored (p. 411)."
When the instructor attempted to take over the

instruction in the traditional manner, ". . .8 either

terminated the session, or continued asking questions as

he had prior to that point, or began looking out the
window (p. 411)."

Sequencing by the subjects was not "logical" when
compared to traditional course sequencing. Even though no
specific learning objectives were provided, there was
considerable commonality among the subjects' independently
generated sequences, especially at the outset of instruc-
tion. Mager says that the sequence for the subjects went
from a simple whole to a more complex whole, not from
abstract to concrete, and not from elements to wholes.
ng_something works preceded questions as to whgt_made it
work. If the questions were about components, function
preceded structure. It should be noted that Crawford
(1962) cites two military training studies which confirm
the above order of sequencing to be more efficient than
others, at least for some types of trainees.

To Mager, the results suggest that the learner's
motivation increases as a function of the amount of learner
control. Reinforcement for content-approaching responses
and/or participation were given as other possible reasons.
Unfortunately, no criterion measures were taken to test
the efficacy of this method of instruction.

In Mager and McCann's (1961) exploratory study in
this area, the effects of learner control of curriculum
and instructional objectives in a real-life industrial

situation were observed. The first "experimental" group

of six newly graduated engineers were permitted to initiate
their own learning by asking for any information they needed
from anyone in the plant. Even though they had only general
objectives to work toward, they soon believed their in-
structions, became cooperative among themselves, demanded
clarification of unclear instruction, and satisfactorily
completed their training in three months instead of the
standard six-month period.

A second "experimental" group of six engineers
repeated the above study except for the addition of a
twenty-page booklet of behaviorally-stated training objec—
tives and reference material (information sources) collected
by the first group. This group completed their training in
an average of 7-1/2 weeks or a reduction in time of 65%.
Subjectively, these engineers were judged by their super-
visors as more confident and more able to work with less
direct supervision than previous trainees.

To Mager, the results suggest that the instructor
who, ". . .tightly controls the curriculum and its
sequencing may even constitute a significant hindrance in

the path of the learners' progress (p. 20)." He adds:

It seems that by providing the learner with a
detailed behavioral blueprint, we have in a sense
provided him with the capability of programming his
own activities. We have given him the necessary
structure with which he can match his own knowledge
and experience to that required of him, and we have
provided him with a means of evaluating many of the
responses that he makes during his training (p. 20).

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It appears as though Mager may have demonstrated
the optimal framework for training. The learner is his own
programmer as Bruner (1961) suggests. In his active
involvement in what he learns, he organizes information into
cognitive structures which are meaningful and useful to him
and his interests, aptitudes, personality, and the learning
set differences. All of this is due to inquiry in an
environment where the learner knows the objectives, is in
control, and sequences the elements in a manner insuring
optimal mediational effect.

However, these "experiments" lacked control. For
example, no control group was used to show how the formal
course might have been redesigned for improvement in
efficiency and effectiveness. Also student control need
not have been the determining factor. Just knowing the
learning objectives may have been a sufficient reason for
the differences observed.

The fact that Mager has gone on to exploit the
learning objectives variable and not the student-control
variable attests to the difficulties inherent in adequately
controlling its components.

There have been a couple of studies where computers
were used as instructors in situations quite similar to
Mager's basic framework. Feurzeig (1964), for example,
developed a sort of "twenty questions" game where, through

inquiry, the learner could solve problems by gradual

10

acquisition of information. The learner had a limited
vocabulary to choose from and the computer typed apprOp-
riate replies according to complex conditional statements.
However, considerable freedom was accorded to the learner
as he could, ". . .specify the information he wants when
he wants it, and can prepare a solution whenever he

likes (p. 39)." So far there have been programs written
for electronic trouble-shooting, management decision
making, and medical diagnosis: but no evaluations have

been made.

The Research of Grubb and Selfridge

 

Grubb and Selfridge (1964) programmed a computer
to help instruct the first half of a one-semester, elemen-
tary course in psychological statistics. Upon inquiry by
the learner, the computer directed each learner to a
specific reading assignment and then verified how he had
mastered the material by giving him problems and tests.
Remedial material and sub-routines were called in case of
incorrect functioning (e.g., error cues were given up to
three times before the correct solution and an alternative
problem was given).

In comparison with eight students, taught conven—
tionally by Grubb, the six experimental subjects were far
superior in performance. The computer-assisted subjects
averaged only 5.8 hours of instruction time, including

text reading and review, and averaged 94.3% on the

 

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mid-semester examination; whereas the conventional sub-
jects averaged 24 class hours, 25 homework hours, 5.3 re-
view hours, and 58.4% on the identical examination. It
should be noted, however, that the latter subjects wrote
diaries elaborating on their difficulties with solving
the problems, etc. These diaries were then used in develop-
ing the special texts and program used by the experimental
subjects. Also, the experimental subjects had a signifi-
cantly higher average grade point average (2.68 vs. 2.14).

The authors point out that in the computer-assisted
instruction, redundancy was low and the steps of informa-
tion were large. The highly dynamic, interacting,
responsive environment with its immediate feedback during
the problem solving also contributed to consistently high
motivation in the students' behavior.

Although the results are extraordinary, it is
obvious that the above "experiment" was not properly con-
trolled since the so-called independent variables were not

manipulated independently.

The Research of Campbell

 

Only one researcher has made a systematic attempt
to theoretically explain and experimentally determine the
effectiveness of student control on learning. After re-
viewing a number of field studies which compared various
self-directing or learner-centered methods with instructor-

centered methods, Campbell (1964) found that not only were

12

the results conflicting but the degree of student control
was confounded with differences between individualized

and group methods of instruction. Invariably the comparison
condition was a group method of instruction, as was true of
Grubb and Selfridge above.

Therefore, Campbell went to individualized pro-
grammed instruction in order to: (a) control for differ-
ences between group and individual differences, (b) equate
the content of instruction presented under the various
conditions, and (c) enable the learner to control the
learning process in greater detail.

The series of experiments reported by Campbell are
similar to those described by Mager (1961) and Mager &
McCann (1961), except that the content was packaged in
such a way as to give students control of what components
were to be used and their sequence of use. Briefly,
Campbell intended to compare the effectiveness of an
instructor-determined sequence of study with student self-
direction.

The theoretical basis on which Campbell hypothesized
student control to be superior was also similar to Mager's:

(a) meaningfulness of the learning task to the student at

 

any given moment, and (b) motivation. Campbell believed
that the learner can best evaluate the meaningfulness of
his progress in learning. Attempts by an instructor to

impose his sense of meaningfulness on the learner may

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either be regarded as an intrusion, a disruption of the
learner's line of thought, or to be considered as an
authoritarian act. Campbell also believed that for many
students, greater freedom and responsibility per se may
increase motivation to learn.

Of the several subject matter areas sampled in a
series of experiments, the only significant difference
favoring self-direction over programmed instruction was for
mathematics, and that difference occurred only after coached
practice in self-direction. The self-directed group was
provided the following material: (a) a short basic text
(actually a short linear program with only occasional
questions), (b) supplementary examples and explanations,
(c) self-testing questions, and (d) a two-page outline of
the entire lesson. These materials were cross-indexed.
The student could use these materials, or only part of
them, in any sequence he wished. The programmed group
used a linear program, self-paced. Both groups were free
to consult the teacher at any time. Without coaching,
there were no significant differences between the two
groups. Of the students who were in the self-direction
group, those who learned most tended to be those who were
high achievers in school.

Campbell explained that coaching was needed to
break up the students' set for passive instruction (i.e.,

do just as they are told), to which they had become

l4

conditioned. In fact, if not coached, nearly half of the
students in the self-direction group reported reading their
materials straight through, one component at a time,
rather than in a back and forth, step-by-step procedure.
Actually, the greatest criticism of Campbell's
experimental procedure lies in his failure to provide
information to the self-directed learner other than that
programmed. Since the self-tests and supplementary infor-
mation were written and designed by the experimenter, there
was no other way for the learner to acquire information he
may have needed to fill in the gaps in either the informa-
tion or in his cognitive structure. The learner cannot
truly be self-directive or in control of his learning if he
reamins locked into the instructor's preconceived and
limited conception of what questions are relevant and what
organization is best. This is not individualized instruc-
tion but a poor substitute. What about questions the
learner might have which are related to his own unique
background and experiences? What about going in depth into
a point of immediate concern to the learner? Since
Campbell offered no evidence of the students having asked
the teacher questions, it can be assumed that the learning
task was not demanding and challenging enough to invite
inquiry beyond the programmed questions and answers. Ex-
cept for the non-program class discussed below, it is

apparent that the learner in these experiments had no need

15

to control his learning or be self-directive. Thus, it
is quite possible that Campbell tested his hypotheses in
situations where the variables under investigation had
little chance of making an impact.

Perhaps coaching was required not because students
were conditioned to accept things as they were, but because
of the inherent order in the basic text (a short linear
program). The text started out somewhere (not necessarily
at any given student's entry level) and proceeded to an
end. Since all examples and self-tests were cross-indexed,
there were no important decisions to make, just the oppor-
tunity to add, repeat, or delete as desired. This was
really not much more than a branching type program, except
that the student had a say as to where and when the branch-
ing occurred. Although Campbell says the students were
free to roam over the material at will, he presents no
evidence to show that this happened.

Mager (1961) noted that his students took con-
siderable time to realize that they were in control of
their learning. In Campbell's case, it is difficult to
believe that the students ever felt they were "self-
directed," what with all of the structure imposed on the
content by the experimenter. If they had been presented
with a genuine challenge, where the starting point was
left up to them and the cross-indexing was related to the

various parts of the content instead of just the questions

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16

and examples, then perhaps Campbell would have been really
tapping the students' cognitive abilities. As it was, the
learning tasks demanded little in the way of self-direction.

In addition, the learning tasks contained the same
drawbacks associated with programmed instruction, as dis-
cussed below. For example, due to the length of these
programs (one was five days, 50 minutes each day), they
were undoubtedly boring. Evidence for this inference comes
from one of Campbell's own experiments. He reported that
one of the self-directed classes was given no program at
all; that is, neither programmed text nor self-testing
questions. Instead, each student was given an outline and
an atlas and instructed to use general classroom references
or to consult freely and privately with the teacher.

Although they generally disliked this free inquiry
method of learning, this class learned the same amount as
the programmed and self-direction groups, but in one-half
the time. Five months later the three groups were again
equal in achievement. This finding implies that all of
the linear programs used were redundant and repetitious.
This finding also implies that under self-direction con-
ditions it was possible for the students to skip the redun-
dant parts and use the extra time to review or practice the
more difficult parts.

Since coaching should have increased the students'

motivation to learn more, this sort of behavior would have

17

increased. However, since the comparison group (who were
also coached) could not deviate from the fixed sequence,

any added external motivation they received was probably

wasted.

One consequence of being able to skip redundant
material and using the time for practice and review is the
gaining of more exposure to the information of importance.
Thus, the comparison group is at a decided disadvantage.
Observed differences between self-direction and programmed
students could be due to this behavior alone, especially

with coaching.

Research Findings and Theories
from Other Areas of Psychology

 

 

The research discussed above constitutes the bulk
of investigations directed toward student-controlled in-
struction. Nonetheless, there are a variety of areas in
psychology which lend support to the contention that
student-control of instruction is a worthwhile instructional
technique.

The first area to be considered is learning theory.
Elements of learning theory: cognitive structure, hier-
archial organization, discovery versus guidance, active
Participation, intrinsic motivation, and S-R theory will
all be shown to advance the rationale for self-direction
in learning. Later, organizational and engineering psy-

chology will be shown to offer additional evidence that

18

student-control should result in increased learner satis-
faction and motivation. Then the humanistic theory of
Rogers will be examined. Finally, the general area of
individual differences in relation to student control will

be discussed.

Learning Theory

 

Much of the experimental research in learning has
been directed toward testing theoretical points which have
had little immediate practical application. Not long ago
Gagné and Bolles (1959) stated that, "relatively little
is known about learning in real life situations (p. 13)."
They added, "On the other hand, educators, who do work with
practical learning situations, have not done the systematic,
controlled type of study that is needed to reveal general
principles of learning efficiency (p. 13)." Nonetheless,
there does exist some theory and research relevant to the
question of student control of instruction.

Cognitive structure theory.--Since Miller, Galanter,
and Pribram's (1960) book renewed interest, cognitive
structure theory has been a fruitful approach in the mean-
ingful learning area. Bruner (1961) has gathered evidence
which suggests that each learner, in a sense, must be his
own programmer. "In his active involvement in what he
learns, he organizes information into cognitive structures
which are meaningful and useful to him (p. 22)." He cites

findings to indicate that if information is organized in

19

terms of a person's own interests and cognitive structure,
it will make that material more accessible for retrieval.
Of particular interest, he concludes that mediators of
words and ideas that children choose themselves are ob-
viously more relevant and familiar to them than mediators
suggested to them by others.

Ausubel (1963), another cognitive theorist, insists
that cognitive structure is the most significant independent
variable influencing the learner's capacity for acquiring
more new knowledge in the same field.

There are processes which enable a learner to pro-
gram himself, to process information, to monitor his own
performance, etc. Rigney and Towne (1970) suggest the
following processes which can mediate learning: memoriza-
tion, the use of familiar analogies, verbalization of con-
cepts, and the use of mental imagery. Since these inter-
nal processes are under the control of the learner, he can
help himself learn. However, it is up to the instructional
systems designer to make it possible, externally, for the
learner to use these mediators within his own cognitive
structure.

Campbell (1964) states that the best evaluator of
meaningfulness of new information is the learner. When a
learner is trying to learn really new ideas and not some
combination of already familiar ideas, most of the infor-

mation he receives has a good deal of uncertainty attached

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to it. The extent to which the learner can abstract the
important points is rather unpredictable. Only the
learner's sense of having grasped or not grasped a new
idea is of real importance. It follows that all the
instructional design in the world is worthless unless it
can be used by the learner for effective manipulation of
the information to be learned.

Hierarchical organization.--Related to cognitive

 

structure is the emphasis placed by some theorists on
hierarchical organization. The capabilities that a person
brings to a task as a result of prior training and experi-
ence are called "learning sets" by Gagné (1962). These
sets presumably mediate transfer from lower-level tasks

to higher-level tasks in a hierarchical manner. Lower-
level "learning sets" are very simple and general, common
to a great variety of complex learning tasks. This implies
that instruction should help the learner select from his
population of "learning sets" and organize them as they
are needed (Rigney & Towne, 1970).

Discovery versus guidance.--The question of how

 

much learning should depend on discovery by the student or
on guidance by the instructor has yet to be answered. Re-
search evidence, to date, when impartially evaluated, shows
no advantage to the independent discovery method per se
over expository teaching in the learning, retention, or

transfer of principles (Ausubel, 1963; Wittrock, 1966).

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21

Ausubel (1963) is convinced that, "the most
efficacious type of guidance (guided discovery) is actually
a variant of expository teaching that is very similar to
Socratic questioning (p. 171)." This type of instruction
demands the learner's active participation and requires
him to formulate his own generalizations and integrate his
knowledge in response to carefully programmed leading
questions.

Campbell (1964) suggests that in the situation
where the learner is expected to develop and pursue a line
of thought, "attempts by an outsider to evaluate the pro-
cess may constitute an intrusion, a disruption of the line
of thought . . . (p. 349)."

Ausubel (1968) says that Bruner and Suchman advo-
cate the use of discovery methods because they believe
learning by discovery is, ". . .a unique and unexcelled
generator of self-confidence, of intellectual excitement,
and of motivation for sustained problem solving and crea-
tive thinking (p. 493)." However, Ausubel counters this
with the argument that unsuccessful discovery experience
can have an Opposite effect. He is convinced other means
are equally motivating and more economical in time spent.

Suchman (1961) has found that the act of inquiry
per se is motivating and builds confidence among elementary
school children. However, it is important for our purposes

to note that for optimal inquiry to take place a special

22

environment was necessary. This environment not only
allowed the learner to reach out for desired information
at any rate or sequence but also consisted of information
sources which responded quickly.

Active participation.--Active participation on the

 

part of the learner, in addition to being considered facili-
tative in the acquisition of new concepts, is motivating
(Rogers, 1969). Ausubel (1968) believes strongly that the
more active the process of relating to existing relevant
concepts, the more meaningful and useful assimilated con-
cepts are.

Intrinsic motivation.--Harlow (1953), in addition

 

to curiosity, has promoted manipulation as a motivational
force in human learning. He has asserted that one reason
why we learn or do anything is to be, ". . .inquisitive
about and playful with an environment which manages to
keep us fairly well aroused much of the time (p. 25)."
Terrell (1959) confirmed Harlow's hypothesis with humans
in an experiment which varied the learning environment
only in the amount of manipulative activity permitted.
There is similarity here to White's (1959) "competence
motivation," the need to deal with the environment (self-
actualization) (Sears, 1967).

The trend in recent research and thinking has been

to place greater emphasis on the motivational power of

such intrinsic motives as curiosity, exploration, activity,

23

manipulation, competency or mastery, and the need for
stimulation. As drives, Ausubel (1968) tells us, they

are ". . .gratified (reduced) merely by the very fact of
successful learning itself (p. 365)." He concludes that

in many human learning situations, adding extrinsic rewards
on top of adequate intrinsic rewards does not dramatically
accelerate the rate of learning as it does in comparable

animal experiments. Thus, Ausubel says:

. . .hunger, thirst, pain, and the like rarely
motivate human learning; and although material rewards
are often effective, intrinsic (task-oriented) and
ego-enhancing motives increasingly tend to dominate
the motivational picture with advancing age (p. 364).

There are hazards inherent in designing instruction
so that it demands exploration. Bruner (1968) warns that
although a task that is cut-and-dried provokes little
exploration, ". . .one that is too uncertain may arouse
confusion and anxiety, with the effect of reducing explora-
tion (p. 43)." Bruner would add that for exploration to
have direction, the objective of the task must be known
and the learner should know where he stands with respect
to it. It is necessary for the learner to learn to recog-
nize when he does not comprehend, so that he can seek help
or translate the information into the learner's way of
thinking.

S-R Theory.--The above elements of learning theory

 

are primarily cognitive in origin and deal with mediational

processes. On the other hand, S-R theory, a major learning

24

theory in its own right, has had a far greater impact on
practical application, as is evidenced by programmed
instruction.

Skinner (1968), a chief advocate of S-R psychology
and programmed instruction, would emphasize reinforcement
of the learner to the exclusion of all other methods.
However, in Annet's (1964) survey of knowledge of results,
the term "reinforcement" is shown to be merely descriptive,
due to its circularity as an explanatory concept.

Bugelski (1964) and Ausubel (1963) both note that
in Skinner's programmed instruction, reinforcement from
knowledge of results is quite small. This, they say, is
because knowledge of results is ubiquitous; the learner's
responses are almost invariably correct. Bugelski adds
that reinforcement for humans can easily be 24 hours later,
and that in programmed instruction, it is unnecessarily
redundant.

But isn't programmed instruction supposed to be
the most promising instructional technique yet devised?
After all, according to Bass and Vaughan (1966) programmed
instruction has paid greater heed to the principles of
learning discovered in the laboratory than any other
training technique to date.

Although‘ it is true that programmed instruction
is individualized instruction and potentially permits

student control (e.g., self-pacing) according to Campbell

25

(1964), it tends to give the learner even less control than
he has with an ordinary textbook.

To understand why there is not a movement in the
opposite direction, toward more freedom and control for
the student, one must consider the source from which pro-
grammed instruction was derived and the environment in
which it is being used.

Skinner (1968), except for specific instances,
promotes instructor control of all phases of learning.
After all, his techniques were derived from pigeons which
could not talk or think for themselves. Consequently, the
following outcomes for programmed instruction have occurred:

1. Since frames are so small, fragmenting the
content, programmed instruction is useful, at present, only
for factual material. Thus, rote learning is fostered
(Ausubel, 1968).

2. The "pall effect" or boredom, due to repetition
and redundancy, sets in after longer sessions of programmed
instruction (Feldhusen, 1963; Smith & Smith, 1966). As a
consequence, careful organization of material is often
ineffective (DeCecco, 1963).

3. Student attitudes toward brief sessions with
programmed instruction contrast sharply with longer sessions.
The latter consider programmed instruction as mechanized,

non-thought provoking, anti-insightful, etc. (Roth, 1963).

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The former, positive attitudes, can be chalked up to the
Hawthorne effect (Grazia & Sohn, 1962).

4. Program sequences are identical for every stu-
dent. Entering behavior is not adjusted for. Even in
branched programs, the remedial 100ps are based on a priori
conceptions of where deficiencies in student response
repertoires or capabilities exist (DeCecco, 1964).

5. Small discrete frames and responses leave little
individual control (Smith & Smith, 1966). Structure is
destroyed (Pressey, 1963). There is no integration of
larger units into patterns which the human is best at
dealing with (Ausubel, 1963).

6. The learner must conform to the arbitrary style
of the programmer: no inquiry, no skimming, no reviewing
of the material, and no reference to the interests of the
learner (Pressey, 1963; Campbell, 1964).

7. Tests of performance reveal only small and
scattered advantage for programmed instruction over other
forms of instruction. Even textbooks have shown better
results in some cases (Feldhusen, 1963; Silberman, Melaragno,
Coulson & Estanan, 1961).

Clearly, programmed instruction has shown little
improvement over other methods in adapting training to the
interests, aptitudes, motivations and background charac-

teristics of learners.

 

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Still, the potential for more student control is
evident, especially when computers are used to assist in
the instruction (CAI). Why has student-control not been
emphasized more by those responsible?

One major reason lies in the fact that programmed
instruction (and S-R theory) in its present mechanistic
form agrees with the assumptions that most trainers hold
about trainees. Although exceptions exist, both educa-
tional and industrial organizations have continued to
assume that learners are: (a) inherently lazy, (b) pri-
marily motivated by extrinsic incentives, (c) naturally
opposed to organizational goals, and (d) incapable of self-
discipline and self-control (Bass & Vaughn, 1966). These
assumptions imply that the organization must plan for its
learners, efficiently organize them, motivate them, and
control by economic rewards and competition.

This practice is in marked contrast with recent
organizational assumptions about peOple. As jobs have
become increasingly complex and demanding, organizational
assumptions about man have been modified from the above
mechanistic model to ones that acknowledge the social and
self-actualizing needs of employees.

Although existing training methods are still
fixated on mechanistic assumptions of man, research evidence

from organizational and engineering psychology will offer

 

In

28

insights into the possible beneficial effects of giving

the learner control of instruction.

Organizational Psychology

 

Organizational psychology emphasizes the systems
approach with its concepts of feedback loops and self-
regulating forces (Schein, 1965). For example, King (1958)
has used a cybernetic View of the production shop to show
it to be an inefficient "split-system." Through role
enlargement the worker was permitted to control his own
activity within agreed limits while the supervisor, freed
from detailed control tasks, had time for long-range
planning. The workers were free to choose their own methods
of getting the work done; those which were found to be most
efficient. Production rose as did worker satisfaction.

Increased production here should not be equated
with increased learning. However, the parallel between
the effect of self-direction on the two environments cannot
be overlooked.

In other cases, Bass and Leavitt (1963) have demon-
strated that self-planners are superior in morale and
productivity over those who have to follow plans developed
by others. In their study even a fool-proof plan (highly
simplified, highly routinized) carried out by others was
performed poorly.

Bass (1965) points out that men are not machines.

Rules need to allow for flexibility so that "decision rules

29

are self-corrective, modifying the job to make better
progress toward relevant goals (p. 255)." He also notes
that research shows that participation and the opportunity
to make decisions results in worker satisfaction and pro-
motes productivity.

Katz and Kahn (1966) argue that of four types of
motivational patterns, two offer much potential benefit if
work environments can be arranged to handle them. One is
satisfaction derived from making decisions about one's own
behavior (self-expression and self-determination). Intrin-
sic job satisfaction from craftmanship or variety and
challenge from job enlargement and responsibility are fac-
tors here. The second is value expression and self-
idealization (self-identity and self-concept). This
pattern includes innovation and spontaneity, but is diffi-
cult to achieve. In most cases there is little Opportunity
for differential task involvement due to standardization
of the work environment. Katz and Kahn admit too little
empirical evidence exists to completely support their
framework; but it fits with existing data.

Smith and Smith (1966) sum the above up by saying
". . .the central problem of industrial training is that
its techniques are geared almost invariably to the needs
of the company rather than to the needs of the worker
(p. 222)." They add that training should "enhance the

worker's ability to control his own patterns of responses"

30

and help him to become "a manipulator of his own job situ-
ation (p. 222)."

On the other side of the coin are the negative
effects caused by the standardized, impersonal, simplified
work environment. Blauner (1964), having studied different
types of industrial workers found four patterns of aliena-
tion depending on the nature of the technology. They are:
a sense of powerlessness or inability to influence the
work situation, a loss of meaning in the work, a sense of
social isolation, and a lack of any self-improvement with

the work.

EngineeringiPsychology

 

In contrast to the emphasis on the social nature
of man by organizational psychology, engineering psychology
emphasizes redesigning the job and its physical environment
to fit the limitations and capacities of the human. For
example, Mayer (1964) would warn us to avoid sensory and
memory overload in the design of instructional systems. At
the same time variability is needed to provide stimulation.
Also, she argues for making the objectives as well as the
interrelationships among them explicit; but the relation-
ship of means to the end should be left unspecified and
as flexible as possible. The danger of surpassing the
learner's memory span in communication to him is stressed

by Campbell (1964). Unless the learner can stop and catch

31

up, rehearse, and rethink, further information may be of
no benefit and may even confuse and distract him.

In recent years, engineering psychology has been
concerned with systems development. Kennedy (1962) views
the organization as an organism. Using synthetic environ-
ments as learning situations, under the assumption that
"experience is the best teacher," an "autotelic" atmosphere
is promoted. Immediate, dramatic, and direct feedback of
information concerning achievement of organizational goals
leads to adaptation and "learning" by crews. In addition
to becoming innovative and more effective in job perfor-
mance, morale, motivation, and involvement by the crew
increased proportionally. Thus, it is possible to develop
a training program which cannot only adapt to the interests,
aptitudes, and motivations of each single learner, but also

"develop" his ability to be innovative and creative.

Humanistic Theory

 

In direct contrast to the mechanistic formulations
of S-R theory and somewhat in agreement with the previously
cited research is the well-known clinical psychologist,
Carl Rogers. Rogers (1969) calls the educational systems
of this country the most traditional, conservative, rigid,
and bureaucratic institution.<3f our time. Among other

things, he says:

32

The young human being is intrinsically motivated
to a high degree. He is curious, eager to discover,
eager to know, eager to solve problems. A sad part
of most education is that by the time the child has
spent a number of years in school this intrinsic
motivation is pretty well dampened. Yet it is there
and it is our task as facilitators of learning to tap
that motivation, to discover what challenges are real
for the young person, and to provide the opportunity
for him to meet those challenges (p. 131).

Learning is facilitated when the student partici-
pates responsibly in the learning process. When he
chooses his own directions, helps to discover his own
learning resources, formulates his own problems,
decides his own course of action, lives with the con-
sequences of each of these choices, then significant
learning is maximized. There is evidence from industry
as well as from the field of education that such
participative learning is far more effective than
passive learning (p. 162).

Rogers (1969), Faw (1949), and Keller (1968) have
all developed means of individualizing classroom teaching
without programmed instruction or computers. Although
they provide a counter to the argument for automated
teaching, they also emphasize the need for student control

of instruction.

Individual Differences

 

Although not directly evident in the above dis-
cussion, there has been a continuing concern for taking
into account the individual differences of learners.

Even Skinner (1968) has said, "Failure to provide
for individual differences among students is perhaps the
greatest single source of inefficiency in education (p.
242)." His efforts with programmed instruction attest to

his attempts to accommodate individual differences.

33

However, self-pacing is the only variable in programmed
instruction that is truly individual (Bulgelski, 1964).

The potential effects of individual differences
(and learning styles) have generally been ignored by both
learning theorists and experimenters (Snow, Tiffin &
Seibert, 1965). Until recently, learner characteristics
have commonly been studied with respect to the content or
difficulty level of instruction. On the other hand,
investigations of learning under different modes of in-
struction have typically assigned subjects randomly to two
or more treatments, compared average performance on some
criterion, and then found no significant differences
(Dubin & Taveggia, 1968).

Investigations of the relationships existing be-
tween learner characteristics and methods of instruction
have been initiated because it was strongly felt that some
students may do better under one method of instruction
while other students may do better under another method.
The use of average performance scores alone, however,
masks this interaction between individual differences and
methods of instruction.

What follows is a brief overview of research on
instruction where individual differences among learners was
the major variable under consideration. The overview be-
gins with a look at individual differences associated with

personality variables of learners. Then cognitive variables

34

will be discussed, followed by individual differences re-
lated to the inquiry process. Finally, individual
differences among learners will be examined in relation to
the sequencing of subject matter.

Personality variables.--There have been a number

 

of experiments where whole classes of one type of student
were taught either in a non-directive or a structured manner.
For example, Dressel (1958) reported that a class of non-
authoritarian students in a social science class, taught

by non-directive methods, performed significantly better
than other students enrolled in other sections of the

course. Application of the same method to a section of
authoritarian students elicited such violent reactions,

the instructor shifted to formal lectures.

Haigh and Schmidt (1956) looked at teacher-centered
and group-centered classes. Their studies suggest that
students who are flexible in thinking, able to cope with
inconsistencies and ambiguities, understanding and accept-
ing of themselves--are probably able to enjoy and profit
from the give and take of group discussion. On the other
hand, students who seek the definite, the concrete, and
the ordered, avoiding the ambiguity and inconsistency,
seeing themselves (and life) in stereotyped ways--are
probably able to gain most in satisfaction and knowledge

from a teacher-centered class.

35

A number of studies have indicated that learning
alone, as is done with programmed instruction or computer-
assisted instruction, is related to the personality of the
student. Doty and Doty (1964), for example, found that
students high in social need performed poorly with pro-
grammed instruction. Traweek (1964) found that learners
who were successful with programmed instruction were also
more withdrawn, less self—reliant, and more test anxious
than less successful learners. Sutter and Reid (1969) add
that students high in sociability and low in test anxiety
achieved better on a problem solving task with a partner,
whereas students low in sociability and high in test
anxiety achieved better alone. Also, Lublin (1965) dis-
covered that low-autonomy-need students performed signifi-
cantly better than high-autonomy-need students.

Significant interactions between instructional
method and a variety of learner-characteristic variables
have been reported by Edgerton (1958); Snow, Tiffin, and
Seibert (1965); and Tallmadge and Shearer (1969). On the
other hand, Ripple, Millman, and Glock (1969) found no
interactions.

Tallmadge and Shearer have found the strongest
evidence to support the existence of learning styles.

They have Operationally defined learning style as an
attribute of an individual which interacts with instruc-

tional circumstances in such a way as to produce

36

differential learning achievement as a function of these
circumstances.

One implication from their research, however, is
that the nature of the material to be learned may affect
relationships existing between learner characteristics
and instructional methods and need to be examined as a
separate, independent‘experimental variable.

In the research carried out by Tallmadge and
Shearer, content governed by logical and inherently
"meaningful" rules and content governed by "arbitrary"
rules interacted with student learning style. The results
also suggested that the correlates of learning styles are
noncognitive individual differences, such as test anxiety,
technological and social/aesthetic interest, and
extraversion/introversion.

Recently, Leith (1970) has confirmed some of
these findings when he found that sixteen year old students,
who were extroverts, learned best with unstructured
material and "non-official" rules as confirmation; where-
as introverts learned best with structured and prompted
situations; nonanxious better with a "discovery" method
of instruction. The latter appear to readily tolerate
ambiguity and prefer not to be carefully prompted and
guided.

One study from industrial psychology is of interest.

Vroom (1959) found evidence of the existence of an

37

interaction between a work role variable (psychological
participation) and personality variable (need for indepen-
dence). Workers who had a high need for independence and
low authoritarianism had a moderate correlation (r=.55)
between reporting participation in making decisions and job
satisfaction; whereas this correlation was low (r=.l3)

for workers with a low need for independence and high
authoritarianism.

Cognitive variables.--Cognitive variables have

 

given contradictory results. King, Roberts, and Kropp
(1969) investigated the influence of verbal and figural
abilities on learning of material presented both verbally
and figurally. They found no differences. However, they
did find that inductive reasoning ability helped students
in a discovery-learning method and deductive reasoning
ability helped those taught in an expository manner. Con-
trary to Skinner's (1968) predictions of no correlation,
Lublin (1965) found scholastic aptitude to be correlated
with performance on a criterion test of a programmed text.
That is, high-ability students learned the concepts better
than lower-ability students. Lublin cites a number of
other studies on programmed instruction which both confirm
and contradict these results.

The correlation between general ability tests (e.g.,
IQ) and individual differences in learning performance,

when gain scores have been used, has generally been

38

negligible (Woodrow, 1946; Carver & Dubois, 1967). On the
other hand, tests of specific relevant abilities (e.g.,
tests of ability to add, subtract, or follow directions)
have had sizable significant correlations with performance,
transfer, and speed-of-learning measures (Gagné & Paradise,
1961). David, Marzocco, and Denny (1970) have recently
investigated the interaction between individual differences
and methods of presenting programmed instruction. They con-
clude that of the two sources of individual differences,
prior learning and general abilities, the former offers
more hOpe in identifying the best instructional method for
a given student. Along with Gagné (1962) they see prior
conditioning, transfer, and "learning sets" as accounting
for most individual differences in learning.

The findings above appear to open up entirely new
fields of investigation. However, since most of the
results were derived from learning through programmed
instruction, for the purposes of this study, they can only
serve as potential areas of investigation. On the other
hand, learning situations closer to those of Mager should
reveal learning style variables with more impact.

Inquiry variables.--When learning by discovery

 

methods, the learner is assumed to be forced to rely on
his own cognitive capacity, to become cognizant of the
relationships of the learning task to his previous experi—

ence or to the patterns of relationships among the elements

39

of that task (Kersh, 1958). Caron (1965) has found an
interaction between individual differences (ego involvement
and aptitude) and learning under conditions where curiosity
was promoted. Measurements of curiosity by Day (1968) re-
veal patterns of interest in approaching and exploring high
levels of novelty, complexity, and incongruity.

Such findings and theory are similar to those
advanced by Shulman, Loupe, and Piper (1968). They studied
the inquiry process and found significant individual
differences between effective and ineffective inquirers.

To study individual inquiry behavior, they developed
an instrument simulating natural conditions for teachers-
in-training. Since there were various degrees of problems,
order and depth of inquiry were up to the individual. The
situation was sufficiently realistic to engage the subjects'
emotional investment. This methodology was in line with
Shulman's (1965) assumptions about the "natural" inquiry

situation:

The "real world" does not consist of carefully con-
structed situations that are presented to individuals
as problems-for-solution. Instead individuals move
through an array of stimulus situations, which are
potentially problematic in varying degrees, selectively
reacting to some and not to others. Those situations
that are problematic do not present themselves one at
a time in a predetermined numerical order but derive
both their definition and order in which they are
handled from cognitive activity of the inquirer (p.
258).

40

From a battery of 17 tests, Shulman et al. came up
with six which they felt were determinants of inquiry per-
formance. For a restricted sample, they accounted for 50%
of the measurable variance in general inquiry behavior.

On the basis of their data, they characterized the person:

. . .high in general inquiry as high in associational
fluency; . . .high in cognitive complexity, preferring
the ambiguous, the assymetrical and the unexpected to
the regular, articulated and predictable; liberal in
political values, willing to risk on a test of logical
thinking; high in verbal problem solving; and low in
expressed test anxiety (p. 94).

Others offer evidence to support these conclusions (Bruner,
Goodman & Austin, 1956; Grimes & Allinsmith, 1961; Sears,
1967; Suchman, 1961, 1964).

Likewise, there is a striking resemblance between
the variables found producing significant interactions
here and in the literature reviewed above. Since the
methodology to be used later will be equivalent in many
respects to the environment used by Shulman et al., his
personality measures are of particular interests. None-
theless, there remains one other variable that deserves
mention.

Sequencing variable.--Recently, a larger number of
studies with programmed instruction material have shown
no difference on achievement measures when "logical"
and "random" sequences are compared (Niedermeyer, 1968;

JRoe, Case, & Roe, 1962; Levin & Baker, 1963; Hamilton, 1964).

41

Many researchers have suggested hypotheses for

these results:

a.

Learners highly motivated are able to overcome
the poor sequence (Briggs, 1968).

High-ability learners are able to overcome the
poor sequence (Niedermeyer, Brown, & Sulzen,
1969).

The content structure is of a low-order
hierarchy; therefore it can be learned in any
order (Niedermeyer et al.).

The content is too short and too redundant
(Payne, Krathwohl, & Gordon, 1967; Briggs,
1968).

The learner can set his own pace and thereby
sort out the confusion (Payne et al.).

The learner is kept more alert during random

sequences (Roe et al.).

Answers to these hypotheses are few. Detamble and

Stolurow (1956) found that for poor sequences (not random)

low ability students obtained lower scores than high

ability students. However, on a better sequence both groups

were equal.

In addition, the correlation between general

ability measures (IQ) and achievement on this latter se-

quence was essentially zero. This finding agrees with

studies cited above.

42

Later, Stolurow (1964) using material with a high-
order hierarchy, found no differences between random and
logical sequences. However, he did find a moderate correla-
tion (.61) between IQ and performance within the random
group.

Recently, Brown (1970) looked at materials of
low-order and high-order in terms of a Gagné-type (1962)
hierarchy. For the low-order task (principle learning)
there were no differences between high and low ability
students and no differences between performance on random
and logical sequences. However, for the high-order cri-
terion task (complex problem-solving), logical sequence
students did better than random sequence students. None-
theless, the high IQ students were able to a large extent
to make up for the random sequence.

As a consequence of the studies in this area, some
researchers (Leith, 1970) have even suggested that randomly
arranged encounters with material may be more beneficial
than systematically arranged instruction. In any event,
the interactions observed are part of a larger effort to
diagnose which types of students should be assigned to which
instructional strategy so that they will reap the greatest
learning gains.

Nonetheless, the purpose of this investigation is
to determine the extent to which the learner can control

his own learning or instructional strategy and profit by

43

it. Mitzel (1970) has coined the phrase "adaptive educa-
tion" to distinguish between the "individualized instruc-
tion" associated with the best of today's computer-assisted
instruction and the tailoring of subject matter presenta-
tions to fit the special requirements and capabilities of
each learner. Thus, in part, the question to be answered
here is: Who controls the instructional functions in
"adaptive education"--the learner, the instructional tech-

nologist, or some combination of both?

The Learner/Instructor Control Dimension

 

It is now appropriate to begin to synthesize what
has been discussed into a more meaningful whole. The focus
will be concerned first with defining more precisely what
is meant by student-controlled instruction. Its efficacy
in allowing for individual differences among learners will
then be discussed. Finally, an environment where student-
control should function effectively will be outlined. It
should be apparent from the above review of the literature
that student control of instruction potentially facilitates
learning. It should also be apparent that the dimension of
student-instructor control of instruction is a complex one.
Although various investigators have developed theories of
instruction (Bruner, 1968), principles of learning for
designing effective training programs (Schein, 1965), or

components of instruction (Gagné, 1969); only one has been

44

concerned with defining the student/instructor control
dimension.

In preparing an exhaustive review of the research
on sequencing of instruction, Briggs (1968) found it con-
venient to classify research on a learner/instructor con-
trol dimension. He described various aspects of control
over learning sequences and procedures which can be
assigned either to the instructor or to the learner.

A somewhat more detailed attempt to identify the
possible range of learner/instructor control than Briggs
is displayed in Figure 1. Selected instructional functions
are related to each of five selected researchers, who
represent anchor points on the learner/instructor control

dimension.

Instructional Functions

 

A brief description of each instructional function
and each researcher's position will now be discussed.

The first three functions in Figure 1 specify what
is to be learned. Experienced instructors not only deter-
mine the content and depth of learning, but also define
the behavioral objectives of the learning and the criteria
for successful completion. To be efficient it is also
worthwhile to determine the entry level of the learner so
as to begin at the optimal level of instruction.

The next six functions in Figure 1 are concerned

with how the content is to be learned and should be

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46

self-explanatory. Function seven is "questioning (cuing)
if by an instructor and "inquiry" if by the learner. The
last two functions in Figure 1 refer to the evaluation of
learning; the first as the learner learns and the second
at some specified terminal point.

Other instructional functions which are rather
difficult to attribute to either the learner or the instruc-
tor include: motivation, attention, and implementation of
mediation of concepts.

Magg£.--Research by Mager (1961; Mager & Clark,
1963; Mager & McCann, 1961) has been highlighted by arrange-
ments under which the learner is given much responsibility
and control over what is to be learned and how it is to be
learned.” "Independent study programs" and "independent
research" are familiar examples. The learner proceeds
entirely on his individual initiative in selecting objectives,
materials, study methods, and sequences of activity. If an
instructor is present, he acts only as an additional source
of information or confirmation.

Campbell.--This researcher (Campbell, 1964) set the
content, objectives and methods of presentation for the
learner. However, the learning materials were in well-
defined packages representing resources the learner could
use in any sequence he wished. In addition to self-testing

questions, an instructor was available for consultation.

47

Rogers.--Along the dimension of learner/instructor
control Rogers (1969) occupies a rather unique position,
mainly because he teaches a humanistic subject matter in a
clinical way. Although he requires his students to read
some books, he only suggests a wide range of possible titles.
Otherwise, the class and he mutually determine the functions
of instruction either as a group or as a dyad. Even ter-
minal evaluation is mutually agreed upon. The inclusion
of Rogers in the dimension is merely to point up the arbi-
trary nature of assigning instructional functions to either
the learner or the instructor.

Pressey.--In recent years, Pressey (1963; Pressey
& Kinzer, 1964), the father of programmed instruction, has
advocated adjunct autoinstruction. This consists of
multiple choice questions to be used by the learner, after
he studies assigned text segments in a sequence of his
choice. The questions are regarded not as examinations
or criteria but as ways to induce the learner to respond,
to receive feedback, and to undertake remedial study where
he is weak.

Ausubel.--A1though Ausubel (1963, 1968) is a
leading cognitive theorist in educational psychology, he
firmly represents those who contend that learning is not
a process of self-instruction. He believes that pedagogi-

cally sophisticated persons should select, organize,

48

interpret, and sequence learning materials. These are the
functions historically assigned to the instructor.

It should be noted that Skinner (1968) and pro-
grammed instruction differ from Ausubel's position in only
one function--pacing of the content. Even the latest
development in this area, "tutorial" computer-assisted
instruction, determines for the learner when to branch,
repeat, or do remedial study (Atkinson, 1968). So called
"individualized instruction" is still mostly under the

instructor's control.

Individual Differences and
Student-Controlled Instruction

 

 

It is possible to conceive of situations where
maximum instructor control (Ausubel and Skinner) means no
consideration of individual differences and where maximum
learner control (Mager) means full consideration of indi-
vidual differences.

For example, at one end of the learner-instructor
continuum is the plausible situation where the instructor
arbitrarily lectures from an arbitrarily prepared text,
insensitive to the learner's presence. At the other ex-
treme, it is possible for the learner to relate to the
instructor and material on an individual basis because he
can demand from the instructor, at will, any information,
examples, reviews or feedback in an order which he feels

is necessary for his personal learning.

49

Since the latter extreme enlarges the role of the
learner by making him more responsible for his learning,
by demanding that he make decisions, and by engaging him
in a dynamic, challenging, and interacting dialogue; it
is quite possible that some learners will not thrive in
such an environment. In other words, the learning environ-
ment ought to match the learning style of the learner.

The simplest test of any hypotheses about the
learner/instructor control dimension would be to compare
the effects on learning of the two extreme positions in
Figure 1 over a range of learning styles. The extreme
position associated with Ausubel and Skinner is well-known
and needs no further explanation. On the other hand, the
extreme position of Mager needs some modification, at least

for experimental purposes.

An Envirgnment Where Student
Control Can Function
Effectively

 

 

Considering just the instructional functions listed
in Figure 1, it can be seen that giving the learner com-
plete control of the first two functions (definition of
content area and definition of objectives), as Mager (1961)
did is impractical. That is, if the experimenter wishes
to compare the achievement of specified learning objectives
with conventional instructional methods. Possibilities for
complete learner control of the fourth function (method/

mode/unit size of presentation) will not be feasible

50

until far more sophisticated computer-learner systems are
available.

Therefore, learner control of at least four of the
instructional functions must, for experimental purposes,
be somewhat arbitrarily determined. The remaining instruc-
tional functions, however, should be designed so that
learner control is as close to that found in Mager's in-
structional environment as is possible. Hopefully, such
an environment will be far more responsive to the learner
than that offered by Campbell (1964).

Optimal prescriptions for each of the 11 instruc-
tional functions follow:

1. Definition of content area and depth. To

 

maintain intrinsic motivation, the learner ought to be able
to satisfy his curiosity by learning beyond the defined
content area.

2. Definition of objectives and criteria levels.

 

The learner ought to be able to add his own learning ob-
jectives and criteria level, as his interests and experi-
ence demand.

3. Entry level of the learner. The learner ought

 

to be able to Engin his learning at a point which is at
his level of achievement.

4. Method/mode/unit size of presentation. To fit
his style of learning, the learner ought to be able to

demand additional methods or modes of presentation. For

 

51

example, if a learner has a need for diagrammatic or pic-
torial representations, he ought to be able to receive
them. So that the learner can integrate the units into
patterns, unit size ought to be larger than that used with
programmed instruction (Grubb & Selfridge, 1964; Ausubel,
1968; Smith & Smith, 1966; Pressey, 1963).

5. Sequencing of content. The learner ought to

 

be able to actually sequence the content. This means:

(a) manipulating and encountering the information in the
most meaningful (to the learner) sequence, (b) selecting
the route to the learning objectives, and (c) the freedom
to generate mediation between new learning and old know-
ledge within the learner's own cognitive structure.

6. Pacing of the content. The learner ought to

 

be able to learn not only at his own pace but also he
ought to be able to start and stop his learning at times
convenient to him (i.e., stopping when he is satisfied).

7. Inquiry of resources. The learner ought to be

 

able to: (a) demand additional information and receive it

 

quickly, (b) seek help on and verification of hypotheses

about unclear or discrepant information, (o) fill in the

gaps in his knowledge or in the information supplied him,
and (d) relate his past experiences to new information on
demand.

8. Practice/application. The learner ought to be

 

able to practice or try out his new knowledge in order to

52

solidify its meaning and to apply it to new situations as
the new knowledge becomes familiar

9. Review/remedial study. The learner ought to

 

be able to review any of the information presented and to
receive remedial help for those skills or "learning sets"
found to be less than adequate for continuing the main
learning task.

10. Knowledge of results as learning proceeds.
.The learner ought to be able to receive immediate knowledge
of results (confirmation) as he learns. Reinforcement from
external sources (motivation) ought to be available if the
learner needs it.

11. Terminal attainment of objective. The learner

 

ought to be able to receive immediate feedback of his ter-

minal performance. Additional feedback ought to be avail-

 

able for the learner's own objectives.

The above prescriptions for each instructional
function should, if properly implemented, result in a
learning environment where the learner can effectively
control his own learning. Only experimental investigation

will tell us if this is true.

CHAPTER III

METHOD AND PROCEDURES

In Chapter II an environment in which student con-

trol could function was described. The research method

and procedures used to investigate the efficacy of student

control will now be described.

Experimental Design

 

A three-factor experimental design was employed to

test the following hypotheses:

l.

The greater the student control, within limits,
over the learning situation, the greater will
be the amount of material learned as measured
by gain and retention scores.

The greater the student control, within limits,
over the learning situation, the greater will
be the satisfaction of the learner with the
learning experience, as measured by attitude
questionnaire.

The above hypotheses will not hold for all
learners. High "inquisitive" learners, as

determined by a battery of personality measures,

53

54

will learn more and be more satisfied than the
average learner. Low "inquisitive" learners
will learn less and be less satisfied than the

average learner.

Factor One

 

The first factor in the experimental design (Table
1) results from the following instructional treatments:

Student-controlled instruction (SCI) treatment.--

 

Under this treatment Ss were given as much control over
their own learning as possible given the constraints of
time, experimental design, and the facilities available.

33 in this group were given the opportunity and
freedom to select the sequence in which material to be
learned was presented to them. The material consisted of
52 videotaped lecture segments about "Computers and How
They Work" and was presented on a nine-inch TV monitor
(Figure 2).

S had a deck of freshly shuffled cards; each card
contained one significant question about computers to
which S might wish to know the answer (Appendix A). For
each card there was a corresponding video tape segment
which, in effect, answered the question on that card.
Therefore, S could make use of previous experiences and
learning to arrive at a decision as to which question he
wanted answered. S indicated to E, by code, which question

he wanted answered (for an explanation of coding, see

55

Table l.--Number of $3 for Each Cell of Experimental
Design.

 
   
 

 

 
 

 

 

 

 

 

Subject-Types Treatments
Inquiry SCI Expert Random lContro
= 1
High 12 12 12 12
High
Low 12 12 12 12
High 12 12 12 12
Low
LOW 12 12 12 12

 

 

 

 

 

 

 

 

 

 

 

56

 

 

Figure 2.--Carrel configuration: earphones,
TV monitor, and microphone.

 

 

Figure 3.--Control room configuration: TV
camera (lens shown only), TV monitors,
microphone, patchpanels, earphones,
and video tape recorders.

57

Appendix A) and B would locate the corresponding video tape
segment and play it to S (Figure 3).

While this new information was being processed,
another question might arise which would prompt S to ask
for another segment. This process continued until all de-
sired segments had been viewed by S. For purposes of ex-
perimental control, 83 were limited to four or fewer
repetitions and/or deletions of the total number of seg-
ments.

In addition, SCI Ss were also given the opportunity
and freedom to ask questions about tape segments after they
had been shown. If asked, E would appear on the monitor
and answer each inquiry posed to him with a direct and suc-
cinct answer. All questions and answers were recorded on
video tape for later use with $5 in both treatments described
below.

Finally, 85 in this group were allowed to proceed
at their own pace. That is, they could pause between seg-
ments for as long as they wished, and they were allowed to
take notes as needed.

"Expert" treatment.--Ss in this treatment viewed

 

video tape segments in a sequence predetermined by "experts"
in the content area. (For definition of "experts", see
below.) The sequence to be followed was presented to S as
a list of questions (Appendix B), identical to those on the

"SCI" cards.

58

Each S in this group was yoked with one of the Ss
under the "SCI" treatment. Therefore, any repetition or
deletion of segments by the "SCI" S was duplicated for
his counterpart S in this group. 85 in the "Expert" treat—
ment were not allowed to ask questions and had no recourse
but to view the recorded questions (and answers) asked by
his "SCI" counterpart. This auxiliary information was
presented directly after the appropriate segment.

Ss in this treatment were presented information in
a completely determined and structured manner. A deliberate
attempt was made to simulate a typical classroom in which
the instructor lectures according to his conception of
what is a "logical" sequence and where most students hear
questions raised by classmates, whether they are meaningful
and relevant questions or not. Unlike the typical class-
room, but for reasons of experimental control, these Ss
were allowed to proceed at their own pace and take as many
notes as needed. '

"Random" treatment.--Again, each S in this treat-

 

ment was yoked with one S from the "SCI" treatment. How-
ever, "Random" Ss viewed the tape segments in a sequence
determined by chance. (For method of randomization, see
below.) The list of questions (Appendix C) were identical
to those of the "SCI" cards. Except for order, "Random"
Ss were presented the information in a manner identical

to the "Expert" treatment.

59

"Control" treatment.--"Control" Ss received no

 

information about computers. Instead, they were given a
ten-minute talk about the experiment and their part in it.
Otherwise, "Control" 85 received the same treatment as the

other Ss.

Factor Two

 

The second factor in the experimental design (Table
1) results from a categorization of Ss in terms of college
aptitude scores. Since this dimension had potential for
interacting with instructional treatments, college aptitude
test scores were obtained for all Ss and divided at the

median (see details below).

Factor Three

 

The third factor in the experimental design (Table
1) results from a categorization of Ss in terms of "inquiry"
scores.

In a "SCI" type instructional system, as discussed
above, where the learner can control his own instruction,
there is an assumption that the learner will interact with
the informational sources in a problem-solving and decision-
making manner.

Since the degree of control an individual may wish
to have in such a learning situation is likely to be depen-
dent on his initiative and since this degree of control

may interact with instructional treatments, some means of

60

including this characteristic in the experimental design
was desirable.

Of the measures of personality which have inter-
acted with instructional method, those delimited by Shulman
et al. (1968) appeared to offer an excellent chance of
separating learners who could take full advantage of the
instructional system and those who could not.

In addition to including many of the dimensions
used in other studies, such as authoritarianism and test
anxiety, Shulman's best six predictors of inquiry behavior,
out of a battery of 17 tests, accounted for 50% of the
measurable variance. More important, perhaps, is the fact
that these measurements were obtained under conditions
similar to the one offered to "SCI" 85 here. I

Therefore, all Ss were given Shulman's battery of
six tests and the dimension, "inquisitiveness", was
treated as the third major independent variable in the
experiment. S's scores on the six tests were standardized
as T scores (i=50, 6=10) and sununed. High total scores
reflected individuals high in cognitive complexity, pre-
ferring the ambiguous, the assymetrical and the unexpected
to the regular, articulated and predictable; liberal in
political values; high in associational fluency; high in
non-stereOpathy; high in verbal problem solving; and low
in expressed test anxiety. This distribution of scores

was then divided at the median.

61

Stratified Random Assignment

 

Using these scores and the college aptitude scores,
it was possible to establish a four cell matrix of Subject-
Types who were, respectively, high on both aptitude and
"inquiry" predictor scores, low on both of these dimensions,
and two groups for which the dimensions were incongruent
(Figure 4).

However, due to the moderate correlation (y=.37)
between the two dimensions, High, High and Low, Low groups
were over-represented. Therefore, some of the Ss in these
two groups who were, in fact, assigned to these groups until
an equal number of Ss (48) were in each.

From each of the four groups, twelve 85 were ran—
domly assigned to each of the four treatments. This tech-
nique insured that an equal number of each subject-type
would be represented in each treatment category (Table 1).

In practice, Ss were assigned to treatments, at
random, after a set of four Ss was drawn, at random, from
one of the groups. This technique helped guarantee that
each set of four Ss would be approximately matched for
aptitude and "inquiry". In addition, since "SCI" members
of each set made inquiry of the subject matter while the
other members could not, it was apprOpriate that that
level of inquiry be consistent with the aptitude and "in—

quiry" level of the other 35 of that set.

"INQUIRY"

standardized)

(Sum of six tests,

62

 

 

 

 

 

 

400 - *
*
380 - * *
* * *
* *
m 360 - * * *
a * ** * * *
m 340 - ** ** ** * *
** * * ** * ** * **
320 _ ** * ** * ** **:*
k * * *
300 - *** * * *
* *
* * ** *** * *
280 ' * * * * * *
** ** * * * *
260 - * ** * * * *
* * * *
3 * * * * *
S 240 - * 3 * *
¢*** * * *
220 - * 5 **
*
*
200 ' *** * *
4:,_
0 10 20 30 40 50 60 70 80 90 100

LOW, HIGH
College Aptitude (Percentile)

Figure 4.--Scatterplot of Experimental Subjects in Terms of

Their College Aptitude and "Inquiry" Scores.

Pilot Experiment

summer term,

ing:

A pilot experiment using 40 53 was run during the

1969. It provided information for the follow—
Modification of instructions and operating
procedures.

Validation data for criteria test items.
Color-coding of cards for "SCI" Ss.

Revision of "Post-Attitude Survey” question-

naire.

63

e. Feedback about the experiment for construction
of "Terminal Interview" questions.
The latter four of these changes are explained in
detail below.
The results of the pilot study were similar to those
of the main experiment described below. Because of the
changes mentioned above, the results of the pilot study are

not reported here.

Subjects

Ss were 192 students enrolled in the introductory
psychology course at Michigan State University during the
fall term, 1969, and winter term, 1970. Due to the time
required from each S (up to five hours), E went to all
introductory psychology classes taught by Graduate Teaching
Assistants where he solicited volunteers. The experiment
was described as a learning experience which would give Ss
the opportunity to become better acquainted with computers.
Potential 58 were given an information sheet (Appendix D).
Some 85 were disqualified because they had had prior courses
in computer science. About 40% of the students contacted
participated in the experiment. Of the Ss who began the
experiment, four failed to complete it. A breakdown of 55
along the dimensions of age, sex, and class is shown in

Table 2.

64

Table 2.--Subject Characteristics

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

CLASS AGE
Fr So Jr Sr Total 17-18 19-20 21+ Total
157 26 6 4 192 137 43 12 192
82 13 3 2 100 71 23 6 100
SEX
Male Female Total
N: 73 119 192
%: 38 62 100

 

 

 

 

 

Pre-Experimental Measures
(Independent Variables)

 

 

Each S began the experiment by taking Shulman's
battery of six tests. Ss also completed two other in-
struments: a "General Inventory" and a measure of their
"Attitude Toward Learning." In addition, scores on some
college entrance tests were available for all Ss. These
tests were administered at the Learning Service office by
one of the secretaries. After testing, Ss were given a

map of the laboratory's location (Appendix E) and told

65

that they would be contacted by telephone for scheduling
of their learning sessions.
What follows is a brief description of the instru-

ments used for the independent measurements:

Shulman's Six Tests

 

1. "Word Association" (Appendix F) is a test of
verbal flexibility used by Getzels and Jackson (1962).

One point was scored for each different meaning and another
point for each different word under each of the different
meanings given.

2. "Political Position" (Appendix F) measures
liberalism-conservatism. Names of political candidates
used were up-dated from those used in Shulman's original
study to reflect the times: i.e., from Goldwater, Johnson,
R. Kennedy, and Romney to Reagan, Nixon, Humphrey, and
T. Kennedy. The form used was actually Shulman's revised
form.

Scoring ranged from 4 (conservative) to 18
(liberal) as follows:

a. Item #1 1-3 points

b. Item #2 1-5 points

c. Item #3a, b 1-5 points each:

if Reagan and Nixon ranked 1, 2--l point
if Humphrey and Kennedy ranked 1, 2--5 points

if some other combination ranked 1, 2--
variable points

66

3. "Inventory of Beliefs" (Appendix F) was used
in the studies by Stern, Stein and Bloom (1956) and
closely parallels the scales developed by Adorno et a1.
(1950). It correlates with the E (authoritarianism)
scale (y=.67), the E (ethnocentrism) scale (y=.66), and
the Egg (political-economic-conservatism) scale (y=.43).
Stern et al. used the "Inventory of Beliefs" as a measure
of college student "stereopathic" and "non-stereOpathic"
personality. They found that non-stereOpathic under-
graduate students had a much higher degree of success in
the unstructured, relatively undisciplined programs at the
University of Chicago. Stereopaths showed much greater
difficulty coping with the generally undirected programs.
Non-stereopaths tended to achieve much more successfully
on comprehensive examinations than did the stereopaths,
even when general aptitude was held constant. These tests
emphasized problem solving and inference far more than
memory for detail.

One point was scored for each item answered in
agreement with Shulman's key.

4. "Attitude Inventory" (Appendix F) measures
cognitive complexity. That is, it measures an individual's
reaction to the ambiguous, unpredictable and assymetrical.
Barron (1953, 1967) used it to distinguish between creative

and non-creative individuals in such fields as architecture.

67

In addition, this instrument also includes a six item
measure of the tendency to "focus" or "scan" when reading
school related materials. For purposes of analysis, the
scores on the two measures were separated. One point was
scored for each item in agreement with Shulman's key.

5. "Attitudes Toward Tests" (Appendix F) is a
measure of test anxiety and is adapted from Alpert and
Haber (1960). One point was scored for each item answered
in agreement with Shulman's key.

6. Michigan State University "Reading Test."
Excerpts of this instrument appear in Appendix G. It
measures the ability to comprehend, analyze, interpret,
and make apprOpriate inferences about selected passages
on social science, literary, historical and natural science
topics. It is therefore a test of general verbal problem—
solving as much as a test of simple reading ability. This
test is routinely administered and scored by MSU's Evalua-
tion Service personnel for all entering MSU students as
part of the orientation program. E obtained test scores

on all Ss from Evaluation Service.

General Inventory

 

This instrument (Appendix F) was designed to deter-
mine the following:
1. Sex: male or female.

2. Age: 17—18, 19-20, and 21 and over categories.

68
Class: Freshman, SOphomore, Junior or
Senior categories.
Socio-economic status: in order to grant a
status to each S, E assigned a number from
0-9, based on the following criteria:
a. Item #2 (father's occupation):

if professional -------------- 4 points

if businessman or
farm owner ------------------- 3 points

if white collar or

skilled tradesman ------------ 2 points
if industrial worker --------- 1 point
if laborer ------------------- no points

b. Items #3, 4, 5 (the highest level of
education attained by father, mother,
and older siblings--the points for the
three items were summed and the average
used in the total):

if post-graduate or professional
training --------------------- 5 points

if college graduate ---------- 4 points

if some college or trade

school ----------------------- 3 points
if high school graduate ------ 2 points
if grades 9-11 completed ----- 1 point
if grades 1-8 completed ------ 0 points

"Pre-Interest in Computers": In order to
grant a "Pre-Interest in Computers" score to
each S, E assigned a number from 0-9 based on
the following criteria:

a. Item #11: if S had plans to take a com-
puter science course--l point. (Other-
wise, this item was used to insure that
only inexperienced 85 were participating.)

69

b. Items #12, 13, l4, 16: If answered in the
direction of showing interest in computers

and:

if strongly agree --------------- 2 points
if agree ------------------------ 1 point
all other ----------------------- 0 points

EQEE‘ Items of the General Inventory instrument
#9 and #10 were not included in any analysis because most
Ss were uncertain how to respond to them. Likewise, items
#1 and #15 were probably not relevant to the dimension under

consideration and were therefore excluded.

"Attitudes Toward Learning"

This instrument (Appendix F) was developed specifi-
cally to measure attitudes toward learning of the type in-
volved in this research. Initially, a number of potentially
useful items were obtained from a study by Davis, Marzocco,
and Denny (1967). To this were added more specific items
for a total of 40. This instrument was given to 72 general
psychology students in the spring term of 1969. A reduc-
tion in the number of items to 21 was made by means of
factor analysis and inspection. The resultant instrument
after being administered to the 192 Ss in the experiment
was again factor analyzed. Table 3 presents the six factors
which account for 53% of the variance after a principal

axes solution followed by a varimax rotation.

70

Table 3.--Proportion of Variance Accounted for by Factors
in "Attitudes Toward Learning" Questionnaire

 

 

 

FACTOR PROP. VAR. ITEMS

1. Fear of asking quest1ons 1n .10 1,8,15

class.
2. Independence from instructor 07 7,14,16,

while learning something new. ° 20
3. Frequency of review or

self-test. '08 2’18
4. Control of instructional 10 4,5,6,9,

functions. ° 11,19,20
5. Ease of learning. .09 3,12,17
6. Autonomy while learning. .09 10,19,21

 

College Aptitude Tests

In addition to the MSU "Reading Test" above,
Evaluation Service provided scores for Ss on the following
dimensions:

Percent of

 

Participants
1. College Aptitude:
a. "Scholastic Aptitude Test" 72%
(SAT)
b. "American College Testing 7%
Program" (ACT)
c. "College Qualification Test" 21%

(CQT)

 

100%

71

Percent of

 

Participants
2. MSU "Mathematics Test" 82%
3. MSU "Arithmetic Test" 92%

Evaluation Service converted scores from the above
tests into standard scores and then reported them as
percentile scores. E reconverted the percentile scores

into standard scores for use in analysis.

Experimental Procedure

Learning sessions were arranged for each 8 indi-
vidually at his convenience. The only stipulation was
that the S had to be able to attend two sessions of about

an hour and one-half each on consecutive days.

First Learning Session

Each S began the first learning session with a 60
item pretest of his knowledge of the subject matter
(Appendix H). This took approximately 30 minutes and was
administered in the carrel room (Figure 2). After the pre-
test was completed, E explained how the audio and video
equipment worked and introduced Ss to either the cards
("SCI" Ss) or one of the lists ("Expert" or "Random" Ss).
Instructions (Appendix I) were read by E over the closed-
circuit TV system from the control room (Figure 3). This
method helped insure that: (a) the presentation would be

uniform for all 83; (b) S would become familiar with the

72

closed-circuit TV system (a warm-up for learning); and (c)
E would be physically removed from S's presence. Thus, S
could relax and pay attention to the content of the in-
struction.

Instructions for the different treatments were as
similar as possible. Of necessity, however, there were
basic differences among them.

After one-half of the segments had been viewed, E
informed S that the first session had been completed. For
"SCI" Ss the stopping point was up to each S. Usually this
point was also after one-half of the segments had been
viewed.

S was then given post-session instructions (Appen-
dix I) which asked him not to learn more about the subject
matter outside the laboratory until the conclusion of the
experiment and not to communicate with other students about
the experimental procedures employed.

Finally, E took S's notes and kept them in the

laboratory overnight.

Second Learning Session

 

When S returned to the laboratory on the following
day, E returned S's notes, if any, and asked if there were
any questions concerning the instructions. If not, S
started and completed the segments and questions.

After viewing the remaining segments, E informed

S that he had as much time as he wished to go over his

73

notes and that when he was ready, a posttest would be given
to him. When S was ready, E removed S's notes, then gave
S a posttest followed by "Post-Attitude Survey" question-
naire (Appendix M).

When the posttest and questionnaire were completed,
E informed S that in two weeks E would contact S for
scheduling a retention test (Appendix H). Finally, S was

again given the post-session instructions, as above.

Subject Logs

 

E kept a Subject Log (Appendix J) on S's perfor-
mance. Initially, E recorded S's name, identification num-
ber, his group and treatment, date, and test sequence.
Then, E began recording, in minutes, the length of the
learning sessions.

For "SCI" 85, E recorded: (a) the number of each
segment in the order chosen by S (E converted code words
into numbers by means of the list in Appendix K), (b) a
check (/) under the apprOpriate column and row for each
segment repeated and/or inquiry made, if any, (c) the num-
ber of each deleted segment, if any, at the end of the
log, and (d) later, a verbatim transcription of every
question asked by S. The total number of questions was
also recorded.

For the "Expert" and "Random" 83, E recorded ini—
tially, a check (/) under the apprOpriate column and row

to indicate when an inquiry on the "SCI" video tape or a

74

repeated segment was due. E wrote word "no" to indicate
when a segment deletion was due. As the learning session
progressed, E recorded a check (/) as each segment was
viewed and an additional check (/) as each repeat, deletion,
or inquiry was completed.

The "Control" Ss required no log.

E also recorded on the Subject Log the time, in
minutes, spent by each S in reviewing his notes. Under
"comments", E recorded any noteworthy behavior or comments

made by S.

Retention Test Session

 

Two weeks after the second learning session, S was
contacted and given a retention test, without review. Upon
completion, S was given the "Terminal Interview" check-
list (Appendix N).

E then asked S to arrange the cards in what he
considered to be the "best" sequence for learning. For
the "SCI" 85 this request was mostly a repeat of previous
instructions. Therefore, in order to motivate them, E
told them to think of the task as if they were about to
relearn the material or as if they were about to sequence
the material for their roommate to learn.

"Expert" and "Random" Ss had to be introduced to
the cards and the color code before they could be asked
to sequence the cards. In addition, these Ss were told

that this was their chance to put the segments in a

75

sequence more "logical," perhaps, than the one they had
viewed. E recorded the order chosen, by number, along
side the 52 numbers on the Subject Logs.

Then E explained the overall experimental design
and hypotheses and showed S's test scores to him if de-
sired. If there was time, E asked S to comment freely on
the experimental environment.

Finally, E signed S's Experimental Credit Card.
By means of this act, S fulfilled one of the requirements
of his psychology course--participation in psychological

experiments.

Control Subjects

 

After completing the pretest, "Control" Ss were
given a short ten-minute talk about the experiment and
their part in it. Then they were given the posttest.
Although this procedure saved both E and S time, carry-
over of learning from the pretest to the posttest, if any,
may have been amplified. In addition, the time between
these tests and the retention test varied from one to two
weeks rather than being exactly two weeks.

When the "Control" 83 were given the "Post-Attitude
Survey" questionnaire, they were instructed to mark "un-
certain" (#3) for any item which they had no knowledge
about one way or another. They were informed that such a
task, even though it may appear to be meaningless, would

serve as a base against which to measure experimental Ss.

76

Development of the Subject Matter

 

E wrote and developed the subject matter (Appendix
L) to be learned in the experiment. The subject matter
learned by 85 ("Computers and How They Work") was chosen
for the following reasons:

1. E was familiar with this area and in repli-
cating Mager's (1961) study had used the same subject matter
area. Therefore, development of the material could be con-
structed around points that naive 85 would want to learn
about.

2. The subject matter fulfilled the requirements
of being: (a) meaningful, verbal knowledge, (b) somewhere
between pure training and pure education extremes, (c)
elaborated on in a number of sources, (d) intrinsically
interesting to most college students, (e) sufficiently
complex and interrelated so as to provide a challenge to
the naive learner, and (f) not so new as to prevent the
learner from coming to the experiment without a great deal
of already acquired bits of information from a variety of
sources.

3. For reasons suggested in the literature above,
this subject matter fulfilled the further requirements of
being capable of being taught and learned in a non-
hierarchical manner. That is, it could be broken down
into segments of an independent nature. Thus, the learner

could enter the material at any point with impunity.

77

This entry could be strictly a matter of personal choice,
not necessarily because one segment "naturally" followed
another.

4. In addition, this subject matter could be con-
structed of units that were larger than the "frames" of
programmed instruction, but still be put into a "scrambled"
or random order.

5. Finally, each segment could be constructed so
that it answered one question about computers. Thus, the
question (title) on each card (or list) served as an access
or an information retrieval device. However, these ques-
tions (titles) were arbitrarily determined by E. No evi-
dence was found to show that this method of input to the

83 was a hinderance to them.

Development of the Segments

The material to be learned was gathered from a
number of sources (Anderson, 1966; Desmonde, 1964; Flores,
1967; Laurie, 1963; Marchant & Pegg, 1967; Schmidt &
Meyers, 1965; Shultz, 1963) designed to:

a. Insure adequate coverage of the area.

b. Be parsimonious. So that all of the material
could be included in an hour's lecture time,
the original 15,000 words were cut to a con-
cise 10,000 words of written material.

c. Be sufficiently difficult to prevent a ceiling

effect in learning.

78

d. Include instructional objectives for each seg-
ment.

e. Encompass an adequate number of concepts (seg-
ments) to make selection of the segments chal-
lenging. In the end, 52 segments were produced,
comprising eight different content areas

(Appendices B and L).

Taping of the Segments

 

The subject matter was transferred to video tape
segments by: (a) typing the content of each segment on
acetate "cue cards", (b) projecting these sheets onto a
screen which was placed directly beneath a television
camera, and (c) focusing the camera on an instructor who
read the projected "cue cards."

The instructor's speaking rate was about 60 words
a minute which is somewhat less than the average news-
caster's rate (approximately 80 words per minute). In
order to conserve tape, the order of taping was identical
to the "Expert" sequence. If errors were detected during
taping, the tape was rewound and the segment retaped.

Two one-inch tapes, a total of one hour and 20 minutes,
were required.

Using an Ampex #7800 editor, these tapes were then
used to electronically reproduce a duplicate set of "Ex-
pert" sequenced tapes and two sets of "Random" sequenced

tapes.

79

Ordering the Segments

 

"Expert" order.--Of the six volunteer experts ob-

 

tained to order the tapes, three were graduate students in
psychology who were or had been consultants in computer
science. The remaining experts were: (a) an instructor
in MSU's Computer Science Department, (b) an Assistant
Professor from the Computer Institute for Social Science
Research (CISSR), and (c) the director of MSU's Data
Processing Department.

E scrambled (shuffled) the complete unindexed set
of 52 segments and instructed each expert to order them
in a sequence which they themselves would use if they were
about to teach the material to naive students. Since the
material for each segment could be put on one sheet of
paper, the task of ordering the segments was readily accom-
plished.

Of the 15 possible rank-order correlations (Table
4) between the rankings assigned to the segments by any
two experts, only three were significant (yhoz.274) and
the average for all 15 was quite poor (yho= .005). Since
agreement between experts over all 52 segments was essen-
tially non-existant, E looked separately at the rank-order
correlations for each of the content areas of the subject
matter. This method of analysis yielded coefficients of
concordance and average inter-correlations among rankings

assigned by the experts that were consistently much

80

Table 4.--Rank-Order Correlations Between Rankings Assigned
to Segments by Pairs of Expert Judges

 

 

 

Judge

1 2 3 4 5 6
l 1.00
2 -.08 1.00

Judge 3 -.13 +.10 1.00

4 -.03 -.38 +.07 1.00
5 -.19 -.02 +.56 -.03 1.00
6 -.09 -.09 -.13 +.44 -.08 1.00

 

Table 5.--Coefficient of Concordance and Average Inter-
correlations Among Rankings Assigned to Segments
by Expert Judges

 

 

'Kendall
Coefficient of
Concordance (w)

Average

Content Area Intercorrelation (7)

 

 

 

1 0.44 0.33
2 0.77 0.72
3 0.56 0.47
4 0.52 0.42
5 0.21 0.05
6 0.44 0.33
7 0.19 0.03
_______8 916.9. 22.52
Overall 0.03 0.00

 

81

higher than the overall concordance and intercorrelation
(Table 5).

Therefore, E, by inspection, sequenced the segments
first by overall content area and then within each content
area according to the consensus of experts. The resultant
"Expert" ordering of the segments is shown in Appendix B.

"Random" order.--Two different random sequences

 

were employed. The second sequence was employed after it
was noted at the half-way point in the experiment that the
"Random" treatment 85 were doing rather well and that it
was possible that this might be due to the random sample
somehow being sequenced "logically." In both cases, the
random sequence was generated by numerous shufflings of the
cards after they had been thrown across the floor and mixed
up thoroughly. Both random sequences are shown in Appendix

C.

Measurement of the Dependent Variables

 

Development of the Tests

 

Initial effort.--In order to show differences be-

 

tween treatments, adequate measures of learning must be
obtained. Initially, three equivalent achievement tests
were developed. Items for these tests were constructed
in sets of three. Either a set asked different questions
about a single concept or it asked a similar type of ques-

tion, such as questions involving numerical memory. Then,

82

the items from each set were randomly assigned to each
test form (Appendix H).

Item analysis - I.--Following the pilot study,

 

item analyses of the three test forms were performed.
Items were inspected for low indices of discrimination,
possibilities of ambiguity, being either too hard or too
difficult, or not having all distractors equally distract-
ing.

The three forms were then revised. Because Form A
had been given only as a pretest, its revision could not
be as precise as forms B and C.

Item analysis - II.--Upon completion of the experi-

 

ment, the test forms were again item analyzed. Because
pretest and "Control" Ss' scores were practically at chance
levels, only scores from posttests and retention tests of
"non-control" treatment 55 were used in these analyses.

From these analyses it was observed that each form
still contained some items which were contributing nothing
to the measurement. A more valid and reliable test would
result from the exclusion of these items. However, dele-
tion of items after testing and then rescoring tests for
those 83 has questionable merit. Normally, items on a
test are deleted and then the revised form is retested on
another population sample before the test is judged fit
for use. Unfortunately, such a procedure could not be

carried out in this case.

83

However, the Rasch method (Wright and Panchapakesan,
1969) of analysis, which is relatively new, has the follow-
ing features, which are not true of conventional item
analyses: (a) test calibrations are independent of the
sample of persons used to estimate item parameters, (b)
person measurements, the transformation of test scores into
estimates of person ability, are independent of the selec-
tion of items used to obtain test scores. This method
identifies poor items during the test calibration phase.
The retained items are then reanalyzed to obtain final
estimates of S ability. 85 who get the same score are
estimated to have the same ability.

Therefore, the ten poorest items from each test
form were deleted. Instead of a decrease in reliability
(KR-20), as one would expect after a reduction in the num-
ber of items, Table 6 shows a slight increase to occur.
This offers evidence that deleting the items was, in fact,
a sound procedure.

Finally, all tests were re-scored. The raw scores
were then standardized as T scores (x = 50,6= 10) prior
to data analysis. This standardization served the purpose

of removing all differences between the three test forms.

84

Table 6.--Change in Test Reliability After Deletion of
Items

 

 

 

 

 

Reliability Measures (Kuder-Richardson 20)
Conventional Rasch Sample-Free Item Analysi
Test Item Analysis .
Form (60 items) Before Deletion After Delet1on
(60 items) (50 items)
A .699 .695 .743
B .805 .802 .824
C .794 .790 .808

 

 

 

 

 

 

Order of the tests.--The three forms were given to

 

the Ss in a systematic fashion, as shown below:

 

S Pretest Posttest Retention
1 A B C
2 A C B
3 B A C
4 B C A
5 C A B
6 C B A

Since there were twelve 85 in each subject-type
treatment cell, this series of alternations was repeated
twice for each cell. Also, the four 55 in each set who
were yoked across subject-type treatment cells always
received the same sequence of test forms.

There was a reason for alternating the sequence of
test forms and not giving all Ss the same sequence. If

Form A had always been the pretest, scores on it would

85

almost always have been associated with chance (i.e., Ss
guessed at most items on the-pretest)». Since this test
could not be evaluated on any other population, it could
never be guaranteed to be a representative and equivalent
test form from which to draw conclusions about gain scores.
Even the Rasch sample-free item analysis demands an equally
representative testing experience for the calibration of

the test to be valid.

"Post-Attitude" Survey

 

E specifically developed this instrument (Appendix
M) to measure Ss' attitudes toward this experiment, es-
pecially the features of the learning environment. Although
designed with four factors in mind, six emerged from a sub-
sequent factor analysis.

Table 7.--Pr0portion of Variance Accounted for by Factors
in "Post-Attitude Survey"

 

 

 

Factor Prop. Var. Items
1. Perceived learning as a .15 7,9,11,16,18,
function of the environment. 19,20,21,22
2. Attitude toward order of the
segments. '97 12,13

3. Attitude toward the method

of instruction. ~13 112,315.15,20
4. Perceived learning as a

function of task difficulty. ’08 2'3'6'11'18'21
5. ReV1ew1ng or relating material .06 10,14

to past experiences.
6. Attitude toward computers. .12 4,8,17

 

86

Table 7 presents the factors which account for 61%
of the variance after a principal axes solution followed
by a varimax rotation. All factors shown, will be used as

criteria in subsequent analyses.

"Terminal Interview"

 

E developed this instrument (Appendix N) specifically
to gather information from 55 which might help explain their
performance and/or attitude toward the learning environment.

In the pilot experiment Ss were given an open-ended
form of this instrument (Appendix N). From the more fre-
quently written answers there, E constructed a new form
which insured that all Ss would consider the same item stems
while still being free to add other information. Conse-
quently, there was a consistent measure of all 55, unlike
the original form.

The first page of the final instrument was identical
for all Ss. For obvious reasons, however, the second page
contained questions specific to either the "SCI" or "Expert"

and "Random" treatments.

"Postelnterest in Computers"

 

E devised a measure of "Post-Interest in Computers"
from parts of the above two instruments. First, all three
items from the sixth factor of the "Post-Attitude Survey"
questionnaire were scored to give a range of from 3 to 15

points for each S. Then, the "Terminal Interview"

87

questionnaire was scored by counting the number of checks
present in any category of item #3 and in the second and
third category of item #4. The total range was from 3-21
points. This information will be compared to "Pre-Interest
in Computers" scores and used as a noncomitant dependent

variable below.

Card Sequencing

 

As explained above, the final task for the 83 was
to sequence the cards. This task was designed to measure:
(a) any change in segment sequencing by the "SCI" Ss, (b)
any difference between "SCI" and "Expert" segment sequence,
and (c) the degree of communality of segment sequences,
after all 83 had become familiar with the material. (No

"Control" 83 performed this task.)

CHAPTER IV

RESULTS

This chapter consists of four sections. The first
is concerned with analysis of variance of the dependent
variables. The second section looks at correlational
analysis of the data. Then, responses to the "Terminal
Interview" will be compared. Finally, agreement among the

rank-orderings of the lecture segments will be presented.

Analysis of Variance

 

Initially, analysis of the data was begun with
posttest and retention test achievement scores as the ma-
jor learning criteria. However, it was discovered that
the average correlation between treatment groups on these
scores was substantial (§=.47). This suggested that there
was a dependency between treatment groups rather than
independency as required by routine analysis of variance
techniques. Upon calculating gain scores (e.g., the
difference between pretest and posttest scores) observed
correlations between treatment groups were reduced signifi-
cantly. The average correlation for the cells shown in
Table 8 is quite small (§;-.os). Therefore, instead of

becoming involved in a complicated 2 x 2 x 4 x 12 factorial

88

89

repeated measures analysis of variance (AOV) design, inde-
pendency between criteria scores was felt to be justified
and a 2 x 2 x 4 factorial AOV was used on all analyses
below.

Finn's (1966) multivariate computer program was
employed in the analysis. Of the 11 variables selected for
use as covariates, only one, "Pre-Interest in Computers"
had a significant overall relationship with the 11 depen-
dent variables. A stepwise univariate multiple regression
analysis was performed to determine the effects of indivi-
dual independent variables on each dependent variable. Of
the 11 variables 10 made no appreciabl£2contribution and
were, therefore, eliminated from the analyses.

Nonetheless, the covariate that remained (Pre-
Interest in Computers) influenced the differences between
means very little. It accounted for less than five per
cent of the variance for the gain scores. Therefore,
covariates are not included in the analyses tabled below.

Tables 9 through 19 below present cell means and
standard deviations, a 2 x 2 x 4 factorial analysis of
variance, and four one-way analyses of variance tables for
each of 11 dependent measures. Throughout the analyses,
the number of Ss in each cell is 12 and probability levels
less than .10 are not shown.

Since the "SCI" instructional treatment was of

primary interest, its mean has been contrasted with the

90

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91

other three treatment means for all 11 analyses. In addi-
tion, in line with the hypothesis of individual differences
being related to learning, Subject-Type groups are broken
out of the overall design and analyzed separately by one—
way AOV's. Due to the number of tests on the same data,
probability levels should be regarded with caution.

Other than gain score measures, responses by "Con-
trol" Ss do not reflect any actual experiences which they
could adequately comment on. Their reSponses serve mainly
as a base against which comparisons may be-made. For a
number of dependent variables, E assigned mean values to
the "Control" Ss for purposes of analysis. Consequently,
other than for the gain scores, control means are not re-

flected in the figures.

Gain 1 (Posttest Minus Pretest) and
Gain 2 (Retention Test Minus Pretest)

Tables 9 and 10 show that for Gain 1 and Gain 2
measures there were significant differences between means
within each of the three factors of the experimental
design.

Aptitude.--Ss with higher college aptitude gained
significantly more knowledge of computers than Ss with low
college aptitude.

Although this result might appear obvious, the
literature has shown that crude gain scores are generally

uncorrelated with measures of intelligence or other

92

characteristics of the learner (Manning & Dubois, 1962).
Situations where similar results have been reported suggest
that low ability students do not do as well as high
ability students when the material is poorly organized or
sequenced (Stolurow, 1964). The fact that the material
presented was quite difficult for most 83 to learn (an
average gain of 13 out of a possible 40) may have served
to accentuate college aptitude differences.

Inguiry.--The more "inquisitive" Ss gain signifi-
cantly more knowledge than less "inquisitive" 85. This
result was predicted, but it must be tempered with the
fact that a moderate correlation (y=.37) exists between
the "inquiry" dimension and college aptitude (Figure 4).

Treatments.--Under the instructional treatment

 

factor, all three overall comparisons were significantly
different. Ss learning under the "Expert" condition
showed the greatest gain in overall achievement and "Ran-
dom" 85 also did significantly better than "SCI" 85.
Such results were expected, since at this point, the
analyses are across Subject-Type factors and Ss would be
most at home with the "Expert" instructional treatment.

A breakdown of the analyses for each Subject-
Type group is presented in the lower sections of Tables 9
and 10 and are displayed in Figure 5. (Since in all cases,
the "Control" 85 made only slight gains, at best, compari-

sons with them and other groups will not be mentioned below).

93

Table 9.--Means and Standard Deviations, 2 x 2 x 4 Factorial AOV, and
Four One-Way AOV of Gain 1 Scores

Means and Standard Deviations

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Subject-Type Treatment
Aptitude nquiry, SCI Expert Random Control Total
High 23.13 » 21.14 22.79 2.35 17.35
High (7.79) g(4.98 g(8.64) (6.21) (7.11)
Low 17.58 24.57 16.19 1.34 14.92
(9.31) (7.65) (6.89) (4.98) (7.34)
High 13.08 15.40 13.80 -0.81 10.37
Low (8.68) (8.98) (9.20), (5.62) (8.17)
Low 10.45 9.05 13.36 -0.99 7.97
(8.54) (9.20), (7.60) (4.76) (7.64)
Total 16.06 17.54 16.54 0.47 12.65
(8.60) (7.88) (8.13) (5.21) (11.02)
2 x 2 x 4 Factorial AOV
Source V df MS F p
A (Aptitude) 1 I 2330.35 40.62 .0001
B (Inquiry) 1 280.58 4.89 .0283
C (Treatment)
SCI-Expert 1 1529.14 26.66 .0001
SCI-Random 1 2188.46 38.15 .0001
SCI-Control 1 5829.73 101.62 .0000
A x B l 0.01 .00
A x C 3 139.82 2.44
B x C 3 33.00 .58
A x B x C 3 142.66 2.49
Error 176 57.37
Total 191
OneAWay AOV for Each Subject-Type Group
High Aptitude/Hi h Inquiry Hi h Aptitude/Low Inquiry
Source df MS F p Source df MS F p
SCI-Exp 1 229.77 4.55 .0386 SCI-Exp 1 1489.32 27.65 .0001
SCI-Ran 1 808.69 16.02 .0003 SCI-Ran 1 362.70 6.73 .0129
SCI-Con 1 2589.60 51.29 .0001 SCI-Con 1 1581.13 29.35 .0001
Error 44 Error 44
Low AptitudeLHigh Inquir Low Aptitude/Low Inquiry,
Source df MS F pp Source df MS F p
SCI-Exp 1 405.02 6.07 .0178 SCI-Exp 1 18.78 .32
SCI-Ran 1 469.71 8.90 .0047 SCI-Ran 1 595.70 10.20 .0026
SCI-Con 1 1157.87 17.35 .0002 SCI-Con 1 785.47 13.45 .0007
Error 44 Error 44

 

 

 

 

 

 

 

 

94

Table 10.--Means and Standard Deviations, 2 x 2 x 4 Factorial AOV, and
Four One-Way AOV of Gain 2 Scores

Means and Standard Deviations

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Subject-Type Treatment
Aptitude nquiry SCI Expert Random Control Total
High 14.78 14.13 14.08 1.11 11.03
High (8.50) (4.47) _fi (6.37) (4.72) (6.08)
Low 9.44 15.38 7.57 0.00 8.10
(6.58) (7.25) _(5.28) (3.96) (6.13)
High 2.92 9.18 8.76 0.12 5.24
Low (5.46) (9.99) (5.45) (5.41) (6.87)
Low 4.84 4.19 2.90 -2.78 2.29
(8.75) (6.37) 4(7.23 ((4.38) (6.80)
Total 8.00 10.72 8.33 -0.39 6.66
(7.45) (7.30) (6.13) (4.64) (8.44)
2 x 2 x 4 Factorial AOV
Source df MS F p
A (Aptitude) 1 1612.40 38.41 .0001
B (Inquiry) 1 415.36 9.90 .0020
C (Treatment)
SCI-Expert 1 1053.54 25.10 .0001
SCI-Random 1 654.92 15.60 .0002
SCI-Control 1 1687.57 40.20 .0001
A x B 1 .01 .00
A x C 3 108.05 2.57
B x C 3 56.36 1.34
A x B x C 3 95.51 2.28
Error 176 41.97
Total 191
One-Way AOV for Each Subject-Type Group
High Aptitude/Hirh Inquiry Hi'h A titudgjLow Inquigz
Source df MS F p Source df MS F p
SCI-Exp 1 153.76 4.16 .0476 SCI-Exp 1 849.24 22.60 .0001
SCI-Ran 1 301.35 8.14 .0066 SCI-Ran 1 64.79 1.72
SCI-Con l 1122 .03 30 . 32 . 0001 SCI-Con 1 534. 87 14. 24 .0005
Error 44 Error 44
Low Aptitude/Hi'h Inquiry Low A titude/Low Inquiry,
Source df ‘MS F p Source df MS F _p
SCI-Exp 1 248.33 5.27 .0266 SCI-Exp 1 58.01 1.26
SCI-Ran 1 419.54 8.90 .0047 SCI-Ran 1 28.00 .61
SCI-Con l 47.04 1.00 SCI-Con 1 348.84 7.55 .0087
Error 44 Error 44

 

 

 

 

 

 

 

 

95

Among High Aptitude/High Inquiry (HA/HI) Ss, "SCI"
53 gained significantly more knowledge than either of the
other two groups on both gain measures. These results were
predicted, but the magnitude of the differences, though
significant, are still small. Apparently, the features of
the "SCI" instructional environment were unable to elicit
the larger differences that were expected.

Among High Aptitude/Low Inquiry (HA/LI) 85, "Ex-
pert" 83 were far superior to all other groups in the ex-
periment. As this instructional treatment matched the
"learning style" of these students, the results are as pre-
dicted. The HA/LI "SCI" 83 did significantly better than
the HA/LI "Random" 83 which probably indicates that the
"learning style" of the "Random" 85 clashed even more with
the demands of the instructional treatment than did that of
the "SCI" SS.

Among Low Aptitude/High Inquiry (LA/HI) Ss, "SCI"
83 did the worst, contrary to expectations. In fact,
under the Gain 2 measure, they did not do significantly
better than the "Control" Ss. Apparently, low aptitude
students cannot make use of their "inquisitiveness" in a
situation where the material is new and difficult for them
to learn. However, it must be acknowledged that few 85
in this group had high "Inquiry" scores (Figure 4).

Among LA/LI Ss, the only significant difference,

except for comparisons with the control group, was between

96

"SCI" Ss and "Random" Ss on the Gain 1 measure. That
"Random" 53 did well here and elsewhere in the experiment
awaits further explanation below.

Interactions.--There were no interactions signifi-

 

cantly beyond the .05 level.

Time Expended in Learning
Sessions by Ss

 

 

Table 11 and Figure 6 show how much time the Ss
expended in the learning sessions. Since "Control" Ss
did not participate in any learning sessions, E assigned
them an average time to serve as a "dummy" variable in
the calculations.

Aptitude.--High Aptitude Ss spent significantly
more time than did Low Aptitude Ss.

Inquiry.--No significant difference.

Treatment.--Overa11, "SCI" 85 expended a signifi-

 

cantly greater amount of time than any other group. In
the breakdown by Subject-Types, the two differences that
were not significant are still in the same direction.

The fact that "SCI" 85 were required to choose a new seg-
ment and then had to wait up to two minutes for it to be
located before it was shown, probably accounts for most
of the difference. "Expert" and "Random" 83, often went
through the lists in a perfunctory manner.

Interactions.--The only interaction shown is be-

 

tween aptitude and treatments, which is consistent with

the above findings.

97

Table 11.--Means and Standard Deviations, 2 x 2 x 4 Factorial AOV, and
Four One-Way AOV of Time Expended in Learning Sessions by 53

Means and Standard Deviggions

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Subject-Type Tregtment,
Aptitude Inquiry SCI Expert Random Control Total
High 148.75 117.50 120.83 125.00 127.52
High (17.47) (9.17) (20.98 (0.00 7(14.4O)
Low 153.33 125.00 112.50 125.00 128.46
(23.39) (17.58) (12.34) (0.00) (15.88
High 137.92 114.17 120.83 125.00 124.00
Low (15.88) (21.30) (20.99) (0.00) (16.93)
Low 138.33 105.83 113.33 125.00 . 120.13
_(18.99) (10.41 (13.37) (0.00) (12.73)
Total 144.58 115.63 116.88 125.00 125.02
(19.14) (15.45) (17.40) (0.00) (19.29)
2 x 2 x 4 Factorial AOV
Source df MS F p
A (Aptitude) 1 1692.19 7.46 .0070
B (Inquiry) 1 102.08 .45
C (Treatment)
SCI-Expert 1 5650.03 24.89 .0001
SCI-Random 1 9157.56 40.35 .0001
SCI-Control 1 11180.17 49.26 .0001
A x B 1 275.52 1.21
A x C 3 610.24 2.69 .0480
B x C 3 242.36 1.07
A x B x C 3 176.91 .78
Error 176 226.97
Total 191
One-Way AOV for Each Subject-Type Group
High Aptitude/High Inquiry Hi h Aptitude/Low Inquiry
Source df MS F p Source df MS F 4p
SCI-Exp 1' 1606.67 7.75 .0079 SCI-Exp 1 191.36 .76
SCI-Ran 1 1810.01 8.73 .0051 SCI-Ran 1 5270.22 20.91 .0001
SCI-Con 1 3978.38 19.19 .0001 SCI-Con 1 5520.67 21.90 .0001
Error 44 Error 44
Low Aptitude/Hi h Inquiry Low Aptitude/Low Inquiry
Source df MS F p Source df MS F p
SCI-Exp 1 1540.56 5.38 .0252 SCI-Exp 1 3268.03 20.18 .0001
SCI-Ran 1 741.13 2.59 SCI-Ran 1 2403.56 14.84 .0004
SCI-Con 1 1335.04 4.66 .0364 SCI-Con 1 1410.67 8.17 .0051
Error 44 Error 44

 

 

 

 

 

 

 

 

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.L 1 I _L
g Aptitude: High High Low Low
Inquiry: High Low High Low

Subject-Type Group

Figure 6.--Mean Time Expended by 85 for Instructional
Treatments Over Four Subject-Type Groups

yumber of Pages of Notes
Taken by Ss

 

 

Table 12 shows the number of notes taken by each
S. E estimated the number by counting the number of pages
or partial pages containing an equivalent of about 250
words. Again since "Control" 85 took no notes, E assigned
them an average for use in the calculations.

There is only one significant difference beyond
the .05 level. In the HA/LI Subject-Type Group, "EXpert"
58 took a significantly greater number of notes than any
of the other groups.

In general, all "Expert" Ss, tended to take more
notes than the other groups. Apparently, the "Expert" 85
felt the learning session was typical of their classes, so

they wrote away as usual.

100

Table 12.--Means and Standard Deviations, 2 x 2 x 4 Factorial AOV, and
Four One-Way AOV of Number of Pages of Notes Taken by 88

Means and Standard Deviations

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Subject-Type Treatment
Aptitude Inquiry SCI Expert Random Control Total
Hi h 6.50 6.42 5.67 7.00 6.40
High 8 (4.89) (3.26) (4.25) (0.00) (3.63)
Low 7.67 .00 6.33 7.00 7.75
(6.00) (4.05) (4.83) (0.00) (4.35)
High 4.67 7.42 6.92 7.00 6.50
Low (lel), (5.82) (3.96) (0.00) (3.85)
Low 5.17 7.42 8.67 7.00 7.07
(5.29) (3.75) (3.77) (0.00) (3.75)
Total 6.00 7.81 6.90 7.00 6.93
(4. 94) (4.33) (4.22) (0.00)) (3.95)
2 x 2 x 4 Factorial AOV
Source df MS F 4p
A (Aptitude) 1 4.08 .27
B (Inquiry) 1 44.08 2.89 .0909
C (Treatment)
SCI-Expert 1 50.17 3.29 .0714
SCI-Random 1 5.01 .33
SCI-Control l 24.00 1.57
A x B 1 7.52 .49
A x C 3 32.76 2.15 .0959
B x C 3 6.76 .44
A x B x C 3 11.95 .78
Error 176 15.25
Total 191
One-Way AOV for Each Subject-Type Group
High Aptitude/Hi h Incuiry Hi h Aptitude/Low Inquiry
Source df MS F p Source df MS F p
SCI-Exp 1 .01 .00 SCI-Exp 1 81.00 4.28 .0446
SCI-Ran 1 9.39 .71 SCI-Ran 1 8.00 .42
SCI-Con l 1.50 .11 SCI-Con 1 2.67 .14
Error 44 Error 44
Low Aptitude/Hi h Inquiry Low AptitudgjLow Inquiry
Source df MS if F p Source df MS F p
SCI-Exp 1 13.44 .91 SCI-Exp 1 2.01 .14
SCI-Ran 1 9.39 .63 SCI-Ran 1 53.39 3.79 .0579
SCI-Con 1 32.67 2.20 SCI-Con 1 20.17 1.43
Error 44 Error 44

 

 

 

 

 

 

 

101

Time Expended on Notes by Ss

 

Table 13 displays the time expended by Ss in re-
viewing their notes prior to the posttests. E assigned an
average score to the "Control" 55 for the analysis.

There was only one significant difference, the
aptitude by treatment interaction. High Aptitude "SCI" 83
and Low Aptitude "Expert" and "Random" 53 appear to have
spent the most time with their notes.

Since "Expert" Ss took more notes, they might be
expected to have expended more time reviewing them. In-
stead the High Aptitude "SCI" Ss and the Low Aptitude "Ran-
dom" Ss took as much, if not more, time than the average

"Expert" 5.

Perceived Learning as a
Function of the Environment

 

 

This dependent measure is the first factor derived
from a factor analysis of the "Post-Attitude Survey"
questionnaire (Appendix M). Table 14 displays the Ss'
group responses where a high score indicates more per-
ceived learning and a low score indicates less perceived
learning.

Overall, the "Expert" Ss had significantly higher
perceived learning scores, followed by "SCI" 83 who had

scores significantly higher than both the "Random" and

"Control" 83.

102

Table 13.--Means and Standard Deviations, 2 x 2 x 4 Factorial AOV, and
Four 0ne4Way AOV of Time Expended on Notes by 38

Means and Standard Deviations

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Subject;Type Treatment
Aptitude nquiry, SCI Expert Random Control Total
High 12.08 9.08 8.50 10.00 9.92
High (9.12) (5.88) (7.08) (0.00) (6.48)
Low 14.50 12.92 7.08 10.00 11.13
7p(12.01) (7.29), (6.04) (0.00) 4_(7.65)
High 8.17 10.25 10.42 10.00 9.71
Low 7 678.05) 10 75(8.99) 12 8(5.70) 10 060.00) 10 31(6.67)
Low . . . . .
(7.14) (5.33) (6.82) (0.00) (5.61)_
Total 10.60 10.75 9.71 10.00 10.27
(9.26) (7.02) (6.43) (0.00) (6.67)
2 x 2 x 4 Factorial AOV
Source df MS F (pp
A (Aptitude) l 12.51 .28
B (Inquiry) l 39.42 .89
C (Treatment)
SCI-Expert 1 15.02 .34
SCI-Random 1 11.28 .26
SCI-Control 1 8.76 .20
A x B 1 4.38 .10
A x C 3 171.17 .88 .0102
B x C 3 10.31 .23
A x B x C 3 32.85 .74
Error 176 44.11
Total 191
One-Way AOV for Each Subject-Type Group
Hi h A titude Hi h Inquiry, Hi h Aptitude/Low Ipquiry
Source df MS F p Source df MS F p
SCI-Exp 1 11.11 .26 SCI-Exp ‘71 51.36 .88
SCI-Ran 1 51.68 1.23 SCI-Ran 1 213.56 3.65 .0626
SCI-Con l 26.04 .62 SCI-Con 1 121.50 2.08
Error 44 Error 44
Low_Ap{itude[HighIn uiry Low Aptitude/Low In uiry
Source df MS F (p Source df MS F p
SCI-Exp 1 V4.69 .11 SCI-Exp 1 3.06 .10
SCI-Ran 1 14.22 .32 SCI-Ran 1 128.00 4.07 .0499
SCI-Con 1 20.17 .45 SCI-Con 1 32.67 1.04
Error 44 Error 44

 

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103

Table 14.--Means and Standard Deviations, 2 x 2 x 4 Factorial AOV, and
Four One-Way AOV of Perceived Learning as a Function of the
Environment Scores

Means and Standard Deviations

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Supject-Type Treatment
Aptitude Inquir SCI Expert Random Control Total
High 35.17 35.25 33.08 24.17 31.92
High (5.18) (3.70) (4.26) _(4.27) (4.89)
Low 34.75 33.83 32.17 26.83 31.90
(5.19) (3.27) (4.37) (4.37) (4.28)(
High 33.08 34.75 34.75 23.75 31.58
Low (5.63) (4.49) (4.71) (3.91) ((4.51)
Low 34.08 34.33 32.00 26.42 31.71
(5.71) (2.67) (4.01 (3.46) (4.11)
Total 34.27 34.54 33.00 25.29 31.78
(5.33) (3.67) (4.48) (4.18) (5.81)
2 x 2 x 4 Factorial AOV .
Sanrce ldf .MS F ((9
A (Aptitude) l 3.25 .16
B (Inquiry) 1 .13 .01
C (Treatment)
SCI-Expert 1 489.52 24.64 .0001
SCI-Random 1 331.53 16.69 .0001
SCI-Control 1 1935.01 97.42 .0000
A x B 1 .26 .01
A x C 3 9.42 .47
B x C 3 45.55 2.29
A x B x C 3 6.28 .32
Error 176 19.86
Total 191

 

 

 

One-Way AOV for Each Subject-Type Group

High Aptitude/High In uiry

 

 

 

 

 

 

 

 

Source df MS F p
SCI-Exp 1 177.78 7.43 .0092
SCI-Ran 1 93.39 3.90 .0545
SCI-Con 1 726.00 30.35 .0001

Error 44 L

Low AptitudeLHi h Inquiry
__§purce df MS V_F p
SCI-Exp 1 160.44 7.88 .0075
SCI-Ran 1 320.89 15.76 .0003
SCI-Con 1 522.67 25.68 .0001

Error 44

 

 

 

 

 

Hi h A titude/Low Inquiry

 

 

 

 

 

 

 

 

Source df MS F p
SCI-Exp 1 60.06 3.28
SCI-Ran 1 15.13 .83
SCI-Con 1 376.04 20.55 .0001

Error 44

Low Aptitude/Low Inquiry

Source df MS F p
SCI-Exp 1 110.25 6.53 .0142
SCI-Ran 1 24.50 1.45
SCI-Con 1 352.67 20.90 .0001
Error 44‘

 

 

 

 

104

Within the Subject-Type groups there are a few
changes from the overall findings. In the HA/LI group the
"SCI" 55 are not significantly higher than either the
"Expert" or "Random" Ss. In the LA/HI group the "Random"
83 had significantly higher scores than the "SCI" Ss, a
reverse of the overall results. Finally, in the LA/LI
group, the difference between the "SCI" and "Random" 55
was not significant.

Hidden in the above contrasts is the fact that
High Aptitude "SCI" 85 perceived themselves as having
learned more in the environment than any other similar

group.

Attitude Toward Order
of the Segments

 

 

The results of this second factor of the "Post-
Attitude Survey" (Appendix M) are shown in both Table 15
and Figure 7.

Overall, the "Expert" Ss recorded the most positive
attitude toward the order of the segments, followed by the
"SCI" Ss. Only in the LA/HI group was there a departure
from the general findings. Here, "SCI" Ss displayed a
more positive attitude (not significant) than "Expert" Ss.
Otherwise the results are consistent, if not significantly
so. As expected, Ss in the "Random" treatment were not

pleased with the order of the segments.

105

Table 15.--Means and Standard Deviations, 2 x 2 x 4 Factorial AOV, and
Four OneJWay AOV of Attitude Toward Order of the Segments

Scores

Means and Standard Deviations

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Subject-Type Treatment
Aptitude Inquiry SCI Expert Random Control Total
High 7.50 7.67 4.50 6.00 .42
High ((1.38) (1150) (1.71) (1.26) (1.23)
Low 6.25 7.67 4.92 5.83 .17
(2.01) (1:65) (1.92) (9.57) (1.62)
High 7.25 6.58 4.58 5.67 .02
Low _1(1.66) (1.88) (1.67) (0.91) (1.63)
Low 6.00 7.50 4.67 6.08 .06
(2.12) (1.92)( (1(1.98) «(1,01)_ (9.32)
Total 6.75 7.35 4.67 5.90 .17
(1.74) (1.74) (1.93) (0.78) (5.03)
2 x 2 x 4 Factorial AOV
Source df MS F _p
A (Aptitude) l 8.33 .36
B (Inquiry) 1 15.19 .65
C (Treatment)
SCI-Expert 1 306.25 13.08 .0410
SCI-Random 1 87.78 3.75 .0545
SCI-Control 1 17.51 .74
A x B 1 31.69 1.35
A x C 3 14.18 .61
B x C 3 40.56 1.73
A x B x C 3 28.95 1.24
Error 176 23.42
Total 191
One-Way AOV for Each Subject-Type Group
H1gh Aptitqu/High Inquiry Hi h A titude/Low Inquiry
_§purce df MS F p Source df MS F p
SCI-Exp 1 25.00 16.50 .0002 SCI-Exp 1 36.00 13.71 .0006
SCI-Ran 1 40.50 26.73 .0001 SCI-Ran 1 10.13 3.85 .0559
SCI-Con l 13.50 8.91 .0047 SCI-Con 1 1.04 .40
Error 44 Error 44
Low Aptitude/Hi h Inquiry Low Aptitude/Low Inquiry
flurce df MS F p Source df MS F p
SCI-Exp 1 5.06 1.91 SCI-Exp 1 473.06 5.44 .0243
SCI-Ran 1 28.13 10.60 .0022 SCI-Ran 1 15.13 .17
SCI-Con 1 15.04 5.67 .0217 SCI-Con 1 .04 .00
Error 44 Error 44

 

 

 

 

 

 

 

 

106

(+)
a)
I

*—'__'_"‘\ Expert

 

MEAN ATTITUDE SCORE
ax
I

W

 

(-)
05
I

 

1 I I T

Aptitude: High High Low Low
Inquiry: High Low High Low

SUBJECT-TYPE GROUP
Figure 7.--Mean Attitude Toward Order of Segments Scores

for Instructional Treatments Over Four Subject—
Type Groups

Attitude Toward Method of Learning

The third factor of the "Post-Attitude Survey" was
concerned with attitudes toward the method of learning.
Table 16 and Figure 8 depict the results.

Overall, "SCI" 85 responded in a significantly
more positive manner than both "Random" and "Control" Ss.
Although "SCI" 83 were also more positive than "Expert"
Ss, the difference was not significant.

Among the Subject-Type groups, only in the LA/LI
group were the "Random" 83 significantly different from

"SCI" 55. Noteworthy, is the fact that in the LA/LI group,

107

Table 16.--Means and Standard Deviations, 2 x 2 x 4 Factorial AOV, and
Four One-Way AOV of Attitude Toward Method of Learning Scores

Means and Standard Deviations

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Subject-Type Treatment ,
Aptitude Inquiry SCI Expert Random Cbntrol Total
“ High 22.33 19.25 17.75 17.33 19.17
(3.45) (3.96) ((3.62)( (3.01), (3.41)
High Low 21.83 18.17 17.17 17.75 18.73
11(5.41) _(4.17) (5.11) (2.63) (4.47)
High 19.83 20.00 19.58 16.83 19.06
(5.08) (4.41) (4.48) (2.15) (4.42)
L0“ Low 22 33 20.25 17.00 18.33 19.48
(3.65) (2.42) (4.29)_ (2.34) (3.34)
Total 21.58 19.42 17.88 17.56 19.11
(4.48) (3.82) 1 (4.41) (2.54) (4.15)
2 x 2 x 4 Factorial AOV
Source df MS (p
A (Aptitude) 1 5.01 .33
B (Inquiry) 1 .01 .00
C (Treatment)
SCI-Expert l 6.04 .40
SCI-Random l 92.25 6.10 .0145
SCI-Control 1 388.01 25.64 .0001
A x b 1 8.76 .58
A x C 3 13.14 .87
B x C 3 18.39 1.22
A x B x C 3 13.03 .86
Error 176 15.13
Total 191

 

 

 

One-Way AOV for Each Subject-Type Group

 

 

 

 

 

 

 

 

High Aptitude/Hi h Inquiry
Source df MS F p
SCI-Exp l .11 .01
SCI-Ran 1 34.72 2.98 .0911
SCI-Con 1 150.00 12.89 .0009
Error 44
Low A titude/Hi h Inquiry
Source df MS F p
SCI-Exp 1 14.06 .79
SCI-Ran 1 12.50 .70
SCI-Con 1 54.00 3.04 .0884
Error 44

 

 

 

 

 

High A titude/Low Inquiry

 

 

Source df MS F (p
SCI-Exp 1 5.06 .25
SCI-Ran 1 55.13 2.76
SCI-Con 1 100.04 5.02 .0302

Error 44

 

 

 

 

Low Aptitude/Low Inquiry

 

 

Source df MS F p
SCI-Exp 1 9.51 .85
SCI-Ran 1 88.89 7.96 .0072
SCI-Con 1 96.00 8.59 .0054

Error 44

 

 

 

 

108

all three means were practically the same. "SCI" 85 in
this group had a less favorable attitude than other "SCI"
Ss; perhaps because they learned so little.

Contrary to expectations, the degree of S "inquisi-

tiveness" bears no relation to the attitude measured here.

 

     
   

 

 

E
a 22 -
8
E120-
? /
E 19 - ‘\\\Expert //
“’18— V
E 17 _ Random
4 L | L
‘T Aptitude: High High Low Low

Inquiry: High Low High Low
SUBJECT-TYPE GROUP
Figure 8.--Mean Attitude Toward Method of Instruction

Scores for Instructional Treatments Over Four
Subject-Type Groups

Perceived Learning as a
Function of Task Difficulty

 

 

This measure is the fourth factor derived from the
"Post-Attitude Survey" questionnaire. Table 17 and Figure
9 reflect the responses, where a low score indicates less
difficulty in learning and a high score indicates more
difficulty in learning.

Overall, the "SCI" 85 indicated significantly less

difficulty in learning than any of the other three groups.

109

Table 17.-~Means and Standard Deviations, 2 x 2 x 4 Factorial AOV, and
Four One-Way AOV of Perceived Learning as a Function of Task

Difficulty Scores

Means and Standard Deviations

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Subject-Type Treatment
Aptitude Inquiry SCI Eupert Random Control Total
High 12.33 12.67 13.25 19.42 14.42
High ((2.64) (1.72 (2.92) (2.15) (2.51)
Low 13.08 13.50 15.75 18.25 15.15
(3.29) (3.27) (3.67) (2.11) ((3.09),
Hi h 14.50 14.08 13.92 18.75 15.31
Low 8 (2.6§) (3.40) (3.05) “(2.56) (2.87)
Low 13.58 13.42 16.25 19.58 15.71
(3.12) (4.85) (3.89)( (2.41) (3.77)
Total 13.37 13.42 14.79 19.00 15.15
(2.83) (3.37) (3.65) (2.36) (3.85)
2 x 2 x 4 Factorial AOV
Source df MS F (p
A (Aptitude) 1 25.52 2.67
B (Inquiry) 1 15.19 1.59
C (Treatment)
SCI-Expert 1 191.36 19.99 .0001
SCI-Random 1 62.35 6.51 .0116
SCI—Control 1 759.38 79.32 .0000
A x B 1 1.33 .14
A x C 3 2.19 .23
B x C 3 18.47 1.93
A x B x C 3 8.61 .90
Error 176 9.57
Total 191

 

 

 

One-Way AOV for Each

 

 

 

 

 

 

 

 

fl1gu Aptitude/Hi h Inquiry

Source df MS F p
SCI-Exp 1 49.00 7.80 .0078
SCI-Ran 1 55.13 8.77 ..0050
SCI-Con 1 301.04 47.91 .0001
Error 44
_A(Low AptitudejHigh Inquir
_§ource df MS F (p
SCI-Exp 1 24.17 2.93
SCI-Ran 1 58.68 7.11 .0107
SCI-Con 1 108.38 13.13 .0008

Error 44

 

 

 

 

 

Subject-Type Group

High A titude/Low Inquiry

 

 

 

 

 

 

 

 

Source df MS F p
SCI-Exp 1 43.34 4.54 .0389
SCI-Ran 1 .06 .01
SCI-Con 1 160.17 16.76 .0002

Error 44

Low AptitudelLow Inquir

Source df MS F (p
SCI-Exp 1 84.03 5.92 .0192
SCI-Ran 1 .89 .06
SCI-Con 1 216.00 15.21 .0004

Error 44

 

 

 

 

110

Among Subject-Type groups the results are mixed.
In the LA/HI group there is a reversal of direction with
"SCI" 53 now the ones expressing significantly greater per-
ceived learning difficulty than "Random" Ss. In the LA/LI
group "SCI" Ss continue to register the second lowest score
while "Random" 83 display the greatest measured learning

difficulty score.

 

 

 

»..
S 316 - Random
88%
ESE:
14 -
8.38
“(2.91313 -
314 m
m.. . T ’1 I I
q Aptitude: High High Low Low
Inquiry: High Low High Low

SUBJECT-TYPE GROUP
Figure 9.--Mean Perceived Learning As a Function of Task

Difficulty Scores for Instructional Treatments
Over Four Subject-Type Groups

Reviewing or Relating
Material to Past Experience

Table 18 shows the fifth factor derived from the
"Post-Attitude Survey." A high score reflects S's indi-
cation that he reviewed or related the material to his
past experiences.

Overall, the only significant difference observed
was between Low and High Inquiry groups. High Inquiry Ss

reported more reviewing or relating of the material.

111

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 18.--Means and Standard Deviations, 2 x 2 x 4 Factorial AOV, and
Four One-Way AOV of Reviewing or Relating Material to Past
Experience Scores
Means and Standard Deviations
Subject-Type Treatment
Aptitude Inquiry SCI Expert Random Control Total
High 5.75 7.00 6.17 5.33 6.06
High (1.66) (1.48) (_(1.73) (1.10) (1.47)
Low 4.58 5.83 5.75 5.33 5.37
((1.31) ((1.49) (1.81) (0.93) (1.50)
High 5.50 5.67 6.08 6.08 5.83
Low (1.57) (2.01) Q.79) (1.48) (1.6g)
Low 5.25 5.33 5.50 5.92 5.50
(1.67)( (2.10) (1.82) (1.27) (1.80)
Total 5.27 5.96 5.88 5.67 5.70
(1.50) (1.74) (1.80) (1.32) (1.62)
2 x 2 x 4 Factorial AOV
Source df MS F p
A (Aptitude) 1 .13 .05
B (Inquiry) l 12.51 4.89 .0284
C (Treatment)
SCI-Expert 1 4.52 1.76
SCI-Random 1 5.28 2.06
SCI-Control 1 3.76 1.47
A x B 1 1.51 .59
A x C 3 5.38 2.10
B x C 3 1.12 .44
A x B x C 3 1.09 .43
Error 176 2.56
Total 191

 

 

 

 

One-Way AOV for Each Subject-Type Group

High Aptitude/Hi h Inqu1ry H1gh Aptitude/Low Inquiry

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Source df MS F p Source df MS F p
SCI-Exp 1 14.06 6.54 .0141 SCI-Exp 1 3.36 1.49
SCI-Ran 1 3.13 1.45 SCI-Ran 1 5.01 2.22
SCI-Con 1 1.04 .48 SCI-Con 1 3.38 1.49

Error 44 Error 44

Low Aptitude/Hi h In uiry Low AptitudelLow Inquiry

Source df MS F p Source df MS F p
SCI-Exp *I* .44 .17 SCI-Exp 1 .44 .14
SCI-Ran 1 .68 .26 SCI-Ran 1 .06 .02
SCI-Con 1 2.04 .79 SCI-Con 1 2.67 .82
Error 44 Error 44

 

 

 

 

 

 

 

 

112

Among the Subject-Type groups, there was also only
one significant difference. In the HA/HI group, "Expert"
83 said they reviewed or related more material to past

experiences than did the "SCI" 85.

Attitude Toward Computers

A high score on the sixth factor from the "Post-
Attitude Survey" indicated a more positive attitude toward
computers by the Ss.

Overall, Table 19 shows that the "Expert" Ss held
a significantly more favorable attitude toward computers
than any of the other groups. Upon addition of the co-
variate "Pre-Interest in Computers", the differences are
still significant. This is true even though "Expert" Ss
were also more interested in computers before the experi-
ment.

Among the Subject-Type groups, there are two sig-
nificant differences. In the HA/HI group, the difference
is consistent with the overall findings and in the LA/LI
group the "Random" Ss register the lowest mean attitude

score .

Number of Questions Asked

Ey "SCI" Ss

Another dependent variable measured in the experi-

 

 

ment was the number of questions asked by "SCI" Ss.
Table 20 presents the mean frequency, standard deviations

and range of questions asked for each of the four "SCI"

1

13

Table 19.—-Means and Standard Deviations, 2 x 2 x 4 Factorial AOV, and
Four One-Way AOV of Attitude Toward Computers Scores

Means and Standard Deviations

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Supject-Type Treatment
Aptitude Inquiry( SCI Expert Random Control Total
High 14.25 15.08 12.92 12.08 13.58
High (3.62)( (2.43) ((2.75) _(3.05) (2.82)
Low 13.83 14.33 11.75 12.67 13.15
((3.56) (1(2.50) ((3.67) (2.64) (3.0§)_
High 13.25 14.25 13.50 11.42 13.10
Low (3.98) (2.99)( (2.07) (2.75) (2128)
Low 14.25 13.75 11.08 13.00 13.02
(2.77) (1.29) (3.85) (2.91 (2.86)_
Total 13.90 14.35 12.31 12.29 13.21
(3.51) (2.38) (3.17) (2.85) (3.10)
2 x 2 x 4 Factorial AOV
Source df MS F p
A (Aptitude) ‘ 1 4.38 .48
B (Inquiry) 1 3.26 .36
C (Treatment)
SCI-Expert 1 83.27 9.20 .0028
SCI-Random 1 19.53 2.16
SCI-Control 1 61.76 6.83 .0098
A x B 1 1.51 .17
A x C 3 1.01 .11
B x C 3 18.35 2.03
A x B x C 3 4.13 .46
Error 176 9.05
Total 191
One-Way AOV for Each Subject-Type Group
High Aptitude/High Inquiry High Aptitude/Low Inquiry
Source df MS F p Source df MS F (p
SCI-Exp 1 36.00 4.54 .0388 SCI-Exp 1 22.56 2.43
SCI—Ran 1 .50 .06 SCI-Ran 1 18.00 1.94
SCI-Con 1 28.17 3.55 SCI-Con 1 8.17 .88
Error 44 Error 44
Low AptitudelHi h Inquiry Low Aptitudg/Low Inquiry
Source df MS F p Source df MS F p
SCI-Exp 1 21.01 1.95 SCI-Exp 1 8.51 1.04
SCI-Ran 1 10.89 1.01 SCI-Ran 1 51.68 6.33 .0157
SCI-Con 1 20.17 1.87 SCI-Con 1 9.38 1.15
Error 44 Error 44

 

 

 

 

 

 

 

 

114

Subject-Type groups. In Table 21, a 2 x 2 factorial
analysis shows that the High Aptitude "SCI" 85 asked sig-
nificantly more questions than their counterparts. Con-
trary to expectations, there is no significant difference
on the "inquiry" dimension. A one-way AOV shows that only
between the HA/HI and LA/LI "SCI" 83 was there a signifi-

cant difference in number of questions asked.

Number of Seqmentg
Repeated by "SCI" 85

 

In addition to the number of questions asked, the
number of segments repeated by "SCI" Ss are shown in Table
22. Although the mean frequency of repeats is in the same
rank-order as the number of questions asked, Table 23
shows no significant differences among them.

In general, these two dependent variables show that
the HA/HI "SCI" 83 asked the most questions and repeated
the most segments. Apparently these type of peOple choose
to make most use of the features of the learning environ-

ment.

Summary of AOV Results

 

1. $3 with higher college aptitude, gained more.
This is unlike many other learning experiments
and suggests the material was difficult to
learn, accentuating individual differences in

college aptitude.

115

Table 20.--Means, Standard Deviations, and Range of the Number of
Questions Asked by "SCI" 88 Within Each Subject-Type Group

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Aptitude
Inquiry High Low
M SD R M SD R
High 17.008 13.92 1-49 9.33 8.05 2-21
Low 13.92 13.18 1-47 6.58 4.21 0-21
aN=12

Table 21.--Ana1yses of Variance of Number of Questions Asked by "SCI" Ss

2 x 2 Factorial AOV

 

 

 

 

 

 

 

 

Source df MS F p
A (Aptitude) 1 675.00 5.12 .0287
B (Inquiry) 1 102.08 .77
A x B 1 .33 .00
Error 44
TOTAL 47
One-Way on
Source df MS F p
High Apt./High Inq. vs. High Apt./Low Inq. 1 78.03 .59
High Apt./High Inq. vs. Low Apt./High Inq. 1 48.35 .37
High Apt./High Inq. vs. Low Apt./Low Inq. 1 651.04 4.94 .0315
Error 44
TOTAL 47

 

 

 

 

116

Table 22.--Means, Standard Deviations, and Range of the Number of
Segments Repeated by "SCI" Ss Within Each Subject-Type

 

 

 

 

 

Group
Aptitude
Inquiry High Low
M SD R M SD R
High 2.67 1.03 1-4 2.17 1.28 0-4
Low 2.33 1.03 1-4 1.92 1.55 0-4

 

 

 

 

 

 

 

 

Table 23.--Ana1ysis of Variance of Number of Segments Repeated by "SCI"
Ss

 

 

Source df MS F p
A (Aptitude) 1 4.08 2.35
B (Inquiry) 1 .33 .19
A x B 1 .75 .43
Error 44
TOTAL 47

 

 

 

 

 

117

35 who are more "inquisitive" gained more--

but the difference is probably due to the
moderate correlation with college aptitude.

As expected, Ss with the "Expert" instructional
treatment learned best overall and the High
Aptitude/Low Inquiry "Expert" Ss learned best
of all.

High Inquiry "SCI" 83 were predicted to learn
more than High Inquiry "Expert" and High
Inquiry "Random" 83. This prediction held

true among High Aptitude Ss and reversed itself
for Low Aptitude 83. High Aptitude/High
Inquiry Ss apparently became more frustrated

by the difficult learning task than the com-
parison groups.

"SCI" 83 expended the most time while learning,
mainly because of the time involved in locating
the desired tape segments.

"Expert" Ss took the most notes, but High
Aptitude "SCI" and Low Aptitude "Random" Ss
spent as much time reviewing their notes.
"Expert" Ss had the highest scores on per-
ceived learning as a function of the environ-

ment.

118

8. "Expert" 83 were most favorable and the "Ran-
dom" Ss were most displeased with the order
of the segments.

9. "SCI" Ss were most pleased with the method of
instruction, except for the Low Aptitude/High
Inquiry "SCI" group.

10. High Aptitude "SCI" Ss and Low Aptitude "Ex-
pert" 85 registered the least amount of per-
ceived learning difficulty.

11. More High Inquiry 83 indicated that they re-
viewed the material or related it to their
past experiences.

12. "Expert" 83 held the most favorable attitude
toward computers at the conclusion of the
experiment.

13. High Aptitude/High Inquiry $5 of the "SCI"
group asked the most questions and repeated

the most segments.

Correlational Ana1ysis

 

The second analysis to be considered consists of
the calculation of product-moment correlation coefficients
between gain scores and scores of the 34 individual-
difference measures. These 280 correlations were calcu-
lated separately for each of the four treatment groups

and are presented in Table 24. The "Control" group

119

correlations are shown, but comparisons with them are not
made since such comparisons would not contribute to explana-
tions of the results discussed above.

Tests of the significance of the differences between
correlations, for each individual-difference measure, were
made for all permutations of pairs of correlations for both
Gain 1 and Gain 2 scores. Only those differences between
correlations which were significant or relevant to the
analysis are analyzed in detail below. In addition, the
remaining ten dependent variables are included with these
analyses. Also, a similar analysis for Subject-Type groups
is shown.

In limiting the correlational analyses to only
those where a significant difference between correlations
on Gain 1 or Gain 2 scores are obtained, the reader's
attention will be focussed on those variables which contri-
bute to an explanation of the observed differences in the
above analyses of variance. Since there are a bewildering
number of possible correlations to compare among the 12
dependent variables and 24 independent variables, this
procedure appears to be justified by its parsimoniousness,
if nothing else. For example, of the over 1900 correla-
tions obtained, only 588 are displayed in following tables.
However, significant correlational differences among other
variables will be presented if they appear worthwhile from

an explanatory point of view.

120

 

 

 

 

 

 

 

 

 

 

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No No No- No oo- mo- mN so “may moHumaoeuoz om: .s
oH oH no- HH oH oN sH- sH monuHHaa mmoHHoo .m
so HH mo- sH so- NH Ho- No monuHuo< 6>HuouHuao=o .N
NN NH so- No- oH NN HN- oo mosuHHa< Hoouo> .H
Houucou Eovcmm uuomxm :Hom: Houucou Eoccmm uuomxm :Hom: whammoz
N zH<o H zHoo mucoumouHo HmooH>HooH

 

macaw unoEumouH zoom cHnuHs mouoom
ummHIcoHuouHuu can monommoz mucouommHa HmopH>Hch comsumm mcoHumHouuoo HHmuo>oII.¢N mHan

121

Hoo.w are,

 

 

 

 

 

 

 

 

Ho.wa.&
no.mwd¥

NNNNs- Hm- NNm- sssamu ¥NNNNI *wmn rst- ***HNI umpuoum .mm
oo No- NN *NN oo No- NNNss sH mouoz :6 omoooaxm oaHe .sN
oo NH- No- NNos oo No- HN NoN .oosme Nouoz No NNNNH No Huneoz .NN

oo No so- NNNN oo NH NH oH Noonmmm
wchumoH cH popcoaxm QEHH .Nm
NH NH- No NNNNs HH- sH- No NHN omxma N=OHummso Ho Hogsoz .HN
NN- HH No No oH No- No HN Numuoasoo Nausea monuHuu< .oN

NN NH sH- NN NH NH HN- NH swam on HNHNmumz
wcHumHom no wcH3oH>om .mN

No No- sH- *sN- NH- oH- NN- *NN- NHHooHHNHo
xmma chHaumoH co>Hooumm .wN

No Ho NsN *NN NH No- No- NN- NaHoumoH
No ocean: onwaoe monuHHHN .NN

No oH- No- NNN Ho- so- NoN NN muomaNNN
No “mono Nausea moooHHHN .NN

sH- so No NN No Ho- NH- NN HomeoouH>oN
"wchummH vo>Houuom .mN
oH- NoN sNos NN so HH NH *sN NHNHNNEON oH HmououoH-NHN .sN
No- NH No so- No- Ho so- No NNNHo .NN
No- NH NHN No- No No- HN No 6N< .NN
No- NH- NNN No- NH- No- No- NH- NNN .HN
Ho No No HH No- NH Ho- No NNN .oN
Ho- *sN NH NH NH NN oN *oN NochmmH 6HHH3 Naoaoua< .NH
NN so oH- NN No NH NHN- sN NcHonmH No comm .NH
No No No- No- HH so- NN- No- mooHuooam NaHoumoH No Houuooo .NH

Houudoo Bovdmm uummxm :HUm: HOHUGOU Eovdmm uhwqxfl :HOm: whammwz
N zHNN H zH<o auamummuHo HmsoHpHooH

 

 

 

N.o.uaooo-.sN NHNNN

122

Also in this vein, the correlations found within
the cells of the 3 x 4 matrices of Treatments and Subject-
Types are not displayed.

Because N for each of these cells is only 12,
making the correlations somewhat spurious, only significant
correlations will be mentioned, unless there is need to
mention lesser ones for cogent reasons. (For N=12, the
probability of a correlation of .57 occurring by chance
is .05, the probability of a correlation of .71 is .01.
Considering the number of correlations observed here,
3,456, significant correlations should be regarded with

caution.)

Attitudes Toward Learning

 

In addition to deriving six factors from the
"Attitudes Toward Learning" questionnaire for use as inde-
pendent variables, E also derived a composite overall
score for each S. This score was based on E's estimation
as to which responses would best distinguish between those
88 who would be most receptive to learning in the "Expert"
instructional treatment versus those 85 who would be most
receptive to learning in the "SCI" instructional treat-
ment. Examples would be: (a) desirous of control versus
not, (b) learn better alone versus with others, (c) at
ease when asking questions versus not, etc.

Table 25 shows that for "Expert" 83, there was a

significant negative correlation between Gain 1 scores

123

Ho.V a:
mo.V as

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

oH sN Ho- oo omxm< NooHuNNomIHo Honsoz
HoI mH so oH muouDQEoo pum3oa opsuHuu<
mH sH HN so- ummm ou HmHuoomz onumHox no wcH30H>om
N No- NN- oN Ho NHH=6HNNHH "NoHoHNNH om>Hmuumm
so wH Non mo wchumoH mo vogue: vumsoa opauHuu<
Hon NH mo NH mucoEwmm onu mo nopuo uum3oa opsuHuu<
No NH Non Non ucoecouH>cm.nwchumoH po>Hoouom
sH Non MH MN Nouoz co woodmaxm oeHH
NH sou NN so coxme mouoz mo wamm mo nonasz
mH NN No mH mcoHNNom mCchmoH cH popcooxm oEHH
oN NH NH- mo N cho
-RI. mo mH mHI DN -H,WHNU
H N H H N H H m N N H m 36H HNHN 36H HNHN "NHHoooH NHHNHHHN
HNH HNH H\m HNH \m m\: 36H on- HNHm HNHm "mmouHHNN
moocmummeo ucmonchHm m mono o NHIuoonnsm
NoI wHI mH -I- voxm< chHummoo mo Nunezz
oo MN mo muouaaaoo cumaoa mpDuHuu<
sHm NoI Hon ummm ou HmHumumz wcHumHom no wcH30H>om
mHI mo- sH NuHaonmHo “wchumoH wm>Hoouom
NH No No NoHoummH No ooNuuz oumsoe monuHuu<
NoI Ho mH mucmeom mnu mo noouo pum3oe opauHuu<
oN mo mo ucoscouH>cm chchmoH po>Hmouom
NN HoI mH mmuoz co poocoaxm mEHH
mo No sN coxma Nouoz mo Nowmm mo Honasz
*om sH oo chHNNom wchumoH cH popcooxm oEHH
mH sN- NH N cho
sH sum- HN H cho
:mmIomm cum-Hum omeHom Eopcmm uuomxm Hum mHuouHuo

 

 

 

 

 

moocoumNMHo ucmonHcmHm

 

masouo ucoEummuH

 

 

mcoHumHouuoo sauna cow3uom moocmuommHo mo moconMchHm onu was Nanouo oamsuuoonnsm can
ucoEumouH cHsuH3 Nouoom aOHuouHuo can wchumoH cumsoe NovsuHuu< comauom chHumHouuoouu.nN oHan

124

and the Attitudes Toward Learning variable. This implies
that only those 85 who were more receptive to the fixed
sequence, structured, instructional situation were really
able to profit by it. Actually, inspection of the 3 x 4
matrix shows that only Low Inquiry "Expert" Ss showed
strong correlations (HA/LI -.56; LA/LI -.51).

On the other hand, a significantly positive cor-
relation with Gain 1 scores for LA/HI "SCI" 85 (.57),
implies that for these "SCI" 85 only those who were more
favorably inclined to the "SCI" treatment, could really
profit from it.

A similar set of correlations, but less strong,
was true for Gain 2 scores. For the remainder of the Ss,
this variable had little relation.

Another interesting correlation was found in the
3 x 4 matrices. There were strong correlations between
Attitudes Toward Learning and Attitude Toward Method of
Learning for only High Inquiry "Random" Ss (HA/HI .59;
LA/HI .55). This says that among High Inquiry "Random" 85
only those who preferred a "SCI" type of learning environ—
ment also preferred the "Random" type of learning environ-
ment.

The lack of significant correlations between the
remaining attitudinal measures and Attitudes Toward
Learning is somewhat surprising since the background for

these variables is very similar.

125

Test Anxiety

 

Table 26 contains one important finding. There is
a mild indication that the less test anxious "SCI" and
"Random" 83 were, especially the lower aptitude ones, the
better they learned. This contrasts to the "Expert" Ss,
where the more test anxious Ss appeared to learn more.
This latter correlation was significant for "Expert" Ss as
a whole, and the LA/LI group in particular correlates .59
with Gain 1 scores. One other group, HA/HI "Random" 83,
had the same correlation (.59) with the Gain 2 measure.

The only other correlations of interest are those
for the HA/LI "SCI" Ss. Of all "SCI" 83 this group had
the only negative correlations with Attitude Toward Method
of Learning (-.51) and Number of Questions Asked (-.40).
These correlations suggest that only the least anxious Ss
in this group liked the learning method and were willing

to ask questions.

Number of Questions Asked

 

Overall, Table 27 shows that the more questions
"SCI" 83 asked, the more they benefited as measured by the
gain scores. More specifically, on the retention test,
the High Inquiry "SCI" 85 profited most by asking ques-
tions (significant correlations of .63 and .64). On the
other hand, "Random" Ss, except for the HA/HI group, gained
less the more questions they heard. Apparently, when the
presentation is not ordered, questions asked by others do

not help.

126

Hover.
mo.Va¥

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

NH- sH- oH- oo omxm< NooHuNoso No Hooasz
oo NHI mHI soI mucusaaoo pumaoe opouHuu<
NH- Ho oN- No- NNNH ou HNHHouNz NoHuNHoN Ho NoHsoH>um
Ho sN NN sN NuHoUHNNHo "NoHoummH o6>H66Hom
Ho oo oN- NH- NoHcHNNH No ocean: Nausea mosuHHHN
NH No- oH- No mucoaNNN 6H0 No Hmouo Nausea NoauHuu<
Ho sHI oHI mo: ucoacouH>cm "wchumoH vo>Hwoumm
mHI NH: sHI mHI Nouoz co covcodxm maHa
wHI NNI *Hmu oou aoxme mouoz mo Nowmm mo Honasz
mHI HNI won NoI chHNmom mchumoH cH povcoaxm QEHH
Ho moI Ho Ho: N ch0
Non oHI Ho. mo H :Hmo
H m H H m H H m m m H m 3oH :me 30H anm "muHawcH mHuouHuo
HNH HNN HNN HNN HNN NNN 36H 36H HNHN HNHN "moauHua<
moocouommHo unmonchHm N some 0 myuuooHHSm
HHI no. mo: omxm< mcoHumond mo Hooaoz
mo oHI mHI Nuouaaaoo vum3oy opauHuu<
HNI mo: mHI ummm ou HmHHoumz wcHumHmM no wcH3mH>mM
NH NH NN NHH=6HNNHH "NoHoummH o6>Houuam
so- sH- MHI wchumoH mo oonuoz cum3oe ovauHuu<
* mo mH NNI mucoawom man no “mono ounBoH «pauHuu<
no: mHI wHI ucoEGoHH>cm chchmmH po>Hmouom
4mm: mo: mHI Nouoz do popcoaxm mEHH
*sm- NHI mN- coxme wouoz mo Nowmm mo Honaaz
HNI *oNI so NcOHmmmm wchumoH cH popcomxm mEHH
mo sH NH: N cHwo
N « mH- *mN oN- H cHwo
cum-axm cam-How ameHom aopgmm uuommm Hum mHuouHuo
mooamuommHo ucmOHmeme ucoaummua

 

 

 

NcoHumHouuoo omens comauom NoocoumHMHo mo moconMchHm mnu can Naaouo ommanuomnnam
pom ucoaummua cHsqu Nouoom cOHuouHuo pom Nuonc< uNoa coosuom NcOHumHopuooII.NN oHan

127

Ho.v.aNN
mo.v.aN

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

II II II II ooxm< NcoHumond mo nmnesz
N Ho- NHN- HN- sH NHNHNNEON onmson mosuHous
oo Ho soI HHI ummm on HmHnoumz wcHumHoN no wcHsoH>oN
No- NN- No- NN- NHHooHNNHo "NcHanmmH om>HmoH6N
No NH- sN NH NNHNNNNH No NNNHNN Numson NN50H00<
NHI HH mo HHI Nucoewom man mo nopno Nnmzoa ocsuHuu<
sH HHI sHI NN ucmEconH>cm "wchnmmH vm>Hmonom
NsN so oN NH Nouoz so omooooxm msHe
NNNs HH oN NoN omxme mmuoz No NNNNN No Hmnasz
NNoN NNNs NNN «NHN NcoHNNoN NcHonmmH NH oooaoaxm meHn
oH No NH HN N aHmo
mo oHI sH oH H cho
H N H H N H H N N N H N 36H NNHN 36H NNHN “NmHoooH NHuuHHHo
H\H HNN HNN H\N HNN NNN 36H 36H NNHN NNHN "moauHNNN
moocmnNNMHo ucmonHc Hm wmaono mo HInumnasm
II II II pwxm< NNOHuNoso mo nonanz
N No- NNN- HH 332.58 Hons-.369 mono-Bus.
No mHI Ho ummm on HmHnmumz wcHanom no wcHSoH>mm
NHI HoI oNI NHHsonmHo chchmoH oo>Hoonom
No No- NoN NNHNHNNH No ooNuNz onmson NoouHuu<
NoI NoI mH mucmawom man we nopno vnmaoa opanuu<
mH oHI sH ucoeconH>cm chchmoH po>Hoonom
N «N NN NH- NNN Nunez so omoooaxm oeHn
N NNN No- NNss ooxmn Nunez No moNNm No nonanz
NNmm mN NNwm NNOHNNmm wchnmoH :H omwcoaxm oEHH
NN N oHI mo NNms N cho
N sHI mo NHm H cho
cum-NNN omN-HNN NxN-HNN ENNNNN unmmxm HoN NHHNHHNN
mooconmmeo namonHame ucoaumone

 

 

 

NcOHumHonnoo amuse

cmm3uom NooconNMMHo mo mocmonchHm man can Nanono mamaIuoonnow vow unmanmony
cHnqu Nonoom coHnoano one pmxm< NcoHumoao mo nonaaz cmo3uom chHumHonnooII.NN pomH

128

For the "Expert" Ss, the results were mixed, but in
a very interesting way. For HA/HI "Expert" Ss, who could
only listen to questions asked by their yoked counterpart,
there was a moderately negative correlation (-.41) with
Gain 1, indicating that the more questions they heard, the
more they were confused. It seems likely that this group
was frustrated in the structured situation in which they
found themselves since they often criticized the questions
they heard other Ss ask as being irrelevant and useless.

However, for HA/LI "Expert" 53 the opposite result
was found. The more questions these 83 heard, the more
they learned, by a significant correlation (.60) with Gain
1. Apparently, these Ss were the type who used information
offered to them by others in a structured situation.

"SCI" Ss, especially those with a high aptitude,
had a significantly more positive Attitude Toward Method
of Learning, the more questions they asked. "Expert" 85
had a significantly less favorable Attitude Toward Compu—
ters, the more questions they heard.

In general, the more questions asked or heard,
the more notes were taken. However, one exception which
relates to the above is the fact that HA/HI "Expert" Ss
had a negative correlation (-.28) between the number of
questions heard and the number of notes taken. This
supports the earlier observation that these 85 may have
been bored or irritated by the questions they had to listen

to.

129

Time Expended in Learning
Sessions

 

From Table 28 it can be seen that for "SCI" 85 the
more time they spent in the learning sessions, the more
they profited in retaining the material learned. In part
this may be a measure of motivation. However, this moti-
vation is not due to interest in computers, because there
is no relation between Time Expended and Attitude Toward
Computers. A possible explanation is the learning environ-
ment: those Ss who thrived in the "SCI" mode, spent more
time and learned more. To support this contention, there
are moderate correlations between Time Expended and Atti-
tude Toward Method of Learning for HA/HI "SCI" (.48) and
HA/LI "SCI" (.54) 83.

As expected, Time Expended was positively correlated
with Notes Taken and Time with Notes for all Ss.

If "SCI" Ss asked more questions, they also took
more time. Noteworthy is the fact that although "Random"
53 Spent more time if there were more questions, "Expert"
83 had no correlation (-.05) between the number of ques-
tions they heard and time in the learning session. Ap-
parently, these 83 were using their time in a manner un-
related to the number of questions heard and still achieved
the greatest gains in the experiment.

Of the remaining correlations, the only worthwhile

observation is that, in general, High Aptitude, High

130

Ho.v&&
mo.VaN

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

NNoN NNNs NNN NNHN omNNN NooHuNmso No Hansoz
NHI No No MN Nnouaano pnmsoh oponHuu<
mH sH oo NNM ummm on HmHnoumz mcHumHom no wcHsmH>om
No- NNHs- sN- NNN- NuHooHNNHo "NoHoumoH om>HN6uoN
No NN NNN NN NoHoummH No ooNHNN Nansen NosuHuHN
N N NH- NH NNNs No- mucosNNN NNN No HNo.5 Nausea moouHNHN
soI NH oN sN ucmaconH>cm "waHonmmH oo>Hoonom
NNNM ON NNoo NNMM Nmuoz co Novooaxm oaHe
NNsN NHN NNNs NNNN omxmn Nouoz No NNNNH No nonaoz
II .II II II NNOHNmom wchnmoH cH Newcomxm wEHH
oH No HoI NoM N cho
mo No Ho NmN H cHwo
H N H H N H H N N N H N 36H NNHN 36H NNHN "NHHNNNH INHnuuHuo
HNH HNN HNN HNN HNN N\N 36H 36H NNHN NNHN "mosuHua<
mmocwnwmeo ucmonHdem monono on HIuoomnow
NNMM MN NNwm ooxm< NcoHnmomd mo noneoz
mo 0N MoI mnouanoo vnmaoH ovouHuu<
NoM NH oo ummm On HmHnoumz wcHanom no wcHaoH>om
NNosI oHI wHI NuHoonwHo "oncnmoH wo>Hoonmm
MN mo oN wchnNoH mo venue: onmaoa mvsuHuu<
mo oHI NN muooEwom mo nopno cnmsoe oNSuHuu<
N NNos sH NoI ucoEoonH>cm chchon No>Hoonom
NNsm NNHs NNos Nonoz no poocomxm oEHH
NNos NNMM NNoM cmxme Nouoz mo Nowmm mo nonaoz
II II II woOHmwom wchnmoH cH cowoomxm oEHH
N Mo soI NNmM N :Hmo
0H NH oH H ono
cmmImxm cmmIHom mmeHom Bopcmm I unmmxm How mHnmano
NooconoNMHo HomoHMHowwm nooaummna
NcoHumHonnoo

omone coosnom mooaonowwHo mo oocmonchHw man can mmoono oaaaIuoonnsm vow ucoanmmna
cHnqu Nonoom coHnmano can Nconmom wchnmoH cH Noccmaxm oEHH coo3uom NcoHumHonnooII.wN «Home

131

Inquiry Subject-Type Ss gained more the longer they stayed

in the learning sessions.

Number of Notes Taken

 

Overall, in Table 29, for only "SCI" Ss and High
Aptitude, High Inquiry Subject-Type Ss were greater gains
in knowledge related to a greater number of notes taken.
This implies that under the conditions these Ss found
themselves in, those who took more notes benefited most.

For "SCI" Ss this could be because their organi-
zation of notes was more in line with their cognitive
structure. However, the LA/HI "SCI" Ss were an exception.
They had negative correlations between Number of Notes
Taken and gain scores.

For HA/HI $5 the positive correlation is probably
related to aptitude and the fact that the material was
hard to learn. Those who took more notes were probably
more motivated.

For the remaining Ss, how many notes they took had
little bearing on their learning achievement.

In general, 83 taking more notes also expended more
time reviewing their notes. Also Ss taking more notes
tended to indicate more perceived learning, less difficulty
learning and a more positive attitude toward computers and
the method of instruction.

"SCI" Ss tended to take more notes if they asked

more questions; especially HA/HI "SCI" 55 (y=.74). In

132

Ho.v.aNN
mo.v.aN

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

NNNs HH oN NoM mem< NcoHummmo mo nobaoz
NH NH sH NN Nnouoosoo onoson oozuHNHN
N NN NHM NHI sN NwM ummm on HmHnmnmz wcHanmm no wcHon>mm
NN- NN- NNH NH- NuHoUHNNHo "NNHNHNNH oo>Hmun6N
oN NN NNNN NN NochmoH No oosumz onmson mosuHuus
N N N NHI MNI NNos NH mucoEwmm ozu mo noono Nnmzoa moanHuu<
NHM NN sN NMM ucmEconH>cm "wchnon Nm>Hmonom
NNNN NNNN NNNN NNoN Nouoz so oooomaxm man
II II II II cmxmh Nouoz mo mmmmm mo nonEoz
NNsN NHN «NNs NNNN NooHNNoN NoHonNoH NH omoomeN meHn
N N NH NoI NoI NNM N cho
NH 0H NH NN H cho

HNN HNH NNH HNN NNN HNN 36H NNHN 36H NNHN "Nunsoon NHnoano

H H H N H N H N H N N N 36H 36H NNHN NNHN "NoouHHNN
mooconommHo ucmonchHm mmoono mm HIuowmnom
N NMM MoI NNss mem< NcoHuNoao «0 nmnEHZ
sH NNN NH mnmuso560 onmaon monuHuu<
NH HH NNM nmmm on HmHnmnmz mcHumHoN no wcHaoH>om
NN- NN- «NN- NHHNNHNNHH "NNHNHNNH oo>H66HNN
sH sN NNNs NoHonmmH No ooNumz onmaon monuHuu<
N NH NHI NN mucoEwmm man no anno Nnm3OH ovounun<
MN NNM NMM ucmEconH>cm "wchnme No>Hoonmm
NNNN «NNN NNoN Nunez o6 oooooaxm NeHn
II II II cmme mmuoz mo Nowmm mo nmnasz
NNNs NNNN NNNN NaoHNNNN NoHonNmH :H oooooNxN maHn
NN N MHI NoI NNos N :Hmo
NoI HN NoM H cho
:mmIqu cmmIHom ameHom Eoocmm unmmxm How mHnmano
NoocmnmmmHo ucmonchHm ncmaummne

 

 

 

m:0Hanonnoo omose coosnom mooconoNMHo mo oocmoHMchHm «nu Nam masono moNHInomnHSN
Nam ucosuwonw cHnqu monoom coHnoano com :owa mouoz mo nmnasz cooauom NooHumHmnnooII.mN mHan

133

general, the number of notes taken by "Expert" 85 was un-
related to the number of questions they heard. For HA/HI
"Expert" 33 the correlation was negative (-.28) indicating
that they took somewhat less notes, the more questions
they heard. "Random" Ss were not similar; they took more
notes, if they heard more questions. Apparently they
needed the additional information, whereas the "Expert" 83

couldn't care less.

Time Expended on Notes

 

Table 30 shows that for "SCI" and "Expert" 83 the
more time they spent with their notes, the better they
did in terms of gain scores.~ This implies that their notes
contained information more worthwhile than notes taken by
"Random" Ss. In fact, the HA/LI "Random" 55 have strong
negative correlations (-.53 and -.36) between Time Expended
on Notes and gain scores. Apparently, these Ss' notes were
somewhat confusing.

As already mentioned, 85 who tended to spend more
time with their notes, tended to spend more time in the
learning sessions and take more notes. Also, "SCI" 83 who
asked more questions also tended to spend more time with
their notes. The finding was similar for "Random" 83 and
the opposite for "Expert" Ss.

In general, the various groups who spent more time

with their notes, also had more positive attitudes toward

134

Ho.V NHNN
mo.VmN

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

NsM so oN NH noxm< NcoHnmooo mo nmnasz
HoI No NH NsM Nnmusano onwsoe ousuHun<
NN MN moI oH NNms ummm cu HmHnonmz mcHuwHom no wcHSmH>um
sNI HNI ONI oNI NnHoonmHo HmchnmoH N0>Hmonom
Ho NH NNNN NNos NoHonmmH No ooNuNN Nansen NNNHHHNN
NN NN N No HoI NNam No Nncwewmm msu «0 nmvno Nnm3oa moouHuu<
Mo oN NH NHM ucmEconH>cm "wchnmoH Nm>Hwonmm
II II II II mouoz co omvooaxm oEHH
NNNN NNNN NNNN NNoN omxmn Nmuoz No NmNNm No nonsoz
«NNN oN NNoN NNNN NoonmoN NoHonmoH oH omoomaxm NeHn
N oN soI NN NNmM N :Hmo
NN No NH NN H cho
H m H H m H H m m m H m 30H :me 30H anm "NnHmch NHnoano
H\H H\N HNN H\N HNN NNN 36H 36H NNHN NNHN "NesuHuN<
mmoconomeo ucmonchHm masono mmNHIuoufimom
N NN NN NHI NNM mem< NcoHuNumd mo nonanz
N NH NNos Ho- NHNHNNENN onmson NosuHHHN
NMM mo NH ummm on HmHnmumz wcHumHom no wcH3mH>mm
sN- NoN- NH- NHHNNHNNHH "NoHonmmH om>Hmonmm
HH HN NNN NoHNNNNH No ooNNNN onaaon ousuHuu<
No- No NNN moamaNoN NNH Ho “mono Nnmaon NNNNHHHN
NH NNos MH ucmaconH>am "wcHanmmH Nm>Hmonmm
II II II Nmuoz co Novgomxm oaHH
NNNN NNNN NNoN amxmn Nunez No NNNNN «6 noneaz
NNMM NNHs NNNs Nconmmm wchnmmH cH voocmoxm mEHH
NoI NN NMM N aHmo
N NoI NNss sH H chw
chIme amNIHom mwmIHom Eoocmm unomxm How NHnwano
NmocmnmNNHn uoNUHmHoNHN unusummnn

 

 

 

NcoHanonnoo wmmse coo3umm mmoaonmmeo mo mocmonchHm man Now Naoonu omNHIuoonnom
New nomanmmna cHnqu Nmnoom NOHnmano pom mwnoz co poocmmxm oaHH cmm3umm mooHnNHmnnoUII.oM mHNNH

135

the experience. However, there doesn't seem to be any
pattern to these relations or meaningful interpretations

possible.

Pre-Interest in Computers

 

Although there are no significant differences be—
tween correlations with treatment gain scores for this
variable, there are some correlations of interest here.

Table 31 shows that, overall, 88 who expressed a
greater interest in computers before the experiment, tended
to make greater gains. The only exception to this finding
are HA/HI $5. In fact HA/HI "Expert" 55 had negative cor-
relations (y=-.34 and -.42) between Pre-Interest in Com-
puters and gain scores. This suggests that the initial
motivation of these Ss was reduced somewhat by the environ-
ment they were placed in.

On the other hand, significant positive correla-
tions, e.g., "SCI" HA/LI (.58), "Expert" LA/HI (.59),
"Expert LA/LI (.85) and "Random" HA/LI (.62), suggest that
in these groups only those Ss who were really interested
in computers made the effort to learn about them. This
implies that the learning environment for these groups was
difficult and therefore the influence due to interest in
computers was accentuated.

In addition, Pre-Interest in Computers was positively

related to the post measure of Attitude Toward Computers.

136

Ho.Vn—NN
mo.VmN

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

NHI NoI HNI oHI Noxm< NaoHummao mo nonasz
NNos NNNs NNHN NNNs Nnmuaasoo onmaon mosuHuu<
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oH- NH- No- so- NuHsuHNNHo "NaHanNNH om>HmunmN
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NHI no sHI Mo mnooewom can no novno onwaoa ovauHuu<
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NN NNNs NN NmN NoI N cho
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moocwnommHo ncmonHmem ammono mmNHIuoufiosm
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NoM NNos MN N ch0
HH NH NsM H cho
cmmImMm cNmIHom QNNIHom Eoocmm unomxm Hum NHnoano
mooomnomeo ucmonHmem ucoanmmna

 

 

 

mcoHanonnoU omega cooBHmm moocmnommHo mo mocmonchHm on» was masonu oaNHIuoonnom
Nam nomanmona cHHqu Nmnoom coHnmanu New mnonoaaoo :H HmonoucHIonm cmo3uom NOHnNHonnooII.HM oHan

.I .1 ‘1' 111! I

 

137

Noteworthy, is the fact that Pre-Interest in Com-
puters bears no relation to the number of questions asked
by "SCI" 53. This means that other factors must have

prompted their questioning behavior.

Miscellaneous Findings From
tfie’3 x 4 Matrices

 

 

LA/HI "SCI" Ss.--For only this group, the Ease of

 

Learning measure correlated significantly with Gain 1
(.53) and Gain 2 (.66) scores. This indicates that in
this group those Ss who thought they were good learners
were actually able to learn best.

HA/LI "SCI" Ss.--Of all the "SCI" Ss, only the

 

HA/LI group had a moderate correlation (.50) between
liking to Ask Questions in Class and the number of ques-
tions asked in the experiment. This suggests that in
this group only those 85 who had past success with asking
questions, took advantage of the experimental situation
and asked questions.

HA/LI "Random" Ss.--The high correlation (.73)

 

between College Aptitude and Gain 1 scores indicates that
in this group only those Ss who were high in aptitude
were able to learn well.

High Aptitude/High Inquiry Ss.--For these Ss,

 

there were interesting differences among the correlations
between Time Expended and College Aptitude. Whereas "Ex-

pert" and "Random" HA/HI 33 had highly significant

138

positive correlations (.79 and .59 respectively), HA/HI
"SCI" Ss produced a moderately negative correlation (-.44).
This contrast indicates that "Expert" and "Random" 85
elected to Spend more time, the higher their aptitude.

For "SCI" Ss, those with a lower aptitude elected to spend
more time in the learning sessions. If time spent is an
indicator of motivation, then the lower aptitude 85 of the
"SCI" group and the higher aptitude Ss of the "Expert" and
"Random" groups were more eager to stay with the learning
situation.

Similar findings were true of Verbal Ability, as
would be expected. Although the "Expert" and "Random"
correlations fell (to .52 and .60 respectively), the "SCI"
Verbal Ability correlation with Time in this case was
significantly negative (-.63).

Summary of Correlational
Analysis Results

 

 

1. Low Inquiry "Expert" Ss with higher "Attitude Toward
Learning" scores, tended to have lesser gain scores.
On the other hand, LA/HI "SCI" 88 had a significant
positive correlation between "Attitudes Toward Learning"
and gain scores. For these groups, only those 83 who
were more receptive to the type of instructional
treatment presented learned more. For only High In-
quiry "Random" Ss with high "Attitude Toward Learning"
scores were positive Attitudes Toward the Method of

Learning found.

139

The more test anxious the "Expert" Ss were, the more
they learned; whereas the reverse was true for the
"SCI" and "Random" Ss, especially those of lower apti-
tude.

The more questions the "SCI" 53 asked, the more they
learned and liked the method of learning. On the
other hand, the more questions the "Random" 83 heard
the less they learned. HA/HI "Expert" 85 did the same,
but HA/LI "Expert" 85 gained more, the more questions
they heard.

Overall, "SCI" Ss and HA/HI Subject-Type Ss gained
more, the more time they expended in the learning
sessions. There were also an overall positive correla-
tions between number of questions asked and time ex-
pended.

Overall, "SCI" 88 and all HA/HI Subject-Type Ss,
regardless of treatment, gained more, the more notes
they took. The more notes taken resulted in more time
expended on notes. "SCI" and "Random" 85 took more
notes, the more questions that were asked. For "Ex-
pert" Ss, there was no relationship.

Overall, "SCI" and "Expert" Ss gained more, the more
time they spent reviewing their notes. "Random" Ss,

on the other hand, had very small or negative correla-
tions. "SCI" and "Random" 85 spent more time reviewing
their notes, the more questions that were asked.

There was an opposite relation for "Expert" 55.

140

7. Except for HA/HI "Expert" Ss, all 83 with greater Pre-
Interest in Computers tended to also have greater
learning gains.

Comparison of Responses to the
"Terminal Interview" Questionnaire

T

 

At the end of the experiment all 85 filled out the
"Terminal Interview" questionnaire (Appendix N). Table 32
displays the frequency and percentage of responses within
instructional treatment groups. Since breakdown by Subject-
Types or by cells (N=12) did not add to the interpretation,
except where mentioned, they are not presented.

Item 1.--Almost 90% of the Ss stated that their
knowledge of computers before the experiment was small or
close to zero when compared to what they learned. Differ-
ences among groups are negligible.

Item 2.--Of the 192 Ss in the experiment, only 15
talked to other students about computers; but 54 talked to
other students about the experiment. Thus, about 28% of
the Ss revealed that they had in some way disregarded E's
request to refrain from discussing the experiment with
outside sources. Differences among the groups are negli-
gible.

Item 3.--The volume of responses to this item
indicates that 83 were favorably motivated by their experi-

ence. However, "Random" Ss show somewhat less enthusiasm.

141

This probably results from their exposure to a difficult,
and possibly frustrating, learning experience.

Item 4.—-The volume of responses here indicates
that most 88 had at least two reasons for participating in
the experiment. The standard reason, of course, was for
research credits. The secondary reason was split between
a curiosity about computers and interest in a "learning"
experiment. Differences among treatment groups are negli-
gible.

Item 5.--The responses here indicate that at least
54% of the Ss used the time between segments to think
about what had been presented. Otherwise, Ss split their
responses between reviewing their notes and interrelating
the material.

Very few 85 used the time between segments to test
themselves. Since they had no help, this was probably a
difficult task.

Except for the obvious non-responses of the "Con-
trol" Ss, differences among the groups are negligible.

Item 6.--For this item, "SCI" Ss had special stems,
only two of which were the same as the rest of the Ss.
"SCI" responses to these two stems are in parentheses.

Ignoring the "Control" responses, over two-thirds
of the "Expert" and "Random" 53 would have liked most to
have been able to repeat the segments they wanted repeated

and to ask their own questions. About one-third of these

142

Ss would have liked to have been able to: (a) assess their
learning as they went along, (b) interact with other stu-
dents, or (c) talk to the instructor.

An obviously higher frequency of responses by "Ran-
dom" Ss is apparent for stems dealing with the ordering of
the segments. Only 6% of the "Expert" Ss would have liked
to have ordered the segments themselves. This low frequency
suggests that requiring the "SCI" 85 to sequence the seg-
ments may have presented them with an undesirable task and
may have interfered with their learning.

In fact, 19% of the "SCI" 85 answered that they
would have liked to have had an "expert" order the segments
for them.

Not shown, but of interest is the fact that only
three "SCI" Ss from the High Inquiry group wanted to have
an "expert" order the segments for them, whereas six Ss
from the Low Inquiry group would have liked to have had an
"expert" order the segments.

In a similar observation, none of the High Inquiry
"SCI" Ss wished to have other students ask questions for
them. Four of the six Low Inquiry Ss who did wish other
students to ask questions for them were from the LA/LI
"SCI" group.

Forty-six percent of the "SCI" 55 were desirous of
being able to assess their knowledge as they went along.

Such a high response percentage to this item suggests that

ll‘llIlll11L11

 

143

"SCI" Ss were not taking advantage of the instructor in
the learning environment to test themselves. The finding
is especially relevant because 15 of the 22 responses were
from High Aptitude "SCI" 88.

Item 7.--For this item "SCI" 85 again had some
additional stems to consider.

"Expert" and "Random" 85 thought the segmented lec-
ture contributed most to their learning. Otherwise, it was
a toss-up among the remaining stems as to which factor con-
tributed most to these Ss' learning.

"Random" Ss seemed to be somewhat less enthusiastic
about having the list of topics in the form of questions.
Perhaps this form was less useful to them than to the other
Ss.

"SCI" 83 thought the self-pace feature was most use-
ful. Contrary to expectations, only about one-fourth of
the "SCI" 85 thought the freedom to order the segments and
to ask questions contributed to how they learned. Of in-
terest is the fact that 8 of the 17 responses to the order-
ing stem and 7 of the 12 responses to the questioning stem
were by High Inquiry "SCI" Ss. Also, only about 8% of the
"SCI" Ss used the Option of testing themselves as they
learned. 4

These results are consistent with similar results
for "SCI" Ss above. Again, these findings lend support to
the contention that "SCI" Ss were not making good use of

the learning environment.

144

Table 32.--Frequency and Percentage of Ss' Response to Items of
"Terminal Interview" Questionnaire Within Instructional
Treatment Groups

Note: All percentages are based on N=48, except total
where N=192.

1. Relative to what I learned, my knowledge of computers before
starting this experiment was:

a. substantial
b. moderate
c. small
d. close to zero
e. other
S91 Expert Random Control Total
2222228212
a. 0 0 2 4 1 2 0 0 3 1
b. 3 6 1 2 0 0 1 2 5 3
c. 9 19 13 28 8 17 6 13 36 19
d. 35 73 29 62 39 81 31 64 134 70
1 2 2 4 0 0 10 21 13 7

e.
TOTAL 48 100% 47 100% 48 100% 48 100% 1 1 100%

2. By chance, during the experiment, I happened to:

a. talk to other students about computers

b. talk to other students participating in the
experiment about the experiment

c. read or view on TV some relevant information
about computers

 

 

d. other

S91 Expert Random Control Total
22222211222

a. 4 8 3 6 3 6 5 10 15 8

b. 11 23 14 29 14 29 15 31 54 28

c. 2 4 1 2 1 2 1 2 5 3
d-11211221_3_61121_442_3
TOTAL 30 63% 33 69% 21 44% 34 71% 11 61%

145

Table 32.--(Cont'd.)

3. This experiment has left me:

a. more curious about computers than before

0. with a more positive attitude toward computers
than before

c. with the idea that I ought to take at least one
computer science course

d. with the idea that I ought to find out more about

 

computers
e. other
SCI Expert Random Control Total
N Z, N Z. N % N % N %

16 33 16 33 16 3T 22 46 69 36
16 33 13 27 14 29 1 2 44 23
12 25 15 31 3 6 7 15 37 19
14 29 18 38 9 19 2 46 63 33
. 5 10 6 13 11 23 _4( 8 26 14

TOTAL 63’ 1312 66' 1422 5 1662 56 1172 239 1242

(DO-DUO)
N

 

4. The reason(s) I participated in this experiment was (were):

research credits

curiosity about computers

genuine interest in computers

interest in a "learning" experiment
nothing better to do (for the fun of it)
other

H'ICD a-no‘m

 

SCI Expert Random Control Total
44 92 37 77 41 85 41 85 163 85
26 54 24 50 18 38 21 44 89 46
8 17 8 17 2 4 4 8 22 11
26 54 21 44 19 40 23 48 89 46
15 1 2 5 10 l 2 14 7
-__3_2 _1_2 2.2 _2_6 .2;

114 238% 92 192% 87 181% 93 194% 386 201%

5‘3"“??‘5'79’
\l

146

Table 32.--(Cont'd.)

5. I used the time between segments to:

 

a. review my notes
b. test myself as I went along
c. interrelate the segmented material
d. think about what had been presented
e. other
S21 Expert Random Control Total
2222222222-
a. 19 40 19 40 19 40 0 0 57 30
b. 1 2 2 4 5 10 0 0 8 4
c. 19 40 10 21 16 33 0 0 45 23
d. 32 67 29 60 26 54 10 21 97 51
e. 6 l3 8 17 9 19 18 38 41 21

 

TOTAL 7 I667. E '17-’27. '76 ”1667. 26 58% 248 1297.

6. My learning would have been more effective, if I had been able
to:

 

a. talk to the instructor
b. select the order of the segments myself
c. repeat the segments that 1_wanted repeated
d. delete the segments that 1 wanted deleted
e. view a more logical ordering of the segments
f. ask my pyp questions after each segment
g. assess my grasp of the new knowledge as I went along
h. interact with other students
i. other
S91 Expert Random Control Total
2222222222
a. (See 17 35 22 46 5 10 44 23
b. below) 3 6 14 29 3 6 20 10
c. 33 69 37 77 3 6 73 38
d. 2 4 6 13 2 4 10 5
e. 2 4 22 46 2 4 26 14
f. 32 67 39 81 5 10 76 40
g. 22 46 16 33 11 23 8 17 35 18
h. 10 21 18 38 19 40 8 17 45 23
i 5 10 1 2 2g 42 _2Q 14

TOTAL 32 672 128' 257.1? T567. 56 1—177. 355 "1862

147

Table 32.--(Cont'd.)

Additional stems for SCI Ss:

E.

J. 9

k. 12

1 2

m 6

n. (12
TOTAL 76

2(

19
25
4
13
.21

158%

have an "expert" order the segments for me
repeat more segments

delete more segments

have other students ask questions for me
other

 

7. What contributed most to how you learned?

N’

a. 13

b. 18

c. 17

d. 21

e. 7

f. (_2
TOTAL 79
Additional
g. 11

h. 12

i. 4
TOTAL 106

H10 040 0‘0:

SCI

2,
27
38
35
44
15

.6.

164%

pretest

segmented lecture

topics in the form of a question

own Speed or pace

being able to check notes at my leisure
other

 

Expert Random Control Total
2 2 11 2 2 2 2 2
17 35 12 25 6 13 48 25
22 46 19 40 1 2 60 31
14 29 5 10 3 6 39 20
10 21 9 19 3 6 43 22
7 15 8 l7 1 2 23 12
_2L 4 (_2 4 (_1 23 18 9

72 2662 55 2162 25 5 2 231 I262

stems for SCI 83:

23
25
8

2262

freedom to order the segments
freedom to ask questions (review)
freedom to test myself as I went along

148

Agreement AmongrRank-Orderings
of the Segments

 

 

Since Mager (1961) found that $8 in his unstructured
learning situation were able to agree somewhat on the se-
quence of the material to be covered, it was appropriate to
test the same hypothesis here.

"SCI" Ss.--For the 48 "SCI" Ss, all of whom ordered
the segments as they learned, the coefficient of concor-
dance, W, was 0.12 (Table 33). Although this is a small
W, with N=48, the agreement is still significantly differ-
ent from zero (p<.0001). About 35% of the "SCI" Ss failed
to have a significant correlation with the consensus. By
inspection, the only correspondence between the ordering

of the segments of the consensus of computer science ex-
perts" and a consensus of the "SCI" Ss is in the last por-
tion, where both groups put the material about future uses
of computers. A rank-order correlation between the two
consensus rankings yields a rho or 0.29, which is just
significant (p=.05).

These findings suggest that on their first exposure
to the segments, "SCI" 85 agreed somewhat among themselves
as to the rank-ordering of the segments and also agreed
somewhat with "experts" in the field.

On their second exposure to the segments, at the

end of the experiment, "SCI" Ss were far more in agreement

(w=0.52). Even more noteworthy is the fact that the second

149

rank-order correlation between consensus rankings yielded

a rho or -0.34. This result agrees with Mager's contention
that students will sequence their material differently from
"experts", if they are given the opportunity. The high
agreement among "SCI" 85 emphasizes that instructional
designers should pay greater heed to students, at least in

the sequencing of content.

Table 33.--Agreement Among Rank-Orderings of the Lecture

 

 

 

 

 

Segments
Kendall Rank-Order
Group Coefficient of Correlation Between
Concordance (W) Consensus Ranks (rho)
"SCI" Ss (1st exposure) 0.12 0.29
"SCI" 83 (2nd exposure) 0.52 -0.34
"Expert" 88 ' 0.40 0.70
"Random" 83 0.29 0.55
All SS (N=144) 0.72 -0.34

For N=48, the probability of a rho correlation of .285
occurring by chance is .05; the probability of a correla-
tion of .376 is .01 (two-tailed tests).

"Expert" Ss.--The results were somewhat different

 

for the "Expert" Ss. The agreement among these Ss was
high (W=.40) and the rank-order correlation between the
consensus for this group and the true "experts" was very
high (rho=.70). However these 83 had been exposed to the
"expert" sequence while they were learning. Apparently
they remembered the "expert" sequence and thought it was

a good one.

150

"Random" Ss.--Although exposed to a random sequence

 

these 83 still had a significant agreement (W=.29). Unex-
pectedly, they also had a highly significant consensus
correlation with the true "expert" consensus sequence.
This result is difficult to explain. Perhaps the time and
effort these Ss spent on reorganizing the sequence while
learning, enabled most of them to see a pattern which was
similar to that chosen by the "experts".

All Ss.--All Ss, except the "Control" Ss, ordered
the cards (segments) after the Retention Test. The co-
efficient of concordance for all these rankings was highly
significant (W=0.72). Only 5 of the 144 Ss failed to agree
with the consensus at a statistically significant level.

However, the rank-order correlation between the
consensus of "experts" and the consensus of the 144 85 was
significantly negative (-.34).

These findings suggest that students familiar with
information to be learned agree substantially on the
sequencing and that this sequencing may be significantly
different from that of "experts". However, the negative
rho correlation is inconsistent with the above positive
rho correlations. This suggests that rank¥order correla-
tions between consensus rankings are somewhat spurious and

should therefore be interpreted with caution.

CHAPTER'V

DISCUSSION

Interaction Between Treatments
and Subject-Types

 

 

The investigation reported here has produced re-
sults which support the hypothesis that different types
of students learn better (or worse) under various types
of instructional treatments. Although there were no sig-
nificant interactions between Inquiry and Treatments for
gain scores, the triple interaction among the variables
Aptitude, Inquiry, and Treatments was close to being sig-
nificant (p=.06 for Gain 1 and p=.07 for Gain 2) and an
analysis of the gain scores by means of a 3 x 4 factorial
design reveals a similar finding for the Treatments x
Subject-Type interactions (p=.07 for Gain 1 and p=.12 for
Gain 2). Although these overall interactions are not
significant at the .01 or .05 level, Figures 10 and 11
indicate that for High Aptitude Ss, at least, significant
interactions do exist between Treatments and Subject-Types.

Given the conditions of this experiment, the
following generalizations appear warranted: to prescribe

the instructional treatment that would result in the

151

.mmsono
ucwEummnu mwnnu nm>o momma
Inconnom now mwnoom H cho cmszI.0H mnomHm

.mmoonm
ucmEumonu mmnnn nm>o mmmma
Iuomnosm now mmnoom N GHNU cmoSII.HH mnomHm

 

 

ezmzeamma nzmzeNNNe
Eoocmm unomxm Hum Eoocmm unwmxm Hum
H H _ H- H
_ o N
N HH\NH oH
HHNNH s NH
2 N W \ VH
5 HE
S
-. N
HNNNH N N HHNNN NH
z
3 x NH
w
HH\4N .m
NH oN
sH NN
Hm\<H.H . NH HHH\¢H.H «N
NH NN

 

 

838033 I NIVO NVHN

153

highest gain scores for a specific student, one would
first determine his Aptitude and Inquiry levels. If he
had High Aptitude and High Inquiry scores, one would
assign him to either an "SCI" or "Random" type of instruc-
tional treatment. If he had High Aptitude and Low Inquiry
scores one would assign him to an "Expert" type instruc-
tional treatment. However, if he had Low Aptitude scores,
one would probably recommend an "Expert" type of instruc-
tional treatment.

Of course, the above prescriptions should be tem-
pered somewhat with reference to student attitudes. For
example, High Aptitude/High Inquiry Ss would almost cer-
tainly prefer the "SCI" method of instruction over a
"Random" type of instruction.

Nevertheless, the wholesale application of such
prescriptions is premature, to say the least. Only one
type of subject matter is represented here and only a few
Subject-Types and Treatments have been tested. Much more
investigation in this area is needed if instructional
selection procedures for students are to be worthwhile.
The main point of this investigation should be clear,
however. Individual differences among students within the
dimension of student control of instructional functions do
appear to exist.

Other investigators, such as Tallmadge and Shearer

(1969), have found individual differences among student

154

aptitudes and characteristics to be correlated with per-
formance on different types of instructional methods.
However, no other research so far as is known has shown
this for the instructional control dimension.

Performance of Ss
in the "SCI" Treatment

 

 

The fact that the best performing "SCI" group
(HA/HI) did not do better or as well as the best "Expert"
group (HA/LI) suggests that the "SCI" treatment was not
as conducive to learning as expected. Why?

One would expect that, overall, Ss who profited
most in the "SCI" treatment would be those who made most
use of the features of the system. This appears to be true
from the high correlation between the number of questions
asked and gain scores. Actually, those 85 of the HA/HI
"SCI" group who learned the most were also the ones who
took more notes, spent more time on their notes, asked
more questions, and spent more time in the learning ses—
sions.

On the other hand, less than half of the HA/HI "SCI"
88 asked enough questions to indicate they were controlling
their learning.

Also, the absence of review or self-testing ques-
tions by these Ss suggests that they did not make full use
of the learning system. Perhaps this was due to the fact
that the learner environment was still too structured--

still too far from Mager's (1961) setting.

155

Another likely reason is student lack of experience
with situations like this one. The fact that Campbell
(1964) had to train 85 to obtain significant differences
between self-directed and programmed Ss corroborates this
view. Campbell felt that since most students are so con-
ditioned to being told what to do, they have difficulty
learning by using their own resources. Unlike Campbell,
this investigation suggests that for certain types of stu-
dents it may be best not to even attempt to train them for
self-control of learning. Apparently, some 85 learn best
under the control of others.

One reason why the differences between "SCI" 85
and the other groups were not greater may be that all Ss
in this experiment were trying to learn as best they could.
Previous research with student control as a variable have
compared an experimentally treated group with a routinely
treated group (Mager and McCann, 1961; Grubb and Selfridge,
1964; and Campbell, 1964). Under these conditions the
experimental groups have displayed greater enthusiasm and
motivation than their counterparts. This could be due
almost entirely to the "Hawthorne effect".

In this experiment all Ss were aware that they
were in an experiment where learning their best was the
major objective. Consistently high motivation, by the vast
majority of Ss, evidenced by the volume of notes, the time

expended, and the excitement and curiosity indicates that

156

the "Hawthorne effect" was at work for all groups. There-
fore, differences observed appear to be free of bias due to
one common source of uncontrolled motivational effects,

but not all.

Performance of Ss
in the "Random" Treatment

 

Although 85 with randomly sequenced material did
less well, overall, than "Expert" Ss, they did have higher
gain scores than "SCI" Ss. However, "Random" 83 were not
pleased with the learning environment in which they were
placed. They had the lowest perceived learning scores,
they disliked the randomness of the segments, they per-
ceived the greatest task difficulty, they consistently had
the least positive attitude toward the method of learning,
and they had the lowest mean attitude toward computers at
the completion of their learning. Why did they perform
as well as they did?

One answer from the literature would suggest that
for tasks involving problem solving, high ability Ss are
able to overcome the sequence impediment (Brown, 1970).

Although the material to be learned in this inves-
tigation cannot properly be called problem-solving material,
it might just be for Ss in the "Random" treatment. If so,
then the results above strongly suggest that the degree of
"inquisitiveness" a high aptitude "Random" S has makes a

marked difference on how well he will be able to overcome

157

the sequence impediment. Actually, "Random" Ss performed
very similar to "SCI" Ss. For example, for all "Random"
Ss, the more questions they heard, the more time they spent
in the learning sessions and the more notes they took.

This implies that these 85 were trying to overcome the
randomness of the segments.

Performance of the Low Aptitude/
High Inquiry "SCI" Subjects

 

 

Since the poor performance of the LA/HI "SCI" 83
was unexpected, it bears some special attention. This
group of Ss managed to do better than the LA/LI "SCI" and
LA/LI "Expert" 58 on the Gain 1 measure, but on the Gain
2 measure performed the worst of any group which was ex-
posed to the subject matter. In fact, they were not sig-
nificantly different from "Control" 33 on this measure.

It has already been stated above that because of
their low aptitude, perhaps this group faced a learning
task that was too difficult for them. Direct support for
this contention is lacking, but the following facts have
a bearing. Of all the "SCI" Ss, the LA/HI 83: (a) spent
the least amount of time in the learning sessions, (b) took
the least number of notes, (c) registered the least per-
ceived learning as a function of the environment, (d) were
the least favorable toward the method of learning, (e)

were most likely to perceive the task as difficult, and

158

(f) were least interested in computers at the conclusion
of the experiment.

Also, because of their lack of a great degree of
"inquisitiveness", they probably were frustrated at being
required to control their own learning. In addition, these
83 asked less questions than the HA/LI "SCI" 53. The
significant correlations between "Attitudes Toward Learning"
and gain scores, suggest that only those 83 in this group
who were receptive to the "SCI" environment were able to
learn in it.

Finally, this group had a significant correlation
between the number of questions asked and Gain 2 scores,
but a negative correlation between the number of questions
asked and Attitude Toward Method of Learning. This suggests
that only those who asked questions were able to retain
the information learned, but the more questions asked, the
less they liked being in the learning environment. Such
findings paint a picture of 83 who did not wish to take
advantage of the features of the learning situation. Only
those few who did were able to benefit. Granting student
control to low aptitude Ss, for the learning of difficult
material, appears to be detrimental to their acquisition

of knowledge.

The Effect of Askingguestions

 

Most instructors assume that questions asked by

students in the classroom are valuable additions to their

159

instruction. The logic is obvious; if one student has a
question, the other students probably have the same ques-
tion and are only uncertain about asking. Bandura's (1969)
research appears to contain the observation that having
questions asked by students in the classroom facilitates
learning for all.

However, the results of this study clearly indicate
that it depends on the type of student. To generalize, in
the classroom High Aptitude/High Inquiry students will
probably learn less, the more questions other students ask.
High Aptitude/Low Inquiry students will probably learn
more the more questions they hear. For low aptitude stu-
dents it probably makes no difference how many questions
they hear. On the other hand, for all types of students,
the more questions they ask, the more they will probably
learn.

In effect, in the classroom, one can't possibly
achieve optimal learning for all students. Only through
individualized instruction, such as that advocated by

Mitzel (1970), would such a goal be possible.

Learning Material

Campbell (1964) and Gagné (1965) have suggested
that material, which is either hierarchical or of a
problem-solving nature, offers the best opportunity for

student self-direction to function effectively. However,

160

the material to be learned in this study was of an inter-
related manner so that it had no specific beginning or end.
It was hoped that under the "SCI" treatment, the learner
could make optimal use of his unique cognitive structure

to make the material "meaningful." The results, however,
show that this was only true for High Aptitude/High Inquiry
"SCI" Ss and in a limited sense.

Tallmadge and Shearer (1970) claim that the nature
of the "content" of the learning experience is a critical
factor affecting the relationships between learner charac-
teristics and instructional methods. Therefore it is
quite possible that different results might have occurred
with different material.

There are two directions future investigations
should take. The first would be to test hypotheses similar
to the ones tested here but with hierarchical or problem-
solving types of material. Second would be to give 85
special training in the use of the "SCI" mode (Campbell,
1964). It seems reasonable to suppose that at least High
Aptitude, High Inquiry students would profit by such

training.

ConcludingiRemarks

 

A number of influential educators, such as Mager
and Rogers, have advocated almost unqualified student

control of instructional functions. The major objective

 

ll I‘ll II! III III!!! I‘ll I!!! i ii.
All,
I
I'll

161

of this research was to test such arguments in a rigorously
controlled environment. The results strongly support
individual differences in learning styles. Thus, one must
use discretion in advising student control of instructional
functions in the design of the instructional systems. Un-
less training is developed to overcome the conditioned or
natural "set" of many students to learn better in struc-
tured, externally controlled, learning environments, then
at least two methods of instruction will be required. In
addition, other research has suggested that different modes
of instruction are preferred by different students for
different types of learning material. Since research in
this area is, at present, only exploratory, much needs to
be done before instructional system designers can decide
what should be the substance of "adaptive education."
Finally, individual differences are known to arise
from either of two sources: (a) basic abilities or (b)
prior learning. These sources are recognized as being
significant for the specification of different modes of
instruction. This study, among others (Tallmadge &
Shearer, 1969) would add a third source, learning style,

to further complicate an already complex area of research.

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APPENDICES

APPENDIX A
CODING OF THE CARDS

Automotive Name
To facilitate an "SCI" Sh communication with E about which

segment he wanted next, each card was coded in the lower right corner
with the name of an automobile. This method met the criteria of: (a)
no inherent order among the names (E had an order known only to him),
(b) commonly known names, (c) sufficiently large population pool from
which to select names, and (d) connotations of the names are indepen-
dent of the subject matter.

The total list of names is shown in Appendix K.

Color Code
During the pilot study E noticed that 83 were having some

difficulty handling all of the information contained on the 52 cards.
Therefore, the cards were also coded in the upper right corner to re-
present the eight major content areas. The cards were marked in each
content area with one of eight different colors. This procedure en-
abled S to know what segments were associated with each other and which
‘were not. 8 could stay within the same area (color) or not depending

‘ O
on personal inclination. Therefore, the color code helped rather than

'

hindered the learner. There was no observed evidence to the contrary.

170

171

In addition, each content area had one segment which was an
"overview" or summary of the remaining segments of the area. The
card representing this segment was also marked in the upper 1s£p
, corner.

Coding of "Expert" and "Random" Lists

Because the above color-coding may have given an unfair ad-
vantage to "SCI" Ss, the "Expert" and "Random" lists of the segments
were also coded. For "Expert" Ss (Appendix B) the eight groups of
questions, corresponding to the eight major content areas, were sep-
arated and the first question in each group was identified as the
"overview" or summary question. For the "Random" Ss (Appendix C)
the eight groups of questions, corresponding to the eight major con-
tent areas, were color-coded the same as the "SCI" cards. "Overview"
or summary questions were identified by asterisks on the random

sequenced list.

172

silver

 

(» How are BINARY NUMBERS CONVERTED T0 DECIMAL
and vice versa?

 

 

 

k TEMPE ST j
Figure 12.--Example of a typical card.
blue orange
\

 

7
What is the LOGICAL STRUCTURE of computers?

 

 

BUICK
\_

Figure l3.--Example of "overview" card.

N‘

 

APPENDIX B
"EXPERT" LIST OF QUESTIONS

What are DIGITAL COMPUTERS?

Of what VALUE are computers?

What are the differences between ANALOG and DIGITAL computers?
What are the BASIC CAPABILITIES of digital computers? '

Can computers THINK?

What are the different CLASSIFICATIONS of digital computers?

What is the HISTORICAL BACKGROUND OF COMPUTERS?
What were the FIRST DATA PROCESSING DEVICES?
What were the PREDECESSORS OF MODERN COMPUTERS?
What is the STORED-PROGRAM CONCEPT?

 

What is the LOGICAL STRUCTURE of computers?

What is the INTER-RELATION among the computer's FIVE FUNCTIONAL
SECTIONS?

What is the function of the computer's INPUT SECTION?

What is the function of the computer's CONTROL SECTION?

What is the function of the computer's MEMORY SECTION?

What is the function of the computer's OUTPUT SECTION?

What is the function of the computer's ARITHMETIC-LOGIC SECTION?

 

 

 

 

How does the computer carry out INTERNAL COMMUNICATION?
How is information put into MEMORY STORAGE?

How are ARITHMETIC-LOGIC CIRCUITS designed?

What are BINARY DIGITS or "BITS?"

How does one perform BINARY ARITHMETIC?

How are BINARY NUMBERS CONVERTED TO DECIMAL and vice versa?
What are "WORDS" AND "ADDRESSES?"

 

 

 

What are the means of EXTERNAL COMMUNICATION with the computer?

What are "PUNCHED CARDS?"

What kind of AUXILIARY COMPUTER EQUIPMENT is associated with
punched cards?

What is the 1NFORMATION CONVERSION CONCEPT?

How do machines "SENSE" INFORMATION?

What is "BUFFER" OPERATION?

What is the difference between "ON-LINE" and "OFF -LINE" COMMUNICATION
with the computer?

 

 

 

173

What
What
What
What
What

What
What
What
What
What
What
What

174

are COMPUTER LANGUAGES?

is MACHINE LANGUAGE?

are TRANSLATING SYSTEMS?

is "FORTRAN?"

are the ADVANTAGES and DISADVANTAGES of a TRANSLATING SYSTEM?

 

 

is PROGRAMMING?

is "FLOW-CHARTING?"

does the PROGRAMMER do?

is a "SUB-PROGRAM?"

does the MAIN PROGRsM consist of?

is "LOOPING" and "BRANCHING?"

is the RELATIONSHIP between 3 PROGRAM, its FLOW CHART, and its

 

 

 

 

DESK CALCULATOR EQUIVALENT?

What

What

is "DEBUGGING" a program?

are the PRESENT and FUTURE USES of computers?

How are computers used for SCIENTIFIC ANALYSIS?
How are computers used for DATA PROCESSING?
How are computers used for CONTROL application?

What
What

is "REAL-TIME" Operation of computers?
is "TIME-SHARING" of computers?

How about FOREIGN LANGUAGE TRANSLATION and INFORMATION RETRIEVAL?

(green)
(black)

(green)
(green)
(black)
(green)

(white)
(orange)

(red)
(orange)

(yellow)
(silver)
(silver)
(orange)
(white)
(yellow)
(blue)

(black)
(silver)
(red)
(silver)
(green)
(blue)
(yellow)
(red)
(green)
(white)

(red)
(orange)

(green)
(yellow)

(orange)

APPENDIX C

"RANDOM" LIST OF QUESTIONS

Fall Term

*What are the PRESENT and FUTURE USES of computers?

 

What were the FIRST DATA PROCESSING DEVICES?

What is "TIME-SHARING" of computers?

How are computers used for SCIENTIFIC ANALYSIS?

What were the PREDECESSORS OF MODERN COMPUTERS?

How are computers used for FOREIGN LANGUAGE TRANSLATION and
INFORMATION RETRIEVAL?

What is MACHINE LANGUAGE?

What is the INTER-RELATION among the computer's FIVE FUNC-
TIONAL SECTIONS?

What is "LOOPING" and "BRANCHING?"

What is the function of the computer's ARITHMETIC-LOGIC
SECTION?

 

 

 

 

 

 

*What are DIGITAL COMPUTERS?

What are BINARY DIGITS or "BITS?"

How is information put into MEMORY STORAGE?

What is the function of the computer's CONTROL SECTION?

What are TRANSLATING SYSTEMS?

What are the differences between ANALOG and DIGITAL computers?

What is the difference between "ON-LINE" and "OFF-LINE"
COMMUNICATION with the computer?

 

*What is the HISTORICAL BACKGROUND OF COMPUTERS?

What are "WORDS" and "ADDRESSES?"
What is "PROGRAMMING?"

*How does the computer carry out INTERNAL COMMUNICATION?

 

How are computers used for DATA REDUCTION?

What is the INFORMATION CONVERSION CONCEPT?

Can computers THINK?

What is a "SUB-PROGRAM?"

What is "REAL-TIME" operation of computers?

What are the ADVANTAGES and DISADVANTAGES of a TRANSLATING
SYSTEM?

What does the PROGRAMMER do?

What is the function of the computer's OUTPUT SECTION?

How are computers used for DATA PROCESSING?

What are the different CLASSIFICATIONS of digital computers?

 

 

 

 

 

 

 

 

*What is the LOGICAL STRUCTURE of computers?

 

175

(blue)

(blue)
(yellow)
(white)
(orange)
(blue)
(silver)
(orange)
(silver)
(black)
(green)
(white)
(yellow)
(red)

(blue)
(red)
(red)
(silver)
(blue)
(red)

(green)
(orange)

(black)
(red)

(white)

(black)
(red)
(silver)
(orange)
(green)
(red)
(blue)
(orange)
(red)
(orange)
(silver)
(blue)
(blue)
(white)
(yellow)

176

What kind of AUXILIARY COMPUTER EQUIPMENT is associated with
punched cards?

What are PUNCHED CARDS?

What are the BASIC CAPABILITIES of digital computers?

What is FORTRAN?

What is the function of the computer's INPUT SECTION?

*What are the means of EXTERNAL COMMUNICATION with the computer?
How are ARITHMETIC-LOGIC CIRCUITS designed?

What is the function of the computer's MEMORY SECTION?

How are BINARY NUMBERS CONVERTED TO DECIMAL and vice versa?
What is the STORED-PROGRAM CONCEPT?

How are computers used for CONTROL applications?

*What are COMPUTER LANGUAGES?

Of what VALUE are computers?

What is the RELATIONSHIP between 3 PROGRAM, its FLOW CHART,

and its DESK CALCULATOR EQUIVALENT?

How do machines "SENSE" INFORMATION?

What is "DEBUGGING" a program?

What does the MAIN PROGRAM consist of?

How does one perform BINARY ARITHMETIC?

What is a "BUFFER" OPERATION?
*What is "FLOW-CHARTING?"

 

 

 

 

 

 

 

 

 

 

 

 

 

Winter Term

 

*What are the PRESENT and FUTURE USES of computers?

What is the INTER-RELATION among the computer's FIVE FUNC-
TIONAL SECTIONS?

What were the PREDECESSORS OF MODERN COMPUTERS?

What is the RELATIONSHIP between a PROGRAM, its FLOW CHART,
and its DESK CALCULATOR EQUIVALENT?

What are the ADVANTAGES and DISADVANTAGES of a TRANSLATING
SYSTEM?

*What is the HISTORICAL BACKGROUND OF COMPUTERS?

What is a "SUB-PROGRAM?"

How are ARITHMETIC-LOGIC CIRCUITS designed?

What is the function of the computer's CONTROL SECTION?

How are computers used for DATA REDUCTION?

What is "LOOPING" and "BRANCHING?"

How do machines "SENSE" INFORMATION?

*What is the LOGICAL STRUCTURE of computers?

What does the PROGRAMMER do?

What is the function of the computer's OUTPUT SECTION?

What are BINARY DIGITS or "BITS?"

What are "PUNCHED CARDS?"

*What are the means of EXTERNAL COMMUNICATION with the computer?

*What are COMPUTER LANGUAGES?

What are the different CLASSIFICATIONS of digital computers?

 

 

 

 

 

 

 

 

 

 

 

 

 

(yellow) Of what VALUE are computers?

(red)
(green)

(black)
(yellow)
(green)
(black)
(orange)

(silver)
(green)
(blue)
(white)
(silver)
(green)
(yellow)
(white)
(blue)
(orange)
(silver)
(yellow)
(orange)
(red)
(yellow)
(green)
(blue)

(blue)

(red)
(red)
(silver)
(silver)
(white)
(green)

177

What is "DEBUGGING" a program?

How are computers used for FOREIGN LANGUAGE TRANSLATION and
INFORMATION RETRIEVAL?

What is the STORED-PROGRAM CONCEPT?

What are the BASIC CAPABILITIES of digital computers?

What is "REAL-TIME" operation of computers?

What were the FIRST DATA PROCESSING DEVICES?

What is the function of the computer's ARITHMETIC-LOGIC
SECTION?

What are "WORDS" and "ADDRESSES?"

How are computers used for DATA PROCESSING?

What is the INFORMATION CONVERSION CONCEPT?

What are TRANSLATING SYSTEMS?

 

 

 

 

*How does the computer carry out INTERNAL COMMUNICATION?

What is "TIME-SHARING" of computers?

 

*What are DIGITAL COMPUTERS?

 

What is FORTRAN?

What is "BUFFER" OPERATION?

What is the function of the computer's INPUT SECTION?

How does one perform BINARY ARITHMETIC?

What are the differences between ANALOG and DIGITAL computers?

What is the function of the computer's MEMORY SECTION?

What is "FLOW-CHARTING?"

Can computers THINK?

How are computers used for SCIENTIFIC ANALYSIS?

What is the difference between "ON-LINE" and "OFF-LINE"
COMMUNICATION with the computer?

What kind of AUXILIARY COMPUTER EQUIPMENT is associated with
punched cards?

 

 

 

*What is PROGRAMMING?

 

What does the MAIN PROGRAM consist of?

How is information put into MEMORY STORAGE?

How are BINARY NUMBERS CONVERTED TO DECIMAL and vice versa?
What is MACHINE LANGUAGE?

How are computers used for CONTROL applications?

 

 

 

 

APPENDIX D
INFORMATION SHEET

Individualized Learning Experiment Information
Dear Psychology Student:

Your interest in participating in an Individualized Learning
Experiment concerning instructional methods and their relation to
individual background, interests, and beliefs is greatly appreciated.

To make your time worthwhile, the material that you will be
asked to learn will be about "Computers and How They Work." You are
probably aware that, today, an understanding of computers is becoming
almost a necessity. Here is an excellent opportunity for you to be-
come very familiar with: (1) how computers could be of benefit to you
in your college work and career choice, (2) what programming is all
about, (3) the present and future uses of computers, and (4) just how
computers can do all those neat things.

In order to gain this new knowledge, all you have to do is:

1. Fill out a questionnaire about your background, interests,

and beliefs. This can be done any weekday, 8:00 a.m. to
5:00 p.m., except noon hour, at Learning Service, 17 Morrill
Hall. One of the secretaries, Mrs. Reimer or Miss Koehler,
will explain the instructions, etc. This will take about

one (1) hour.

178

179

NOTE: In order for us to know who is really interested
in this experiment, we ask that this part of the

experiment be completed within the next week.

 

Come to the Experimental Classroom Facility, 25A Instruc-
tional Media Center, for your learning sessions. You will
have two sessions, on consecutive days, of about an hour
and one-half each. Since students will be learning one at
a time, we will telephone you when we are ready to schedule
you, at your convenience; that is, just about any time of
the day, night, or weekend. (Rides are even possible,
upon request.)
Two weeks later, again at your convenience, we would like
to determine how much knowledge you have retained. When
this final part of the experiment has been completed, you
will receive nine (9) research credits.

John Fry

Graduate Research Assistant

Learning Service

17 Morrill Hall

Telephone: 353-8940 office
355-5930 home

APPENDIX E

MAP OF LABORATORY LOCATION

Wells
Hall
Stadium [:::]
Power
N Plant
International Center
Shaw Lane 0 i
\

 

    

 

 

 

  

‘1

Stadium Road

Engineering

::Locomotive Building

.m—

  

[F

I
II I I I I I I1 I I 1 1

Instructional
Media Center
ENTRANCE

 

l

 

l

l

 

 

 

Red Cedar Road

 

 

 

O

 

 

111

l
llll"'lI
lA__L/

lllll

 

 

 

 

EXPERIMENTAL CLASSROOM FACILITY *
25A - Instructional Media Center

180

APPENDIX F

ATTITUDE SURVEY TEST BATTERY

Dear Psychology Student:

The purpose of this survey is to obtain information about your
personal beliefs and attitudes, especially as they are related to learn-
ing. This information will be correlated with your performance in the
learning sessions to follow. All information obtained will be held in
strict confidence. There is no possible way for your grades to be
affected.

General Instructions In this packet there are seven questionnaires:
General Inventory

. Word Association

Political Position

Attitudes Toward Learning

Inventory of Beliefs

Attitude Inventory
Attitudes Toward Tests

\JO‘U‘J-‘LONH

The latter four questionnaires have IBM-type answer sheets
attached. They may be detached. For these, please use a scoring
pencil.

Answer the questions rapidly. There is no right or wrong, good
or bad answer to any of the questions. Just fill them out as you
really feel -- to honestly represent yourself. If you find anything

objectionable, you do not have to return the packet.

181

182

So that the information obtained is indeed representative and
unbiased, the need to obtain your personal response (reaction) to each
question cannot be over-emphasized. If you must discuss the questions
with your friends, do it EEEEE you have privately completed answering
all of the questions.

Thank you, your cooperation is greatly appreciated.

John Fry
Graduate Research Assistant

Learning Service
17 Morrill Hall

183

General Inventory
Name
Student #
Date

 

 

Please circle the appropriate reaponse.

1.

10.

11.

I was raised in a predominately: a. Urban area b. Suburban area
c. Small town d. Rural area

My father's occupation is (was):

a. Professional (doctor, lawyer, etc.) b. Self-employed business-
man c. Farm owner d. Skilled tradesman (e.g., carpenter)

e. White collar (clerical, sales, etc.) f. Industrial worker

g. Other

 

The number of years of schooling my father had was:

a. 1-8 b. 9-11 c. High school graduate d. Some college or
trade school e. College graduate f. Post-graduate or professional
training

The number of years of schooling my mother had was:

a. 1-8 b. 9-11 c. High school graduate d. Some college or
trade school e. College graduate f. Post-graduate or professional
training

The highest educational level attained by elder brothers or sisters
was: a. I have no older brothers or sisters b. 1-8 c. 9-ll

d. High school graduate e. Some college f. College graduate

I am a: a. male b. female

I am: a. 17 b. 18 c. 19 d. 20 e. over 20 years old

My class standing is: a. Freshman b. Sophomore c. Junior d. Senior

My major is

 

My cumulative grade point average is

 

My formal education relative to computers consists of:

a course in high school (e.g., FORTRAN programming)
a course in college

plans to take at least one course in the future

no courses or plans for them

moo‘m

 

12.

13.

14.

15.

16.

Computers are interesting
to me.

When the tepic is about com-
puters, I feel inadequate.

I am indifferent toward
computers.

I feel threatened by compu-
ter science.

I am curious about how
computers work.

184

 

Strongly Strongly
Agree Agree Disagree Disagree
4+ + - --
++ + - --
++ + - --
++ + - --
4+ + - --

 

 

 

185

Word Association
Name
Student #
Date

 

 

Listed below are 25 words that have more than one meaning. In
the space following each word, you should write down as many of the
meanings as you can. The meanings need not be written out in full;
writing down one word will usually do. For example:

BARK tree, dog, seal) boat

These four words bring to mind three different meanings for the
word BARK: the outer covering of a tree; a certain noise made by some
animals like dogs and seals; and a kind of boat.

Notice that the meanings were not written out in full; only
some words to remind us of these meanings were given. This is all you
have to do.

Your score will depend both on the number of different words
you write (in the example above this was four) and on the number of
different meanings the words remind us of (in the example above this
was three). 80 if you had time to write only two words for BARK, you
would choose tree and 52g, say, rather than 52g and seal because the
former words stand for two meanings, but the latter words stand for
only one meaning.

When you are sure of what to do, you may begin.

 

 

 

 

 

 

 

 

 

 

1. ARM
2. BIT
3. BOLT
4. CAP
5. COIL
6. DUCK

 

 

7. FAIR

 

10.

11.

12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

22.

23.

24.

25.

186

FAST

 

 

FILE

 

 

GRAVE

 

 

HOST

 

 

LEAF

 

 

MORTAR

 

PINK

 

PITCH

 

PLANE

 

 

POKE

 

 

POLICY

 

PORT

 

 

PUNCH

 

 

 

SACK

 

STRAND

 

 

TACK

 

TENDER

 

187

Political Position
Name
Student #
Date

 

 

To the best of your knowledge, what are (were) the predominant
political leanings of your parents? Please circle the letter
corresponding to your answer.

a. Democratic
b. Republican
c. Independent
d. Other (specify)

 

Politically speaking, would you consider yourself: (circle)

. Quite conservative
Somewhat conservative
Middle-of-the-road
Somewhat liberal
Quite liberal

(DO-00"!)

Presidential preference

a. For this item, think back to last November, election eve. Rank
the following four people according to your preference assuming
that they were all eligible candidates for the presidency. Rank
them as you would have preferred them on that night regardless
of whether or not they actually were nominated by their parties
or were elected. Rank the most preferred as number one, etc.

Ronald Reagan
Richard Nixon.
Hubert Humphrey
Ted Kennedy

waI-I

 

b. Were the election held today, what would be your preference?
Rank the most preferred as number one, etc.

Ronald Reagan
Richard Nixon
Hubert Humphrey
Ted Kennedy

DWNH

188

Attitudes Toward Learnipg

Fill in only the tap line of the answer sheet supplied. For each
statement, respond to the following key:

10.

11.

12.

13.

14.

15.

16.

(l) Strongly agree (4) Disagree
(2) Agree (5) Strongly disagree
(3) Uncertain

I am afraid to speak up in class.

I usually review what I've learned, before going on to new material.

I usually have to have things repeated over again in order to learn
them.

When I'm learning a new subject, I like to skip around and let my
interests lead the way, rather than proceed through it in a fixed
step-by-step sequence.

I should have control over the sequencing of classroom material.

Lectures should not be interrupted by the typical student request.

Instructors should use examples etc., which are more closely re-
lated to my personal interests and experiences, than to his own.

I feel at ease when asking questions in class.

Questions raised by students in class usually interfere more with
my learning than they help.

I learn better when I am with other students.

The instructor should be solely in control of the sequencing of
classroom material.

Even when material is quite disorganized, I can usually make sense
out of it.

If an instructor made himself freely available, I would take advan-
tage of his help.

When learning something new, I prefer to follow the lead of someone
else.

To me, asking questions in class is worth the effort.

How the instructor presents classroom material, should not be in-
fluenced by my personal interests.

17.

18.

19.

20.

21.

189

Compared to other students, I learn quickly.
I usually do not test myself as I learn.
I should be able to start and stop lectures at my command.

I prefer to decide for myself how to go about learning something
new.

I learn better when I am alone.

190

Distribution of Answers to Items from Factors in
"Attitude Toward Learning" Questionnaire

Factor 1. Fear of asking questions in class.

 

 

Item No. Loading
1. I am afraid to speak in class. (.81)
Strongly Strongly
Agree Agree Uncertain Disagree Disagree Total M_ ‘55
10.0 38.0 35.0 77.0 32.0 192
5.2 19.8 18.2 40.1 16.7 100% 3.4 1.1
8. I feel at ease when asking questions in class. (-.86)
11.0 65.0 46.0 59.0 11.0 192
5.7 33.9 24.0 30.7 5.7 100% 3.0 1.1

15. To me, asking questions in class is worth the effort. (-.63)

60.0 97.0 22.0 11.0 1.0 191
31.3 50.5 11.5 5.7 .5 99% 1.9 0.8

Factor 2. Independence from instructor while learning something new.

7. Instructors should use examples etc., which are more closely re-
lated to my personal interests and experiences, than to his own.

(-.57)
30.0 69.0 51.0 37.0 5.0 192
15.6 35.9 26.6 19.3 2.6 100% 2.6 1.0

14. When learning something new, I prefer to follow the lead of some-
one else. (-.34)

9.0 63.0 44.0 70.0 6.0 192

4.7 32.8 22.9 36.5 3.1 100% 3.0 1.0

16. How the instructor presents classroom material, should not be
influenced by my personal interests. (.67)

2.0 36.0 47.0 92.0 14.0 191
1.0 18.8 24.5 47.9 7.3 99% 3.4 0.9
20. I prefer to decide for myself how to go about learning something
new. '(.40)
17.0 55.0 60.0 54.0 4.0 190
8.9 28.6 31.3 28.1 2.1 99% 2.8 1.0

191

 

 

Factor 3. Frequency of review of self-testing.
2. I usually review what I've learned, before going on to new
material. (-.65)
Strongly Strongly
Agree Agree Uncertain Disagree Disagree Total M_ 55
10.0 63.0 38.0 75.0 6.0 192
5.2 32.8 19.8 39.1 3.1 100% 3.0 1.0
18. I usually do not test myself as I learn. (.72)
6.0 62.0 22.0 89.0 13.0 192
3.1 32.3 11.5 46.4 6.8 100% 3.2 1.1
Factor 4. Control of instructional functions.

4. When I'm learning a new subject, I like to skip around and let
my interests lead the way, rather than proceed through it in a
fixed step-by-step sequence. (-.45)

15.0 38.0 31.0 82.0 25.0 191
7.8 19.8 16.1 42.7 13.0 99% 3.3 1.2

5. I should have control over the sequencing of classroom material.

(-.66)
5.0 42.0 68.0 64.0 13.0 192
2.6 21.9 35.4 33.3 6.8 100% 3.2 0.9

6. Lectures should not be interrupted by the typical student request.

(.52)
3.0 14.0 26.0 79.0 70.0 192
1.6 7.3 13.5 41.1 36.5 100% 4.0 1.0
9. Questions raised by students in class usually interfere more with
my learning than they help. (.41)
3.0 12.0 24.0 111.0 42.0 192
1.6 6.3 12.5 57.8 21.9 100% 3.9 0.8
11. The instructor should be solely in control of the sequencing of

classroom material. (.69)

.0 23.0 52.0 94.0 1
6

7 .0 192
3. 3

6
12.0 27.1 49.0 8. 100% 3.5 0.9

19.

20.

Factor

3.

12.

17.

Factor

10.

19.

21.

192

I should be able to start and st0p lectures at my command. (-.32)

 

 

 

Strongly Strongly
Agree Agree Uncertain Disagree Disagree Total M_ ‘55
3.0 13.0 29.0 103.0 43.0 191
1.6 6.8 15.1 53.6 22.4 99% 3.9 0.9

I prefer to decide for myself how to go about learning something
new. (-.39)

l .0 55.0 60.0 54.0 4.0 190
9 2.1

7
8. 28.6 31.3 28.1 99% 2.8 1.0
5. Ease of learning.

I usually have to have things repeated over again in order to
learn them. (-.61)

15.0 59.0 43.0 66.0 9.0 192
7.8 30.7 22.4 34.4 4.7 100% 3.0 1.1

Even when material is quite disorganized, I can usually make
sense out of it. (.69)

2. 61.0 54.0 61.0 14.0 192
1 7.3

0
.0 31.8 28.1 31.8 100% 3.1 1.0
Compared to other students, I learn quickly. (.75)

4.0 51.0 93.0 37.0 7.0 192
2.1 26.6 48.4 19.3 3.6 100% 3.0 0.8

6. Autonomy while learning.
I learn better when I am with other students. (.69)

15.0 50.0 65.0 55.0 6.0 191
7.8 26.0 33.9 28.6 3.1 99% 2.9 1.0

I should be able to start and stop lectures at my command. (-.40)

3.0 13.0 29.0 103.0 43.0 191
1.6 6.8 15.1 53.6 22.4 99% 3.9 0.9

I learn better when I am alone. (-.76)

39.0 56.0 48.0 42.0
20.3 29.2 25.0 21.9

191
99% 2.6 1.1

000‘
HO

193

Inventory of Beliefs
Fill in only the top line of the answer sheet supplied.

This inventory consists of 100 statements which range over a
wide variety of t0pics. As you read each statement you are asked to
indicate quickly your agreement or disagreement with it in terms of
the key given at the top of each page. Peeple have different reactions
to these statements. This is not a test in which there are "right"
and "wrong" answers. What is wanted here is your own quick personal
reaction. You should be able to finish taking the inventory in 20
minutes or less.

In responding to these statements you will notice that there
is no way provided for indicating a neutral position. It is desired
that you indicate a tendency toward either agreement or disagreement
even though you may prefer to remain undecided. It is important that
you respond to every one of the 100 statements. Note that you will
never use the §_response on your answer sheet.

KEY: strongly agree or accept the statement.
tend to agree or accept the statement.
tend to disagree or reject the statement.
strongly disagree or reject the statement.

Dow>
HHHI—l

1. Literature should not question the basic moral concepts of society.
2. The main thing about good music is lovely melody.

3. Lowering tariffs to admit more foreign goods into this country tends
to raise our standard of living.

4. When things seem black, a person should not complain, for it may be
God's will.

5. Science is infringing upon religion when it attempts to delve into
the origin of life itself.

6. Literature which questions the basic moral concepts of our society
is good.

7. In our present society only a wartime economy can provide full
employment.

8. A man's conscience is an unreliable guide to right and wrong.

9. No task is too great or too difficult when we know that God is on
our side.

10. A work of art which provides only entertainment is useless.

11.

12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

22.

23.

24.

25.

194

KEY: strongly agree or accept the statement.
tend to agree or accept the statement.
tend to disagree or reject the statement.

strongly disagree or reject the statement.

UCW>
HHHH

A person gets what's coming to him in this life if he doesn't
believe in God.

Young people today are in general more immoral and irresponsible
than young people of previous generations.

More playgrounds and fewer strict fathers would eliminate
juvenile delinquency.

The many different kinds of children in school these days force
teachers to make a lot of rules and regulations so that things

will run smoothly.

Organized labor has done more to further economic progress than
business and industry.

Poverty can be eliminated.
Europeans criticize the United States for its materialism but
such criticism is only to cover up their realization that American

culture is far superior to their own.

The worst danger to real Americanism during the last 50 years has
come from foreign ideas and agitators.

The scientist that really counts is the one who turns theories
into practical use.

There is only one real standard in judging a novel or play--that
is convey a message of social significance.

Nudist colonies are a threat to the moral life of a nation.

Allowing more immigrants of all kinds into this country will
improve our culture.

No world organization should have the right to tell Americans
what they can or cannot do.

Despite the material advantages of today, family life now is not
as wholesome as it used to be.

Raising our standard of living requires government regulation of
business enterprize.

26.

27.

28.

29.

30.

31.

32.

33.

34.
35.

36.

37.

38.

39.

40.

41.
42.

195

KEY: strongly agree or accept the statement.
tend to agree or accept the statement.
tend to disagree or reject the statement.

strongly disagree or reject the statement.

UCW>
HHHH

The United States doesn't have to depend on the rest of the world
in order to be strong and self-sufficient.

Foreigners usually have peculiar and annoying habits.
The best assurance of peace is for the United States to have the
strongest army, navy, and air force, as well as the most atom

bombs.

It is only natural and right for each person to think that his
family is better than any other.

Any man can find a job if he really wants to work.

Strikes are caused by the unwillingness of an employer to meet the
needs of his employees.

American films emphasize sex more than foreign films do.

Being a successful wife and mother is more a matter of instinct
than of training.

The only way to eliminate prejudice is through forceful legislation.
A person often has to get mad in order to push others into action.

There is only one real standard in judging art works-~each to his
own taste.

Business enterprise, free from government interference, has given
us our high standard of living.

There is no art for art's sake.

The existence of poverty is an infallible sign of a poorly organized
society.

Many social problems would be solved if we did not have so many
immoral and inferior people.

Picket lines ought to be respected and never crossed.

You can't do business on friendship: profits are profits, and good
intentions are not evidence in a law court.

43.

44.

45.

46.

47.

48.

49.

50.

51.

52.

53.

54.

55.

56.

57.

58.

59.

196

KEY: I strongly agree or accept the statement.
I tend to agree or accept the statement.
. I tend to disagree or reject the statement.

I strongly disagree or reject the statement.

UGW>

A person has troubles of his own; he can't afford to worry about
other people.

Books and movies should start dealing with entertaining or up-
lifting themes instead of the present unpleasant, immoral, or
tragic ones.

The minds of many youth are being poisoned by bad books.
Speak softly, but carry a big stick.
Military service should be by choice rather than by conscription.

Peace can only be achieved when the United States abandons its
attempt to establish military superiority.

Honesty, hard work, and trust in God insure neither material nor
spiritual rewards.

Ministers in churches should not preach about economic and poli-
tical problems.

Each man is on his own in life and must determine his own destiny.

The moral good or evil of people has little bearing on any possible
destruction of the world.

The successful merchant can't allow sentiment to affect his business
decisions.

No intelligent man today can really believe in God.

The United States should make no attempt to exercise control over
any world organization.

Ministers who preach socialistic ideas are a disgrace to the church.

Labor unions don't appreciate all the advantages which business and
industries have given them.

We should impose a strong censorship on the morality of books and
movies.

European criticism of the United States is quite justified.

60.

61.

62.

63.

64.

65.

66.

67.

68.

69.

70.

71.

72.

73.

74.

75.

76.

197

I strongly agree or accept the statement.

I tend to agree or accept the statement.

I tend to disagree or reject the statement.
I strongly disagree or reject the statement.

U0tfi>

If we allow more immigrants into this country, we will lower our
standard of culture.

Modern paintings look like something dreamed up in a horrible
nightmare.

The greatest contribution to real Americanism during the last
fifty years has come from the intermingling of foreign immigrants
and native-born.

What a person gets in this life has little to do with whether he
believes in God or not.

Voting determines whether or not a country is democratic.

In our society, a person's first duty is to protect from harm
himself and those dear to him.

Europeans have no faults as bad as the provincial smugness and
intolerance of Americans.

Members of so-called racial minorities are no more alike than any
other group of American citizens.

A belief in divine guidance is of little help in meeting difficul-
ties.

Those who can, do; those who can't, teach.

Philosophers on the whole act as if they were superior to ordinary
people.

We would be better off if peOple would talk less and work more.

Most intellectuals would be lost if they had to make a living in
the realistic world of business.

Science will eventually explain the origin of life.

A lot of teachers, these days, have radical ideas which need to
be carefully watched.

Now that America is the leading country in the world, it's only
natural that other countries should try to be like us.

Prayer does little toward relieving one's problems.

77.
78.

79.

80.

81.

82.

83.

84.

85.

86.

87.

88.

89.

90.

91.

92.

198

strongly agree or accept the statement.
tend to agree or accept the statement.
tend to disagree or reject the statement.
strongly disagree or reject the statement.

UCUUI>
hlhikihi

Capital punishment does not serve to lower the crime rates.
Foreign films emphasize sex more than American films do.

Our rising divorce rate is a sign that we should return to the
values which our grandparents held.

Pride in craftsmanship and in doing an honest day's work is a
rare thing these days.

The United States may not have had much experience in interna-
tional dealings, but it is the only nation to which the world
can turn for leadership.

A sexual pervert is an insult to humanity and should be punished
severely.

Labor, since it represents the majority, should be given a greater
voice in a democracy than capital.

The actions of the United States in world politics clearly demon-
strate its unfitness for world leadership.

Both beauty and purpose can be found in all modern paintings.

There may be a few exceptions, but, in general, members of a
racial group tend to be pretty much alike.

There are too many people in this world who do nothing but think
about the Opposite sex.

Modern people are superficial and tend to lack the finer quali-
ties of manhood and womanhood.

It is more important for a book or movie to be realistic than to
be pleasant.

Members of religious sects who refuse to salute the flag should be
punished for their lack of patriotism.

As young peeple grow up, they ought to get over their radical ideas.

The twentieth century has not had leaders with the vision and
capacity of the founders of this country.

199

KEY: strongly agree or accept the statement.
tend to agree or accept the statement.
tend to disagree or reject the statement.

strongly disagree or reject the statement.

cow»
HHHH

93. Books on tragic and sordid themes help youth face the world
of reality.

94. There are a lot of things in this world that will never be
explained by science.

95. The world will get so bad that some of these times God will
destroy it.

96. Other countries don't appreciate as much as they should all the
help that America has given them.

97. If a person is honest, works hard, and trusts God, he will reap
material as well as spiritual rewards.

98. The welfare of others is more important than one's own self-
interests.

99. Nothing but profit to our country would result from the relaxa-
tion of our present strict immigration laws.

100. No censorship on the presumed morality of books and movies can
be justified.

Adapted in part from the Inventory of Beliefs copyrighted by the
Cooperative Study of Evaluation in General Education of the American
Council on Education, 1951.

10.

11.

12.

13.

14.

15.

16.

200

Attitude Inventory

Fill in only the tOp line of the answer sheet supplied.
For each statement, respond according to the following key:

(1) True
(2) False

I like to have a place for everything and everything in its place.

I often start writing the answer to an essay question and find
I've written myself into a corner.

Some of my friends think that my ideas are impractical, if not a
bit wild.

I cannot skim reading material; I must examine each word and
sentence.

I don't like to undertake any project unless I have a pretty good
idea how it will turn out.

In reading, I try to master each idea before passing on to the next.

For most questions there is just one right answer, once a person
is able to get all the facts. '

When text material is too difficult for me, I try to grasp what-
ever I can and at least try to get the big picture.

Politically I am probably something of a radical.

I am better at getting the main ideas of a lecture than at remem-
bering the details.

Perfect balance is the essence of all good composition.

I find it pleasant and easy to skim a chapter in a book and pick
out the major points.

I prefer to engage in activities from which I can see definite
results rather than those from which no tangible or objective

results are apparent.

I find that a well-ordered mode of life with regular hours is not
congenial to my temperament.

The unfinished and the imperfect often have greater appeal for me
than the completed and the polished.

I like to listen to primitive music.

I Illllllll.l'.| III l¢illlq4'|.1ll ll 11'
II I. I I 1.11l‘ til

:1] J‘l I 1 I i 1‘
XIII“ (ll

l7.

l8.

19.

20.

21.

22.

23.

24.

25.

26.

27.

28.

29.

30.

31.

32.

33.

34.

35.

36.

201

I have always had goals and ambitions that were impractical or
that seemed impossible for me to realize.

When a teacher lectures on something other than what he originally
announced, I feel uneasy.

Trends toward abstractionism and the distortion of reality have
corrupted much art of recent years.

It bothers me to have different news commentators give different
interpretations of the news.

I like to fool around with new ideas, even if they turn out later
to have been a total waste of time.

I don't like to work on a problem unless there is a possibility of
coming out with a clear-cut unambiguous answer.

I have always hated regulations.

Many of my friends would probably be considered unconventional by
other people.

It doesn't bother me when things are uncertain and unpredictable.
My way of doing things is apt to be misunderstood by others.
Facts appeal to me more than ideas.

I have had strange and peculiar thoughts.

I don't like things to be uncertain and unpredictable.

The worst thing an instructor can do is to make very specific
plans for each lesson.

It is a good rule to accept nothing as certain or proved.
I dislike following a set schedule.

Usually, I prefer known ways of doing things rather than trying
out new ways.

I like to go alone to visit new and strange places.

I much prefer friends who are pleasant to have around to those who
are always involved in some difficult problem.

I have had very peculiar and strange experiences.

10.

ll.

12.

l3.

14.

202

Attitude Toward Tests

 

Fill in only the top line of the answer sheet supplied.

Nervousness while taking an exam or test hinders me from doing
well. (1-Always 2-Never)

I work most effectively under pressure, as when the task is very
important. (l-Always 2-Never)

In a course where I have been doing poorly, my fear of a bad
grade cuts down my efficiency. (l-Never 2-A1ways)

While I may (or may not) be nervous before taking an exam, once
I start, I seem to forget to be nervous. (l-I always forget
2-1 am always nervous)

When I am poorly prepared for an exam or test, I get upset and do
less well than even my restricted knowledge should allow. (l-Never
2-Practically always) ‘

Nervousness while taking a test helps me do better. (l-Never
2-Often)

The more important the exam, the less well I seem to do. (l-Always
2-Never)

When I start a test, nothing is able to distract me. (l-Always
true 2-Never)

During exams or tests, I block on questions to which I know the
answers, even though I might remember them as soon as the exam is
over. (l-Always 2-Never)

In courses in which the total grade is based mainly on one exam,
I seem to do better than other people. (l-Never 2-Almost always)

I find that my mind goes blank at the beginning of an exam, and it
takes me a few minutes before I can function. (l-Almost always
2-Never)

I look forward to exams. (l-Never 2-Always)

I am so tired from worrying about an exam, that I find I almost
don't care how well I do by the time I start the test. (l-Never
2-Almost always)

Although "cramming" under pre-examination tension is not effective
for most people, I find that if the need arises, I can learn
material immediately before an exam, even under considerable pres-
sure, and successfully retain it to use on the exam. (l-Always
2-Never)

15.

l6.

17.

18.

203

Time pressure on an exam causes me to do worse than the rest of
the group under similar conditions. (1-A1ways 2-Never)

I enjoy taking a difficult exam more than an easy one. (l-Always
2-Never)

I find myself reading exam questions without understanding them,
and I must go back over them so that they will make sense.
(l-Never 2-Almost always)

The more important the exam or test, the better I seem to do.
(l-This is true of me 2-This is not true of me)

APPENDIX C

EXCERPT FROM MICHIGAN STATE UNIVERSITY READING TEST

But in all politics we observe two sources of decay existing
from natural causes, the one external, the other internal and self-
produced. The external admits of no certain or fixed definition, but
the internal follows a definite order.

When a commonwealth, after warding off many great dangers, has
arrived at a high pitch of prosperity, and undiSputed power, it is
evident that, by the continuance of the great wealth within it, the
manner of life Of its citizens will become more extravagant; and the
rivalry for office, and in other spheres of activity, will become
fiercer than it ought to be. And as this state of things goes on more
and more, the desire of office and the shame of losing reputation, as
well as the ostentation and extravagance of living, will prove the
beginning of deterioration. And of this change the peOple will be
credited with being the author, when they become convinced that they
are being cheated by some from avarice, and are puffed up by flattery
from others from love of office. For when that comes about, in their
passionate resentment and acting under the dictates of anger, they
will refuse to obey any longer, or be content to have equal powers
with their leaders, but will demand to have all or far the greatest
themselves. And when that comes to pass the constitution will receive
a new name, which sounds better than any other in the world, liberty
or democracy; but, in fact, it will become the worst of all governments,
mob-rule.

38. Which of the following best describes the author's concept of
history?

a. The golden age--all history is a decline from some perfect
period.

b. The cyclical--nations develop and then decay.

c. The progressive--nations continue to improve.

d. The static--nations do not change.

39. According to the passage, the ultimate authority of government
derives from

a. aristocratic leaders
b. wealthy landowners

204

40.

41.

ordinary

c
d. virtuous men

The writer st

a. anarchy
b. fascism
c. communism
d. all of th
Which of the
decay?

a citizens
b. conquest
c disputes
d mob-rule

205

people

ates that internal corruption might finally lead to

e above

following might be called an "external source" of

demanding universal suffrage
by a rival power

among rival politicians

by the masses

APPENDIX H

THREE EQUIVALENT FORM
60 ITEM TESTS

Form A

Fill in only name and student number on the answer sheet supplied.
Choose the best answer for each item.

1. Digital computers differ from analog computers in that digital
computers

a. calculate with quantities that are continuous in nature (not
discrete)

b. print out final results which are limited in accuracy and
precision

(c.) print out final results which are very accurate and precise

d. are electronic and use stored programs

e. are slightly more pOpular with industrial users

2. Calculating the planned orbit of a space vehicle would be an ex-
ample of using the computer for

a. information retrieval

b. data processing

(c.) mathematical modeling or simulation
d. control purposes

e. data reduction

3. The functional section of the computer which interprets the program
of instructions is the .

a. input section

(b.) control section

c arithmetic-logic section
d. memory section

e. all of the above

4. What is the name of the program which translates an artifical com-
puter language into machine language?

a. "interpreter" program
b. "source" program

206

207

(c.) "compiler" program
d. "assembly" program
e. FORTRAN program

A device which can represent one "bit" of information is

a a magnetized metal "donut"
b. an electro-mechanical switch
c a light bulb

d. a photoelectric cell

(c.) all of the above

In "core" type memory, the third wire that intersects each core .

a. carries one-third of the current required to change the mag-
netic orientation

senses which direction the core is magnetized

just holds the three-dimensional structure rigid
demagnetizes the core after use

does not exist

man?
COOCV

The time taken in "debugging" programs often takes up to

a none of the computer's time

b. one-tenth of the computer's time
(c.) one-third of the computer's time
d. one-half of the computer's time
e. two-thirds of the computer's time

The keypunch automatically punches, feeds, moves, and

notches each correct card

verifies each correct card

prints out the information on each card on paper, line by line
duplicates each card in a stack automatically

(c.) none of the above

Dan U'm

While the cost of each computer computation has been decreasing,
the cost of has been increasing.

a. electronic components

b. input and output units
(c.) programming

d. information retrieval

e. all of the above

In order to avoid a lot of time "debugging" his programs, the
programmer must

a. be well-trained in flow-diagramming and logic
b. be compulsive and lucky

11.

12.

13.

14.

15.

16.

208

(c.) conform exactly to the rules of the translating system used

d. use the correct translating system for the type of problem
to be solved

e. all of the above

Complete the following arithmetic operation in binary.

 

10000 a. 1010
-101 b. 1101
(c.) 1011

d. 1110

e. 0011

Computer systems in the year 2000 A.D. will probably use

a "off-line" operation

b. "buffer" operation

c. punched cards and tape

(d.) source programs and object programs
e. all of the above

Computer size usually refers to

a. the physical dimensions of the system installation
(b.) memory storage capacity

c input/output capacity

d. arithmetic/logic capacity

e. all of the above

Which of the following is not a basic capability of digital
computers?

a. control

b. data reduction

c. data processing

(d.) data programming

e. scientific analysis or simulation

The working memory of computers usually has

a. quick access time and a relatively large capacity

b. quick access time and a relatively small capacity

c. constant access time and a relatively large capacity
(d.) constant access time and a relatively small capacity
e. "real-time" access time and unlimited capacity

A compiler program is .

one part of a translating system

written by experts and kept in a library

available already prepared in machine language

d. must be loaded in the computer's memory before use
(e.) all of the above

00"!»

209

17. Decimal "17" is equivalent to binary

 

(a.) 10001
b. 01111
c. 10011
d. 10111
e none of the above

18. Complete the following arithmetic Operation in binary.

110 (a.) 1101
+111 b. 0101
c. 1111

d. 1011

e. 1001

19. In computer programming jargon, "branching" means

a. going from one "loOp" to another "loop"

(b.) the same as carrying out conditional instructions (i.e.,
"if" statements)

c. performing over and over again a given sequence of instruc-
tions

d. that the computer can distinguish between "yes" and "no"
instructions

e. the same as "looping"

20. The most common "buffer" used with the computer, works much the
same as

a a Xerox copying machine

b the cathode-ray tube in the home TV set
c. a photoelectric cell

(d.
e.

) an audio tape recorder
all of the above

21. The concept of the modern computer was formulated, initially,
about years ago.

a. 25
b. 30-35
c. 80
(d.) 145
e. 2500

22. The main reason computers are sometimes built to handle only one
type of problem is because

(a.) it is less expensive to build them this way
b. the purchaser has limited uses

23.

24.

25.

26.

27.

210

c. it does away with the need for a stored-program
d. they can then calculate with greater precision
e. it makes them more reliable

Punched cards trace their history and form to

a. John von Neuman and UNIVAC corporation
b. Professor Aiken of Harvard University and IBM corporation

(c.) Dr. Hollerith and the U.S. Census Bureau

d. Charles Babbage and the size of the U.S. dollar bill
e. Alfred E. Newman and Control Data Corporation

If you were in charge of buying a computer for a large-sized
airline, which of the following would you be least interested in?

a. number of "words" in the memory
b. access time
c. "multi-terminal" Operation

(d.) "buffer" operation

e. digital operation

The number of electronic digital computers in Operation in the
U.S. today is approximately

a. 10,000
(b.) 50,000
c. 100,000
d. 500,000

e. 1,000,000
Computer science promises in the future

a. just more of today's same uses

b. a small computer in every home

(c.) small communication consoles in every home linked to a large
computer

d. to reduce the need for programmers

s. all of the above

The functional section of the computer to which information always
goes directly after input and directly before output is

the arithmetic section
the control section
the logic section

.) none of the above

all of the above

(DO-00“!)

211

28. FORTRAN was primarily designed to assist

a. all programmers everywhere

b. business and banking programmers

c. only programmers working with IBM computers
(d.) engineering and scientific programmers

e. U.S. Census Bureau programmers

29. Binary "100" is just a compact way of writing

a. 1x103+0x102+0x101+1x100
(b.) 1 x 23 + 0 x 22+ 0 x 2 + l x 20
c. 1x24+ox2+0x22+1x21
d.lx22+0x2 0x2+1x22

e. none of the above

30. What will be the output from the indicated input to the following
series of gates?

 

0 ----) (a.) 01
0 ----9 b. 00
0 ----------- ) c. 10
d. 11
e no result possible

31. Even though a program translates into machine language okay, and
runs through to completion, it may still have logical errors in
it. In order to detect these errors, the programmer would

a. show the output to expert programmers and the originators of
the problem

b. check with the computer for a detailed "debugging" analysis

(c.) manually calculate a sample answer and compare it with the
program's result

d. compare the source program with the object program

e. all of the above

32. A card reader can "read" a punched card

a. only one row or one column at a time
b. all twelve rows at a time

c. all thirteen rows at a time

(d.) all eighty columns at a time

e. all eighty-eight columns at a time

33. The devices used by Dr. Hollerith to analyze the data from the
1890 census were

a. Charles Babbage machines

b. mechanical adding and multiplying machines
(c.) card-sorting machines

d. banks of photo-electric cell machines

e. electro-mechanical computers

34.

35.

36.

37.

38.

212

The computer uses the binary number system because

a.

b.
c.
(d-)

e.

it has been traditional since 1945, when punched cards were
introduced

the decimal number system is too complicated for the com-
puter's simple operation

electricity can only be positive or negative

the only components which work fast and reliably, are binary
type

all of the above

Auxiliary computer equipment is of value to computer Operation
because

it translates decimal into binary code and vice-versa

it prepares input information in a form acceptable to the
computer

it takes over in the event of a major computer breakdown

it does the dull and routine tasks, leaving the computer to
do the "thinking"

all of the above

Which of the following could be least adapted for individualized
student learning?

(d-)

e.

cathode-ray tube operation
foreign language translation
"off-line" operation
"on-line" operation

control (feedback) Operation

of the more remarkable things computers can do is

take over almost any job and do it better than man

win chess and checker games every time they play man
"reproduce" themselves, when linked to automated processing
machines

improve their ability through trial and error learning

all of the above

"Real-time" computer Operation means

a.
b.
c.

d.
(e-)

simultaneous multi-programming is possible

the time difference between the computer's sensing information
and responding to it approaches zero

the programmer can "talk" directly with the computer by means
of a typewriter

"on-line" operation is also necessary

all of the above

39.

40.

41.

42.

43.

44.

213

An example of a logical question, which the computer's arithmetic-
logic section cannot answer, is

a. is "X" greater than 5? (where "X" is 8)

b. is a number "+" or "—?"

(c.) is "A/B" less than "C?" (where "A"=2="C" and "B"=0)
d. is "A" equal to "B?" (where "A"=+2 and "B"=—2)

e. none of the above

Which of the following is not a legal FORTRAN operation symbol?

a (*)
b (=)
c (/ )
(d ) (X)
e. none of the above

Michigan State's CDC 6500 computer has in its memory the capacity
for "words?"

a. 36

b. 360

c. 3, 600
(d. ) 36, 000
e. 360, 000

Flow charts are especially helpful to the programmer because they
enable him to

a. easily code the instructions

(b.) represent solutions in a graphic form
c. find the best method of programming
d. be logical in his thinking

e. all of the above

"Sub-programs" differ from "lOOpS" in that "sub-programs"

a can include many "loops" within its own sequence

b. are independent of the main program, but are called on, for
repeated use, by it

c. are used primarily to solve frequently recurring, specific,
problems

d. are part of a translating system's library

(e. ) all of the above

"On-line" communication with the computer differs from "off-line"
in that "on-line" in general,

a. speeds up the computer in its operation
(b.) delays the computer in its normally efficient Operation
c. causes the computer to make more electronic errors

45.

46.

47.

48.

49.

50.

214

d. works well with electrO-mechanical output devices
e. makes use of "buffer" units

One factor that was not different between the "ENIAC" and the
"MARK I" was

a. the speed of Operation
b. the type of components
c. the financial backing
(d.) the type of operation
e. the first application

The inside of a computer looks like it was made mostly of

.) spaghetti

tiny little black boxes

tiny electronic switches
spinning disks and drums
all of the above

maoo‘m

A situation where "on-line" and "off-line" computer communications
would be most likely practiced is

a. an industrial processing complex

b. an airline reservation inventory system

(c.) a university complex (e.g., MSU)

d. a commercial-business data processing concern
e. all of the above

Of the following, which would least likely be used for medical
diagnosis by computer?

a. simulation

b. data reduction

(c.) "off-line" operation

d. "time-sharing" Operation
e. information retrieval

The computer can carry out higher order mathematics, such as
calculate, by means of

a. its engineered capability to "think" logically

b. repeated comparisons

c. repeated looping and branching

(d.) repeated successive approximations to the correct answer
e. looking up answers in indexed tables stored in the memory

Before any information in a computer's memory can be output (e.g.,
printed) it must be

a. converted from binary into decimal or alphabetic characters
b. called for by the computer operator

51.

52.

53.

54.

55.

215

(c.) programmed for by appropriate instructions
d. prepared prOperly for the translating system
e. all of the above

FORTRAN means

a. First Order Translator

b. For Translating

c. Formula Translating Algebra/Numbers
(d.) Formula Translation

e. none of the above

Which is a disadvantsgs of translating systems?

 

a. an artificial language must be used to write the source
program

(b.) computer time must be used to translate the source program

c. memory location definition must be done by the translating
system

d. the compiler program must be written by experts

e. the object program must be written by the translating system

In computer terminology, the term "word" can be defined as

(a.) a group of consecutive memory cells

b. the location for storing four bits of information
c. any instruction which is near-English

d. the basic building block of a computer's memory

e all of the above

One difference between the programmer and the computer operator
is that the programmer

a. knows more about how the computer works inside

(b.) "debugs" the programs he writes

c. loads the programs he writes

d. loads the apprOpriate compiler program for the translating
system he is using

e. all of the above

The number of alphanumeric characters that are usually punched in
each column of an IBM type card is

(a.) one
b. two
three

c
d. one per each pair of columns
e none of the above

56.

57.

58.

59.

60.

216

Information translation or conversion is the basic principle upon
which all coding and information processing equipment rests. All
is well, as long as the information which is converted

(3.) remains consistent within each form it's put into

b.
c.
d.
e.

The

e.

does not deviate from its original form too much
is not amplified too many times

is not stored for too long a time period

all of the above

stored-program concept essentially refers to

storing a compiler program in the computer's memory in order
to translate into binary, the program entered

storing instructions in the computer's memory and having them
modified conditionally

storing data in the computer's memory and having it modified
conditionally

storing tables of indexed information which can be searched
through for answers

all of the above

The two criteria for effective computer translation of foreign

languages, which have not been met, are

 

the storage of precise translation rules and idioms

better agreement of meanings and precision in translation
programming all the possible meanings and their subtleties of
usage for each word or phrase

enough storage space and rapid enough access to it

none of the above

Main programs differ from sub-programs in that main programs

are written by experts

are defined as having a running time of one minute or more
are not part of a translating system

are usually written in some artificial language

all of the above

The functional section(s) of the computer which translate(s) the
data and program of instructions from decimal into binary and
vice versa is (are) the

mantra:

memory section

control section
arithmetic-logic section
all of the above

none of the above

217

Form B

Fill in only your name on answer sheet supplied.
Choose the best answer for each item.

1. Examples of mechanical analog computers are

a. the card sorter and the keypunch

(b.) the bathroom scale and the Speedometer
c the abacus and the desk calculator

d. the thermostat and the alarm clock

e. the slide rule and the turnstile

2. An example of data reduction use would be

(a.) Census Bureau operation

b. industrial feedback operation

c airline reservation operation

d. computer simulation of space vehicle flights
e all of the above

3. The control console of a computer is not used for

. warning of trouble

. starting and stOpping the computer

. inserting special instructions into the computer
. monitoring the computer

(c.)"debugging" computer programs

D-OU‘m

4. A translating system consists of an artificial language and

a. a source program
(b.) a compiler program
c. an assembly program
d. an object program
e. a FORTRAN program

5. In the binary number system as used with the computer, the symbol
"0" differs from "1" in that "0" signifies

no value

a counter-clockwise charge
a negative charge

. the opposite state of "l"
(e.) all of the above

CLO o‘m

218

6. Core memories are usually used for the working memory of a
computer because

(a.) access time to them is very rapid and constant
b. access time to them is very rapid and variable
c access time to them is relatively slow and constant
d. access time to them is relatively slow and variable
e they are less expensive than other types of memories

7. Of all the foul-ups associated with using computers, over ninety
percent of the trouble is with

a electronic component failure

b. memory storage and communication errors

(c.) programming errors

d. program language compilation (translation) errors
e. input and output errors

8. The card sorter can sort cards

a. into eleven piles or less
(b.) only one column at a time
c. only two columns at a time
d. all seventy-two columns at a time

9. The two key factors that have led to the incredibly fast, reliable,
and versatile computers which we have today are

a. the use of the binary number system and the stored-program
concept

b. the use of the binary number system and electronic components

c. electronic components and the time-sharing concept

(d.) electronic components and the stored-program concept

e. the stored-program concept and the time-sharing concept

10. Even the very best programmers make mistakes, because the compu-
ter

 

(a.) does exactly what it is told, like a robot

b. has the ability to modify its own instructions

c. is so tricky to program

d. is so logical in operation

e. sometimes fails to translate the instructions into binary code
correctly

11. Complete the following arithmetic operation in binary.

1001 (a.) 0011
-110 b. 0101
c. 1001
d. 0111
e. 1101

12.

l3.

14.

15.

16.

219

Computer systems in the year 2000 A.D. will probably

O-OU'Q'J

(e)

eliminate the need for bank checks and paper money
provide instant information access

have terminals in the average person' 3 home
provide efficient foreign language translation

do all of the above

Generally, as computer operation cost, in dollars per hour,
increases, the number of computations per second

-)

(DD-00“!)

has no relation

decreases

remains the same

increases

is related to the efficiency of the programmer

Which of the following is not a current general use of computers?

(DO-06m

The

(d-)

control purposes
scientific analysis
data processing
information retrieval
data reduction

auxiliary memory of computers is usually designed for

quick access time and relatively large capacity

quick access time and relatively small capacity
relatively slow access time and relatively large capacity
relatively slow access time and relatively small capacity
"real-time" access time and unlimited capacity

proper order of steps in using a translating system is .

compiler program loaded, source program loaded, translation of
source program into machine program, and execution of the
machine program

FORTRAN program loaded, source program loaded, translation of
FORTRAN program into the machine program, and execution of

the source program

translating system loaded, compiler program loaded, transla-
tion of compiler into the source program, and execution of the
source program

source program loaded, compiler program loaded, translation of
the source program into the object program, and execution of
the object program

none of the above

220

17. Binary "1111" is equivalent to decimal

a. 13
b. 14
(c.) 15
d. 16

e. none of the above

18. Complete the following arithmetic operation in binary.

1100 a. 010100
x 11 b. 101010
c. 001001
d. 011001
(c.) 100100

19. Getting the computer to decide, in effect, "yes" or "no" is
based on

a. the computer's ability to distinguish between "yes" and "no"
instructions

b. the computer's ability to operate on instructions in a set,
fixed order

c. the computer's stored-program capability

d. the computer's ability to "think" logically

(e.) a comparison of the results of each loop with some criterion

20. "Buffers" would be used when the system is Operated
a with "time-sharing"
b. in "real-time"
c. "on-line"
(d.) "Off-line"
e "on-line" and "off-line" at the same time
21. The first true counting device was invented about years ago.
a. 500
b. 1,000
(c.) 2,500
d. 5,000
e. 10,000

22. Computers are valuable because compared to man they process
information .

. automatically

with greater precision

without forgetting, unless told to
. more reliably

(e. all of the above

(Lou‘s:

23.

24.

25.

26.

27.

28.

A
0‘3)

221

Binary operation traces its history to

a. the fact that decimal electronic components couldn't be built
b. Charles Babbage
c. Dr. Hollerith

(d.) John von Neuman

e. the stored-program concept

If you were in charge of buying a computer for a commercial-
business concern, which of the following would you be least
interested in?

type of memory

.) control Operation

c "buffer" operation

d. "off-line" operation

e. types of translating systems offered

A computer that performs Operations at the rate of one every
billionth of a second would be said to be Operating

a in micro-seconds
b. in mega-seconds
c. in pica-seconds

(d.) in nano-seconds

e. at the speed of light

Using the computer in the future, as in automated industries,
involves replacing men who

a. design the processing equipment
b. maintain the processing equipment
c. monitor the automated system

(d.)control the processing equipment

e. make "logical" decisions about the use of the automated system

The section(s) of the computer which translate(s) the results
(information) from binary machine language into decimal alphanu-
meric characters for printout is the

 

control section

memory section
arithmetic-logic section
.) all of the above

none of the above

GQOU‘N

The FORTRAN translating system was COpyrighted in 1952 by

a. CDC and is available for almost all computers
b. CDC and is available only with CDC computers

222

(c.) IBM and is available for almost all computers
d. IBM and is available only with IBM computers
e. UNIVAC and is available only with similar large computers

29. To convert decimal numbers to their binary equivalent, the decimal
number is repeatedly divided by two. Therefore, decimal 68 would
be equivalent to binary

 

a. 0100001
b. 1100000
c. 1010000
d 1001000

(e.) 1000100

30. The computer's arithmetic-logic circuits are made up of three
basic gates,

 

a. AND, IF, BUT
b. YES, OR, BUT
(c.) AND, OR, NOT
d. YES, IF, NOT
e. none of the above

31. In computer programming jargon, "debugging" means finding and
removing

3. errors between the source program and the Object program

b. errors between information punched on the cards and the source
program

c. electronic errors by test runs on the computer

(d.) programming errors by test runs on the computer

e. the bugs that eat holes in the punched cards

32. When "sensing" information from punched cards, the two factors
which distinguish one character from another are the number of
holes and

(a.) the space between them

b. the pattern among the holes

c the magnetization of the electrical impulses
d. the frequency of the electrical impulses

e the speed of the electrical impulses

33. The first true counting device was

a. counting on one's fingers and toes

(b.) the abacus

c. the mechanical adding machine, invented in 1642
d. the Babbage machine

e. the Hollerith machine

34.

35.

36.

37.

38.

39.

223

The two major features of computer design that make modern com-
puters possible are

(a.) the stored-program concept and electronic components
b. the stored-program concept and binary operation

c "real-time" and "time-sharing" operation

d. "real-time" and "on-line" operation

e "time-sharing" and remote console terminal operation

Machine language instructions differ from their equivalent arti-
ficial language instructions in that the former

are always written in the binary number system

always exceed the latter in quantity for the same result
are tedious for humans to work with

include specification of memory locations

(e. ) all of the above

900'!»

Which of the following could be least adapted for individualized
student learning?

a. "time-sharing" operation

b. "real-time" operation

(c.) "off-line" operation

d. remote terminal console operation
e. general-purpose operation

Computers should not be feared; because

a they can only process data, nothing else

b they have no way of taking any direct action

c. they cannot improve their ability, independent of man
(d
e

.) man is in control of whatever they do
man can always pull their plug

"Time-sharing" computer operation means

simultaneous multi-programming is possible

multi-user sharing of a central computing facility is possible
remote terminals are feasible

"real- time" and "on- line" operation is necessary

(e. ) all of the above

O-OO‘OJ

The arithmetic-logic section of the computer is often compared to
a mechanical desk calculator because

a. it performs the same functions

b. it uses the same type of electro-mechanical components

(c.) information has to be fed into its registers before it performs
any operations

d. it is capable of only the same simple Operations

e. it calculates in the same "logical" way

40.

41.

42.

43.

44.

45.

224

In FORTRAN, the algebraic expression é_§_§.+ C2, would be writ-
ten

/A * B//2 + /C ** 2/
(A x B)/2 + (C * C)
(A x B)/2 + (C x C)
.) (A * B)/2 + (C ** 2)
(A : B)/2+ (C* 2)

(DO-00“!)

The typical computer "word" contains "bits" of information.

2
4
24
.) 48
80

A

(DD-00"!”

Flow charts

help to code the data prOperly

show where information goes inside the computer

make sure all possible solutions are obtained

.) represent solutions to problems in a graphic form

use squares, circles, rectangles, triangles, and diamonds to
represent logical operations

(DO-DUO)

"Sub-routines" differ from "sub-programs" in that "sub-routines"

 

a. are smaller and more specific than "sub-programs"

(b.) are written locally, rather than being part of a translating
system

c. are an integral part of "sub-programs"

d. are for calculating commonly used algebraic functions rather
than for special, unique functions

e. are kept "canned" in libraries for use with any program

"On-line" communication with the computer is essential for

a. scientific and engineering problem-solving
(b.) "time-sharing” and "real-time" applications
c. digital and stored-program computers

d. data reduction and processing operations

e. electro-mechanical input and output devices

The world's first all electronic digital computer (the ENIAC) was
financed by

a. IBM and the University of Pennsylvania
b. IBM and Harvard University

46.

47.

48.

49.

50.

225

(c.) The U.S. Army and the University of Pennsylvania
d. The U.S. Army and Harvard University
e. none of the above

Programs or instructions which cause the computer and its auxil-
iary equipment to function are commonly known as

"Operational design"
"systems design"
"flow charts"

.) "software"
"routines"

(DO-IOU‘m

An example of "off-line" computer communication operation is

a. an airline reservation inventory system

(b.) an electro-mechanical output of processed data

c. thirty engineers using the same computer at the same time,
from individual terminals

d. a college student problem-solving at a remote console

e. all of the above

Of the following, which would most likely be used for vocational
guidance by computer?

a foreign language translation

b. control operation

(c.) scientific analysis or simulation
d. general-purpose operation

e. remote terminal Operation

An example of the use of a computer-based mathematical model
would be

simulating a nuclear power plant operation before it is built
simulating the way the human brain operates

calculating the orbit of satellites

d. designing the wing shape of the SuperSonic Transport (SST)
(e.) all of the above

00‘s:

The form and amount of information output from a computer is de-
termined by

(a.) appropriate instructions

b. the nature of the problem that was solved
c. the way the computer was designed

d. the type of output media

e the translating system used

51.

52.

53.

54.

55.

56.

226

COBOL stands for

(a.) Common Business Oriented Language

b. Commor Or Basic Operational Language
c Computer-Oriented Business Or Law

d. Computer-Based Operating Language

e none of the above

In general, the closer the artificial language of a translating
system is to English, the

longer it takes for a problem to be computed on the computer
harder it is to translate it into machine language

easier and quicker it is for man to use

d. less efficient use of the computer's capabilities is made
(a.) all of the above

00‘s:

The difference between the contents of a "word" and an "address"
is

 

the address is actually part of a word

the address never changes, while the word does
the address is used to locate a word in memory

d. the address is made up of fewer cells than a word
(c.) all of the above

00“!)

In order for the programmer to determine exactly what he wants
the computer to do, he must know how

a to translate decimal numbers into the binary number system
b. to flow-chart each computer operation

(c.) to solve each problem independently of the computer

d. the computer works inside

e. all of the above

Punched cards can be "read" only by

a. Optical scanners

b. cathode-ray tubes

(c.) humans and card readers
d. photo-electric cells

e. all of the above

An example of the information conversion concept is

a. mechanical energy converted into electrical energy to feed a
computer

(b.) sound impulses converted into electrical impulses

c. the results of a computer program converted into a new air-
craft wing shape

d.
e.

227

a Xerox copying machine converting an original into a COpy
all of the above

57. Since the modern computer stores the program of instructions
within its memory, this enables the computer to

(a-)
b.
c.
d.

modify and select instructions internally

"think" logically, just like humans

solve problems which the programmer does not know how to solve
create its own lOOps and branches

58. Until computers have extremely rapid access to an extremely large
memory, the future looks bleak for

a. true "real-time" Operation

b. true automation of data processing

c. "on-line" operation Of numerous remote consoles

d. mathematical modeling and simulation

(e.) information retrieval and foreign language translation
59. For efficient and versatile use of main programs, they are often

written

a. for use with any machine

b. directly in machine language

c. as sub-programs or sub-routines, and then put together as

needed
(d.) for the general case and then stored in libraries
e. all Of the above

60. Which of the following is not a type of input unit?

a.

b.

(d-)

those which record the information to be entered upon some
input medium

those which permit an Operator to enter the information
manually

those which enter information directly from its original
source

those which translate the information from decimal into
binary

those which store information until a convenient time for
input arrives

228

Form C

Fill in only name on answer sheet supplied.
Choose the best answer for each item.

1. The slide rule and the speedometer in your car are examples of

electronic digital computers
electronic analog computers

mechanical digital computers
.) mechanical analog computers

none Of the above

A
(DO-DUO:

2. Examples of data processing are

inventory and record-keeping

payroll preparation and billing preparation

reading the numbers on checks and reading handwriting
file sorting and searching

(c.) all of the above

O-OU‘m

3. The control section Of a computer

a is a closed-loop feedback system

b. consists of the computer operator and the control console
(c.) interprets the program of instructions

d. is used in "debugging" programs

e. all of the above

4. Information, to be used by the computer, must be in

(a.) a binary language

b. a programming language

c a translating system language

d. FORTRAN or a similar algebraic language
e all Of the above

5. When one speaks of a "bit" one is really speaking of a contraction
for

a. binary integer

(b.) binary digit

c. binary information theory
d. Boolean information theory
e. none of the above

6. Drum and disk type memories differ from core type memories in that

 

a. their access time is variable and faster than core
b. they are magnetic type memories, whereas core is not

(d.)

e.

229

information is "read" on to them at the speed Of light, unlike
core

they revolve like records in jukeboxes, whereas core is sta-
tionary

they are made up Of many small switches, unlike core

7. Usually forty percent (40%) of all computer programming instruc-
tions are concerned with

a.

03-)
c.
d.
e.

moving information internally

input and output of information

performing arithmetic and logical Operations
controlling the sequence Of information
"debugging" computer programs

8. The card verifier differs from the keypunch in that the card veri-
fier

(a.)
b.

c.
d.
e

punches ss_holes, but notches correct cards

punches holes and notches incorrect cards

punches holes and notches correct cards

punches no holes, but notches incorrect cards

prints out, line for line, the information on a series of
punched cards

9. Charles Babbage never got to build the first computer because

a.

(b.)
c
d.
e.

of a lack of a means Of storing information
of a lack of electrO-mechanical components
he died at a young age

no one would provide money and facilities
his formalized plans were incomplete

10. An electronic digital computer will

(a.)
b.

c.
d.
e

do exactly what it is told to do

solve problems that man doesn't know how to solve
never make errors, electronically

reject any instructions that are not logical

do all of the above

11. Complete the following arithmetic operation in binary.

11011 a. 10111
— 101 (b.) 10110
c. 11010
d. 01111

e. 01010

12.

13.

14.

15.

16.

17.

230

Computer systems in the year 2000 A.D. will probably

a.
b.

c.
d

(e:)

use "time-sharing" and "real-time" operation

be a network of remote terminals connected to a central
facility

be small and inexpensive enough to be in the average home
replace most Office workers

all of the above

Initial computer cost is generally related directly to

(a.)
b.

c.
d.
e

The

data processing, data reduction, and

Q-OU‘m

The

mano‘m

speed
physical size
versatility
purpose
precision

four general uses of computers today are scientific analysis,

 

monitoring systems
language translation
information retrieval
technical computation
control Operation

memory section of a computer ought to have

variable access time to each "word"

many more "words" than "addresses"
"auxiliary," "main," and ”working" units
equal access time to each "word"

all of the above

Writing a program directly in machine language

a.
b.
Ce

(d-)

e.

can be accomplished by means of a translating system

can be accomplished by means of a compiler program

is easy because it's in binary all 1's and 0's

may be the most efficient way of programming a problem in
terms of computer time

all of the above

Decimal "11" is equivalent to binary

a.
b.
(c.)
d.
e.

 

00011
00111
01011
01001
11011

18.

19.

20.

21.

22.

231

Complete the following arithmetic Operation in binary.

10 11100 a. 10100
(b.) 01110
c. 01010
d. 11000
e. 11100

In computer programming jargon, "looping" means

a.
b.

(c-)
d.

e.

the computer is hung-up within a circle of instructions
jumping from one instruction to another according to a

given sequence

performing over and over again a given sequence of instruc-
tions

leaving the main program, performing a sub-program, and then
going back to the main program

the same as "branching"

Information is usually stored temporarily on "buffers" because

a.
b.
c.

d.

(e-)

buffers provide a convenient place to store information until
it is needed by the computer

information can then be "read" into the computer at a faster
rate than what otherwise would be possible

buffers provide a convenient place to store results until
they are needed by the programmer

results can then be "read" out Of the computer's memory at a
rate faster than what Otherwise would be possible

all of the above

In 1945, the "ENIAC" computer was 500 times faster than the
"MARK I" and faster than a desk calculator by a factor of

a.
b.

(C-)
d.

10,000
50,000
100,000
500,000

e.1,000,000

Most of the computers in the U.S. today can be described as

a.

b.

c.
(d.)

e.

specific purpose, electronic, and digital
general purpose, stored-program, and analog
specific purpose, electronic, and analog
general purpose, stored-program, and digital
electronic, analog, and digital

23.

24.

25.

26.

27.

28.

232

"On-line" computer Operation traces its history to

a. the invention Of the photo-electric cell
b. "real-time" Operation

c. "time-sharing" Operation

(d.) control of industrial processes

e. the invention of "buffer" units

If you were in charge of buying a computer for a major university,
which Of the following would you be least interested in?

a stored-program Operation

b. precision of Operation

(c.) analog Operation

d. number of "words" in the memory
e. remote terminal Operation

The larger computers available today are capable of performing
up to operations per second.

a. 300,000
b. 500,000
c. 1,000,000
d. 2,000,000

(a.) 3,000,000
Data processing in the future will probably

a. be about as good as it is today
(b.) eliminate bank checks

c eliminate paper money and coins

d. eliminate most paperwork

e. eliminate more jobs than it creates

Which section(s) is (are) the functional core of the computer?

a. memory
(b.) arithmetic-logic
c. control
d. console
e. all of the above

Which of the following is true of a FORTRAN translating system?

a. it uses standard algebraic expressions (+, -, x, =)

(b.) it can be learned by humans relatively quickly, compared to
learning machine language

c. it is compiled and translated by computers in an insignificant
amount of time

d. one can write commercial-business programs easily in FORTRAN

e. it requires the definition of memory locations

29.

30.

31.

32.

33.

233

Decimal "321" is just a compact way Of writing

a.
03-)
c.
d.
e.

3 x 103 + 2 x 102 + 1 x 101

3 x 102 + 2 x 101 + 1 x 100

3 x 23 + 2 x 22 + 1 x 21
binary 1011100011100011110010100
none of the above

What will be the output from the indicated input to the following
series of gates?

 

1---+‘:i', a. 01
1-————9 (b.) 00
1 )4‘il’ ‘GIE’ 7 c. 10

d. 11

e. no possible result

One of the two general ways a program can be in error is to be in
violation Of one Of the rules Of the artificial language used.
Such a violation will cause the program to

(a.)

b.
c.

d.

e.

fail during compilation or translation and be rejected before
any computation is performed

produce incorrect results after computation

produce electronic signals on the computer's control console
during computation

be marked, where the error is, with an appropriate remark and
computed as well as it can be

all of the above

More recently, devices which can "sense" magnetic ink characters
on bank checks or read ZIP code numbers have been developed. They
are called

electronic "eyes"
infrared readers
optical scanners
magnetic sensors
electronic detectors

four individuals credited with the development or invention Of
first mechanical data processing tools were

the Chinese, Aiken, Eckert, and Steinmetz
Pascal, Babbage, Hollerith, and Aiken

Pascal, Leibnitz, Babbage, and Hollerith
Babbage, Hollerith, Eckert, and von Neuman

the Chinese, von Neuman, Babbage, and Steinmetz

34.

35.

36.

37.

38.

39.

234

In addition to the stored-program concept, John von Neuman was
also the first to urge the use of

electro-mechanical components

logical gates that work according to Boolean algebra
"on-line" versus "Off-line" Operation

.) the binary number system for internal computer communication
punched cards for external computer communication

A
(DO-Oc‘m

Punched cards are used more Often for input of data than punched
tape because punched cards are

a. easier to read by humans, as they are the same size as U.S.
dollar bills

b. more resistant to humidity and temperature effects

c. easier to read by machines

(d.) easier to correct errors on, while programming

e. all of the above

Which of the following could be least adapted for individualized
student instruction?

a. closed-loop feedback Operation

b. information retrieval

(c.) binary number system Operation

d. foreign language translation

e. scientific analysis or simulation

In essence, the result Of the invention and develOpment of the
computer has been to

a. cause automation

(b.) extend man's mental ability

c. decrease man's physical workload

d. decrease the number of white-collar workers

e. threaten man's privacy and decrease his individuality

A familiar example of a closed-100p, feedback control device is

 

(a.) a thermostat

b. a speedometer

c. a television camera
d. a telephone

e. an elevator

A computer can be programmed to integrate the sine Of x from 0
to l by

(a.) a series of simple arithmetic and logical Operations
b. a series of simple comparisons and loops

235

c. going into its memory bank of indexed tables for the answer
d. going to a sub-program previously written
e. going to a "canned" program library

40. Machine language would look like

a. -H-+----H---+-+-----+I--+--H---+-H---+-+
b. -H-+ ++ ++ +1- + ++ +++ + +
c. 111 11 1 l 11 l 11 111 1 l
(d.) 11100011001010000110100110011100101
e. 22211122112121111221211221122211212

41. The minimum number Of binary digits which can represent any single
decimal digit is

a. one
b. two
c. three
(d.) four
e. five

42. If one were to flow-chart the problem " X plus Y equals Z;" which
one of the following would be a correct instruction? (X, Y, and
Z are variables)

a. READ X,Y,Z
b. PRINT SUM
c. A + B = C
(d.) Z = 0

e. SUM = 0

43. "Sub-programs" differ from regular main programs in that "sub-
programs" .

a. are kept "canned" in libraries, one just adds "data"

(b.) come as a part of translating systems and are already program-
med by experts

c are just small programs

d. are really just large "loops" within a main program

e. are all of the above

44. "Off-line" communication with the computer usually

a does ESE make efficient use of the user's time

b. does make efficient use Of the computer's time

c. prevents the computer from being used as an expensive print-
ing press

d. makes use of "buffer" units

(a.) all Of the above

45.

46.

47.

48.

49.

50.

The

(b-)

9-0

The

03-)

c.
d.
e.

236

"ENIAC" differed from the "MARK I" in that the "ENIAC"

was built earlier

was built completely of electronic components
was built at Harvard University

was built with money from IBM

was designed by Alfred E. Newman

physical components of a computer system are called the

"servo-mechanisms"
"hardware"

"black boxes"
"logical gates"
"cybernetics"

An example of an "on-line" computer communication operation is

 

a
b.
c.

d.

(e-)

a computerized airline reservation inventory system

control (feedback) Of a steel-making process

a "real-time" computer Operation, with "time-sharing"
capability

continuous computer "up-dating" of a space vehicle's flight
path

all of the above

Of the following, which would most likely be used for a compu-
terized match-up for dating?

(Dana‘s:

In scientific analysis,

control Operation

"on-line" Operation
"time-sharing" Operation
scientific analysis or simulation
analog Operation

scientists Often use computer simulation

(mathematical modeling) for .

a. calculating results before the real thing is built
b. testing dangerous situations without risk

c. saving time and money

d. expanding or compressing the time dimension

(e)

all of the above

Output from a computer can be displayed on

O-OU‘O)

(eo)

punched cards and punched tape

two-dimensional plotters and televison monitors
electric typewriters and teletypewriters
perforated paper and magnetic tape

all of the above

51.

52.

53.

54.

56.

237

What are the two translating systems in common use today?

a.
b.
c.
d.

(e.)

FORTRAN and EDP
DATRAN and COBOL
IPL-V and EDP
DATRAN and FORTRAN
COBOL and FORTRAN

of a translating system means

the programmer must write the Object program

efficient use is made of the computer's hardware capabilities
an expert is required to write programs

it is possible to write programs which are independent Of the
type of machine to be used

more time is involved in writing programs than if one were

to write in machine language

"Words" differ from "addresses" in that "words"

(a.)
b.

c.
d.
e

The

change their content with each new program
are fixed in size, unlike addresses

use different memory cells

contain binary digits, unlike addresses
all of the above

usual sequence of steps associated with programming are;

defining the problem, flow-charting the major tasks, reducing
each task to a set of simple Operations, and writing one instruc-
tion for each Operation. The step which is missing is

A
(DO-DUO)

writing the Object program instructions

loading the computer

writing sub-programs for repeated Operations

picking out an artificial language suitable for the problem
actually solving a similar problem independently Of the
computer

standard IBM type of punched card has

72 columns and 12 rows
80 columns and 10 rows
80 columns and 12 rows
88 columns and 12 rows
88 columns and 13 rows

Which of the following does not make use of the information
conversion concept?

a.
b.

a thermostat
an Optical scanner

57.

58.

59.

60.

C.

238

a card reader

(d.) a Xerox or copying machine

e.

a telephone

Which Of the following is not relevant to the stored-program
concept?

a.

b.
c.

(d-)

e.

the computer will perform numerous calculations and print
out results, all automatically

punched data cards need be entered only once

instructions can be modified depending upon results Obtained
during processing

Charles Babbage

John von Neuman

At present, computers are not well-suited for information re-
trieval because they still

The

ing?

(a.)
b.

c.
d.
e.

have an unsophisticated memory organization
cannot be linked remotely to console terminals
cannot hold large enough amounts of information
cannot do fast file searching

cannot be Operated in "real-time"

storing of main programs in program libraries

is not Often done

means that one only needs to add his own data for "instant
programming"

means programmers have to always program for the general case
is done only in machine language

is done only in some translating language

input section of the computer does not do which Of the follow-

convert information into binary code

provide a means of inserting the compiler program

arranges the information (data) in the proper format

provides an interface between the programmer and the computer
stores information until a convenient time for input arrives

APPENDIX I

INSTRUCTIONS

Pre-Instructions for All Ss

The information you are about to learn is about computers and
how they work. To determine how much knowledge you already have in
this area, it will be necessary for you to take a pretest. Even though
it may be rather difficult to answer, go through it quickly and guess
if you need to. The pretest is useful to you in that it gives you a
preview of what you will be learning. Begin the pretest now and sig-
nal me when you have finished. (WHEN FINISHED, REMOVE TEST, SHOW CARDS
(OR) LIST, EXPLAIN AUDIO SET-UP.)

After your learning sessions, a posttest will give you the
chance to see how much you have learned. Two weeks from tomorrow, a
third test, a retention test, will let us know how much knowledge is
still alive. I want to make it quite clear that these tests are all
quite similar to each other, but do differ in detail. That is, you
will not see the same question twice.

We will learn about half of the material today, and the other

half tomorrow.

Post-Instructions for All 83
We are conducting this experiment in order to study the learn-

ing process. Therefore, during the course of the experiment, please do

239

240

not discuss what you have been learning here with anyone else until
the experiment is over. Please do not go out of your way to learn
about computers. If you are influenced by outside sources, the val-
idity of the results of this scientific experiment will Obviously be
jeopardized.

It is also important that you do not discuss the experiment
with other students who are participating in it. Your influence, even
casual discussion, may alter their performance and thereby bias our
findings.

At the time of the retention test, I will be glad to discuss
the experimental design and how much you have learned. You will also
receive your research credits at that time. I will phone you to arrange

a convenient time.

Instructions for "SCI" 83

 

This experiment is concerned with individualized methods of
learning by television. Your task is to learn the material presented
as best you can.

This learning experience may be quite different from any for-
mal learning experience you may have had. In this learning situation,
you are able to control the pace, the sequencing, and the questioning
of the material presented.

You might ask, "How is this possible?" Well, stored on video
tape are 52 segments of a lecture entitled, "Computers and How They
Work." These segments are all interrelated and can be learned in any

sequence. Therefore, it is possible for you to start with any one of

241

the segments and thereafter decide in what order the remaining segments
will be presented to you. Each segment is about one minute in length.

Each card before you represents one of the 52 taped segments.
The information at the tOp of each card is in the form of a question
you might have asked, had you made an inquiry into this subject matter.
The word in the lower right-hand corner of each card is the code word
for access to the information represented by that card.

So that there is no doubt that the sequencing of the information
is under your control, the cards have just been freshly shuffled. Which
segments you select first should be determined primarily, at this time,
by which ones interest you the most.

You should take some time now to become familiar with the infor-
mation on the cards by carefully reading them and sorting or organizing
them into piles or groups. There is some help in doing this. The cards
are color-coded into eight groups. That is, these segments which go
together, in the same area, are all colored the same in the right corner.

Also each of these groups has one card which is an overview or
summary of that group. These eight cards, one for each group, are also
marked in the left corner.

As you proceed through the 52 segments, you may wish to use the
color-code to organize or re-organize your selections. In general, you
should be flexible in your organization, cutting across colors or re-
maining with one color as you learn. The eight overview segments may
serve as previews ahead of other segments, as summaries after other seg-
ments, or just as additional sources of information. The choice is yours.

Please sort the cards now. Signal me when you have finished.

242

(WAIT)

Now that you have sorted the cards, please choose one of
interest and ask me for its tape by the code word. You may take notes
if you wish. (SHOW TAPE TO S)

Now that you have viewed the first tape segment, you may choose
any other one, from any group, next. The choice of new information
should be determined by what new information is most useful and mean-
ingful to you in your learning. For example, one segment may be of
interest to you because of your personal interests...and another may
be meaningful to you because of what you have learned from previous
tape segments. The selection of each segment should be relevant to
your personal inclination as to what information is needed to fill in
the gaps in your knowledge about computers.

In any event, by the time you see the last segment, everything
should fit into place, just like the pieces in a jigsaw puzzle. Half-
way through today's learning session, I will ask you to reread the cards
you have not yet seen. This will help insure that all the information
you might wish to know next is kept fresh in your mind. In other words,
you should not necessarily follow a fixed sequence, but instead remain
open to new avenues as you learn.

In addition to the freedom of selecting the order in which you
see the tape segments, you also have the freedom of questioning each
tape segment after it has been shown. This means that you should feel
free to ask that I clarify, correct, or review any of the points covered
on the tapes. Depending on your personal needs as a learner, you may

press me for further explanations and examples until you are satisfied

243

that you understand what was presented. (SHOW S DEMONSTRATION SIGN)

It is quite possible that you will want to use me just for
verifying your understanding. For example, you might say, "Do I
understand that 2 + 2 = 4?" And of course, I would answer, "Yes."

Thus, you may test your new knowledge as you go along. All I ask is
that, since your questions will be recorded, just give me time to get
ready before you begin.

Further ground rules of the experiment are:

a. You may view up to four of the segments a second time. You

may ask for these at any time.

b. You may delete up to four of the segments. These may be
any of the segments.

c. You may take as many notes as you wish. However, notes will
not be of as much value to you here as in the typical class-
room, since they will be kept here after each learning session,
in order to prevent you from studying at home. The reason
is that we want you to do all of your learning in the labora-
tory and nowhere else. This keeps things equal for everyone
in the experiment. Also you will not have access to your
notes during the posttests. However, on the other hand, you
may have as much time as you wish before the posttest to
review your notes.

d. Finally, you may use the time between tape segments to write
notes, review notes, or interrelate what you have learned.

In other words, you may go as fast or as slow as you wish.

244

When I have located the next tape segment that you have
chosen, I'll ask for your "OK" before I play it. By all
means, don't let me hurry you.
Are there any questions? (PAUSE)
OK, your task is fairly straight forward; that is, to learn
the material presented as best you can, within the given conditions

of the experiment.

Instructions for "Expert" and "Random" 83

This experiment is concerned with individualized methods of
learning by television. Your task is to learn the material presented
as best you can.

This learning experience may be quite different from any for-
mal learning experience you may have had. In this learning situation,
you are able to learn and review at your own pace.

You might ask, "How is this possible?" Well, stored on video
tape are 52 segments of a lecture entitled, "Computers and How They
Work." Each segment is about one minute in length.

Before you is a list of the segment topics. Each topic is in
the form of a question you might have asked, had you made an inquiry
into this subject matter. The information on the tape segments, in a
sense, answers these questions.

The tape segments will appear in the same order as on the list.
Thus, you may use the list to follow the presentation of the segments.

You should take some time now to carefully read the questions

on the list, so you will get a good idea of what you will be learning

245

and when. As you do this, you will notice that there are eight ...

EXPERT: groups of questions. The first segment in each group is a
preview of the information contained in the remaining seg-
ments of that group.

RANDOM: questions which are marked with an asterisk. Tape segments
for these questions represent previews or summaries of the
information contained on other segments of the same color
code. That is, all questions of the same color sort of go
together in the same topic area.

Please read the list now. Signal me when you have finished.

(WAIT)

I will now show you the first tape segment. You may take notes
if you wish. (SHOW TAPE)

Now, another student saw these same segments, but he, unlike
you, was permitted to ask questions about them. If he chose to ask
questions about one of the tape segments, the questions were recorded.
When you see the same segment, it will be directly followed by his
questions and the answers. (This means that you can't ask any questions.)

Possibly his questions will be the same questions you have. In
any event, they should help to clarify, correct, or review points covered
on each tape segment. Also, if this student chose to delete any of the
segments, they will also be deleted for you.

As you view each tape segment, try to relate the information
presented to your personal interests or background or to what you have
learned from previous tape segments. Half-way through today's learning
session, I will ask you to reread the remaining questions on the list.
This will help insure that all information to be learned and its loca-

tion is kept fresh in your mind.

246

EXPERT: The segments are all interrelated; so by the time you have
seen the last tape segment, everything should fit into place,
just like the pieces in a jigsaw puzzle.

You may take as many notes as you wish. However, notes will
not be of as much value here as in the typical classroom, since they
will be kept here after each learning session, in order to prevent
you from studying at home. Due to the experimental nature of this
situation we want you to do all of your learning in the laboratory and
nowhere else. This keeps things equal for everyone in the experiment.
Also, you will not have access to your notes during the posttests. On
the other hand, you may have as much time as you wish before the post-
test to review what notes you do take.

You may use the time between tape segments as you see fit. For
example, you may wish to write notes, review notes, or just interrelate
what you have learned. In other words, you may go as fast or as slow
as you wish. When you are ready for the next segment, just give me
some signal that you are ready and I'll play it. You are in control of
the pace, so by all means, don't let me hurry you.

RANDOM: Finally, the tape segments may appear in what seems to be a
haphazard order. The problem of sequencing is one of the
purposes of this experiment. However, the segments are all
interrelated, so by the time you see the last tape segment,
everything should fit into place, just like the pieces of a
jigsaw puzzle.

Are there any questions about the instructions? (PAUSE)

0K, your task is fairly straight forward; that is, to learn the

material presented as best you can, within the conditions of the experi-

ment .

APPENDIX J

SUBJECT LOGS

"SCI" and "Expert" Ss

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

GROUP ID #
QUADRANT NAME
TREATMENT TEST SEQUENCE TERM
L- 1 REPEAT N_O. ?_ REPEAT N9. 1 REPEAT mg. 1 REPEAT
1 14. 27. 40.
2 15. 28. 41.
3 16. 29. 42.
4. 17. 30. 43.
5. 18. 31. 44.
6 19. 32. 45.
7 20. 33. 46.
8 21. 34. 47.
9. 22. 35. 48.
10. 23. 36. 49.
11. 24. 37. 50.
12. 25. 38. 51.
13. 26. 39. 52.
REPEATS: TIME
pg, 3_ WHEN START STOP DIFF
1. SESSION 1
2. SESSION 2
3. TOTAL
4. NOTES
QUESTIONS/COMMENTS (TOTAL # of questions )

247

248

"Random" Ss--Fall Term

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

GROUP ID #
QUADRANT NAME
TREATMENT Random I TEST SEQUENCE _ _ TERM Fall
&. 1 REPEAT NQ. ?_ REPEAT N9. 3_ REPEAT 13g. 1 REPEAT
45. 14. 36. 15.
8.1 34. 39. 23.
51. 3. 16. 10.
46. 31. 48. 49. f
9. 7. 6. 32.
52. 24. ll. 2.
33. 38. 27. 43.
12. 18. 26. 29.
42. 47. 4. 44.
17. 28. 35. 41.
l. 5. 13. 22.
21. 40. 25. 30.
19. 50. 20. 37.
REPEATS: TIME:
.119: 1 WHEN START STOP DIFF
1. SESSION 1
2. SESSION 2
3. TOTAL
4. NOTES
QUESTIONS/COMMENTS (TOTAL # of questions ___)

 

GROUP

 

QUADRANT

 

TREATMENT Random II

 

&. _?_ REPEAT

249

"Random" Ss--Winter Term

TEST SEQUENCE

m. 1 REPEAT

ID #

NAME

TERM

1E . ?_ REPEAT

 

 

Winter

 

Mg. 3_ REPEAT

 

 

 

 

 

 

 

 

 

 

 

 

 

45. 39. 8. 3.
12. 16. 17. 15.
9. 21. 24. 38.
43. 26. 48. S.
36. 25. 28. 46.
7. 32. 34. 31.
40. 6. 18. 27.
20. 2. 51. 37.
14. 44. 1. 41.
47. 52. 35. 19.
42. 10. 30. 23.
29. 4. 13. 33.
11. SO. 22. 49.
TIME:
START STOP DIFF

SESSION 1

SESSION 2

TOTAL

NOTES

COMMENTS:

 

 

 

10.

11.

12.

13.

14.

15.

16.

17.

18.

Name

OLDSMOBILE

ROAD RUNNER

GRAND PRIX

CHARGER

REBEL

T-BIRD

CHEVROLET

EL CAMINO

CYCLONE

SPECIAL

BUICK

ELECTRA

CHEVELLE

STING-RAY

FAIRLANE

BELAIR

GALAXIE

RAMBLER

APPENDIX K

E'S CODE CONVERSION LIST

Location
Tage #1
15 19. FALCON 10:
38 20. MALIBU 11
62 21. LE MANS 13
87 22. CAMARO 14:
110 23. TEMPEST 17
130 24. AMX 21
151 25. PONTIAC 22:
167 26. MONACO 23:
186 27. BONNEVILLE 25:
206 28. MONTEGO 26:
Tage #2
00:30 29. IMPALA 28:
01:40 30. IMPERIAL 29:
03:00 31. FIREBIRD 30:
04:15 32. MERCURY 32:
15:15 33. TORINO 33:
06:25 34. BARRACUDA 35
07:30 35. DART 36:
08:45

250

00

:45

:00

30

:40

:00

30

45

20

50

00

15

30

00

30

:00

30

36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.

52.

CORVAIR

FORD

FURY

LE SABRE

MUSTANG

CATALINA

LTD

CUTLASS

GTO

PLYMOUTH

COUGAR

NOVA

JAVELIN

MAVERICK

NEWPORT

38:00
39:40
41:00
43:20
44:40
46:00
47:30
49:00
50:15
52:00
53:30
55:00
56:00
57:20

58:30

NEW YORKER60:OO

SKY LARK

61:30

APPENDIX L

SUBJECT MATTER SEGMENTS

Each of the 52 pages of Appendix L contains the content of
one of the subject matter segments. The order presented here is

in the "Expert" sequence.

251

252

Digital Computers

 

Let us say that a digital computer is an unusually fast and
versatile machine for processing data. It can accept both the data to
be processed and the instructions for processing into internal memory.
It can transmit data and instructions to and from internal sections.

It can perform simple arithmetic and simple logical Operations. It
can discharge the results. By combining these abilities, however, a
computer can also be used to do such things as perform complicated
scientific calculations, do file searching, keep inventories, and simu-
late industrial plant operations.

In short, a computer is something that will accept information,
process it, and give results. Computers are valuable because they can
do these things faster, cheaper, and more reliably than humans. Some
people even profess that computers have the ability to think.

The types of electronic computers vary widely in size and
cost. However, most of the more than fifty thousand computers in the
United States today can be described as general purpose, stored-

program, and digital.

253

The Value of Computers

The value of computers lies in their ability to help man to
store and process information, solve problems, make decisions, and
control processes.

Compared to human performance of similar operations, digital
computers are very, very fast, accurate, reliable, and inexpensive.

Why is this so? It is because the modern digital computer is built of
electronic components and operates automatically.

Electronic components mean the computer performs its operations
at tremendous rates of Speed. Today the internal operations of compu-
ters are measured in nanoseconds or billionths of a second. For example,
MSU's Control Data 6500 computer can perform three million additions or
subtractions in one second.

Automatic operation means that no human control is necessary
once the computer has started on a program. Usually it is wise to eli-
minate direct computer interaction with humans because humans are so
much slower and have a remarkable tendency to make mistakes.

Recently, however, "real-time" man-to-machine dialogue has
become possible. Such direct communication with the computer will even
further enable man to use the computer as an extension of his mental

ability.

254

Analog;yersus Digital Computers

There are two basic classes of computers: analog and digital.
Note: the information that is being presented to you, other than this
segment, is exclusively concerned with digital computers, not analog.

In analog computers, some physical quantity, such as voltage,
is considered analogous to the magnitude of the numbers under considera-
tion. Answers to problems are found by measuring voltages in the
circuits. As a consequence, these answers are not necessarily whole
numbers, but rather are continuous in nature. As a further consequence,
the ability to read the final results accurately is limited. Hence,
there are few analog computers in use. Familiar examples of mechanical
analog devices are the slide rule and the speedometer in your automobile.

Digital describes any calculating mechanism that handles and
calculates with integers. In other words, a digital computer works
with discrete units and calculates in much the same way as a person
doing arithmetic by hand. This is similar to counting in chunks. As
a consequence, digital computers can be built to calculate very precise
results. Over fifty thousand digital computers are now operational in

the United States.

The

255

The Basic Capabilities of Digital Computers

digital computer has the basic capabilities of:
Information storage--some computers are built only for
storage of information.

Data processing and editing-~computers can boil down reams
of information to produce a handful of statements or a
summary.

Problem solving--computers can mathematically analyze and
abstract the relationships which exist among many factors.
Decision making--depending on rules and formulas supplied
by the programmer, computers can be directed to make logi-
cal decisions.

Control--computers can monitor and regulate on-going

industrial processes.

In the field of computations, the computer now permits calcula-

tions that

require too

were literally impossible before. Not only would such tasks

many human operators, but the errors that would creep in

would make the results inaccurate. A computer has no such difficulties,

electronically. The reliability of a computer depends mainly on the

programmer's instructions which have to be accurate, logical, and com-

plete. A computer will do exactly what it is instructed to do--not

one thing more nor one thing less.

256

Can Computers Think?

The question "Can computers think?" should be left unanswered
for the present since a definition of thinking is a difficult one to
make, and also because the computer field is so young. However, a
machine that can play checkers and improve its play through trial-
and-error learning is on the road toward approximating human thought
processes.

Many people are worried about this trend, saying that cold-
blooded computers will replace people and run the country. They may
be right--there is no question that job transitions are being made
and that the habits and security of all, either directly or indirectly,
will be affected by this technological change. But familiarity breeds
contempt. Computers are not as complicated as most people believe.

In fact, computers owe most of their capabilities to their inherent
simplicity. The overall operations are merely detailed combinations
of simple steps. And these are used and controlled by man, as an

extension of his mental ability--much as man uses a car to get some-

where in a hurry.

257

The Classification of Digital Computers

Digital computers can be classified under the following
criteria:
Purpose

General-purpose computers are versatile machines that can
handle a variety of different computational problems. On the other
hand, special-purpose computers are built for more speed, more effi-
ciency, and greater economy. For example, the computer can be set up
to handle only one type of problem, such as airline reservations or
banking Operations.
§l£2

Generally, when speaking of computer size, reference is being
made to the number of "words" in the memory section. The physical
size of the computer is gradually being reduced as modern construction
comes up with smaller and smaller components.

Cost

 

The cost of computers is generally related to the speed of
operations. Small computers run around $50,000 and cost $100 per hour
to run. They are capable of about 500 operations per second. Large
computers run over $1,000,000 and cost about $500 per hour to run.
However, they are capable of up to 3,000,000 operations per second.

MSU's CDC 6500 computer cost around $3,000,000.

258

Historical Background of Computers

The computer is a machine which handles information. Modern
electronic digital computers are the newest link in a chain of calcu-
lating machines that trace their beginnings back to the first data
processing machine, the abacus. It was invented some twenty-five
hundred years ago.

Throughout history, computational devices have been invented
and refined to relieve the repetitious effort required to perform
arithmetical calculations manually. It is not the concept of the
computer that is new, but rather the availability of precision parts
and man's ability to combine them in a new way.

The means to build the incredibly fast, reliable, and versatile
machines which are in use everywhere today, began only twenty-five
years ago with the availability of electronic components and the stored-
program concept. Since then, although computers have gotten faster

and more versatile, no major design changes have been made.

259

The First Data Processing Devices

The first data processing devices to facilitate counting were
completely non-automatic. The abacus, invented independently by the
Chinese and Greeks in 600 B.C., was the first true counting device.
Today, it is still used by half of the earth's population.

The French mathematician Pascal is credited with inventing the
first mechanical adding machine in 1642. In 1690, the German mathema-
tician Leibnitz invented the first machine that could multiply. Both
of these machines were manually operated; a human operator had to feed
the numbers in individually for each operation.

In 1834, the British mathematician Charles Babbage develOped
logical plans for the construction of an automatic, mechanical computer.
In principle, at least, it would follow instructions and solve problems
automatically. Unfortunately, the lack of electro-mechanical components
and funds kept Babbage from realizing his dreams.

By 1889, Dr. Hollerith of the U. 8. Census Bureau had constructed
automatic sorting machines capable of sorting three hundred cards per
minute. Although not computers, these machines were the first to use
punched cards to store information on. By ordering, merging, and separ-

ating cards, the data from the 1890 census were reduced and analyzed.

260

Predecessors of Modern Computers

International Business Machines (IBM) and Harvard University
supported Professor Howard Aiken in the construction of the world's
first automatic digital computer. When it was started in 1939, the
machine was called the "Automatic Sequence Controlled Calculator."

When it was finished in 1944, it was called the "Mark I." It used
existing telephone equipment for components; that is, electro-
mechanical relays, counters and cams.

Although possessing the same basic capabilities as the Mark I,
another computer, built by professors at the University of Pennsylvania
between 1943 and 1945, was 500 times faster. The reason? It was the
first all-electronic computer; that is, it operated with no moving
parts. It was called ENIAC, short for "Electronic Numerical Integrator
and Calculator." Its initial use was to make up mathematical tables of
ballistic shell trajectories for U.S. Army ordnance use.

In ten seconds the ENIAC could do the calculations that would
have taken a desk calculator twenty hours to do. This boils down to a

ratio of 100,000 to l.

261

The Stored-Program Concept

In 1945, John von Neuman, one of the world's greatest mathe-
maticians, suggested the use of the stored-program in computer design.
Prior to this time, only one type of operation at a time could be
performed. Control panels, like plugboards in telephone switchboards,
were used to control the internal operation from outside the computer.
For each additional type of Operation, the data cards had to be re-
entered and a new plugboard attached to the computer.

A stored-program computer, on the other hand, can be defined
as a machine that can accept and store a set of instructions in its
memory. Therefore, the computer is capable of automatically executing
these instructions in any desired sequence. It is also capable of
changing the stored instructions, depending on results obtained during
processing. The stored-program concept provides not only adaptable
and automatic processing of data, but unlimited versatility as well.

In addition, John von Neuman also was the first to urge the use
of the binary number system for internal computer communication. Since

then, 1945, no major changes have been made in digital computer design.

262

The Logical Structure of Computers

Big or little, fast or not so fast, all digital computers are
organized along similar lines; they all are made up of five functional
sections that work together in close harmony. They are: the input
section, the memory section, the arithmetic-logic section, the control
section, and the output section.

The order of communication of information in the computer for
a typical problem would be: (1) input section to memory section,
(2) memory section to arithmetic-logic section, (3) arithmetic-logic
section back to the memory section, (4) memory section to the output
section, and (5) the control section overseeing and monitoring these
inter-communications of information.

In a physical sense these sections are hard to locate, but
functionally the manner in which they are organized can be called the

logical structure.

263

The Input Section

The input section is essentially the communication entry point
through which problem numbers, data, and instructions are actually fed
into the computer's memory section. Prior to this time, however, a
great deal of effort, external to the computer, has been accomplished
in order to prepare the information for this entry.

In general, the information is made ready on one of three types
of input media: those which require that the information be recorded
upon some input unit, such as magnetic tape; those which permit an
operator to enter information manually, such as an electric typewriter;
and those which enter information directly from its original source,

" device. The input section for one computer may

such as a "sensing
consist of several different such input media.
If the data and instructions are not in binary code, they must

be translated by the computer into this code, the only form acceptable

to the computer's memory.

264

The Control Section

The control section or "brain of the brain" directs the overall
functioning of the computer by interpreting the program of instructions
and then generating electrical impulses which tell the other four sec-
tions what to do.

Although control is considered a single function, the circuits
of the control section are spread throughout the computer--channeling
the flow of information, carrying out the instructions, checking the
results of each step for error, and otherwise coordinating the different
sections at the right time. Automatic control makes sure, by precise
timing mechanisms, that messages do not "collide" going and coming over
the same wires. There also are checks to make sure each message being
transmitted is accurate.

If there are any internal difficulties, special warning lights
on the control console notify the computer Operator. Otherwise, the
control console is used by the computer Operator for monitoring the

processing in a routine manner.

265

The Memory Section

The memory section is the "storage depot" where instructions
and problem numbers are kept until needed by the control and arithmetic-
logic sections. The memory section is divided into many, many, individ-
ual cells, each capable of storing one bit of information.

One of the most important characteristics of a memory section
is its access time, which is the time required to put information into
or take information out of the memory cells. It is essential that the
time be very short for high speed computation. The memory must also
have a large capacity so that long programs can be accommodated.

It would be best to have an extremely large memory section with
every part equally available in as short a time as possible. But effi-
cient and economical design calls for division into at least two areas,
working memory and auxiliary memory. Working memory is relatively small
in capacity but quick in access time. In general, the auxiliary memory

is large in capacity but relatively slow in access time.

266

The Output Section

The output section enables the computer to communicate with the
user. Any information stored in the computer's memory may be brought
out. This is accomplished by appropriate instructions.

Of course, the stored information is still coded in binary and
must be translated for convenient use. Typically, because the computer
can output information at high rates of speed, the information is re-
corded on magnetic tape units called "buffers." Later this tape is
converted to readable, printed documents by "off-line," high speed print-
ers. Printers may also be used "on-line" for direct printing of output.

In addition, output can be recorded directly on punched cards,
punched tapes, electric typewriters, cathode-ray display scOpes, and two-
dimensional plotters for graphic display.

The output section for one computer may have several different

output units.

267

The Arithmetic-Logic Section

The arithmetic-logic section is the actual calculating mechanism
which performs the arithmetical and logical operations called for by the
instructions. It is the functional core of a digital computer; the
other four sections oversee its Operation and control the flow of infor-
mation into and out of it.

This section is often compared to a desk calculating machine
since numbers must be fed into its registers before it can be directed
to perform any arithmetic or logical operations.

Only simple numnerical and logical operations can be performed.
If a programmer wishes to do a complicated calculation, he must solve
the problem using only addition, subtraction, multiplication, and divi-
sion. If a programmer wishes to ask a sophisticated question, he must
do so through a combination of simple logical questions: is a number
zero or not; is number positive or negative? A relatively simple prob-
lem, such as integrating the sine of x from 0 to 1, would require a

number of both arithmetic and logical steps.

268

Internal Communication

The primary purpose of the computer is to process information
by means of internal communication. Initially the program instructs
the computer as to what tasks and in what order they are to be performed.
Programs are usually written in Special languages which permit the use
Of decimal numbers and some English. However, all incoming decimal
numbers and alphabetic characters are converted into the machine's binary
language. Therefore, internal communication and the storage of instruc-
tions and data is carried out only in binary numbers.

Each group of binary digits or "word" has a location or "address."
The storage locations are of a variety of types: drum, disk, and core.
Processing of the information involves binary arithmetic and arithmetic-
logic circuit operations, specific to the binary number system.

Actually the insides of a computer is a spaghetti-like network
of wires stretching in a variety of ways from one end of the machine to
another. It is on these wires by means of electrical impulses, that the

communication of bits physically takes place.

269

Methods of Memory Storage

Current memory cells are magnetic. In one type, information is
recorded in tiny donut-shaped metal cores, the size of pinheads. The
cores are strung on wires like beads in a permanent three-dimensional
arrangement. Each magnetic core is at the intersection point of two
current-carrying wires. Each wire carries only half the amount of cur-
rent required to change the magnetic orientation of the core. Thus, only
where two wires intersect can a core receive enough current to change its
direction of magnetization. Cores magnetized in one direction, clockwise,
denote the bit "1," and cores magnetized in the opposite direction, coun-
terclockwise, stand for the bit "0." A third wire senses which way the
core is magnetized. For the core memory, access time to each bit is
constant and very rapid.

In the other type of memory, information is recorded as spots
of magnetism on some surface which revolves, such as a disk or a drum.
Information is input onto these devices at the speed of light, so there
is plenty of time to write or read from the drum or disk without slowing
it. An analogy can be made to a slowly moving escalator. There is
plenty of time for an individual to write and erase as it moves by, as
long as he is accurate and has good timing. Drums and disks can store
large amounts of information, but their access time is variable and re-

latively slow.

270

Arithmetic-Logic Circuits

 

The computer's arithmetic-logic circuits are made up of three
basic components: AND, OR, and NOT gates. Their names indicate their
function. That is, the AND gate will emit a 1 bit as output only when
both input bits are 1's. The OR gate will emit a 1 bit as output when-
ever at least one input bit is a l. The NOT gate will reverse the
entering bit to its opposite. That is, if a 1 bit enters, a 0 bit
exits, and vice versa.

With just these three gates, in combination, the computer can
perform simple arithmetic and logical operations. For example, bits,
in the form of electronic impulses, can be called into an accumulator or
register of the computer's arithmetic—logic section and added. The re-
sult is then sent back to memory. The corresponding instructions would
be "Add what is stored in location A and location B and store the re-

sult in location C" or in FORTRAN, the algebraic A + B = C.

271

Binary Digits--(Bits)

The number system we normally use and are most familiar with is
the decimal number system. The decimal system uses a base of ten,
which means that counting depends upon sets of ten, one hundred, and
one thousand, etc. It is possible, however, to construct number sys-
tems with a different base. The base used depends on convention,
tradition, and convenience. It is possible to use the base ten in the
computer, like it is used in desk calculating machines. But it is much
simpler, in terms of speed and reliability, to use the base two inside
the computer.

The binary, or base of two, number system uses exactly two sym-
bols--0 and 1. One (1) signifies a value, whereas a zero (0) signifies
no value. In the language of computers, zero and one are referred to
as bits, a contraction of binary digits.

Any physical device which can be switched back and forth, such
as a lightbulb, can be used to represent bits: l=on, 0=off. Typically,
computer components consist of magnetic devices which are charged either

positively or negatively to represent bits.

272

Binary Arithmetic

 

Binary arithmetic operations are simpler to perform than their
decimal number system equivalents. However, the rules of arithmetic
are identical in both systems.

The binary addition table contains only four entries: (example).

Binary carry-overs are treated in the same fasion as decimal
carry-overs.

The binary subtraction table also contains only four entries:
(example). Just as in decimal subtraction, a borrow must be made in
order to subtract a larger digit from a smaller one. (Example)

The binary multiplication also contains only four entries:

(0 x 0 = 0, 0 x 1 = 0, 1 x 0 = 0, and l x l = 1). (Example)

Binary division is much simpler than decimal long division
because there are only two combinations: (%-= 0.«% = 1). Division by
zero (1/0 or 0/0) is meaningless, just as it is in the decimal system.

(Examples)

273

BinarygAddition Table

0 0 l l
flfl tail
0 l l 10 (carry 1)
Examples:
3 ll 3 ll
ig_= +10 :1 =-+lll
5 101 10 1010

Binary_Subtraction Table

 

I
ch>c>
I
old H
I

14:3

0
:1
l (with a borrow
of 1)

Example:

274

 

Binarprultiplication
0 0 l 1
meme
0 0 0 1
Example:
111
x100
7 000
53 = 000
28.1.11.
11100

BinaryrDivision

r-IIO
ll
0

1 1 O
— = . — — = 99?
1 1, 0 and 0 ...

Examples:

2 10
3/3 = 11/110
11

0

7 111
4/‘2? = 100 /111OO
100
110
100
100
1.02

275

Binary to Decimal Conversion and Vice Versa

Although we have only ten digits to work with in the decimal
system, we can write numbers that express all quantities, no matter
how large or small they may be. This is possible because of posi-
tional notation. The value of any digit depends on its relative
position within a number. The digits of the number 3333 are identical,
and yet each has a different value: the first "3" (from right to left)
refers to the units column, the second to the tens column and so on.
3333 is just a compact way of writing: (example).

A similar breakdown of binary numbers can be made. Since
binary number system has a base of two, the positions of the digits in
a binary number refer to increasing powers of two. Therefore, decimal
14, for example, equals binary 1110 since 1110 in binary can be written:
(example).

This represents a simple addition method to convert binary num-
bers into their decimal equivalents. The reverse is trickier, but a
simple method has been found. Essentially you repeatedly divide the
decimal number by two. Because you are dividing by two, the remainder
of any division can only be 1 or 0. These remainders are the digits

of the binary equivalent. (Example)

276

Decimal Number System

Decimal 3333 =

3 thousand + 3 hundred + thirty + three

(or) 3 x 1000 + 3 x 100 + 3 x 10 + 3 x l

3 2

+ 3 x 10 + 3 x 101+ 3 x 10°

(or) 3 x 10

Binagy Number System

Binary 1110 =

3 + 1 x 22 + 1 x 21 + O x 2°

1 x 2
(or) 1 x 8 + l x 4 + l X 2 + 0 x l

Equals decimal 14

277

Example of
Decimal to Binary Conversion

 

 

 

 

 

 

 

2 69 --------- ) l
34 ------- ) 0
l7 ----- ) 1
8 ---) 0
4 -)
l lleVv
1 0 O O l 0 1

So decimal 69 = binary 1000101

278

Words and Addresses

All computers employ a systematic method for placing and
finding information in the form of binary digits. Each binary digit
or bit of information is stored in one cell. Groups of consecutive
cells, or groups of bits, are called words. Each word, in turn, is
assigned a number, which is its address. The addresses are used to
designate where in memory the words are to be placed or found.

There is a difference between the contents of a word and its
address. The address of a word never changes. Its content changes
every time new information is put into the cells of the word. Some
of the cells of the word are, in fact, the address of the word.

Usually word size is fixed for a given computer. Today, the
typical size of a word is 48 bits. Forty-eight bits can be broken
down into twelve sets of four bits each. Since it is possible to re-
present any single decimal digit by four bits, this means that twelve
decimal digits can be represented by each 48 bit computer word. For
example, with 36,000 words in its memory, MSU's CDC 6500 computer can

hold the equivalent of over 400 thousand decimal digits.

279

External Communications

 

External to the computer are devices which do no computing
but still are very essential to computer operations. As auxiliary
equipment, their value lies in the readying of the information for
input into the computer.

Punched cards are the most common means of entering informa-
tion into the computer. Therefore most of the auxiliary equipment
is designed for use with punched cards.

Along with their own special equipment, punched paper tape,
magnetic tape, paper imprinted with magnetic ink characters, and
typewriters are also means of entering and withdrawing information
from the computer.

All input and output devices, however, may be connected to
the computer either "on-line" or "off-line." If connected "off-line,"
they usually communicate through an auxiliary storage medium called
a buffer.

Eventually all information is "sensed" or "read" from this
auxiliary equipment. This means that it is translated into electri-

cal impulses, the only form the computer can make use of.

280

Punched Cards

Punched cards can trace their form and history to Dr. Hollerith
of the Census Bureau. They originally provided a very useful means of
storing, sorting, and tabulating large masses of information. Familiar
examples today would be paychecks and registration cards. They are
sturdy, easy to enter information on, compact, standard-sized, semi-
permanent, and disposable. In addition, they can be read by humans
and machines, although much more quickly by the latter.

The most common type of punched card is the IBM type with 80
columns and 12 rows. Information is punched on the cards by means of
a keypunch. Each character takes up exactly one column. Numbers are
encoded as one punch per column, letters by two punches per column,
and special characters like $ and * take up to three punches per column.
Obviously there can only be 80 characters total per card. Therefore,
only one instruction is written on each card. For human reading, the

characters are also printed on the top edge of the card.

281

Auxiliary Computer Equipment

The auxiliary equipment discussed here includes only that
which is associated with punched cards.

The keypunch is an electro-mechanical device which punches
holes in cards, one column at a time. The machine also automatically
feeds, moves, and stacks the cards. The Operator uses a keyboard
which is very similar to that of a typewriter.

The card verifier is a similar machine but does no punching.
The punched cards are entered and the operator types as on the key-
punch. If the punches are correct, the card is notched as having
been verified. Otherwise, the machine locks and errors are detected.

The reproducer is a machine which quickly duplicates a stack
of punched cards.

The card printer will quickly list line by line the contents
of a stack of cards.

The card sorter sorts punched cards into 13 different piles.
The cards drop into pockets corresponding to each of the punch posi-
tions in any one of the columns. In order to sort within another
column, another run and a switch on the card sorter must be set.

The card reader is the input unit through which punched cards
are "read," that is, the information is "sensed" and converted into

electrical impulses.

282

The Information Conversion Concept

In computer operations, information from the source document
is converted into holes in a card, into electric impulses in a wire,
into tiny magnetic spots on a tape, into electric charges in the
computer's memory, into magnetic spots on a tape, and finally into
printed characters on paper. This is similar to the voice being
converted from sound waves into electrical impulses and back again
over telephone wires. As long as the transmission code is consis—
tent, all is well, orderly, and understandable. This concept of
translation is the foundation upon which all coding and information
processing rests.

For example, consider the special graphite pencil marks you
make on multiple-choice answer sheets. A mark sensing device con-
verts the marks into electrical impulses. They, in turn, are matched

with the correct answers and the total is accumulated automatically.

283

Sensing Information

Auxiliary computer equipment, such as the card reader, "sense"
the information recorded on punched cards and convert it into electri-
cal impulses. The electrical impulses are then either recorded on a
magnetic tape buffer unit or they may be sent directly into the com-
puter's memory.

One means of sensing is to move one card at a time between
a contact drum and a set of small metal brushes. The brushes make
electrical contact whenever one drops through a hole in the card.
Each contact produces an electrical impulse which represents informa-
tion. The timing and the number of electrical impulses per column
differentiates the punched characters from each other. Ordinarily
there are 80 brushes, one for each column. By rotating the drum,
impulses from all 80 columns can be "read" simultaneously.

In a similar way, the magnetic character reader "senses" spots
of magnetism in particular configurations, such as those on bank checks.
More recently, Optical scanners have been designed to read printed mat-

ter, such as ZIP codes.

284

Buffer Operation

Usually information is not transmitted directly to or from
the computer. Instead it is transferred to an intermediate storage
medium. Such intermediate storage mediums are called buffers. The
most common buffer is the magnetic tape unit. Information is recorded
as tiny spots of magnetism on the surface of the tape. As the tape
is moved past a multichannel reading head, similar to the recording
and reading heads of an audio tape recorder, information is read in
or out as desired.

One can transcribe information onto a buffer at a convenient
rate. Once it is accumulated, it can then be read into the computer
at high rates of speed. For example, magnetic tape can be read into
the computer at a rate 500 times faster than punched cards can.

The computer also produces output information at high rates
of speed. Therefore, it is convenient to pile up the information
on a buffer, holding it for later and slower retrieval. Meanwhile,
the computer can go on doing other processing without waiting. Buf-

fers are strictly for efficiency of operation.

285

"On-Line" Versus "Off-Line Communication

Auxiliary computer equipment can communicate with the computer
in one of two ways: on-line and off-line.

On-line communication takes place when input and output units
are wired directly to the computer. Unless the method of input or
output is at a high rate of speed, on-line communication generally de-
lays the computer. Because of their capability of Operating in "real-
time," computerized control applications in industry have input and
output information on-line for years. "Real-time" and "time-sharing"
operations as used now are on-line. Any improvements in these two
areas will only increase the use of on-line communication.

Off-line communication means that the input and output units
are independent of the computer. Communication is through "buffer"
units. This permits the computer to carry on its processing and cal-
culation without interference. For example, the printing of output
information is an electro-mechanical process and therefore is rela-
tively slow. If it were not possible to store output on a "buffer,"
computers would spend half of their time being very, very, expensive

printing presses.

286

Computer Languages

 

A computer program is a sequence of instructions which a
computer is to carry out. Instructions must be written in a code or
language. The basic code a computer can accept, interpret, and exe-
cute is called machine language. This language is in binary and is
very difficult for humans to learn and to use.

However, translating systems exist. They enable the programmer
to state his problem in a language closer to English. A translating
system consists of an artificial language and a compiler program. The
compiler program automatically translates the artificial language
statements into machine language. Thus, the translating system eases
the programmer's job by providing him with an artificial language which
is easier to learn and to use.

Today there are two commonly used translating systems--FORTRAN
and COBOL. FORTRAN stands for Formula Translation and uses an algebraic-
oriented language. COBOL is short for Common Business Oriented Language

and, as its name implies, is used for business data processing Operations.

287

Machine Language

Instructions are written in a code. The basic code a computer
can accept, interpret, and execute is called machine language. Most
computers use a binary coded machine language. For example, 000000000-
1000010000001111000001110010101ll, might mean clear and add to the
accumulator the quantities stored in location 383.

Although such a machine language is ideal for the computer,
people find it tedious to work with these strings of numbers. Also,
because the binary number system is unfamiliar, programs are rarely
written in machine language. Instead, several artificial languages,
which are convenient to the programmer, are used. These, however,
must be eventually converted (translated) into machine language, the
only language the computer is designed to understand. A clerk could
do this translating, but the cheapest, quickest, and most accurate
clerk available is the computer itself. This is where the translating

system comes into play.

288

Translating78ystems
A translating system has two parts: an artificial language
and a compiler program. The artificial language is devised in terms
of the problems to be solved, such as FORTRAN. The compiler program
has been written by experts and is available already prepared in
machine language. It is not unusual to have several translating sys-
tems available for use with one computer.

The basic steps in using a translating system are:

l. The programmer writes instructions in the language of the
translating system. This is called the source program.

2. The source program is put on an input medium and entered
into the computer.

3. The compiler program is also loaded into the computer's
memory.

4. Instruction by instruction, the compiler program trans-
lates the source program into machine language or what is
called the object program.

5. If everything is okay, execution of the object program will
begin, causing the processing of the data and the output
of the results.

One of the outstanding features of this process is that many

machine language instructions may be produced from each instruction

written in the language of the translating system.

289

FORTRAN

FORTRAN is the name of a problem-oriented translating system.
It has been designed primarily to assist engineers and scientists in
programming.

FORTRAN's artificial language very closely approaches standard
algebraic expressions. Some examples of FORTRAN arithmetic operations
are: (example) (add (+), subtract (-), multiply (*), divide (l), and
exponential (**). This means that the algebraic expression A + %?
would be written A + (B **2) / 2 in FORTRAN.)

The FORTRAN compiler, in addition to translating the language
also includes many sub-programs, conveniently pre-written in machine
language.

The name FORTRAN was copyrighted by IBM in 1956. It is now
available for almost all computers. It is very successful because it
can be learned very quickly, it does not take long to write or change
a FORTRAN program, the computer can quickly translate FORTRAN programs
into the computer's language, and it works efficiently for most scien-

tific and engineering problems.

290

FORTRAN
Add +
Subtract -
Multiply *
Divide /
Exponential **

2

A+ =A+(B**2)/2

mu:

291

Advantages and Disadvantages of Translating Systems

The use of a translating system has many advantages. Since
its artificial language is closer to English, it is easier and quicker
to learn and to use. Further, the universality of a programming
language makes it possible to write programs which are machine inde-
pendent.

Also, the time and effort for both writing and correcting a
program are reduced. Defining memory locations is not required, since
the compiler allocates them automatically to each instruction in the
object program. It is very simple for the programmer to insert or de-
lete instructions from the source program since no memory locations
have been specified.

However, using a translating system consumes up to one-third
of the computer's time in translating the source program into the
object program. Also some object programs may not make as efficient
use of the computer as a program written by an expert programmer directly
in machine language. The expert programmer can take full advantage of
the computer's hardware, and if skillful, may devise techniques to

shorten the program considerably.

292

Programming

 

The physical components of the computer are called the
"hardware." "Software," on the other hand, refers to the programs
or instructions which cause the hardware to function.

The computer's ability to solve complex problems is based
almost completely upon the ingenuity of the person who writes the
instructions, the programmer. By means of flow charts, he designs
and prepares the schedule of computer operations, or program, that
transform given inputs into desired results.

The simplest type of program he could write is the straight-
line or fixed order program. However, the vast majority of programs
involve some repeated parts. If the repetition occurs as part of the
main program, it is called looping and permits branching of instruc-
tions. On the other hand, if the repetition involves a sequence of
specific operations, called on by the main program, it is called a
sub-program.

There are, in addition, diagnostic programs available to assist

the programmer in "debugging" the programs that he writes.

293

Flow-Charting

Problems run on the computer usually require thousands of
instructions. Before the coding is prepared, the programmer creates
a flow chart which helps him find a good method of processing the data.

Flow charts are very much like problem-solving road maps, in
that they represent solutions to problems in a graphic form. Symbols
such as circles, diamonds, and rectangles are used to show exactly what
operations are to be done. The order of Operations is indicated by
directional arrows. All possible outcomes are considered.

Sample problem: add five numbers.

Solution: Let Xi represent the five numbers, where i goes from
1 to 5. Put zeros in the location reserved for "sum" to insure that it
is empty before you start. Assume five numbers will be read into the

computer on a single punched card. The flow chart would look like the

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

   
 

following:
( START) ‘———-> i=1
1 __SL__
SUM-=0 r—fl SUM==SUM + Xi
READ DATA
CARD DISPLAY

  

 

  

"SUM: ll

 

 

 

 

 

 

 

Flow Chart Diagram

294

The Programmer

 

The programmer writes the instructions or sequence of tasks
that the computer is to carry out. He takes the original problem
presented by the scientist or accountant and restates it concisely.

In order to determine exactly what he wants the computer to do,
however, the programmer must know how to solve the problem indepen-
dently of the computer. The problem is then broken into a set of
simple arithmetic or logical statements, each corresponding to one
instruction the computer can perform.

Usually, the programmer writes these final instructions in
some artificial language, such as FORTRAN. If so, he must conform
exactly to the rules of that language or risk a false solution. The
computer has no magical powers; all it can do is follow instructions
blindly. In fact, 90 percent of the problems associated with compu-
ters are due to programming errors.

The programmer works with ideas and peOple; he has practically
no physical contact with the computer itself. It is the computer opera-
tor who starts and stOps the computer, loads the programs, monitors the

processing, and mounts the magnetic tapes.

295

The Sub-Prpgram

A sub-program is any sequence of operations which is called on
for repeated use. As a consequence, sub-programs usually make up a
substantial portion of an entire program. However, they are written
only once, separate from the main program. They are called into the
main program, as needed by the main program, in order to solve specific
problems. An example would be a frequently recurring mathematical
function, such as computing the square root of any number.

Normally, each translating system, such as FORTRAN, includes
a library of such commonly needed subsidiary programs. Thus, the pro-
grammer can select proven sub-programs from the library for use with
any of his programs. This fact greatly reduces the time required to
write and test new programs.

If such "canned" sub-programs are written locally, rather than
being an integral part of a translating system language, they are then

called sub-routines.

296

The Main Program

Basically, any program can be construed as a sequence of detailed
instructions which route information through the computer's circuits.

The major categories of a main program are: (l) instructions which trans-
fer data from input media to computer memory, (2) instructions which

move data internally and perform arithmetical and logical calculations,
(3) instructions which control the program sequence, and (4) instructions
which transfer results from the computer's memory to output media.

Up to forty percent of all instructions are concerned with input
and output functions. The computer running time of programs, including
compiling, is usually one minute or less.

The most important feature of modern computers is the fact that
instructions are stored whithin the computer's memory. Therefore, it
is possible to write a single program which, through looping and branch-
ing techniques, will enable the computer to repeat, modify, or select
its own instructions.

For efficient use of programs, they are often written for the
general case and then stored in program libraries. Other people just

add their own data for "instant" programming.

297

Looping and Branchigg

 

Straight-line portions of a program are groups of instructions
individually stated and operated upon by the computer in a set, fixed
order. However, practically every program requires lOOping or itera-
tion; that is, performing over and over again a given sequence of
instructions. Loops are produced when one of the instructions specifies
a return to a previously executed instruction and repetition of the sub-
sequent group of instructions.

The instruction to loop is usually based on a comparison of the
results of the computation with some criterion. By testing or compar-
ing the result of each reiteration against a criteria, we can make the
computer, in effect, answer "yes" or "no." This is called branching,
the determining of which series of instructions the computer will carry
out next.

A programmer can also write conditional branching instructions.
For example, he can write: "If A is greater than B, do this; if not,
do something else." This simple branching ability coupled with the
fact that the program is stored in the memory and is available for modi-

fication, makes the computer an extremely powerful tool.

298
Comparison of a Prggram, Its Flow Chart,
and Its Desk Calculator Equivalent

What follows is a comparison of a program to add two numbers,
the flow chart of that program, and the equivalent Operations on a
mechanical desk calculator. We shall begin with the desk calculator.
The steps would be: (1) obtain the first number from the prOper
location, (2) enter it on the keyboard, (3) press the prOper key to
enter the value internally, (4) obtain the next number from the prOper
location, (5) enter it on the keyboard, (6) press the proper key to
enter the value internally, (7) press the addition button, (8) read
the result, and (9) write the result in the proper location.

The flow chart of this problem would be: (slide)

Translation of the problem into FORTRAN language would result

in the following:

(Slide) (Read)

SUM = 0 set accumulator to zero before starting
READ A, B read in data card

SUM = A + B = formula for adding the numbers

WRITE SUM print out answer

STOP tell computer program is ended

299

Flow Chart

 

 

 

‘ START > ’3\READ IN TWO NUMBERS /Z-

 

 

 

 

 

\

 

 

ADD NUMBERS E KPRINT SUM /—

:

 

 

300

W

The finding and removing of errors by test runs on the computer
is pOpularly known as "debugging" a program. Since the computer itself
has built-in error detectors, it is rarely at fault. Even the best
programmers make mistakes, if only because the computer does exactly
what it is told, like a robot.

There are two general ways programs can have errors: first, the
program can fail during translation if the programmer has violated any
of the rules of the language used. Since this is rather common, the
compiler is programmed by experts to automatically produce diagnostic
messages to help the programmer locate his errors. Second, the program
may have been programmed incorrectly, even though the program had been
translated into binary code. For example, the compiler program would
accept a division by zero. In order to detect this error, the manual
calculation of a sample answer is usually undertaken. The results are
then compared with the computer's answer. If this method fails, one
can trace the flow of information, one instruction at a time, by means
of a program which prints out just what is in the computer's memory.

One-third of the computer's time may be consumed in debugging
programs. A single error may take several days and many reruns to

discover.

301

Present and Future Uses of Computers

The develOpment of the electronic digital computer has been

heralded as the beginning of a second industrial revolution as tremen-

dous in scope as that initiated by the invention of the steam engine.

In general, the present use of computers can be broken into

four categories:

1.

Scientific analysis-~usually in scientific analysis there
is relatively little input and output, but the calculations
are many and repetitious.

Data reduction--data reduction usually consists of routine
tasks, such as comparing, sorting, and organizing scien-
tific or technical information.

Data processing--data processing usually involves large
quantities of input and output, with few and simple compu-
tations, the typical business-commercial situation.
Control--the computer is often used in industrial settings

to monitor and regulate on-going processes.

Future computer systems will most likely make use of two of the

newer innovations in computing: "time-sharing" of facilities and "real-

time" operation. However, the potential use of computers for foreign

language translation and information retrieval have not yet been

realized.

302

Scientific Analysis

 

Use of the computer has vastly expanded the size, sophistication,
and complexity of scientific analysis. By means of repeated successive
approximations a computer can do calculus and other higher order mathe-
matical calculations which are so essential to science.

When a scientist can formulate a body of mathematical expres-
sions to describe some situation, condition, or object, he has, in
effect, constructed a mathematical model. It can be tested in the place
of the actual or real thing. Time can be expanded or compressed. Dan-
gerous situations can be safely observed. For example, space vehicles,
nuclear power plants, river systems, and the human brain have all been
modeled.

What is the best wing shape for a new aircraft? Previous re-
search methods would have called for wind-tunnel experiments followed
by many tests on full-scale prototypes. Much of this can now be avoided
by means of a mathematical model and the computer. Equations solved
by the computer are compared with actual wind-tunnel tests. From this,
the math model is modified. Better and better solutions are worked out,
while wind-tunnel experiments are reduced. Computer simulation can thus

cut in half development time and costs.

303

Data Reduction

Data reduction means to compare, sort, and organize large
masses of data. The purpose is to reveal patterns or to provide sum-
mary information from which conclusions may be drawn by scientists
and engineers. It is quite economical for computers to be programmed
to do such tasks.

For example, the enormous amount of data gathered during
missile or satellite flight is meaningless until correlated and re-
duced. Processing the information, manually, is h0pelessly time-
consuming. Therefore, computers are called upon to handle the routine
part of such tasks.

Fields of study, such as weather forecasting, medicine, and law
are characterized by masses of information increasing in quantity and
sophistication faster than they can be reduced and classified. Use of
the computer appears to be their only salvation. For example, the
Census Bureau would be ten years behind the facts, if it were not for

computers.

304

Data Processing

 

Except for electronic data processing, the major part of
America's population might have had to become bank clerks in order to
meet the rising demands for paperwork. Computers can hold large amounts
of information and do file searching. This enables a company to keep
voluminous records and yet have fast access to any one of them. Bill-
ing, inventory, and record-keeping are familiar examples.

Commercial computer systems commonly use programs which are
extremely repetitive and have little need for change over long periods
of time. A prime example is payroll preparation. New data is compared
with each individual record on a master file for swift computation of
pay, taxes, and other deductions.

Banking systems now include devices which can read magnetic
numbers printed at the bottom of checks. Reliable computer reading of
handwriting is in the near future. In the more distant future the very
nature of money and commercial accounting may be transformed. Instead
of physical checks and paper, computers will simply receive electronic

messages to add or subtract amounts from subscriber's records.

305

Control by Computer

The use of computers for control purposes is usually some way
tied in with automation. In a manufacturing situation like steel
making, factors contributing to the process are sampled or measured
continuously. These measurements are then compared with certain crit-
eria, by means of a computer program, to determine whether the con-
tributing factors are producing the desired output. Should the
computations indicate a present variation, or predict a future variation
from tolerances, the computer can generate signals to correct the errors
or alert personnel to the undesired state. These are called closed-100p
feedback systems. Many repetitions, a small memory, and little compu-
tation is usually required for such Operations.

The thermostat is a familiar example of a closed-lOOp feedback
device. It governs the furnace's output depending on room input tem-
perature. Such devices, as in automation, need a minimum of people;
but man is really responsible for the creation and develOpment of the

computer programs which produce the desired output.

306

"Real-Time" Operation

As the operating Speed of the computer increases, the time
difference between input of information and output of an appropriate
response approaches zero. Such a computer is said to be operating in
"real-time." However, "real-time" Operation is a recent innovation.
Usually, one submits his program and then waits hours or days, depend-
ing on his priority, program length and complexity, before receiving
any output of results.

Today, genuine "real-time" operation is found only in control
applications, where feedback of control signals can be almost instan-
taneous. This is because computations are short and simple.

There are many other applications, however, where immediate
response to a problem is required, but a slight delay can be tolerated.

For example, in an airline reservation system, passengers'
inquiries concerning the availability of seats must be answered at once.
Precise, up-to-date inventory records must be kept of the unfilled
seats in present and future flights. To handle situations of this
type, the nationwide system consists of a central computer with informa-
tion flowing in from remote terminals and results going back almost

immediately.

307

Time-Sharing

 

Time-sharing means the pooled utilization of computing resources,
so that many users can simultaneously share a central computing facility,
either through local or remote stations. With faster and larger compu-
ters, rates are now approaching that of telephone use--$5-$10 per hour.

Time-sharing permits multi-programming, the handling of up to
sixty programs simultaneously. The control section of the computer
allocates time to each program on the basis of a priority system. This
means that all sections of the computer can be Operating at maximum
efficiency.

This also means fast and direct access to the computer when the
user wants it--in minutes or "real-time" instead of hours or days. The
programmer can exert continual control over his program and is free to
change his mind and do things differently, as he interacts with the com-
puter.

There is a trend toward centralized computer complexes with
multiple and essentially simultaneous access by users at remote loca-
tions. Eventually, we shall see home communication consoles linked to
computerized public information centers. Nonetheless, people in the
future will probably also have small and inexpensive computers in their

homes.

308

Foreign Lapguage Translation and Information Retrieval

Since computers can hold large amounts of information and do
fast file searching, they are well-suited for the translation of
foreign languages and the retrieval of information. However, to date,
the expected develOpments in these two areas have not materialized.
Nonetheless, it is still hoped that the computer will be the answer
to the information explosion that is upon us.

Machine translation of foreign languages is being carried out
today at various levels of sophistication. Success has been limited
to the translation of technical material, which contains a minimum of
idioms and subtleties of language usage. For even the simplest of
translations, today's computers cannot handle the requirements of rapid
access to large amounts of computer storage space.

Information retrieval depends for success and speed on the
sophistication of the computer's memory organization. Storing any sub-
stantial body of literature into an artificial memory is both costly
and time consuming. Therefore, its practicality is severely limited at
present. Ideally, subscribers would merely dial the particular publi-

cation or data file they are interested in.

APPENDIX M

POST-ATTITUDE SURVEY

Appendix M consists of two parts: the "Post-Attitude Survey"
Questionnaire and a distribution of answers to items resulting from

factors in the questionnaire.

309

APPENDIX M

"POST-ATTITUDE SURVEY" QUESTIONNAIRE

This learning situation was different from what you are normally

used to. In order for us to know what you thought of it, please re-

spond to the following:

10.

11.

12.

13.

KEY: (l) Strongly Agree
(2) Agree
(3) Uncertain
(4) Disagree
My college courses should be taught this way. (5) Strongly Disagree
I found I had difficulty paying attention.
I expect to retain the material covered.
The information presented was uninteresting.
I would recommend this experience to a friend.
The material presented was too difficult for me to grasp.
I tried to learn as much as I could.

Computers are a dull subject.

Having the material broken up into segments increased my learning
effectiveness.

I was able to relate the material presented to my past experiences.

I was able to relate prior segments to the learning of new
segments.

The order in which I viewed the segments should be called hap-
hazard.

Had the segments been ordered differently, I would have learned
better.

310

14.

15.

l6.

l7.

l8.

19.

20.

21.

22.

311

I took time to review what I learned, before going on to the next
segment.

I felt that in this environment, I was in control of my learning.
********************
Respond to the following statements according to the following key:
KEY: (1) Greatly
(2) Moderately
(3) A little
My vocabulary has increased. (4) None
My interest in computers has grown.

My overall knowledge about computers has increased.

I have improved in my ability to recognize significant facts and
to interpret information.

The features of this learning environment increased my motivation
to learn in it.

When talk turns to computers, I'll be able to hold my own.

This experiment has been a satisfying experience.

312

Distribution of Answers to Items from Factors in
”Post-Attitude" Survey Questionnaires

Factor 1. Perceived learning as a function of the environment.

ll.

l6.

l8.

19.

20.

 

 

 

Item No. Loading
7. I tried to learn as much as I could. (.55)
Strongly Strongly
Agree Agree Uncertain Disagree Disagree Total M, SD
37 123 24 5 l 190
19.3 64.1 12.5 2.6 .5 99% 2.0 0.7
9. Having the material broken up into segments increased my learning

effectiveness. (.50)

31 77 69 10 2 189
16.1 40.1 35.9 5.2 . 1.0 98% 2.3 0.9

I was able to relate prior segments to the learning of new
segments. (.46)

30 97 47 15 2 191

15.6 50.5 24.5 7.8 1.0 99% 2.3 0.9
My vocabulary has increased. (.65)

16 73 78 22 l 190

8.3 38.0 40.6 11.5 .5 99% 2.5 0.9
My overall knowledge about computers has increased. (.73)

70 62 39 19 l 191

36.5 32.3 20.3 9.9 .5 99% 2.0 1.0
I have improved in my ability to recognize significant facts and
to interpret information. (.65)

4 47 78 58 2 189

2.1 24.5 40.6 30.2 1.0 98% 3.0 0.9

The features of this learning environment increased my motivation
to learn in it. (.47)

20 60 51 58 l 190
10.4 31.3 26.6 30.2 .5 99% 2.8 1.0

21.

22.

313

When talk turns to computers, I'll be able to hold my own. (.68)

 

 

’Strongly Strongly
Agree Agree Uncertain Disagree Disagree Total M, 'gg
6 64 77 41 3 191
3.1 33.3 40.1 21.4 1.6 99% 2.8 0.9
This experiment has been a satisfying experience. (.43)
68 73 32 16 l 190
35.4 38.0 16.7 8.3 .5 99% 2.0 1.0

Factor .

12.

13.

2 Attitude toward order of the segments.

The order in which I viewed the segments should be called hap-
hazard. (.83)

17 34 61 62 16 190
8.9 17.7 31.8 32.3 8.3 99% 3.1 1.1

Had the segments been ordered differently, I would have learned
better. (.86)

Factor .

3.

5.

ll 28 94 46 11 190

5.7 14.6 49.0 24.0 5.7 99% 3.1 1.0
3 Attitude toward method of instruction.
My college courses should be taught this way. (.77)

10 18 90 50 24 192

5.2 9.4 46.9 26.0 12.5 100% 3.3 1.0
I found I had difficulty paying attention. (-.53)

9 43 38 89 13 192

4.7 22.4 19.8 46.4 6.8 100% 3.3 1.0
I expect to retain the material covered. (.52)

4 55 90 39 4 192

2.1 28.6 46.9 20.3 2.1 100% 2.9 0.8
I would recommend this experience to a friend. (.57)

39 86 50 13 4 192

20.3 44.8 26.0 6.8 2.1 100% 2.3 0.9

314

 

 

 

15. I felt that in this environment, I was in control of my learning.
(.65)
Strongly Strongly
Agree Agree Uncertain Disagree Disagree Total M_ g2
15 59 66 41 11 192
7.8 30.7 34.4 21.4 5.7 100% 2.9 1.0
20. The features of this learning environment increased my motivation
to learn in it. (.48)
20 60 51 58 l 190
10.4 31.3 26.6 30.2 .5 99% 2.8 1.0
Factor 4. Perceived learning as a function of task difficulty.
2. I found I had difficulty paying attention. (.38)
9 43 38 89 13 192
4.7 22.4 19.8 46.4 6.8 100% 3.3 1.0
3. I expect to retain the material covered. (-.56)
4 55 90 39 4 192
2.1 28.6 46.9 20.3 2.1 100% 2.9 0.8
6. The material presented was too difficult for me to grasp. (.82)
4 21 61 81 24 191
2.1 10.9 31.8 42.2 12.5 99% 3.5 1.0
11. I was able to relate prior segments to the learning of new seg-
ments. {-.34)
30 97 47 15 2 191
15.6 50.5 24.5 7.8 1.0 99% 2.3 0.9
18. My overall knowledge about computers has increased. (-.36)
70 62 39 19 l 191
36.5 32.3 20.3 9.9 .5 99% 2.0 1.0
21. When talk turns to computers, I'll be able to hold my own. (-.36)

6 64 77 41 3 191
3.1 33.3 40.1 21.4 1.6 99% 2.8 0.9

315

Factor 5.

Reviewing or relating material to past experiences.

 

10. I was able to relate the material presented to my past
experiences. (.73)
Strongly Strongly
Agree Agree Uncertain Disagree Disagree Total M_ SD
5 49 46 69 23 192
2.6 25.5 24.0 35.9 12.0 100% 3.3 1.1
14. I took time to review what I learned, before going on to the
next segment. (.60)
4 69 54 57 8 192
2.1 35.9 28.1 29.7 4.2 100% 3.0 1.0
Factor 6. Attitude toward computers.
4. The information presented was uninteresting. (-.66)
4 13 34 100 40 191
2.1 6.8 17.7 52.1 20.8 99% 3.8 0.9
8. Computers are a dull subject. (-.72)
4 10 24 95 58 191
2.1 5.2 12.5 49.5 30.2 99% 4.0 1.0
17. My interest in computers has grown. (.66)
48 77 51 14 0 190
25.0 40.1 26.6 7.3 0 99% 2.1 0.9

APPENDIX N

TERMINAL INTERVIEW

Pilot Experiment

ID #
Name
Date

 

 

 

What has this experinece done for you?

What did you do for this experience that increased your learning
effectiveness?

What contributed most to how you learned from this experience?

What prevented this experience from being more effective?

How would you improve this experience?

EXP: Did being given the opportunity and responsibility of
selecting the order of the segments yourself, please you?

CON: Should you have been given the Opportunity and responsi-
bility of selecting the order of the segments yourself?

EXP: Did being given the opportunity and responsibility of asking

questions after each segment, please you?

CON: Should you have been given the opportunity and responsibility
of asking questions after each segment?

316

317

Page 1--All Ss

 

ID #
Name
Date

 

 

 

Check the choices that pertain to you.
(You may check none or more than one.)

1.

 

Relative to what I learned, my knowledge of computers before
starting this experiment was:

substantial
moderate
small

close to zero
other

 

 

By chance, during the experiment, I happened to:

talk to other students about computers

talk to other students participating in the experiment about
the experiment

read or viewed on TV some relevant information about computers
other

 

 

This experiment has left me:

more curious about computers than before

with a more positive attitude toward computers than before
with the idea that I ought to take at least one computer
science course

with the idea that I ought to find out more about computers
other

 

 

The reason(s) I participated in this experiment was (were):
(indicate which was most important with an asterisk *)

research credits

curiosity about computers

genuine interest in computers

interest in a "learning" experiment
nothing better to do (for the fun of it)
other

 

 

I used the time between segments to:

review my notes

test myself as I went along
interrelate the segmented material
think about what had been presented
other

 

318

Page 2--"SCI" SS

My learning would have been more effective, if I had been able to:

have an "expert" order the segments for me

interact with other students

repeat more segments

delete more segments

have other students ask questions for me

assess my grasp of the new knowledge as I went along
other

 

 

What contributed most to how you learned?

pretest

segmented lecture

topics in the form of questions

own speed or pace

being able to check notes at my leisure
freedom to order the segments

freedom to ask questions (review)
freedom to test myself as I went along
other

 

 

How would you improve this learning experience?

319

Page 2--"Expert," "Random," and "Control" Ss

My learning would have been more effective, if I had been able to:
(indicate most important one with an asterisk *)

talk to the instructor

select the order of the segments myself

repeat the segments that I wanted repeated

delete the segments that I_wanted deleted

view a more logical ordering of the segments

ask EX.2EE questions after each segment

assess my grasp of the new knowledge as I went along
interact with other students

other

 

 

What contributed most to how you learned?

pretest

segmented lecture

topics in the form of a question

own speed or pace

being able to check notes at my leisure
other

 

 

How would you improve this learning experience?

APPENDIX 0

TABLE OF INTERCORRELATIONS

BETWEEN ALL VARIABLES

320

321

 

l 2 3 4 5 6

l. Pretest 100

2. Gain 1 ~27 100

3. Gain 2 ~25 79 100

4. Verbal Aptitude 29 34 35 100

5. Quantitative Aptitude 29 35 38 63 100

6. College Aptitude 31 38 41 90 90 100
7. MSU Mathematics Test 28 24 25 43 67 61
8. MSU Arithmetic Test 22 19 27 46 61 60
9. Test Anxiety 22 27 30 51 48 54
10. Complexity Scale 06 08 14 12 21 19
ll. Focus-Scan 17 14 21 28 27 30
12. Beliefs 03 16 16 24 23 26
13. Political Preference ~08 02 03 02 10 07
14. Word Association 22 19 23 46 35 45
15. MSU Reading Test 29 40 42 70 60 72
16. Attitude Toward Learning ~03 02 06 11 ll 13
17. Ask Questions in Class 02 ~06 ~01 ~03 06 02
18. Independence ~06 ~07 {-04 ~07 ~04 ~06
19. Review of Material ~07 ~05 ~04 ~06 ~08 ~08
20. Control of Instructional Functions 00 ~10 ~05 ~03 ~02 ~03
21. Ease of Learning 25 17 24 42 40 .46
22. Autonomy While Learning 23 23 19 03 06 05
23. SES 10 09 12 09 13 13
24. Sex 01 01 10 01 30 18
25. Age ~09 ~09 ~09 ~06 ~17 ~11
26. Class ~08 ~08 ~10 07 ~02 05
27. Pre-Interest in Computers 11 ll 19 03 10 07
28. Perceived Learning: Environment ~01 45 38 06 05 06
29. Attitude Toward Order of Segments ~08 12 03 07 ~04 05
30. Attitude Toward Method of Learning 00 18 23 05 ~02 02
31. Perceived Learning: Task Difficulty ~05 ~53 ~43 ~15 ~17 ~18
32. Reviewing or Relating Material to Past 11 08 l4 14 08 13
33. Attitude Toward Computers 07 20 18 07 08 08
34. Number of Questions Asked 10 15 23 21 28 29
35. Time Expended in Learning Sessions 08 l7 16 22 15 19
36. Number of Pages of Notes Taken ~13 12 10 03 ~02 02
37. Time Expended on Notes ~12 14 17 04 ~04 00

322

 

7 8 9 10 11 12 l3 14 15 l6 17 18 19

l.

2.

3.

4.

5.

6.

7. 100

8. 70 100

9. 26 29 100

10. 07 15 24 100

ll. 20 22 35 18 100

12. 17 08 23 43 18 100

13. 06 05 19 24 04 35 100

14. 14 21 35 18 23 13 12 100

15. 47 49 50 17 31 32 06 46 100

16. 06 09 27 21 24 12 ll 14 14 100

17. ~02 ~00 17 19 21 03 05 13 01 49 100

18. ~03 00 ~20 ~18 ~09 ~06 ~04 ~02 ~01 ~13 ~11 100

19. ~02 ~05 ~01 ~10 09 ~10 04 ~10 01 34 11 ~09 100
20. ~03 ~02 03 21 09 13 06 10 06 47 19 ~18 ~05
21. 31 33 53 15 32 10 02 32 39 40 19 ~11 02
22. 03 02 12 ~08 02 02 08 02 09 21 ~11 00 08
23. 00 04 l6 13 10 08 ~25 04 10 ~05 05 01 ~18
24. 26 26 07 20 22 08 ~07 ~04 ~03 02 13 ~10 ~18
25. ~09 ~01 ~14 07 10 02 ~06 03 ~08 ~04 ~01 ~15 ~11
26. 05 09 ~04 06 11 10 ~01 06 05 05 ~01 ~11 ~05
27. 09 05 04 08 07 03 07 ll 08 13 08 11 ~05
28. ~06 ~05 17 05 10 ~07 ~13 09 10 ~03 ~10 03 06
29. 00 02 ~04 ~02 00 ~06 ~10 01 ~03 ~01 00 ~10 05
30. ~07 ~04 ll 08 15 01 ~01 12 02 01 ~10 ~11 01
31. ~07 ~07 ~29 ~08 ~18 ~02 00 ~17 ~22 Ol 04 11 ~03
32. 09 13 17 02 20 00 09 20 20 16 07 ~07 19
33. 07 01 16 ~03 13 ~05 ~10 14 19 05 ~07 O3 05
34. 18 20 20 08 04 16 .03 17 25 06 07 ~03 ~05
35. 18 08 17 ~04 ~01 14 00 08 17 04 ~01 ~18 ~10
36. ~02 ~01 11 ~10 ~06 ~02 ~01 04 04 07 ~04 ~01 07
37 04 00 12 ~09 ~05 02 03 06 03 09 ~06 ~06 04

323

 

20 21 22 23 24 25 26 27 28 29 30 31 32

l.

2.

3.

4.

5.

6.

7.

8.

9.

10.

ll.

12.

l3.

14.

15.

l6.
17.

18.

19.

20. 100

21. 00 100

22. ~01 05 100

23. 00 17 ~17 100

24. ~06 13 02 11 100
25. ~04 ~07 ~02 ~09 14 100

26. ~01 ~01 04 ~05 14 75 100

27. ~02 12 07 02 01 06 00 100

28. ~09 06 10 07 02 ~08 ~06 11 100

29. ~03 ~10 02 06 ~04 07 ~07 ~03 08 100

30. ~03 04 01 ~01 08 00 ~03 18 64 10 100

31. 11 ~12 ~13 ~05 ~08 07 05 ~09 ~75 ~35 ~62 100

32. 05 15 00 01 ~08 02 06 17 20 ~19 20 ~16 100
33. ~07 13 15 00 02 03 02 45 54 07 54 ~41 31
34. 00 17 01 02 12 05 15 ~09 05 ~04 09 ~13 ~01
35. ~06 12 02 ~03 09 ~02 09 ~04 15 ~03 33 ~26 03
36. ~06 05 06 06 ~17 ~16 ~05 ~04 19 ~02 20 ~15 16
37. ~06 ~04 07 ~01 ~13 ~10 ~07 ~05 16 08 25 ~18 12

\OQNOU‘IJ-‘LAJNH

33

100
~03
ll
15
12

34

100
34
20
13

35

100

36 37

27 100

39

69 100

324

APPENDIX P

RAW DATA FOR ALL SS

Appendix P contains the complete raw data for all subjects

participating in the experiment. Cell membership is identified by

the following code:

Student Number Code

lst digit: 1 low aptitude, 2 high aptitude

2nd digit: 1 low inquiry, 2 = high inquiry

3rd & 4th digit: Numerical position of S in cell

5th digit: 1 = "SCI" treatment
2 = "Expert" treatment
3 = "Random" treatment
4 = "Control" treatment

325

326

mm Hm mm New mmq «a «HH mom «on mmo omq NNHNN

 

 

OO ON OO OOO NOO OH NHH OON OOO OHN ONO NHHNN
OO OO NN OOO OON HN OOH OOH HON NHO NOO NOHNN
OO NH OO HOO ONO ON ONH NOH OHO ONO OOO NOONN
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H HHOU

327

 

 

ONH OO OO OHH OH OO N ON OH NNHNN
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328

 

 

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O O O N OH O OH OH mo OOH MH MO OOH MH Om MNONN
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O HHOO

331

O O H M OH O OO OO MH ONH OH NM NOH NH ON HNHHN
O H H N OH O NO MO HO OMH OO MM NOH OO NO HHHHN
O O O O NH O NH ON NN OMH OH OM OOH OH MM HOHHN
O O H O OH M OO OO OO OOH OM OM HHH NO OM HOOHN
O O H O OH O OO OH NO OOH HO NM OOO OH OO HOOHN
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O O H O NH O OO ON HH OHN NO NM OOO OH NO HHOHN

 

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O OH NO OM ON OO ONO ONO OH MN: HO: MOO ONO OHO HOOHN
M NH NO HM ON OO OOO OOO MH ON HOH NOO OOO MOM HOOHN
M OO OO OM ON NO OOO OOO HN OHH OON HOO HMN ONO HOOHN
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M HH MH OM ON OO HOO HNO ON OOH OMH NOO MOO NOO HHOHN
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O O O O OH O OO ON NH OOH OH OO MOO OO OO NNHHN
O O O O OH O NH OM OH OOH OO MO NOH MH OO NHHHN
O O H O NH O OH OH HH OHH OH ON NNH NH OO NOHHN
O H H N OH O OO HH HH OHH OM NM ONH OH OO NOOHN
O O O O NH O NH NO OO OOO HO OM OOO OH NO NOOHN
O H H O NH O OO OH OO OHH HH HM NOO HH NO NNOHN
O O H O OH O OH OO MH OOH HO MM ONH HH OO NOOHN
M N H O OH M OH ON OH OOH OO OM OOO MH MO NOOHN
O O H O OH O MO OH OH ONH OH OM ONH OO OO NOOHN
O O O O HH M HH OO HH ONH OO OM HOH NH NO NMOHN
O O O O MH O OO OH MO OOH NH HO OOO OH OO NNOHN
O O O N OH N MO OH OO OMH NO NM OOO OO OO NHOHN

 

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O OO MH NM NN MO OOO OOO ON NMH OON HOO OHO OOM NHHHN
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M NN OO NM HH HO OHO ONO OH NOH OOH HOO OOO OOM NOOHN
N HN OO MO ON OO MOO ONO NH OMN OHM MOO OOO OOM NNOHN
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O O O O OH N OO MN OH OMH OM OM NOH NH OO MOOHN
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O O O O MH O OO OH OH OMH NH Om. OHH MH OO MNOHN
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O ON MO OM OH OO ONO ONO MN OM MOH NMO OOO MOO MNHHN
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O O O M OH O MH OO OO MNH NH OO HMH OH MO ONOHN
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334

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N OH MH HM NH OO OOO OOO OH OO: HO: MOO NNO MOO OOOHN
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N H O M OH O OH NH OO OOH NH OO OMH OH OO MNHNH
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H H H O NH N MH OO OO ONH NO ON MOH OH OO MOHNH
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