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This is to certify that the
thesis entitled

STATUS OF INFORMATION TECHNOLOGY IN HIGH
SCHOOL AGRISCIENCE CURRICULA IN MICHIGAN
AND CALIFORNIA

presented by

Matthew Charles Golzynski

has been accepted towards fulfillment
of the requirements for the

MS. degree in Agricultural and Extension
Education

 

/ Major Professor’s Signature

2/ ”W 2003
Datefl

 

MSU is an Affinnative Action/Equal Opportunity Institution

PLACE IN RETURN Box to remove this checkout from your record.
TO AVOID FINES return on or before date due.
MAY BE RECALLED with earlier due date if requested.

 

DATE DUE DATE DUE DATE DUE

 

 

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6/01 cJCIRC/DateDuepGS—p. 1 5

 

STATUS OF INFORMATION TECHNOLOGY IN HIGH SCHOOL AGRISCIENCE
CURRICULA IN MICHIGAN AND CALIFORNIA

BY

Matthew Charles Golzynski

A THESIS

Submitted to
Michigan State University
In partial fulfillment of the requirements
For the degree of

MASTER OF SCIENCE
Agriculture and Natural Resources Education and Communication Systems

2003

ABSTRACT

STATUS OF INFORMATION TECHNOLOGY IN HIGH SCHOOL AGRISCIENCE
CURRICULA IN MICHIGAN AND CALIFORNIA

By
Matthew Charles Golzynski
This thesis research set out to answer the question of what are the
knowledge, skill level, potential usefulness, and value of information technology
for Michigan and California high school agriscience educators. A survey was
distributed to agriscience educators in both Michigan and California. This survey
identified the educators use and knowledge of information technology in the
classroom. The results of the survey were analyzed using the SPSS statistical
package. Descriptive statistics including means, standard deviations, and
frequency distributions were used for demographic data and research objectives.
Mean scores were compared between Michigan and California respondents
using independent t-tests. Relationships between variables and the value placed
on information technology were examined using Pearson Product Moment
Correlation coefficient. The responses from both the Michigan and California
respondents indicated a significant correlation between their perceived value of
information technology with both their technology and software knowledge and

skills.

DEDICATION

To my father, David Golzynski.
He always told me to strive towards my potential.

ACKNOWLEDGEMENTS

I would first like to thank my advisor Dr. Luke Reese for guiding me
through this process, keeping me focused and reining me back in when I needed
it. I would like to thank Dr. Arthur Parham here at California State University,
Fresno for accepting the responsibility to be on my committee to finish this thesis
at a distance. I would also like to thank the other member of my committee Dr.
David Krueger, who has given me some insights and made me think more about
this project. I also need to thank Dr. Constance Schneider for running my
statistics and helping me understand what all those numbers meant.

I wouldn’t have been able to do this without the support of my employers
both in Michigan and California, WLAJ-TV 53 and Saint Agnes Medical Center;
especially my two immediate supervisors Michael Winsky and Michael Maxwell.

The support and encouragement that I received from my mother and step-
father, Carol and Richard Perlick, as well as my brothers Jeff and Jon and my
step-brother Allen have be much appreciated. To all my friends along the way,
thank you for your support.

I also must thank my daughter Allie for her understanding, as much as a
three year old has, during this long process of finishing. We didn’t get to spend
much time together, but we will now.

Most importantly, the person that makes me strive and attain more than I
thought that I ever could, is my inspiration and the person that gave me more
support than I deserved through this process is my beautiful wife Dr. Diane

Golzynski. Without her, this project would never have finished.

TABLE OF CONTENTS

LIST OF FIGURES ............................................................................................. vii
LIST OF TABLES ................................................................................................ viii
CHAPTER 1 INTRODUCTION ............................................................................. 1
The Problem ...................................................................................................... 1
Problem Statement ............................................................................................ 2
Purpose of this Research .................................................................................. 3
Research Objectives ......................................................................................... 3
Significance of the Study ................................................................................... 4
Definitions ......................................................................................................... 4
CHAPTER 2 REVIEW OF LITERATURE ............................................................. 6
Introduction ....................................................................................................... 6
Basic Computer Skills ....................................................................................... 8
Computer Software ........................................................................................... 9
Internet/World Wide Web and E-mail .............................................................. 10
Satellite Distribution ......................................................................................... 10
Interactive Video .............................................................................................. 11
Agriscience Educators ..................................................................................... 12
Summary ......................................................................................................... 14
CHAPTER 3 METHODOLOGY ........................................................................... 15
Introduction ..................................................................................................... 15
Respondents ................................................................................................... 16
Survey Instrument ........................................................................................... 16
Data Analysis .................................................................................................. 21
CHAPTER 4 RESULTS ...................................................................................... 24
Introduction ..................................................................................................... 24
Objective One .................................................................................................. 25
Objective Two .................................................................................................. 28
Objective Three ............................................................................................... 34
Objective Four ................................................................................................. 36
Objective Five .................................................................................................. 40
Objective Six ................................................................................................... 41
Objective Seven .............................................................................................. 46
Objective Eight ................................................................................................ 48
Summary ......................................................................................................... 51

CHAPTER 5 DISCUSSION AND CONCLUSIONS ............................................. 53

Introduction ..................................................................................................... 53
Objective One Discussion and Conclusions .................................................... 54
Objective Two Discussion and Conclusions .................................................... 55
Objective Three Discussion and Conclusions ................................................. 56
Objective Four Discussion and Conclusions ................................................... 57
Objective Five Discussion and Conclusions .................................................... 58
Objective Six Discussion and Conclusions ...................................................... 59
Objective Seven Discussion and Conclusions ................................................ 60
Objective Eight Discussion and Conclusions .................................................. 61
Recommendations for Agriscience Educators ................................................. 62
Recommendations for Further Research ........................................................ 63
Summary ......................................................................................................... 63
BIBLIOGRAPHY ................................................................................................. 65
APPENDICES ..................................................................................................... 68
APPENDIX A ................................................................................................... 69
APPENDIX B ................................................................................................... 82
APPENDIX C ................................................................................................... 84
APPENDIX D ................................................................................................... 87
APPENDIX E ................................................................................................... 89
APPENDIX F ................................................................................................... 92

vi

LIST OF FIGURES

Figure 1. Part A Information Technology Values Survey Scale. ......................... 17
Figure 2. Part B Information Technology Knowledge/Skill Survey Scale. ........... 18
Figure 3. Part C Software Application Knowledge/Skill Survey Scale. ................ 18
Figure 4. Part D Availability of Computer Technology Survey Scale ................... 19

Figure 5. Part E Usefulness of Information Technology in Program/Instruction
Management Survey Scale .................................. 19

Figure 6. Part F Information Technology Training Sources Survey Scale ........... 20

vii

LIST OF TABLES

Table 1. Summary of Demographic Information. ................................................ 27
Table 2. Mean Comparison of Value of Information Technology as Perceived by
Michigan and California High School Agriscience Teachers ........................ 32
Table 3. Mean Comparison of Michigan and California High School Agriscience
Educators' Report of Information Technology and Skills .............................. 37
Table 4. Mean Comparison of Michigan and California High School Agriscience
Educators‘ Perception of Software Specific Knowledge and Skills. ............. 40
Table 5. Mean Comparison of Michigan and California Agriscience Educators'
Report of Usefulness of Information Technology in Program and Instructional
Management. ............................................................................................... 42
Table 6. Availability of Computer Technology as Reported by California and
Michigan High School Agriscience Teachers. .............................................. 47
Table 7. Self-Reported Information Technology Training Received within the Past
Three Years. ................................................................................................ 49
Table 8. Relationships Between Perceived Value of Information Technology

Scale and Selected Variables. ..................................................................... 5O

viii

CHAPTER 1

INTRODUCTION

The Problem

When you look at the world of education today there has been a
significant change from just five to ten years ago. There are a greater number of
computers and other technological innovations that have made their way into the
classrooms of the schools throughout the country. There also has been a
change in the type of students that are sitting in the chairs In these classrooms.
These students have a greater access to the information world around them and
are completely involved in the world of technology (Tapscott, 1998). This new
generation of student has been termed an “N-Gener" (Tapscott, 1998) for their
use and understanding of the technology that is available to them at home, in
school and virtually everywhere that they go.

To keep up with the ever changing demands of these students, educators
must keep up-to-date with the burgeoning world of technology and determine
how they can best use these technologies to help their students obtain
knowledge. This can be a problem for some educators who have been in the
classroom for a number of years. Possibly these educators do not have the time
or the motivation to keep up-to-date and therefore are falling behind
technologically and not reaching their students. There also is the possibility that

the school districts in which these educators are working do not have the

resources to make the technological updates that are necessary for the
educators to keep up—to-date.

Agriscience educators do feel that there is a need for training when it
comes to computers and their use in the classroom. The findings from Garton
and Chung (1996) showed that teachers ranked the value of having computers in
the classroom and some form of training on their use ranked sixth on their list of
50 in-service topics as a need for agriscience teachers. These findings almost
mirrored those of an earlier study conducted by Birkenholtz and Harbstreit (1987)
where the beginning agriscience educator’s greatest need was in computer
training. Seeing these needs, Kotrlik, Redmann, Harrison, and Handley (2000)
did a study entitled Information Technology Related Professional Development
Needs of Louisiana Agriscience Teachers. The Kotrlik study is the conceptual

basis for this research project.

Problem Statement

Agriscience educators need to have a knowledge base for teaching their
students what is to be expected from them in the future so that they will be able
to succeed in what ever they decide to do after high school; whether it be going
to work on the farm or other business or going off to college. Accomplishing this
task is getting harder and harder with the technological innovations that are
happening and the speed at which students are learning and expecting these

innovations to be used in the classrooms.

Purpose of this Research

This research aims to answer the question of what are the knowledge, skill
level, potential usefulness, and value of information technology to Michigan and
California high school agriscience educators. An analysis of these answers in
relation to independent variables will determine if a relationship exists. This will
be accomplished by replicating the study that Kotrlik et al. (2000) performed in
Louisiana. In replicating this study the researcher hopes to accomplish a greater
understanding of where the agriscience education communities in Michigan and
California are in relation to the information technology that is being used in

today’s classroom.

Research Obiectives
The research objectives for this study are the same objectives as those of
Kotrlik et al. (2000):

1. To determine the demographic characteristics (degrees held, age, gender,
ethnicity, years teaching experience, school location [rural, urban or
suburban], grade level, and participation in professional associations) of
the selected subjects.

2. To determine the value of information technology as perceived by the
agriscience educator.

3. To determine the general information technology knowledge and skill level

possessed by the agriscience educator.

4. To determine the general software knowledge and skill level possessed by
the agriscience educator.

5. To gain the educator’s perception of the potential usefulness of
information technology in program and instructional management.

6. To determine the availability of information technology to the agriscience
educator.

7. To find out the source of the agriscience educators information technology
training.

8. To find if a relationship exists between selected variables and the value

placed on information technology by the agriscience educator.

ggnificance of the Studv

Technological innovations are not going to stop just because teachers can
not keep up with them. The same can be said of education. There seems to be
a growing trend in wanting to get more things done in a shorter amount of time,
and one of the ways to do this is with the use of technology. These eight
research objectives will help determine Ahere the field of agriscience education

in Michigan and California feeds to improve in information technology.

Definitions
Compressed Video: Computer-based technology that allows a teacher to

instruct one or more classes at different locations —

Information Technology:

Multimedia:

Satellite Downlink:

Video Conferencing:

students and teacher can see and hear each other
and interact live.

All technology that is used to communicate
information within our profession and to students.
This includes the World Wide Web, Internet, satellite
technologies, and computers.

Computer-based applications that allow the user to
see and hear different types of information using one
screen and speakers (e.g., text, pictures, video,
animation, sound, music).

A satellite dish that allows schools to receive televised
transmissions broadcast from anywhere in the world
and enables a school to view many programs that are
not available via cable or regular television
transmission.

A method of conferencing in which people at different
locations can see and hear one another, as well as
communicate using different types of media
equipment (e.g., computer, image viewer, slide

projector).

CHAPTER 2

REVIEW OF THE LITERATURE

Introduction

Computer technology is an ever-expanding presence in the lives of most
people in the United States. This is especially true for children who are growing
up in the 21St Century. Today’s students in all likelihood have a computer in their
home; at least one computer lab in their high school; and could quite possibly
attend a college or university that requires incoming students to own a computer
(Tapscott, 1998). When it comes to the video quality and graphic capabilities of
today’s computers, students would not believe the changes in computers from
just five to ten years ago. Monochrome green or yellow characters on a black
background and if someone was creative they could create an “image” using
characters from their keyboards (Tapscott, 1998).

Tapscott (1998) identifies that most children today under 19 years old
have been aware of technologically advanced electronic gadgets and devices for
as long as they can remember. Twenty to thirty years ago the general public
never thought that you could carry a computer around in your backpack that was
faster and more powerful than most computers in use at the time. This general
population is where the agriscience educators of today have been drawn. This
discrepancy then causes a problem for the technology-challenged educator when
the technologically literate child of today gets to high school (Tapscott, 1998).

Stellwagen (1999) believes that the learning environment that the agriscience

instructor has developed is at a distinct disadvantage when it comes to the use
and knowledge of technology. The instructor has to try to teach to the students
of today with a more fast paced multi-input class style using emerging
technologies rather than the lecture/lab based class that has been used in the
past. Agriscience instructors, in particular, seem to be behind in their knowledge
of the newer technologies (Stellwagen, 1999). These technologies would
include, but are not limited to, the lnterneWVorld Wide Web, video conferencing
systems, satellite downlinks, compressed video, and compact disc players
(Kotrlik et al., 2000).

There is a great deal of controversy and many challenges that have arisen
with the influx of people who want educators to move into the digital world.
Educators from all disciplines must figure out what is the good information and
what is the bad information that is now coming into their classrooms (Nobel,
1997, 1998a, 1998b, 1999). There are many who would argue that information
technology is nothing more than a tool that is used to enhance teaching just like
a book or any other peripheral instrument that is used by the instructor to get his
or her point across to the students (Winsboro, 2002).

Information from the literature regarding the agriscience educators’ use
and knowledge of information technology can be divided into technological
categories. These technological categories are: 1) Basic Computer Skills, 2)
Computer Software, 3) Internet/World Wide Web and E-mail, 4) Satellite
Distribution (Kotrlik et al., 2000), and 5) Interactive video (Swan & Brehmer,

1994).

Basic Comolfir Skills

Basic computer skills knowledge differs when comparing agriscience
educators to their students. Educators use any method, such as role playing,
field trips, videos, or guest speakers, so that they can to get their point across.
Most agriscience educators feel that they have enough training and experience
with traditional technologies like computers and basic word processing software
but have little time to learn and use these technologies to their fullest capabilities
(Trede & Miller, 1993).

One of the reasons for this basic skills difference is the fact that
agriscience educators feel that they are, for the most part, novice computer
users. Most of the instructors are only using the computers that they have
available to them for one to three hours per week (Fletcher & Deeds, 1994).
Educators must combine two or more information technologies together to help
them make their classes more up to date. The educators’ lack of knowledge, skill
and acceptance are a limiting factor in allowing them to make the change from
the traditional ways that they have been teaching to the technologically advanced
ways that society deems necessary (McCaslin & Torres, 1992).

The lack of skill and knowledge do not follow a demographic pattern
where older educators are less inclined to use the technologies than educators
just entering the profession (Peasley & Henderson, 1992). The main reasons for
a lack of technological skill and knowledge are: 1) there is a lack of funding at the
local school level for many if not all technological upgrades (Trede 8 Miller, 1993;

Swan & Brehmer, 1994; Alston, 2000), and 2) self directed learning on the part of

the agriscience educators themselves is the main way that they receive training
in new technologies and the time is just not there for these educators to learn all
of the new technologies (Kotrlik et al., 2000).

Alston (2000) found that the basic computer and word-processing skills
are prevalent with educators, but when it comes to using interactive video or web
page design programs these teachers are not very knowledgeable. Even the
simple skills of searching the Internet are a challenge (Kotrlik et al., 2000). There
is a feeling among some in the agriscience field that educators are not being
encouraged by their principals, superintendents, school boards, and state
representatives to use computers in the classroom. There also is a lack-of-
funding issue that goes right along with this lack of support and many feel if the
educators had the support of their supervisors, the educators’ acceptance and

anxiety level about computers would decrease (Fletcher & Deeds, 1994).

Computer Software

According to Fletcher and Deeds (1994), when it comes to actually
programming in a computer language such as BASIC, there is a great amount of
anxiety that the agriscience educator feels because of the lack of educator
training. Many feel that additional computer training courses need to be
implemented into the teacher education programs at the college level to help in
the understanding of the computer applications they can expect to encounter
(Fletcher 8 Deeds, 1994). There also are many software titles, such as Microsoft

Excel, PowerPoint, Word, etc., used in agriscience classrooms by both

I

instructors and students that are used by many I ‘ conferencing

 

different academic programs (Alston, 2000). ‘tellite
* t of
Internet/World Wide Web and E-mail y
Instructors, who are more computer lite <5?"
Internet is beneficial and should be used in th If

percentage of their students use the lntemet (Layfield, WWW

reports that a significant factor is the limited access to and support of the World
Wide Web provided by the school districts.

When it comes to the use of e-mail the findings are even worse. The
number of educators that use e-mail in their educational setting is basically non-
existent and the use of e-mail by their students is contrary to this finding

(Layfield, 1998). {'Access and support is a factor in these resultslaWeld (1998)

th‘n‘.‘

a...

reports that the majority of student e-mail account use is for literature searches

and not correspondence between individuals.

Satellite Distribution

Satellite distribution of educational material has been used in many
curricula when a particular school or district has been unable to attract an
educator for a specific subject matter (Pelton, 1991 ). Using this tool, schools
have the opportunity to keep courses that may have been eliminated because of
the lack of a teacher or limited student numbers to justify a full-time instructor.

Many agriscience educators felt that they have no experience with newer

10

technologies like satellite uplink/downlink distribution and video conferencing
systems (Trede & Miller, 1993). When educators are exposed to satellite
distribution, their benefits in professional development mirror the amount of
learning that is gained by the student (Larson 8: Bruning, 1996). Satellite
distribution does have a problem that is not easily overcome, especially when
you compare it to its relatively close counterpart Interactive Video; there is no
way to communicate with the satellite instructor and the student at the distant
site. Larson and Bruning (1996) indicate that students had very few opportunities
to interrupt and interact with the television teacher’s presentation or be able to

ask clarifying questions, as they do in their regular learning environments.

Interactive Video

There are several reasons that many agriscience programs and educators
throughout the country do not make use of an interactive video system.
Foremost is the significant cost that is assumed when programs decide to go into
the interactive video world (Miller & Miller, 2000; Swan & Brehmer, 1994; and
Alston, 2000). Miller and Miller (2000) found that even if a system is in place,
educators still tend to not use the interactive system to teach their classes
because of conflicts with course scheduling, coordinating with other agriscience

programs, and training sessions on how to use the technology.

11

Agriscience Educators

Kotrlik et al. (2000) utilized these technology categories in a research
project completed in 1998 with secondary agriscience educators in Louisiana.
Objective one of the study found that the majority of respondents (58%) had a
master’s degree and none had doctoral degrees. Ninety-four percent of the
respondents were white with an average age of 42 years old. The majority of the
respondents taught in rural schools (81%) where over half (51%) were connected
to the Internet (Kotrlik et al., 2000).

In objective two the respondents strongly agreed that information
technology was an important aspect to agriscience education (M 2 4.5). They
agreed that all of the technology listed in the survey was important, and had a
high educational value (M = 354.49) and should be available for use in the
classroom (M 2 4.5). When it came to the area of cost for the technology the
respondents were undecided (M = 1.5-2.49) on the cost effectiveness of
technology in the classroom (Kotrlik et al., 2000).

In objective three, the respondents rated themselves average (M = 2.5-
3.49) in regards to their expertise of the information technologies. The first eight
technologies listed in this section were at least a decade old and available to the
majority of respondents in the study. When it came to the newer technologies,
the last seven in the section, the respondents rated themselves as below
average (M = 152.49) (Kotrlik et al., 2000)

The findings were somewhat the same for objective four. The

respondents rated themselves as average (M = 25-349) in their expertise with

12

basic computer software. With the newer software’s (Web browsers, e-mail,
FTP, etc.) the respondents rated themselves as below average (M = 1.5-2.49)
(Kotrlik et al., 2000)

Objective five of the Kotrlik et al. (2000) study was to determine the
usefulness of the information technology in the program and instructional
management areas. The respondents found information technology to be
moderately useful (M = 354.49).

The results for objective six also revealed that the majority of the
respondents had access to a computer at home (51%) or at work (79%). The
majority (84.5%) also had access to multimedia machines; mainly these
computers were in the work setting (63.1%) (Kotrlik et al., 2000).

The results of objective seven revealed that the majority of respondents
had received their information technology training in a self directed environment
(69.8%). This was also true for the learning that they did in the last three years.
Respondents (40.3%) reported that this learning was more self-directed in
regards to information technology (Kotrlik et al., 2000).

Objective eight showed the relationship between selected variables and
the value that was placed upon them from an information technology standard.
The variable that had the highest relationship (r = 0.25) was between
accessibility of computer technology and the value of information technology

(Kotrlik et al., 2000)

13

Summary

In the Kotrlik et al. (2000) study, agriscience educators are ill prepared
and undereducated when it comes to information technology in the classroom.
Their results support this idea and indicate that more information technology
training is needed to make agriscience educators more comfortable and
competent with the use of these technologies in the classroom.

Computer technology changes approximately every eight years. Given
this assumption and the five years since the Kotrlik et al. (2000) study, have their
results changed in this time period? This study will replicate a modified version
of the Kotrlik et al. (2000) study reflecting current technology options and

advancements.

14

CHAPTER 3

METHODOLOGY

Introduction

The purpose of this research was to determine how high school
agriscience educators in Michigan and California are keeping up with changing
technologies. To complete this research project, the researcher replicated the
project done by Kotrlik et al. (2000) in Louisiana. This project used a modified
version of the survey instrument used for the Kotrlik research project. The
modified survey (Appendix A) was updated to reflect changes in technology since
the original survey was conducted. The survey was distributed to agriscience
educators in both Michigan and California via the United States Postal Service.
The current survey identified the educators use and knowledge of information
technology in the classroom. The results of the survey were analyzed using
descriptive statistics including means, standard deviations, and frequency
distributions for demographic data and research objectives. Mean scores were
compared between Michigan and California respondents using independent t-
tests. Relationships between variables and the value placed on information
technology were examined using Pearson Product Moment Correlation
Coefficient. Since this project involved two different states and advisors on the
project from two different universities, human subjects approval was obtained
from the University Committee on Research involving Human Subjects at

Michigan State University (Appendix B). This human subjects approval was

15

given verbal acceptance and approval by the Committee on the Protection of
Human Subjects at California State University, Fresno prior to beginning this

research project.

Respondents

Respondents for this research project were high school agriscience
educators in both Michigan and California. These respondents were identified by
a list of names obtained from Michigan State University for respondents in
Michigan and from the Internet website www.calagedorg for respondents from
California. An equal number of subjects (n = 120) were selected randomly from
each list. Each name from the two states was numbered and a random numbers
list was generated using Microsoft Excel. When the first 120 corresponding
names and numbers from the random numbers list were matched those were the
names of the participants to be sent a survey. A separate random numbers list

was generated for Michigan and California.

Survey Instrument

The survey instrument that was slightly modified was obtained from Kotrlik
and colleagues. Reliability and content validity of the survey had been
established prior to its use in the Louisiana research project. The instrument that

was used for this research project is divided up into seven parts “A through G” as

_.._, _ _, .. Hm“...
r __-.-—r

_—.__._u;
m‘—*

follows: Information Technology Values; Information Technology Knowledge and

Skill; Software Applications Knowledge and Skill; Availability of Computer

16

Technology; Usefulness of Information Technology in Programs and Instructional
Management; Information Technology Training Sources; and Demographic
Characteristics. Selected definitions for terms used in the instrument were
printed on the last page of the instrument and asterisks were placed throughout
the survey directing respondents to this area. The specifics of each of these
sections will be discussed in the following paragraphs. Each survey instrument
was coded for either Michigan (M-XXX) or California (C-XXX) so that the data
could be analyzed to separate the two different regions and to provide
confidentiality to the respondents. The code number was also used to complete
a reminder post card for non-respondents and then removed form the survey.
Part A, the Information Technology Values section, of the instrument used
a Likert scale (Figure 1). This section consisted of four multi-part statements that
determined how the respondents value information technology for themselves,

their colleagues and their students. See Appendix A for the complete Part A.

 

 

 

 

 

1 Strongly Disagree
2 Disagree

3 Undecided

4 Agree

5 Strongly Agree

 

 

 

 

Figure 1. Part A lnforrnation Technology Values Survey Scale.

Part B, the Information Technology Knowledge and Skill section, of the
instrument and used a modified Likert scale (Figure 2). This section measured

the individual educator’s knowledge and skill of information technology with eight

17

individual statements and one multi-part statement with a space for them to add
technological equipment that is not on the list. See Appendix A for the complete

Part B.

 

I DON’T KNOW enough about this area to respond

My knowledge/skill in this area is BELOW AVERAg.

My knowledge/skill in this area is AVERAGE.

My knowledge/skill in this area is ABOVE AVERAGE.

My knowledge/skill in this area qualifies me as an EXPERT.

 

 

 

 

(TI-th-b

 

 

 

 

Figure 2. Part B Information Technology Knowledge/Skill Survey Scale.

Part C, the Software Applications Knowledge and Skill section, of the
instrument and used a modified Likert scale (Figure 3). This section measured
the respondent’s knowledge of computer programs and software. There were
fifteen general areas of software and programs listed each having specific
examples of the types of software that belong to these areas. See Appendix A for

the complete Part C.

 

I DON’T KNOW enough about this area to respond

My knowledge/skill in this area is BELOW AVERAGE.

My knowledge/skill in this area is AVERAGE.

My knowledge/skill in this area is _A_B(_)VE AVERAGE.

My knowledge/skill in this area qualifies me as an EXPERT.

 

 

 

 

WAGON—l5

 

 

 

 

Figure 3. Part C Software Application Knowledge/Skill Survey Scale.

18

Part D, the Availability of Computer Technology section, had eight
statements where the subjects placed an “X” in a particular column that
describes the availability of computers to themselves and their students.
Availability choices include DO NOT HAVE, HAVE NOW, PLAN TO ACQUIRE
(Figure 4). The statements in this section consist of three multi-part statements

and five individual statements. See Appendix A for the complete Part D.

 

 

Availability
Information Technology Do Not Have Nowj Have Now {Plan to Acquire

 

 

 

 

Figure 4. Part D Availability of Computer Technology Survey Scale.

Part E, the Usefulness of Information Technology in Program and
Instructional Management, used a Likert scale (Figure 5). This section measured
how the respondent feels about the potential usefulness of information
technology in their program. This section consisted of ten task examples to
clarify certain areas for the respondents. See Appendix A for the complete Part

E.

 

Not Useful
Low Usefulness
Undecided
Moderately Useful
Highly Useful

Figure 5. Part E Usefulness of Information Technology in Program/Instruction

 

 

 

 

(fl-waA

 

 

 

 

Management Survey Scale.

19

Part F, the Information Technology Training Source section, of the
instrument consisted of eight specific sources of training and one area where the
respondent added any other source of training that he/she has used. The
respondent circled either “YES” or “NO” if they have used this training source. If
they respond “YES” they were asked to then tell whether or not they have had
training from this source in the last three years (Figure 6). See Appendix A for the

complete Part F.

 

 

Information Have you ever received If Yes, have you had training from
Technology training from this this SOURCE in the past three (3)
TRAINING source? years?

SOURCE (circle your answer)

 

 

 

Figure 6. Part F Information Technology Training Sources Survey Scale.

Part G of the instrument was the Demographic Characteristics section. In
this section the respondents were asked to provide their education level, gender,
race, age, teaching experience, teaching level, number of professional
workshops or conferences attended, and whether their current employer was
involved in any projects that would connect their school to any other schools in

their district. See Appendix A for the complete Part G.

Mion of S_urvev Instrument

The distribution of the survey instrument was based upon techniques
following Dillman’s Tailored Design Method (2000) for survey research to collect
the desired data for this project. The numbered research instrument, cover letter,

and postage paid return envelope was mailed to the selected participants in

20

 

Michigan on April 9, 2003 and to participants in California on April 11, 2003 via
the United States Postal Service (Appendix C). A postcard thank you/reminder
mailing to all participants occurred on April 19, 2003 just over one week after the
initial mailing (Appendix D). As surveys were returned, the code on the front
page of the survey was used to cross names off of the participant list of those
who returned the survey to protect these respondents from any further mailings.
A second mailing of the instrument, cover letter (Appendix E), and a postage paid
return envelope was sent on May 5, 2003, two weeks after the postcard
reminder. This second complete mailing was sent to only those participants who
had not responded to the first mailing or the reminder postcard. All returned
survey envelopes were discarded to protect the confidentiality of the respondents
returning the survey. The researcher limited the time that the responses will be
accepted to five weeks and began analysis of the data received on approximately

May 30, 2003 (Dillman, 2000).

Data Analvsis
Survey responses were tracked on a log as they were returned. A data
coding sheet was utilized to assist with accuracy for data entry. Data was

entered, checked and verified to decrease error. Descriptive statistics including

w—w“

 

___—___.-———~—

means, standard deviations, and frequency distributions were used for
demographic data and research objectives. Mean scores were compared

between Michigan and California respondents using independent t-tests.

Relationships between variables and the value placed on information technology

21

were examined using Pearson Product Moment Correlation Coefficient.
Educators who did not answer certain sections or certain questions within a
section of the survey were not discarded. Rather, those sections or questions in
which they did not respond were not used in the data analysis. In these cases, a
valid percent is reported. The survey instrument was retested for reliability of
scales using Cronbach’s alpha (See Appendix G). Results were considered
significant at p = 0.05 (Rubin & Babble, 2001).

In order to accomplish objectives seven and eight and to replicate the
methodology of Kotrlik et al. (2000) it was necessary to assign professional
development points to the answers given in Part F: Information Technology
Training Sources. Each time a respondent indicated that they received training
from one of the eight identified training sources, a point was given. Additional
points were given to each respondent who indicated that the training had
occurred in the past three years. As a result, each respondent could earn a
maximum of 16 points. The available number of points was multiplied by the
frequency in which the available number of points occurred. This total number of
points was then used to establish a baseline for the Pearson Product Moment
Correlation analysis. See Appendix F for the Working Table for Professional
Development Points.

In the Kotrlik et al. (2000) survey, Cronbach’s alpha (or) was used to
determine the internal consistency coefficients for four sections of the instrument:

Value of Information Technology (a: .87), General Information Technology

Knowledge and Skill (a: .95), Software Specific knowledge and Skill ((1: .94),

22

and Usefulness of Information Technology in Program and Instructional
Management (OF .93) (Kotrlik et al., 2000). For the Louisiana project the alpha
level was set at a priori, a=0.05 (Kotrlik et al., 2000). The same alpha level was
used for this study.

Post-hoc non-respondent reliability was determined by running a t—test
comparison of the grand means between the early and late responders to the
study. It was found that there was no significant difference in the grand mean
comparison between the two groups from either state.

Data were first entered into a Microsoft XP Excel spreadsheet and then
transferred to the statistical software. All statistical tests were performed using

the computerized Statistical Package for Social Sciences, version 11.0.

23

CHAPTER 4

RESULTS

Introduction
This research addressed the following research objectives:

1. To determine the demographic characteristics (degrees held, age, gender,
ethnicity, years teaching experience, school location [rural, urban or
suburban], grade level, and participation in profesSional associations) of
the selected subjects.

2. To determine the value of information technology as perceived by the
agriscience educator.

3. To determine the general information technology knowledge and skill level
possessed by the agriscience educator.

4. To determine the general software knowledge and skill level possessed by
the agriscience educator.

5. To gain the educator’s perception of the potential usefulness of
information technology in program and instructional management.

6. To determine the availability of information technology to the agriscience
educator.

7. To find out the source of the agriscience educators information technology
training.

8. To find if a relationship exists between selected variables and the value

placed on information technology by the agriscience educator.

24

A total of 240 surveys were distributed for the purposes of this research
out of a possible 698 possible subjects from both Michigan (n=139) and
California (n=559). An equal number of surveys were sent to agriscience
teachers in both Michigan and California. The research had a 49.2% (n=118)
response rate. The distribution of respondents from each state was 48.3% from
Michigan (n=58) and 50% from California (n=60). There were two respondents
from Michigan (2.0%) that did not fill out the survey because they both had
stopped teaching agriscience and had moved on to other disciplines. In both
cases the agriscience programs had been eliminated from their Michigan school
districts. These two respondents’ surveys were discarded and not used in the

data analysis.

Obiective One

In Michigan there were 33.3% (n=20) who had earned a Bachelor degree,
46.7% (n=28) with a Master degree, 13.3% (n=8) with Master +30 or Educational
Specialist degree, and 1.7% (n=1) with both a PhD and other degrees. In
California there were 63.3% (n=38) who had earned a Bachelor degree, 16.6%
(n=10) with a Master degree, 20.0% (n=12) with Master +30 or Educational
Specialist degree, and no one with either a PhD or other degrees.

In Michigan the distribution of the respondents was 53.3% (n=32) male
and 43.3% (n=26) female. The ethnic makeup of the respondents was 95.0%
(n=57) Caucasian and 1.7% (n=1) other. The average age of the respondents

was 39.6 years (range=23-58) with an average of 12.9 years (range=1-34) in the

25

lclassroom. There were two respondents (1 .7%) that did not answer the
question. In California the distribution of the respondents was 66.7% (n=40)
male and 31.7% (n=19) female. The ethnic makeup of the respondents was
86.7% (n=52) Caucasian, 8.3% (n=5) Hispanic, and 5.0% (n=3) other. There
were no respondents from either state that self reported their race being Black.
The average age of the respondents was 41.2 years (range=25-61) with an
average of 13.1 years (range=1-34) in the classroom.

The respondents in Michigan indicated that 65.0% (n=39) were teaching in
rural schools while 6.7% (n=4) were teaching in urban schools and 25.0% (n=15)
were teaching in suburban schools. Those that responded indicated that they
had been to an average of 7.7 (range=1-25) professional development
conferences/workshops in the past three years. The percentage of respondents
who said that their school currently is connected to the other schools in their
district was 60.0% (n=36). Those that said that they were involved in any project
that would connect their school to the other schools in the district was 15.0%
(n=9). The majority of the respondents, 75.0% (n=45), indicated that they taught
only at the high school level while 3.3% (n=2) taught at a middle school/junior
high school level and 15.0% (n=9) taught at both the middle/junior high and high
school levels. In California the respondents indicated that 50.0% (n=30) were
teaching in rural schools while 25.0% (n=15) were teaching in urban schools and
25.0% (n=15) were teaching in suburban schools. The respondents indicated
that they had been to an average of 7.1 (range=2-20) professional development

conferences/workshops in the past three years. The percentage of respondents

26

who said that their school currently is connected to the other schools in their
district was 53.3% (n=32). Those that said that they were involved in any project
that would connect their school to the other schools in the district was 10.0%
(n=6). The majority of the respondents, 98.3% (n=59), indicated that they taught
only at the high school level with 1.7% (n=1) teaching at both the middle/junior

high and high school levels. Table 1 summarizes demographic characteristics.

Table 1. Summary of Demographic Information.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Michigan California
Category Options n % n %

Bachelor’s 20 34.5 38 63.3

Highest Degree Master’s 28 48.3 10 16.7

Held Master’s +30 8 13.8 12 20.0
PhD 1 1.7 0 0.0
Other 1 1.7 0 0.0
Male 32 55.2 40 66.7

Gender Female 26 44.8 19 31 .7
No Response 1 1.6
Caucasian 57 98.3 52 86.7
Black 0 0.0 0 0.0

Race Hispanic 0 0.0 5 8.3
Other 1 1.7 3 5.0

_Age Wears) M=39.6 R=23-58 M=41.2 R=25-61

Yea's.Tea°“'”9 M=12.9 R=1-34 M=13.1 R=1-34

Expenence

The area where Rural 39 67.2 30 50.0

your school is Urban 4 6.9 15 25.0

located Suburban 15 25.9 15 25.0

How many

professional

development

conferences/works M=7.7 R=1-25 M=7.1 R=2-20

hops have you

aflendedinthe

past three years?

 

27

Table 1 (cont’d)

 

 

 

 

 

 

 

 

 

Is your school Currently
currently Connected 36 62'1 32 53'3
connected or Involved in
involved in any Project 9 15'5 6 10'0
project that would
connect your
school to the other No Response 13 22.4 22 36.7
schools in your
district?
High school
students only? 45 77.7 59 98.3
Middle
school/junior
high school 2 3'4 O 0'0
students only?
Both high
Do you teach school and
middle
school/junior 9 15'5 1 1'7
high school
students?
Other 0 0.0 0 0.0
No Response 2 3.4 0 0.0

 

 

 

 

 

 

@ectjve Two

Objective two was to determine the value of information technology as

perceived by the agriscience educator. The respondents rated each statement

on a scale where 1 = Strongly Disagree (MS1.5), 2 = Disagree (M=1.5-2.49), 3 =

Undecided (M=2.5-3.49), 4 = Agree (M=3.5-4.49), and 5 = Strongly Agree (M .>.

4.5) (Appendix A). The data revealed that respondents strongly agreed on the

value of knowing how to use the internet in both Michigan (M=4.93) and

California (M=4.88). It also revealed that the respondents strongly agreed on the

value of knowing how to use a computer in Michigan (M=4.93) and California

(M=4.85).

28

 

Statement three in this section was divided up into 11 different areas. The
Michigan respondents data revealed that they strongly agreed on the value of
programs having computers for teachers to use (M=4.97), an Internet connection
for teachers (M=4.91), having computers for students to use (MfiJZ), and
having an Internet connection for students to use (M=4.71). The data revealed
that the respondents from Michigan agreed with the value of having multimedia
computers for teachers (M=4.47), having multimedia computers for students
(M=4.16), and having DVD players for teachers (M=3.72). Michigan respondents
data revealed that they were undecided about the availability of satellite downlink
capabilities for teachers (M=3.48), having video conferencing capabilities for
teachers (M=3.50), having a compressed video system capability for teachers
(M=3.45), and having student access to DVD players (M=2.96). In California, the
respondents data revealed that they strongly agreed on the value of programs
having computers for teachers to use (M=4.92), having an Internet connection for
teachers (M=4.92), having computers for students to use (M=4.75), having an
Internet connection for students to use (M=4.72) and having multimedia
computers for teachers (M=4.56). The data revealed that the respondents from
California agreed with the value of having multimedia computers for students
(M=4.25), having DVD players for teachers (M=3.94), and having the availability
of satellite downlink capabilities for teachers (M=3.57). The data from the
California respondents revealed that they were undecided about having video

conferencing capabilities for teachers (M=3.43), having compressed video

29

capabilities for teachers (M=3.42), and having student access to DVD players
(M=3.12).

Statement four of Part A was divided into 20 different areas that the
respondents were to rate. The data of the Michigan respondents revealed that
they strongly agreed with the value that Information Technology helps individuals
apply knowledge (M=4.58). The Michigan respondents agreed with the value of
Information Technology as a useful instructional tool (M=4.48), that it can
improve the quality of programs (M=4.38), that it is essential to prepare students
for workplace (M=4.31), that it allows teachers the flexibility in planning their
instruction (M=4.04), that Information Technology adds interest in instruction
(M=4.46), is important in instruction (M=4.05), encourages teacher innovation
(M=4.00), enhances student learning (M=4.09), is necessary for the success of
students in the workplace (M=4.10), that it can improve teacher effectiveness
(M=4.23), and that it promotes self-directed learning (M=4.03). The Michigan
data revealed that the respondents were undecided (M=2.79) on whether
Information Technology creates a problem for the teacher. In the section with the
negatively worded statements the data revealed that the Michigan respondents
disagreed that Information Technology was too expensive to be cost effective
(M=2.35), that it makes learning too mechanical (M=2.18), that it will limit
student-teacher interaction (M=2.14), that it causes more problems than it solves
(M=1.99), that Information Technology will isolate teachers from one another
(M=2.20), and that it has an adverse effect on teachers (M=2.11). The data

revealed that the Michigan respondents strongly disagreed that Information

30

Technology has little value in agriscience education (M=1.46). The data of the
California respondents revealed that they agreed with the value that lnforrnation
Technology helps individuals apply knowledge (M=4.43), that Information
Technology was a useful instructional tool (M=4.45), that it can improve the
quality of programs (M=4.43), that it is essential to prepare students for the
workplace (M=4.32), that Information Technology allows teachers the flexibility in
planning their instruction (M=4.22), that it adds interest in instruction (M=4.20), is
important in instruction (M=4.18), encourages teacher innovation (M=4.18),
enhances student learning (M=4.17), that it is necessary for the success of
students in the workplace (M=4.05), that Information Technology can improve
teacher effectiveness (M=4.07), and that it promotes self-directed learning
(M=3.95). The data revealed that California respondents were undecided
(M=2.58) on whether Information Technology creates a problem for the teacher.
The California data in the section with the negatively worded statements revealed
that the respondents disagreed that Information Technology was too expensive
to be cost effective (M=2.27), that it makes learning too mechanical (M=2.23),
that it will limit student-teacher interaction (M=2.08), that it causes more problems
than it solves (M=2.03), that lnforrnation Technology will isolate teachers from
one another (M=2.02), that it has an adverse effect on teachers (M=1.83), and
that Information Technology has little value in agriscience education (M=1.57). In
Part A: Information Technology Values there were no significant differences
between the responses from Michigan and California. Table 2 shows a complete

mean comparison of Part A: Information Technology Values.

31

Table 2. Mean Comparison of Value of Information Technology as Perceived by
Michigan and California High School Agriscience Teachers“.

 

Value of Information
Technolggy

Michigan

California

 

n

M

SD

n

M

SD

t

 

Teachers should
know how to use the
Internet

58

4.93

0.32

60

4.88

0.32

-0.809

 

Teachers should
know how to use
computers

58

4.93

0.26

60

4.85

0.36

-1.413

 

Programs should have the following technology

available for use in instruction

 

Computers for
teachers

58

4.97

0.18

60

4.92

0.28

-1.127

 

Internet
connections for
teachers

58

4.91

0.28

60

4.92

0.28

0.056

 

Computers for
students

58

4.72

0.49

60

4.75

0.51

0.282

 

Internet
connections for
students

58

4.71

0.56

60

4.72

0.45

0.104

 

Multimedia
computers for
teachers

58

4.47

0.71

59

4.56

0.70

0.708

 

Multimedia
computers for
students

57

4.16

0.82

60

4.25

0.91

0.573

 

DVD players for
teachers

58

3.72

1.01

60

3.94

1.07

1.135

 

Satellite
downlink
capability for
teachers

58

3.48

0.98

60

3.57

1.05

0.449

 

Video
conferencing
capability for
teachers

58

3.50

1.03

60

3.43

1.08

-0.343

 

Compressed
video capability
for teachers

58

3.45

1.06

60

3.42

1.11

-0.158

 

 

DVD players for
students

 

56

 

2.96

 

1.11

 

60

3.12

 

 

1.22

 

0.701

 

32

 

Table 2 (cont’d)

 

Value of Information
Technology

Michigan

California

 

 

n I

SD

 

nIM

ISD

 

 

Information Technology

 

Helps
individuals
apply
knowledge

55

4.58

0.57

60

4.43

0.70

-1.245

 

Is a useful
instructional
tool

56

4.48

0.54

60

4.45

0.62

-0.296

 

Can improve the
quality of
programs

56

4.38

0.70

60

4.43

0.77

0.426

 

Adds interest in
instruction

56

4.46

0.57

59

4.20

0.81

-1.996

 

Is essential to
prepare
students for

the workplace

56

4.31

0.80

60

4.32

0.87

0.027

 

Can improve
teacher
effectiveness

56

4.23

0.74

60

4.07

0.80

-1.156

 

Allows teachers
flexibility in
planning their
instruction

56

4.04

0.63

60

4.22

0.64

1.532

 

Enhances
student
learning

56

4.09

0.67

60

4.17

0.69

0.612

 

ls important in
instruction

56

4.05

0.77

60

4.18

0.89

0.835

 

Encourages
teacher
innovafion

56

4.00

0.76

60

4.18

0.75

1 .306

 

ls necessary for
the success of
students in the
workplace

56

4.10

0.95

60

4.05

0.89

-0.281

 

 

Promotes self-
directed
learning

 

56

 

4.03

 

0.69

 

60

3.95

 

0.83

 

 

-0.542

 

33

 

Table 2 (cont’d)

 

Value of lnfon'nation

Technology Michigan California

 

n M SD n M SD t

 

Creates
problems for 56 2.79 1.26 60 2.58 1.28 -0.857
the teacher

 

Is too expensive
to be cost 56 2.35 0.96 60 2.27 1.13 -0.463
effective

 

Makes learning

too mechanical 56 2.18 0.79 60 2.23 0.95 0.340

 

Will limit
student-
teacher
interaction

56 2.14 0.94 60 2.08 0.98 -0.334

 

Will isolate
teachers from 56 2.20 0.86 60 2.02 0.91 -1.092
one another -

 

Causes more
problems than 56 1.99 0.81 60 2.03 0.99 0.252
it solves

 

Has an adverse
effecton 56 2.11 1.09 60 1.83 0.91 -1.466
teachers

 

 

Has little value
in agriscience 56 1.46 0.71 60 1.57 0.95 0.661
education

 

 

 

 

 

 

 

 

*a S 0.05

Obiective Three
Objective three was to determine the general information technology

knowledge and skill level possessed by the agriscience educator. The
respondents rated each statement with 1 = I DON’T KNOW enough about this
area to respond (M149), 2 = my knowledge/skill in this area is BELOW
AVERAGE (M=1.5-2.49), 3 = my knowledge/skill in this area is AVERAGE
(M=2.5-3.49), 4 = my knowledge/skill in this area is ABOVE AVERAGE (M=3.5-

4.49), and 5 = my knowledge/skill in this area qualifies me as an EXPERT

34

 

(M245) (Appendix A). The data of the Michigan respondents revealed that they
felt that their knowledge and skills were above average when it came to knowing
how to operate a computer (M=3.77), locating computer-based teaching
materials for use in instruction (M=3.74), integrating computer-based teaching
materials into instruction (M=3.57), knowing the basic components of a computer
(M=3.57), and evaluating software for instruction (M=3.57). Michigan
respondents data also revealed that they felt they were average in their
knowledge and skill in selecting information technology that fits program needs
(M=3.42), knowing how to prepare students to use information technology
(M=3.32), and evaluating software for program management (M=3.30).
Statement nine of this section was divided into seven parts. The Michigan
respondents data revealed that they felt that their knowledge and skill was above
average when it came to using the Internet E-mail (M=4.31), the World Wide
Web (M=4.22), and DVD players (M=3.57). The data showed that they were
average in the knowledge and skills when using multimedia computers (M=2.91).
The data also showed that the Michigan respondents felt that their knowledge
and skills were below average when it came to using video conferencing
equipment (M=2.12), satellite downlink technologies (M=1.95), and compressed

video technologies (M=1.84).

The data of the California respondents revealed that they felt that their
knowledge and skills were above average when it came to knowing how to
operate a computer (M=3.87), locating computer-based teaching materials for

use in instruction (M=3.77), integrating computer-based teaching materials into

35

instruction (M=3.68), knowing the basic components of a computer (M=3.65),
and selecting information technology that fits program needs (M=3.59).
California respondents data also revealed that they felt that they were average in
their knowledge and skill in evaluating software for instruction (M=3.47), knowing
how to prepare students to use information technology (M=3.47), and evaluating
software for program management (M=3.32). In statement nine of this section
the California respondents data revealed that they felt that their knowledge and
skills were above average when it came to using the Internet Email (M=4.17),
the World Wide Web (M=4.16), and DVD players (M=3.67). The California data
showed that the respondents were average in the knowledge and skills when
using multimedia computers (M=2.97). The California data showed that the
respondents felt that their knowledge and skills were below average when it
came to using video conferencing equipment (M=2.10), satellite downlink
technologies (M=2.02), and compressed video technologies (M=2.00). There
were no significant differences in the responses from the Michigan and California
respondents in the Information Technology Knowledge/skill section of the survey.
Table 3 is a complete mean comparison of Part B: lnforrnation Technology

Knowledge/Skills.

Qbiective Four
Objective four was to determine the general software knowledge and skill
level possessed by the agriscience educator. The respondents rated each

statement with 1 = I DON’T KNOW enough about this area to respond, 2 = my

36

Table 3. Mean Comparison of Michigan and California High School Agriscience
Educators' Report of Information Technology and Skills*.

 

Technology
Knowledge and
Skills

Michigan

California

 

M

SD

M SD

 

Know how to operate
a computer

58

3.77

0.62

60

3.87 0.70

0.814

 

Can locate computer-
based teaching
materials for use in
instruction

58

3.74

0.76

60

3.77 0.83

0.172

 

Can integrate
computer-based
teaching materials
into instruction

58

3.57

0.82

60

3.68 0.87

0.730

 

Know the basic
components of a
computer

58

3.57

0.92

60

3.65 0.88

0.489

 

Know how to select
information
technology that fits
program needs

56

3.42

0.85

55

3.59 0.85

1.064

 

Can evaluate
software for
instruction

58

3.57

0.92

60

3.47 0.97

0.063

 

Know how to prepare
students to use
information
technology

57

3.32

0.76

56

3.47 0.71

1.075

 

Can evaluate
software for program
management

58

3.30

0.88

60

3.32 1.00

0.086

 

Know how to use

 

Internet Email

58

4.31

0.78

58

4.17 0.82

-0.930

 

World Wide
Web

58

4.22

0.75

58

4.16 0.81

-0.475

 

DVD players

58

3.57

1.03

58

3.67 1.05

0.536

 

Multimedia
computers

58

2.91

1.19

58

2.97 1.11

0.242

 

 

Video
Conferencing

 

58

 

2.12

 

1.09

 

58

2.10 1.00

 

 

 

-0.088

 

37

 

Table 3 (cont’d)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Technology
Knowledge and Michigan California
Skills
n M SD n M SD t
Satellite
downlink 58 1.95 1.05 58 2.02 1.07 0.351
technologies
Compressed
video 58 1.84 0.99 58 2.00 1.08 0.809
*aS0.05

knowledge/skill in this area is BELOW AVERAGE, 3 = my knowledge/skill in this
area is AVERAGE, 4 = my knowledge/skill in this area is ABOVE AVERAGE, and
5 = my knowledge/skill in this area qualifies me as an EXPERT (Appendix A).
The data from the Michigan respondents revealed that they felt their software
knowledge and skill was above average when it came to word processing
software (M=3.93), Internet E-mail software (M=3.94), World Wide Web browsers
(M=3.92), and grade book software (M=3.54). The Michigan respondents felt
that their knowledge and skill was average when it came to using presentation
software (M=3.40), spreadsheet software (M=3.13), operating systems (M=3.43),
graphics software (M=3.00), desktop publishing software (M=2.94), database
software (M=2.39), instructional software (M=2.79), and desktop utility software
(M=2.68). The data revealed that the Michigan respondents were below average
in their knowledge and skill in lesson planning software (M=1.97), World Wide
Web page creator software (M=2.33), and file transfer software to and from other

computers using a modem or a local area network (LAN) (M=2.22).

38

The California respondents data revealed that they felt their software
knowledge and skill was above average when it came to word processing
software (M=4.10), Internet E-mail software (M=3.93), World Wide Web browsers
(M=3.90), grade book software (M=3.59), and spreadsheet software (M=3.67).
The California respondents felt that their knowledge and skill was average when
it came to using presentation software (M=3.45), operating systems (M=3.13),
graphics software (M=3.13), desktop publishing software (M=3.13), database
software (M=3.09), instructional software (M=2.93), and desktop utility software
(M=2.88). The California data revealed that the respondents were below
average in their knowledge and skill in lesson planning software (M=2.40), World
Wide Web page creator software (M=2.33), and file transfer software (M=2.33).
Table 4 is a complete mean comparison of Part C: Software Applications
Knowledge/Skill. There were significant differences between the California and
Michigan respondents’ rating of their software knowledge and skills in three
areas. For skills in using spreadsheets, California respondents rated their skills
significantly higher (M=3.67; SD=1.00) than the Michigan respondents (M=3.13;
SD=0.99) at p=0.01. In their knowledge and skills in the use of database
software California respondents also rated their skills significantly higher
(M=3.09; SD=1.11) than their Michigan counterparts (M=2.39; SD=0.94) at
p=0.05. This also was true when it came to the knowledge and skill in using
lesson planning software. California respondents rated their skills higher
(M=2.40; SD=1.24) than the skills of the Michigan respondents (M=1.97;

SD=1.08) at p=0.05.

39

Table 4. Mean Comparison of Michigan and California High School Agriscience
Educators' Perception of Software Specific Knowledge and Skills*.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Software Specific
Knowledge and Michigan California
Skills

n M SD n M SD t
Word Processor 57 3.93 0.84 60 4.10 0.86 1.084
Internet E-mail 57 3.94 0.87 60 3.93 0.88 -0.033
World Wide Web
Browser 58 3.92 0.76 60 3.90 0.84 -0.152
Grade Book 57 3.54 1.24 60 3.59 1.09 0.222
Presentation Software 58 3.40 1.01 60 3.45 1.08 0.277
SJIreadsheet 58 3.13 0.99 60 3.67 1 .00 2936*
Operating System 58 3.43 0.98 60 3.13 1.02 -1.619
Graphics 58 3.00 0.96 60 3.13 1.02 0.732
Desktop Publishing 58 2.94 1.12 60 3.13 1.14 0.890
Database 57 2.39 0.94 58 3.09 1.11 3.641 *
Instructional Software 58 2.79 1.12 60 2.93 1.24 0.711
Utilities 58 2.68 0.99 60 2.88 1 .02 1 .046
World Wide Web
Page Creator 58 2.33 1.23 60 2.33 0.97 0.028
File Transfer to and
from other computers
using a mo dem or 58 2.22 1.20 60 2.33 0.97 0.543
LAN
Lesson Planning 58 1.97 1.08 60 2.40 1.24 2037*
*a50.05

Obiective Five

Objective five was to gain the educator’s perception of the potential
usefulness of information technology in program and instructional management.
The respondents rated each statement on a scale where 1 = Not Useful, 2 = Low
Usefulness, 3 = Undecided, 4 = Moderately Useful, and 5 = Highly Useful
(Appendix A). The data from the Michigan respondents revealed that all of the

categories in this section were moderately useful in instructional management

40

(M=4.40), in program planning, development and evaluation (M=4.05), in student
vocational organizations (MM.06), in instructional planning (M=3.96), in
professional role and development (M=4.02), in instructional execution (M=3.88),
in the coordination of cooperative programs (M=3.64), with student guidance and
career development (M=3.95). school community relations (M=3.87), and with
instructional evaluation (M=3.81).

The California respondents data also revealed that all of the categories in
this section were moderately useful in instructional management (M=4.35), in
program planning, development and evaluation (M=4.23), in student vocational
organizations (M=4.20), in instructional planning (M=4.20), in professional role
and development (M=4.07), in instructional execution (M=3.93), in the
coordination of cooperative programs (M=3.92), with student guidance and
career development (M=3.88), school community relations (M=3.82), and with
instructional evaluation (M=3.78). There were no significant differences in the
responses from the Michigan and California respondents in the Usefulness of
Information Technology in Program/Instructional Management section of the
survey. Table 5 is a complete mean comparison of Part E: Usefulness of

Information Technology in Program/Instructional Management.

Obiective Six

Objective six was to determine the availability of information technology to

the agriscience educator (Appendix A). In question one of the section, the

41

Table 5. Mean Comparison of Michigan and California Agriscience Educators'
Report of Usefulness of Information Technology in Program and Instructional

Management*.

 

Usefulness of

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

lnforrnation Michigan California
Technology
n M SD n M SD t

Instructional

Manafiment 57 4.40 0.86 60 4.35 0.73 -0.362
Program Planning,

Development& 57 4.05 0.85 60 4.23 0.85 1.127

Evaluation
Student Vocational

Organizations 57 4.06 0.91 60 4.20 0.71 0.924
Instructional Planning 57 3.96 0.96 60 4.20 0.78 1.457
Professional Role and

Development 57 4.02 0.81 60 4.07 0.90 0.309
Instructional

Execution 57 3.88 0.97 60 3.93 0.80 0.342
Coordination of

Cooperative 57 3.64 0.93 59 3.92 0.92 1.601

Programs
Student Guidance

and Career 57 3.95 0.84 60 3.88 0.90 -0.504

Development
School Community

Relations 57 3.87 0.95 60 3.82 0.91 -0.301
Instructional

Evaluation 57 3.81 0.93 59 3.78 0.87 -0.163
*a50.05

percentage of Michigan respondents who now have a computer available with

World Wide Web access at home was 93%, who do not have World Wide Web

access was 5% and those who plan to a acquire access was 2%. Those

respondents who had Internet Email access was 93%, who did not have access

was 5% and those who plan to acquire access was 2%. Michigan respondents

whose home computer had multimedia capabilities was 81%, who did not have

42

 

the capabilities was 18% and those who planned to acquire a computer with
these capabilities was 2%. The percentage of Michigan respondents who now
have a computer available with World Wide Web access in their office or
classroom was 98%, who do not have World Wide Web access was 2% and
those who plan to a acquire access was 0%. Those respondents who had
Internet E-mail access was 98%, who did not have access was 2% and those
who plan to acquire access was 0%. Michigan respondents whose work
computer had multimedia capabilities was 86%, who did‘not have the capabilities
was 11% and those who planned to acquire a computer with these capabilities
was 3%. The percentage of Michigan respondents who now have a computer
available with World Wide Web access in a computer lab was 78%, who do not
have World Wide Web access was 16% and those who plan to acquire access
was 2%. Those respondents who had Internet Email access was 77%, who did
not have access was 21% and those who plan to acquire access was 2%.
Michigan respondents whose computer lab computers had multimedia
capabilities was 79%, who did not have the capabilities was 18% and those who
planned to acquire a computer with these capabilities was 4%.

The percentage of Michigan respondents that had multimedia computers
in their schools currently was 88%, those who did not have was 11% and those
that planned to acquire the technology was 2%. There were 64% of Michigan
respondents who currently had a DVD player in their school and 36% who did not
have a DVD player and 0% planned to acquire. When it came to satellite

downlink technologies in their school, 42% had the technology currently, 53% did

43

not have the technology, and 2% were planning on acquiring the technology. In
Michigan 40% of the respondents stated that they had a compressed video
system currently in their school, 60% said that they did not have such a system,
0% planed to acquire a system. There were 46% of Michigan respondents who
currently have video conferencing systems in their schools while 52% said that
they do not have these systems and 2% plan to acquire a video conferencing
system.

The percentage of California respondents who now have a computer
available with World Wide Web access at home was 83%, who do not have
World Wide Web access was 14% and those who plan to a acquire access was
3%. Those respondents who had Internet E-mail access was 81%, who did not
have access was 14% and those who plan to acquire access was 5%. The
respondents from California whose home computer had multimedia capabilities
was 67%, who did not have the capabilities was 24% and those who planned to
acquire a computer with these capabilities was 7%. The percentage of
California respondents who now have a computer available with World Wide
Web access in their office or classroom was 95%, who do not have World Wide
Web access was 3% and those who plan to a acquire access was 2%. Those
California respondents who had Internet Email access was 97%, who did not
have access was 2% and those who plan to acquire access was 2%. California
respondents whose work computer had multimedia capabilities was 81%, who
did not have the capabilities was 13% and those who planned to acquire a

computer with these capabilities was 5%. The percentage of California

respondents who now have a computer available with World Wide Web access in
a computer lab was 64%, who do not have World Wide Web access was 26%
and those who plan to acquire access was 10%. Those respondents who had
Internet Email access was 60%, who did not have access was 31% and those
who plan to acquire access was 9%. California respondents whose computer lab
computers had multimedia capabilities was 59%, who did not have the
capabilities was 35% and those who planned to acquire a computer with these
capabilities was 7%.

The percentage of California respondents that had multimedia computers
in their schools currently was 78%, those who did not have was 17% and those
that planned to acquire the technology was 5%. There were 52% of California
respondents who currently had a DVD player in their school, 38% who did not
have a DVD player and 10% who planned to acquire a DVD player. The
percentage of California respondents who currently had satellite downlink
technologies in their school was 19%, 74% did not have the technology, and 7%
were planning on acquiring the technology. In California, 14% of the
respondents stated that they had a compressed video system currently in their
school, 83% said that they did not have such a system, 4% planed to acquire a
system. There were 13% of California respondents who currently have video
conferencing systems in their schools while 80% said that they do not have these
systems and 7% plan to acquire a video conferencing system. Table 6 is the

complete frequency distribution for Part D: Availability of Computer Technology.

45

Obiective Seven
Objective seven was to determine the source of the agriscience educator’s
information technology training (Appendix A). The percentage of the Michigan
respondents who reported that their information technology training was self-
directed was 88% and of those 92% had received training from this source in the
last three years. School districts or state sponsored in-service training accounted
for 91% of the technology training and 88% reported participating in the last three
years. Michigan respondents reported that 84% of them had received
technology training at a professional conference and 86% had received
technology training from this source in the last three years. Of those reporting,
76% of the Michigan respondents had received training from a college course
and 57% had received this training in the last three years. Those receiving
technology training from a written source reported at 70% while 87% of those
had done so in the past three years. Michigan respondents reported that 62%
had received technology training from a college workshop and 72% said that this
training had occurred in the past three years. The percentage of Michigan
respondents who reported receiving training from an industry workshop was 29%
and 88% had had received training in the last three years. Those receiving
technology training from suppliers reported at 27% while 67% had received this
training in the past three years.

The percentage of the California respondents who reported that their
information technology training was self-directed was 87% and of those 98% had

received training from this source in the last three years. School districts or state

46

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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47

sponsored in-service training accounted for 85% of the technology training and
92% reported of participating in the last three years. California respondents
reported that 46% of them had received technology training at a professional
conference and 96% had received technology training from this source in the last
three years. Of those reporting, 76% of the California respondents had received
training from a college course and 49% had received this training in the last three
years. Those receiving technology training from a written source reported at
71% while 93% of those had done so in the past three years. California
respondents reported that 75% had received technology training from a college
workshop and 79% said that this training had occurred in the past three years.
The percentage of California respondents who reported receiving training from
an industry workshop was 36% and 76% had received training in the last three
years. Those receiving technology training from suppliers reported at 27% while
72% had received this training in the past three years. Table 7 is a complete

report on Part F: Information Technology Training Sources.

Obiective Eight

Objective eight was to determine if a relationship exists between selected
variables and the value placed on information technology by the agriscience
educator. Table 8 reports the Pearson Product Moment Correlation coefficients
along with the levels of significance and the number of respondents used for
each correlation analysis. The correlations were interpreted using the scale

developed by Davis (1971) where 0.01-0.09= negligible correlation, 0.10-0.29=

48

Table 7. Self-Reported Information Technology Training Received within the Past
Three Years.

 

 

 

 

 

 

 

 

Michiggt California
Attended Within Past Attended Within Past
3 Years 3 Years
Training Source n % n % n % n %
Self-Directed 49 88.0 43 92.0 52 87.0 48 98.0
School
District/State 51 91.0 44 88.0 50 85.0 44 92.0
Professional
Conference 46 84.0 38 86.0 50 46.0 46 96.0
College 42 76.0 24 57.0 45 76.0 22 49.0
Written Materials 39 70.0 32 87.0 42 71.0 37 93.0

 

College Workshop 34 62.0 23 72.0 44 75.0 34 79.0

 

IndustryWorkshop 16 29.0 14 88.0 21 36.0 16 76.0

 

 

 

 

 

 

 

 

 

 

 

Suppliers 15 27.0 10 67.0 13 72.0 6 46.0

 

low correlation, 0.30-0.49= moderate correlation, 0.50-0.69: substantial
correlation, and 20.70= very strong correlation.

There was a positive moderate correlation (r=0.40) between the perceived
value of information technology and the Technology Knowledge/Skills Scale for
the Michigan respondents at p=0.05, as well as the California respondents
(r=0.33). In the Software Knowledge and Skills Scale, the Michigan respondents
had a positive moderate correlation (r=0.32) and the California respondents had
a low correlation (r=0.26) with the perceived value of information technology. A
low correlation (r=0.29) was found between the perceived value of information
technology and the number of professional development points for the California
respondents. Michigan respondents also had a low positive correlation with the
location of their school (r=0.18), their school involvement with other schools

(r=0.11), and the age of the respondents (r=0.13). A negative low correlation

49

was observed with the Michigan respondents if the indicated that they taught
both high school and middle school (r=-0.12) and with the number of professional
development activities (r=-0.1 1) that they attended. None of the other selected
variables, school location, school involvement with other schools, age, highest
degree held, teach middle school only, teach high school only, gender, years
teaching experience, teaching both high school and middle school, the number of
professional development activities attended, or race showed a significant

correlation with the perceived value of information technology for the California

respondents.

Table 8. Relationships Between Perceived Value of Information Technology

Scale and Selected Variables‘.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Variable M r3 p‘ n3
Technology Knowledge/Skills Michigan 3 31 0.40 0.005 48
Scale grand mean2 California ' 0.33 0.014 54
Software Knowledge & Skills Michigan 3 13 0.32 0.026 50
Scale grand mean California ' 0.26 0.047 57
Professional Development Michigan 9.67 0.05 0.737 53
Points6 California 8.81 0.29 0.026 59
. Michigan 0.18 0.196 52

S°h°°' L°Cat'°" California 0.02 0.859 59
School Involvement with other Michigan 0.11 0.500 39
Schools California 0.01 0.954 37
A e Michigan 0.13 0.390 47
9 California 002 0.915 55

. California 0.01 0.980 52
H'gheSt Degree He'd Michigan 0.06 0.635 59
Middle School Only Michigan 0.03 0.82 52
. Michigan 0.04 0.765 52
”'9“ S°h°°' 0"" California 004 0.781 59
Michiggn 0.08 0.594 52

Gender California -0.06 0.684 58
. . Michigan 006 0.674 52

Years Teaching Experience California _0.02 0.886 58

 

 

 

 

 

 

50

 

Table 8 (cont’d)

 

 

 

 

 

 

 

Variable r7 p‘ n5
. . Micligan -0.12 0.411 52
High School and Middle School California 005 0.701 59
Number of Professional Michigan -0.11 0.431 50
Development Activities California 002 0.911 55
Race Michigan 012 0.389 52
California 005 0.727 59

 

 

 

 

 

 

 

 

1 Variables selected were the grand means of the Technology Knowledge/Skills,
Software Knowledge/Skills, demographic variables, and professional
development activities.

2 Grand Mean = all respondent’s means for each variable within the scale were
averaged to yield one mean for the scale.

3 r = Pearson correlation coefficient, rounded to the second significant digit.
4p= Level of significance — correlations were significant at the 0.05 level (2-
tailed).

5n= the number of respondents used in the correlation analysis based upon
respondents’ decision to participate in the survey question.

6 Professional development points were calculated under objective seven to
replicate the methodology used by Kotrlik et al. (2000). See Appendix F.

Summa[y

This study was designed to replicate the methodology of Kotrlik et al.
(2000). The objectives were duplicated and a modified questionnaire was mailed
to 240 High School agriscience teachers in Michigan and California. Their
responses indicated a significant correlation between their perceived value of
information technology with both their technology and software knowledge and
skills. A large number of surveys were received from both Michigan (n=58) and
from California (n=60) although, the percentage of respondents for this survey
was less than the percentage, 65.0%, of respondents in the Kotrlik et al. (2000)
study. The respondents from each state showed a high level of computer and

software related knowledge and skill. Respondents from both states identified

51

the usefulness of information technologies for the betterment of their students’

education and as assistance for accomplishing this goal.

52

CHAPTER 5

DISCUSSION AND CONCLUSIONS

Introggction

This research attempted to answer the question of what are the
knowledge, skill level, potential usefulness, and value of information technology
to Michigan and California high school agriscience educators. This was
accomplished by replicating the study that Kotrlik et al. performed in Louisiana in
1998. In replicating this study the researcher hoped to accomplish a greater
understanding of where the agriscience education communities in Michigan and
California are in relation to the information technology that is being used in
today’s classroom. This research addressed the following research objectives:

1. To determine the demographic characteristics (degrees held, age, gender,
ethnicity, years teaching experience, school location [rural, urban or
suburban], grade level, and participation in professional associations) of
the selected subjects.

2. To determine the value of information technology as perceived by the
agriscience educator.

3. To determine the general information technology knowledge and skill level
possessed by the agriscience educator.

4. To determine the general software knowledge and skill level possessed by

the agriscience educator.

53

5. To gain the educator’s perception of the potential usefulness of
information technology in program and instructional management.

6. To determine the availability of information technology to the agriscience
educator.

7. To find out the source of the agriscience educators information technology
training.

8. To find if a relationship exists between selected variables and the value

placed on information technology by the agriscience educator.

Obiective One Discussion and Conclusions

There were a greater number of respondents from Michigan that held
advanced degrees than their counterparts in California. The number of male
respondents was greater than their female counterparts in California while the
respondents from Michigan were dispersed fairly equally. In both states, the
overwhelming majority of respondents self reported their race to be Caucasian,
while there were no respondents from either state that represented themselves
as being black. The age and number of years teaching experience was
approximately equal between both states while the majority of the respondents
taught at rural schools with an equal number of respondents from California
teaching in urban and suburban schools. The respondents from both Michigan
and California attended approximately the same number of professional
development workshops and conferences. There were a greater number of

Michigan respondents who taught at the middle school and junior high level than

54

their counterparts in California and it is believed that this is because there are
fewer agriscience educators in Michigan.

This information reflects that the two populations are very similar in most
of their characteristics with the largest discrepancy being in the number of
respondents with advanced degrees. The Louisiana study (Kotrlik et al., 2000)
had a majority of respondents with advanced degrees (58.0%), much like the
respondents from Michigan, were almost exclusively male ((94.0%), like the
California respondents, and Caucasian (94.0%), like the respondents from both
Michigan and California except that there were respondents in the Louisiana
study that reported themselves as black (5.0%). The Louisiana respondents’
average age was slightly higher (42) and their years of teaching experience were
also greater (18) than the respondents from Michigan and California. The
majority of the respondents taught in rural areas and in high school like the

respondents from Michigan and California.

Mm Two Discgssion and Conclusions

The Michigan and California respondents were very similar in their
perceived value of information technology with no significant deviation found in
any of the positively or negatively worded statements. In comparing the findings
of these values with those of Kotrlik et al. (2000), there is an increase in the
means of the responses in the positively worded statements and a decrease in
the means of the negatively statements. These results showed an increase in

the value that is placed on information technology and the belief that there are

55

fewer obstacles to implementing information technologies in agriscience
educafion.

The reason for this could possibly be the ever changing attitude towards
technology that has occurred in the five years since Kotrlik et al. (2000)
performed their study. These results help to reinforce that educators must keep
up-to-date with the burgeoning world of technology and determine how they can

best use these technologies to help their students obtain knowledge.

Obiective Three Discussion and Conclusions

The respondents from both Michigan and California were very similar in
their information technology knowledge and skill levels with no significant
difference in any of the different areas of technology listed in the survey
instrument. As stated before, their knowledge and skills means were higher all
across the board than those of the respondents in the Kotrlik et al. (2000) study.
For example, the mean for knowing how to use a computer was 2.93 in the
Kotrlik et al. (2000) study and 4.93 in Michigan and 4.85 in California. A reason
for this could be that the respondents in the Kotrlik et al. (2000) study are of an
age when they would not have been exposed to computer technologies until after
many of them would have graduated from college. With this information one
could assume that their technology skill levels would be lower than that of
persons who graduated from college five years later when the computer

technology growth and innovation was taking off.

56

This falls in line with the responses from the previous section. Since there
is a greater perceived value of information technology by both of the subsets in
the study, it just follows along that both groups would have a greater knowledge
and skill level with the technology than their counterparts did five years ago. The
results of this objective follow along with the results of objective six and the
percentage of respondents who have access to technology both at home and in

the classroom.

Obiective Fmfliscussion and Conclusions

In this objective there was a significant difference in three distinct software
knowledge and skill areas between the respondents from California and the
respondents from Michigan. The areas in which this difference occurred were
spreadsheet software, database software, and lesson planning software. In each
of these areas the mean score for California was higher than that of Michigan
indicating a greater knowledge and skill in these areas. A reason for this could
be that these software’s are required by more school districts in California to
possibly record grades or other tasks on a more regular basis than their Michigan
counterparts. Another possible explanation for this is the number of California
respondents who attended college and industry sponsored workshops as part of
their professional development practices. Another possibility is that the majority
of California respondents taught at the high school level only where software
programs such as these would be more likely used in the classroom. The

number of Michigan respondents that taught at the middle school or junior high

57

school level could possibly not be covering material where these software
programs would be needed.

The results of this objective differed from those that Kotrlik et al. (2000)
found. Their findings indicated that the respondents perceived themselves at an
average to below average level when it came to software specific knowledge and
skill; especially in the areas of World Wide Web browsers (M=1.92), Internet E-
mail (M=1.91), file transfer (M=1.79), and presentation software (M=1.97) where
the Michigan and California respondents rated themselves above average and
average. These findings follow along with the discussion from the previous
section. The advent of the World Wide Web as we know it today has really been
since the Kotrlik et al. (2000) study was performed five years ago. It is believed
that if the study was done with this population again that the findings would be

more in line with the results from Michigan and California.

Obiective Five Qscgssion and Conclusions

The respondents from both Michigan and California all rated the potential
usefulness of information technology in program and instructional management
moderately useful to highly useful for all of the categories that were available to
them. There was no significant difference in the respondents reporting for either
Michigan or California.

The possible reason for this goes along with the same reasons in the
responses to objectives two and three. There is a growing acceptance and

understanding of technology in today’s workplace and a feeling that information

58

technology can assist in the completions of many tasks that were time
consuming and monotonous in the past (Tapscott, 1998), such as calculating
grades or keeping a database of contact information for student vocational
opportunities. Technology has made these types of endeavors easier to access
and maintain. The respondents in the Louisiana study rated the potential
usefulness of information technology in program and instructional management
only moderately useful in all categories (Kotrlik et al., 2000). For example, the
instructors in Louisiana had a mean of 4.02 in the usefulness in student
vocational organizations and a mean of 3.89 in instructional execution compared
with means of 4.06 and 4.20 in Michigan and California in the usefulness in
student vocational organizations and 3.88 and 3.93 in Michigan and California in

instructional execution.

Obiective Six Discussion and Conclusions

The results of this objective showed that the availability of information
technology, more specifically computer technology, has almost reached a 100%
saturation level with the sample that was used for this study; whether it was at
home, in their classroom or office or in a computer lab in their school. The
number of respondents who had access to higher priced technologies was much
lower than those with access to computers. Although, the Michigan respondents
reported a greater number of them had access to satellite downlink technologies,
compressed video systems, and video conferencing systems than their

counterparts in California.

59

This area also falls in line with the others when it comes to the value that
the respondents are placing on the usefulness of information technology. They
are embracing it by bringing it into their homes and using it in many different
ways. A possible reason that the Michigan respondents have a greater level of
availability of the technologies that would be used to transmit information on a
more face to face level is that Michigan has fewer agriscience programs than
California and the need to communicate and share information with their
colleagues at other schools is possibly greater. There is a possibility that these
systems could be used to share teachers so that a school district can keep an
agriscience program going when there is a lack of funds to hire a teacher for that
district. The results from this section were far greater than those of Kotrlik et al.
(2000) and the Louisiana study, where just over half of the respondents had
computers at home and only three fourths had computers in their offices or
classrooms. Less than one-quarter of the respondents had access to Internet E—
mail or the World Wide Web at home, classroom, office, or lab. When it came to

the higher end technologies Louisiana and California had similar response rates.

Obiective Seven Discussion and Conclusions

The training sources used for information technology showed that the
majority of the respondents were self-directed learners. There were a greater
number of California respondents who reported attending college and industry
workshops than their counterparts from Michigan, but there was not a significant

difference between the two.

60

This area is expected to have a high response rate because of the
requirements of educators to obtain continuing education credits to keep their
teaching credentials. The fact that these respondents are using technology as
an area to increase their knowledge is not surprising given the information that
has been discussed in the previous sections of this chapter. The need to
educate oneself to keep up with technology falls in line with the need to keep
educating oneself to keep their teaching credentials. The results of the Kotrlik et
al. (2000) study followed the same lines as this current study where the majority
of the respondents in Louisiana were self directed learners (69.8%) when it came
to information technology. The Louisiana respondents also reported a high level
of training from college and industry workshops (54.0%) as did the California

respondents.

Obiective Eight Discussion and Conclusions

In the results for this objective there are several significant relationships
which suggest that the selected variables may be associated with the
respondents’ value placed on information technology. The General Information
Technology Knowledge/Skill variable can be interpreted to have a moderate
association which can suggest that if the respondent knows how to operate a
certain technology and can use various components of said technology that they
will in turn place a higher value on information technology as a whole. The
Michigan respondents had a significantly higher association between technology

knowledge and skills than that of the respondents from California. This can

61

possible mean that it is more important for the Michigan respondents to believe
that they are knowledgeable about information technology than the California
respondents when it comes to predicting the value placed on information
technology.

The data from this study shows a stronger association than the data
reported for the study done by Kotrlik et al. (2000) where all of the correlations
were interpreted as either low or negligible. There is a strong indication that if
educators have the technology skills and attend professional development
activities that there is a greater value placed on information technology. This

also goes along with the tenor of the findings for this study.

Recommendations for Agriscience Educators

Since the Kotrlik et al. (2000) study was performed, a great deal of
innovation in the world of information technology has happened. What was
discovered with this current survey is that the agriscience educators are
developing a value of information technology that was not seen in the 1998
study. This is a good trend and they must not stop pursuing a higher knowledge
and skill level when it comes to information technology. They also need to
remember that the need to push their students into the using these technologies
because it will not only help them in the field of agriscience, but in many of the
challenges they will receive from many other areas. Technology is not going
away and the fact that the agriscience education community is starting to grasp

this concept is going to help the field to keep moving fonlvard.

62

Recommendations for Further Research
There are four areas in which the researcher feels further research is
recommended and/or needed in the area of information technology:

1. Look further into the significant differences that arose in the software
knowledge and skill section of this research project to determine the
reasons for the differences.

2. Use the same survey instrument and perform the study again in 5 years
(the same interval between this study and the Kotrlik et al. (2000) study)
and see what changes, if any, have come about in the value and
knowledge of information technology.

3. Use the same survey instrument and perform the study in another region
of the country and compare the findings to the two previous research
projects to see how different regions of the country compare.

4. Use the data gathered from a subsequent research project, if any occur, to
validate the relationships discussed in objective eight of this research

project.

Summary

Technological innovations are not going to stop just because the general
public can not keep up with them. The same can be said of education. There
seems to be a growing trend in wanting to get more things done in a shorter
amount of time, and one of the ways to do this is with the use of technology.

From these eight research objectives, one can assume that educators in the field

63

of agriscience education in Michigan and California see a great value in their use
and knowledge of information technologies. This knowledge and these skills are
vastly improved in regards to information technology and their use in the
classroom and with information dissemination. Educators are starting to keep up
with the trends of their students when it comes to the information technologies
making their classrooms a more vibrant, energetic, fun, and realistic learning

environment.

64

BIBLIOGRAPHY

65

Alston, A. J. (2000). An assessment of instructional technology in secondary
agricultural education curricula in North Carolina and Virginia.
Unpublished doctoral dissertation, Iowa State University, Aimes.

Birkenholz, R. J., 8 Harbstreit, S. R. (1987). Analysis of the in-service needs of
beginning vocational agriculture teachers. The Journal of the American
Association of Teacher Educators in Agriculture, 28(1), 41-49.

Davis, J. A. (1971). Elementary survey analysis. Englewood Cliffs, NJ:
Prentice-Hall.

Dillman, D. A. (2000). Mail and internet surveys: The tailored design method.
New York, NY: John Wiley & Sons, Inc.

Fletcher, W. E., & Deeds, J. P. (1994). Computer anxiety and other factors
preventing computer use among United States secondary agricultural
educators. Journal of Agricultural Education, 35(2), 16-21.

Garton, B. L., & Chung, N. (1996). The in-service needs of beginning teachers of
agriculture as perceived by beginning teachers, teacher educators, and
state supervisors. Journal of Agricultural Education, 37(3), 52-58.

Kotrlik, J. W., Redmann, D. H., l-larrison, B. C., & Handley, C. S. (2000).
Information technology related professional development needs of

Louisiana agriscience teachers. Journal of Agricultural Education, 41 (1 ),
1 8-29.

Larson, M. R., & Bruning, R. (1996). Participant perceptions of a collaborative
satellite-based mathematics course. The American Journal of Distance
Education, 10(1), 6-22.

Layfield, K. D. (1998). A national assessment of secondary agriculture teachers'
perceptions of and use of the internet. Unpublished doctoral dissertation,
The Pennsylvania State University, College Park.

McCaslin, N. L., & Torres, R. M. (1992). Factors underlying agriculture teachers'
attitude toward using microcomputers for in-service education. Journal of
Agricultural Education, 33(3), 47-52.

Miller, G., & Miller, W. (2000). A telecommunications network for distance

learning: If it's built, will agriculture teachers use it? Journal of Agricultural
Education, 41(1), 79-87.

Nobel, D. F. (1997). Part I: The automation of higher education. Retrieved
December 10, 2002, from the University of California-San Diego,

66

Department of Communications Web site
http://communications.ucsd.edu/dI/ddm1 .html

Nobel, D. F. (1998a). Part II: The coming battle over online instruction.
Retrieved December 10, 2002, from the University of California-San
Diego, Department of Communications Web site
http://communications.ucsd.edu/dl/ddm2.html

Nobel, D. F. (1998b). Part III: The bloom is off the rose. Retrieved December
10, 2002, from the University of California-San Diego, Department of
Communications Web site http://communications.ucsd.edu/dl/ddm3.html

Nobel, D. F. (1999). Part IV: Rehearsal for the revolution. Retrieved December
10, 2002, from University of California-San Diego, Department of
Communications Web site http:/Icommunications.ucsd.edu/dllddm4.html

Peasley, D. D., & Henderson, J. L. (1992). Agriscience curriculum in Ohio
agricultural education: Teacher utilization, attitudes, and knowledge.
Journal of Agricultural Education, 33(1), 37-45.

Pelton, J. N. (1991). Technology and education: friend or foe? Research in
Distance Education, 3, 2-9.

Rubin, A. & Babbie, E. (2001 ). Research methods for social work. Belmont, CA:
Wadsworthfl’homson Learning.

Stellwagen, J. B. (1999). Social studies teaching and technology: reflections of a
veteran teacher. International Journal of Social Education, 14(1), 118-129.

Swan, M. K., & Brehmer, J. (1994). Educational instruction via interactive video
network. Journal of Agricultural Education, 35(1), 13-20.

Tapscott, D. (1998). Growing up digital: The rise of the net generation. New
York, NY: McGraw-Hill.

Trede, L. D., & Miller, W. W. (1993). Educational technology tools used by
extension professionals in the north central region. Journal of Agricultural
Education, 34(2), 85-92.

Winsboro, l. D. S. (2002). Technology and distance learning lessons form the

nations newest university: Perceptions and reality. The Educational
Forum, 66(3), 247-252.

67

APPENDICES

68

APPENDIX A

69

STATUS OF INFORMATION TECHNOLOGY
IN
HIGH SCHOOL AGRISCIENCE CURRICULA

The purpose of this study is to determine the status of information technology in
Michigan and California’s high school agriscience curricula. Instructions are
provided at the beginning of each section. This IS NOT a test and there are no
“correct" answers. Your individual answers will be kept confidential. We
appreciate your cooperation in completing this questionnaire.

T— T}
, I
/T :3

 

 

 

 

 

lnforrnation Technology is defined as all technology that is used to
communicate information within our profession and to our students. This
includes, but is not limited to, the World Wide Web, Internet, satellite
technologies, and computers.

 

Code No.

70

 

 

 

PART A: INFORMATION TECHNOLOGY VALUES

 

INSTRUCTIONS: Using the scale below, please circle the response that

indicates your level of agreement with each statement.

 

 

 

 

 

 

 

 

 

 

 

 

 

1 Strongly Disagree
2 Disagree
3 Undecided
4 Agree
5 Strongly Agree
Information Technology VALUES Value Level
Strongly Strongly
Dlsagroe Agge
1 Agriscience Teachers should know how to use 1 2 3 4 5
computers.
2 Agriscience Teachers should know how to use the 1 2 3 4 5

Internet.

 

3 Agriscience programs should have the following

technology available for use in instruction ........

 

. . computers for teachers

 

. . computers for students

 

. multimedia computers for teachers*

 

. multimedia computers for students*

 

. Internet connections for teachers

 

. Internet connections for students

 

. DVD players for teachers

 

. DVD players for students

 

. . video conferencing equipment for teachers*

 

. . satellite downlink capabilities for teachers*

 

 

. . compressed video capabilities for teachers*

 

A—k—t—i—t—L-L—L—t—t—L
NNNNNNNNNNN
wwwwwwwwwww
bkh-hb-bhA-b-k-b

U'IU'IUIU'IU'IUTU'IUIUTU'IU'I

 

 

*Selected definitions of terms may be found on the back cover.

 

71

 

 

 

Information Technology VALUES Value Level

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

3:22;: 3333::
4. Information Technology . . .
. helps individuals apply knowledge 1 2 3 4 5
. . creates problems for the teacher 1 2 3 4 5
. improves the quality of programs 1 2 3 4 5
. makes learning too mechanical 1 2 3 4 5
. . adds interest in instruction 1 2 3 4 5
. improves teacher effectiveness 1 2 3 4 5
. is a useful instructional tool 1 2 3 4 5
. . causes more problems than it solves 1 2 3 4 5
. is important in instruction 1 2 3 4 5
. is too expensive to be cost effective 1 2 3 4 5
. isolates teachers from one another 1 2 3 4 5
. limits student-teacher interaction 1 2 3 4 5
. promotes self-directed learning 1 2 3 4 5
. . allows teachers flexibility in planning their 1 2 3 4 5
instruction
. . encourages teacher innovation 1 2 3 4 5
. . enhances student learning 1 2 3 4 5
. has little value in agriscience education 1 2 3 4 5
. is essential to prepare students for the 1 2 3 4 5

workplace

 

. . . is necessary for the success of students in 1 2 3 4 5

the workplace

 

 

. . . has an adverse effect on teachers 1 2 3 4 5

 

 

72

 

 

 

PART B: INFORMATION TECHNOLOGY
KNOWLEDGE/SKILL

 

INSTRUCTIONS: How much knowledge and skill do you have in the area of
information technology? Using the scale below, please circle the response that
represents your level of knowledge/skill in each area listed.

 

I DON’T KNOW enough about this area to respond

My knowledge/skill in this area is BELOW AVERAGE.

My knowledge/skill in this area is AVERAGE.

My knowledge/skill in this area is ABOLE AVERAGE.

My knowledge/skill in this area qualifies me as an EXPERT.

 

 

 

 

(fi-th-K

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Knowledge/Skill
Level
Information Technology KNOWLEDGE AND 00""
SKILL Know Export
1. Know how to operate a computer. 1 2 3 4 5
2. Know the major components of a computer. 1 2 3 4 5
3. Can evaluate software for instruction. 1 2 3 4 5
4. Carl evaluate software for program management. 1 2 3 4 5
5. Can locate computer-based teaching materials for 1 2 3 4 5
use in instruction.
6. Can integrate computer-based teaching materials 1 2 3 4 5
into instruction.

 

 

73

 

 

Knowledge/Skill
Level

 

lnforrnation Technology KNOWLEDGE AND Don’t
SKILL Know Expert

 

Know how to select information technology that fits

program needs. (computers, printers, modems/LAN, 1
CD/DVD players/bumers, etc.)

N
00
A
0"

 

A
01

Know how to prepare students to use information 1 2 3
technology.

 

Know how to use . . .

 

. Internet Email.

 

. . World Wide Web.

 

. . video conferencing.*

 

. . satellite downlink technologies.*

 

. . compressed video.*

 

. multimedia computers.*

 

. DVD players.

 

 

A-A—Ar—L—l—LAA
NNNNNNNN
0000000000wa
#h-h-k-b-kbh
(”01010101010101

. . other:

 

 

 

 

*Selected definitions of terms may be found on the back cover.

 

74

 

 

 

PART C: SOFTWARE APPLICATIONS
KNOWLEDGE/SKILL

 

 

INSTRUCTIONS: How much knowledge and skill do you have in using each of
the following computer programs? Please respond to the following items by
circling the response that represents your level of knowledge/skill using the scale
below.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1 I DON’T KNOW enough about this area to respond
2 My knowledge/skill in this area is BELOW AVERAGE.
3 My knowledge/skill in this area is AVERAGE.
4 My knowledge/skill in this area is ABOVE AVERAGE.
5 My knowledge/skill in this area qualifies me as an EXPERT.
Knowledge/Skill
Level
Information Technology SOFTWARE Don’t
Know Expert
Grade Book 1 2 3 4 5
2. Word Processor (Examples: Microsoft Word, 1 2 3 4 5

WordPerfect, etc.)

 

3. Spreadsheet (Examples: Excel, Microsoft Works, Quatro 1 2 3 4 5

 

 

 

Pro, etc.)
Database (Examples: dBase, Microsoft Access, etc.) 1 2 3 4
5. Lesson Plannirg (Examples: 4Mation, PET, etc.) 1 2 3 4
6. Instructional Software (Examples: My Resume,
Microsoft Money, Quicken, Injured Engine, livestock feed 1 2 3 4 5

ration formulation, nutrition, house design, health
diagnostics, etc.)

 

7. Graphics (Examples: Photoshop, Paintbrush, MacPaint, 1 2 3 4 5
Harvard Graphics, Print Shop, etc.)

 

 

8. Presentation Software (Examples: Microsoft
PowerPoint, WordPerfect Presentations, Freelance . 1 2 3 4 5
Graphics, Harvard Graphics, etc)

 

 

 

75

 

Knowledge/Skill

 

 

 

 

 

 

 

 

 

Windows98, Windows Millennium, Windows XP,
Macintosh, Power MA C, etc)

 

Level
Information Technology SOFTWARE 000"
Know Expert

9. World Wide Web browser (Examples: lntemet 1 2 3 4 5
Explorer, Netscape, AOL, etc.)

10. World Wide Web page creator (Examples: Microsoft 1 2 3 4 5
FrontPage, Adobe GoLive, Netscape Composer, Dream
weaver, etc.)

11. File Transfer to and from other computers using 1 2 3 4 5
a modem or LAN (Examples: WS_FTP, Fetch, etc.)

12. Internet Email (Examples: America On-Line, Microsoft 1 2 3 4 5
Outlook, Eudora, Juno, etc.)

13. Desktop Publishing (Examples: Pagemaker, Microsoft 1 2 3 4 5
Publisher, etc.)

14. Utilities (Examples: Norton, PC Tools, Norton Antivirus, 1 2 3 4 5
McAfee Antivirus, etc.)

15. Operating System (Examples: Windows95, 1 2 3 4 5

 

76

 

 

 

PART D: AVAILABILITY OF COMPUTER
TECHNOLOGY

 

 

INSTRUCTIONS: How accessible is information technology to you? Please
indicate the availability of the technology listed by placing an X in the appropriate

column (You May Check More Than One Column).

 

Availabili r
Information Technology Do Not Have Plan to
Have Now Now Acquire

 

 

1. Computer at home with. . .

 

. . . multimedia capabilities.*
. . . World Wide Web access.

. . . Internet Email.

 

 

 

2. Computer available in office or
classroom with . . .
. . . multimedia capabilities.*

. . . World Wide Web access.

. . . Internet Email.

 

 

 

 

3. Computer lab in department with .

 

. . . multimedia capabilities.*
. . . World Wide Web access.

. . . lntemet E-mail.

 

 

 

Video conferencing in school*

 

5. Satellite downlink technologies in
school*

 

DVD players in school

 

Compressed video in school*

 

 

Multimedia computers in school*

 

 

 

 

 

 

*Selected definitions of terms may be found on the back cover. 4|

 

77

 

 

PART E: USEFULLNESS OF INFORMATION
TECHNOLOGY IN PROGRAM/INSTRUCTIONAL
MANAGEMENT

 

 

INSTRUCTIONS: We want to know you opinion of the potential usefulness of
information technology in your program. Using the scale below, rate the
potential usefulness of information technology for each task listed by circling
your response.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1 Not Useful
2 Low Usefulness
3 Undecided
4 Moderately Useful
5 Highly Useful
USEFULNESS of Information Technology Usefulness
Not Highly
Useful Useful
1. Program Planning, Development and Evaluation
(Examples: youth organization activities, program reports, budget, 1 2 3 4 5
equipment/maintenance reports, long-range planning, funding
requests, fund raising, instructional material, equipment purchases,
etc.)
2. Instructional Planning (Lesson/Unit/Curriculum Planning) 1 2 3 4 5
3. Instructional Execution (Presentation of Instruction) 1 2 3 4 5
4. Instructional Evaluation (Testing, Assessment) 1 2 3 4 5
5. Instructional Management (Grade Reports, Student Records) 1 2 3 4 5
6. Student Guidance and Career Development 1 2 3 4 5
7. School Community Relations (Public Relations) 1 2 3 4 5
8. Student Vocational Organizations 1 2 3 4 5
9. Professional Role and Professional Development 1 2 3 4 5
10. Coordination of Cooperative Programs 1 2 3 4 5

 

 

 

 

78

 

 

PART F: INFORMATION TECHNOLOGY TRAINING
SOURCES

 

INSTRUCTIONS: Please indicate in the middle column if you have received
information technology training from the source listed by circling either “yes” or
“no.” IF you circled “yes” in the middle column, then circle either “yes” or “no” in
the right column indicating if you have had training from this source in the past
three (3) years.

 

 

 

 

 

 

 

 

 

 

 

Information Technology Have you ever If Yes, have you had
TRAINING SOURCE received training training from this

from this source? SOURCE in the past
(circle your answer) three (3) years?

University/college course YES NO YES NO

University/college workshop YES NO YES NO

Industry workshop YES NO YES NO

Professional conference YES NO YES NO

Self-directed YES NO YES NO

learning/personal experience

Suppliers of equipment and YES NO YES NO

software

School, district or state YES NO YES NO

sponsored in-service traini_ng

Written materials such as YES NO YES NO

information booklets, training

manuals, etc.

Other — please identify: YES NO YES NO

 

 

 

79

 

 

 

 

PART G: DEMOGRAPHIC CHARACTERISTICS

 

 

 

Instructions: We would like to know some basic information about you. Please
respond to these questions by placing an X in the correct blank or by providing
the information requested.

 

Highest Degree Held: _Bachelor’s _Doctorate
_Master’s _Other
_Master’s + 30/Ed. S.

Gender: _Male _Female

Race: _Caucasian _Black
_Hispanic _Other

Age: years

Years Teaching Experience: years

The area where your school is located: _Rural _____Urban

_Suburban

In what district do you teach?

 

How many professional development conferences/workshops have you attended
in the past three years?

Is your school currently connected or involved in any project that would connect
your school to the other schools in you district?
currently connected _involved in project

Do you teach: _High school students only?
_Middle school/junior high school students only?
_Both high school and middle school/junior high school
students?
_Other (please identify level)

 

80

Definitions

Comgressed Video- this computer-based technology allows a teacher to instruct
one or more classes at different locations — students and teacher can see
and hear each other and interact live.

Information Technology- all technology that is used to communicate
information within our profession and to our students. This includes the
World Wide Web, Internet, satellite technologies, and computers.

Multimedia- computer-based applications that allow the user to see and hear
different types of information using one screen and speakers (e.g., text,
pictures, video, animation, sound, music).

Satellite Downlink- a satellite dish that allows schools to receive televised
transmissions broadcast from anywhere in the world and enables a school
to view many programs that are not available via cable or regular
television transmission.

flea Conferencing— a method of conferencing in which people at different
locations can see and hear one another, as well as communicate using
different types of media equipment (e.g., computer, image viewer, slide
projector).

THANK YOU!!

 

 

An addressed envelope with the correct postage was included with this
survey. If you have lost the envelope, please return this survey to:
Golzynski, California State University, Fresno 5300 N Campus Dr. MIS
FF17 Fresno, CA 93740.

 

 

81

APPENDIX B

82

 

OFFICE or
RESEARCH
ETHICS AND
STANDARDS

mom's
mm
Imam
WSiannivesity
2020111511311
Enticingfll

48824

517055-210)
FAX 517/432-4503

Well: Minsu edit/Itsellllcrihs
E-Mail: uaihsOmsuedu

MSU is m alumnus-um.
WWO minim.

6 % ;4/W
As umar, MD. /?

MICHIGAN STATE
u N l v E R SIT Y

March 4. 2003
TO: Luke REESE
409L Agriculture Hall

RE: IRBI 03-173 CATEGORY: EXEMPT 1-2

APPROVAL DATE: March 3. 2003
EXPIRATION DATE:February 3. 2004

TITLE: STATUS OF INFORMATION TECHNOLOGY IN HIGH SCHOOL
AGRISCIENCE CURRICULA IN MICHIGAN AND CALIFORNIA

The University Committee on Research Involving Human Subjects' (UCRIHS) review of this
projectiscompleteandlam pleasedtoadvisethattherightsandwelfareofthehuman
wbjectsappeabbeadequatdyprotectedandmedndsbobtalnhfamedcomentare
appropriate. Therefore, the UCRIHS W this project.

RENEWALS: UCRIHS approval is valid until the expiration date listed above. Projects
continuing beyond this date must be renewed with the renewal form. A maximum offour such
expedited renewals are possible. Investigators wishing to continue a project beyond that time
need to submit a 5-year application for a complete review.

REVISIONS: UCRIHS must review any changes in procedures Involving human subjects. prior
to initiation of the change. If this is done at the time of renewal. please include a revision form
with the renewal. To revise an approved protocol at any other time during the year, send your
written request with an attached revision cover sheet to the UCRIHS Chair, requesting revised
approval and referencing the project's IRB# and title. Include In your request a description of
the change and any revised Instruments, consent forms or advertisements that are applicable.
PROBLEMSICHANGES: Should either of the following arise during the course of the work,
notify UCRIHS promptly. 1) problems (unexpected side effects, complaints. etc.) involving
humansubjects02)changesmmereseerchemkmnuNanewIanaummdicefing
greater risk to the human subjects than existed when the protocol was previously reviewed and
approved.

If we can be of further assistance. please contact us at (517) 355-2180 or via email:
UCRIHS@msu.edu. Please note that all UCRIHS forms are located on the web:
http://www.msu.eduluserlucrihs

Sincerely,

O

UCRIHS Chair

AK: jm

cc: Matthew Golzynski
13 Royal Oak Dr.
Gladstone. Ml 49837

83

APPENDIX C

84

Status of Information Technology in High School Agriscience Curricula in
Michigan and California

April, 2003

Name
Address
City, State Zip

Dear Sir or Madame:

Enclosed you will find a questionnaire about your knowledge of information
technology. It should only take about 15 to 20 minutes to complete the 55
question survey. The questionnaire is 12 pages long only because a larger font
size was used to make it easier to read. ‘

The benefit of your participation in this research project is to help the Agriscience
profession better understand the usefulness of informational technology in the
high school Agriscience classroom. By completing and returning this survey, this
indicates your consent to be part of the research project.

Your participation is completely voluntary and at any time during the completion
of the survey you may decline to respond to any given item. You may choose
not to participate at all, but we would really appreciate your input.

The only cost to you in this whole procedure is your time. You may return your
survey in the self addressed stamped envelope that is enclosed.

If at any time you have questions regarding this survey you can reach me at
(559) 324-7197 or by email at golzynsk@msu.edu. If you have questions or
concerns regarding your rights as a study participant, or are dissatisfied at any
time with any aspect of this study, you may contact — anonymously, if you wish -
Ashir Kumar, M.D., Chair of the University Committee on Research Involving
Human Subjects (UCRIHS) by phone: (517) 355-2180, fax: (517) 432—4503, e-
mail: ucrihs@msu.edu, or regular mail: 202 Olds Hall, East Lansing, MI 48824.

Your participation in this survey will be completely confidential. The survey has
an identification number on it that will enable us to check your name off the
mailing list when the questionnaire is returned. The identification number will be
removed and destroyed prior to any data entry or analysis to maintain the
confidentiality of all responses. Your name will never be placed on or related to
your completed survey.

All returned surveys will be maintained in a locked file cabinet within my office.

After the raw survey data is entered, summarized and reported, all surveys will
be destroyed. Collected data will be entered and stored on a password-protected

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computer. Your privacy will be protected to the maximum extent allowable by
law.

The information from this study may be published in refereed journals or
presented at appropriate conference and meetings, but your identity will be kept
strictly confidential.

Thank you for your time. If you have any questions at any time, please feel free
to call or email.

Sincerely,
Luke E. Reese Matthew Golzynski
Thesis Advisor Graduate Student

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APPENDIX D

87

About 1 week ago you should have received a survey of lnfonnation Technology.
If you have completed and returned your survey, thank you very much for your
time. If you haven’t yet done so, please take a few minutes and fill out the
survey. If you have not yet received your survey, please let one of us know so
that we can send you another. The purpose of this research is to determine the
status of information technology in Michigan and California’s high school
agriscience curricula.

Please be sure that your survey is postmarked by May 9, 2003.

Thank you again; we greatly appreciate your effort in helping us complete this
research project. If you should have any questions, please do not hesitate to
contact us.

Luke Reese (517) 355-6580 Matthew Golzynski (559) 278-5294

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APPENDIX E

89

Status of Information Technology in High School Agriscience Curricula in
Michigan and California

May, 2003
Dear Sir or Madame:

A few weeks ago you received a survey packet in the mail about your use and
knowledge of Information Technology. We have not yet heard from you and are
giving you one more chance to respond. The deadline for receiving these
surveys is May 23, 2003. Please take a few minutes and fill this out.

Enclosed you will find a questionnaire about your knowledge of information
technology. It should only take about 15 to 20 minutes to complete the 55
question survey. The questionnaire is 12 pages long only because a larger font
size was used to make it easier to read.

The benefit of your participation in this research project is to help the Agriscience
profession better understand the usefulness of informational technology in the
high school Agriscience classroom. By completing and returning this survey, this
indicates your consent to be part of the research project.

Your participation is completely voluntary and at any time during the completion
of the survey you may decline to respond to any given item. You may choose
not to participate at all, but we would really appreciate your input.

The only cost to you in this whole procedure is your time. You may return your
survey in the self addressed stamped envelope that is enclosed.

If at any time you have questions regarding this survey you can reach me at
(559) 324-7197 or by email at golzynsk@msu.edu. If you have questions or
concerns regarding your rights as a study participant, or are dissatisfied at any
time with any aspect of this study, you may contact — anonymously, if you wish —
Ashir Kumar, M.D., Chair of the University Committee on Research Involving
Human Subjects (UCRIHS) by phone: (517) 355-2180, fax: (517) 432-4503, e-
mail: ucrihs@msu.edu, or regular mail: 202 Olds Hall, East Lansing, MI 48824.

Your participation in this survey will be completely confidential. The survey has
an identification number on it that will enable us to check your name off the
mailing list when the questionnaire is returned. The identification number will be
removed and destroyed prior to any data entry or analysis to maintain the
confidentiality of all responses. Your name will never be placed on or related to
your completed survey.

All returned surveys will be maintained in a locked file cabinet within my office.
After the raw survey data is entered, summarized and reported, all surveys will

90

be destroyed. Collected data will be entered and stored on a password-protected
computer. Your privacy will be protected to the maximum extent allowable by
law.

The information from this study may be published in refereed journals or
presented at appropriate conference and meetings, but your identity will be kept
strictly confidential.

Thank you for your time. If you have any questions at any time, please feel free
to call or email.

Sincerely,
Luke E. Reese Matthew Golzynski
Thesis Advisor Graduate Student

91

APPENDIX F

92

Working Table for Professional Development Points

 

Available

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Points‘ n2 Points3
0 4 0
1 1 1
2 1 2
3 3 9
4 4 16
5 1 5
6 9 54
7 4 28
8 20 160
9 10 90
10 18 180
11 10 110
12 16 192
13 6 78
14 8 112
15 1 15
16 3 48
Total 119 1100

 

 

1 One point was given for each professional development activity that was
attended. An additional point was given if the professional development activity
was attended within the past three years. A maximum of 16 points could be
accrued per educator.

2 Frequency or count representing the number of respondents accruing the
available points.

3 Points accrued by frequency (the available number of points was multiplied by
the frequency) resulting in 1,100 points from a possible 1,904 points or 58%.

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