Adult Self-Directed Learning,

Personal Computer Competency,

and Learning Style:

Models for More Effective Learning

A dissertation submitted

by

HELEN C. BARRETT

to

THE FIELDING INSTITUTE

in partial fulfillment ofthe requirements for the

degree ofDoctor of Philosophy in Human Development

This dissertation has beenaccepted by the faculty ofThe Fielding Institute by:

___________________________________________Richard P. Appelbaum, Ph.D., Mentor (Chair)

___________________________________________Don D. Bushnell, Ph.D., Program Director

___________________________________________Jeremy J. Shapiro, Ph.D., Second Faculty Reviewer

___________________________________________Marisa Guerin, Student Reviewer

ii

January 28, 1991

Copyright by

Helen C. Barrett

1991

iii

Acknowledgements

There are many people to acknowledge throughout the years when I formulated this research project,

and the many months that it took to bring it to completion.

To Dan, my husband of twenty-six years, who may have sacrificed the most throughout the last eight

years because of my working habits, I'm not sure I could have gotten through this process without a

partner to share my life and keep me organized. Your patience, love and concern for me are very

much appreciated.

To my daughter Erin and my son Chris, you are both the most important "accomplishments" of my life.

You have grown into fine young adults over the last eight years, during many hours when I was not

available, and I also thank you for your understanding, patience and love.

To my mother, Mary Alice, who is one of the best managers I know, and my primary role model, you

always encouraged me to excel in whatever I wanted to do. A lot of where I am today is because of our

family values and the energy that you gave both as a single working parent and tireless volunteer

leader.

For my late grandparents, married on July 27, 1921, and on whose 69th wedding anniversary I gave

my final oral presentation of this research project, I am proud to be the first in the family to earn a Ph.D.

and to carry on the collegiate academic tradition that they both initiated.

To Gretchen, my "dissertation buddy" and co-developer of one instrument in this study, and to Sue

who brought us together, I am grateful for the retreat I found in both of your Anchorage homes as I

struggled to find the solitude and inspiration I needed to complete this project.

And my final thanks go to all of the participants in this study, to my colleagues at the University of

Alaska and at the Fairbanks North Star Borough School District, the students and faculty of The

Fielding Institute, and to my wonderful committee members, who were a constant joy to work with over

the last three years. All of you made terrific contributions which enriched this study far beyond my

original ideas.

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TABLE OF CONTENTS

Copyright .................................................................................................................. iiAcknowledgements ............................................................................................... iiiTable of Contents................................................................................................... ivList of Figures.......................................................................................................... viList of Tables.......................................................................................................... viiAbstract

Chapter 1 - Introduction .....................................................................................................1Rapid Technological and Social Change ..........................................................1Personal Computer Revolution ............................................................................1Learning as an Individual Activity ........................................................................2Self-Directed Learning Research ........................................................................4Research Purpose ..................................................................................................4Design.......................................................................................................................5Significance of the Research................................................................................6

Chapter 2 - Literature Review...........................................................................................8Context for Change - Personal Computers in Society.....................................9

Technology and Social Change ............................................................10The Information and Learning Society..................................................11Computer Literacy/Competency.............................................................12

Process of Learning..............................................................................................28General Definition of Learning ...............................................................28Domains of Learning................................................................................29Nature of Learning....................................................................................29Conditions of Learning.............................................................................30Adult Learning ..........................................................................................30Independent Learning and Independent Study..................................31Self-Directed Learning.............................................................................31

Learning Models ...................................................................................................38Information Processing Model................................................................41Gagn�'s Hierarchy of Learning ..............................................................43Caroll's Model of School Learning........................................................45Barbazette's Situational Training Methods..........................................47Consciousness and Competence Model .............................................49Dreyfus & Dreyfus Five-Stage Model of Skill Acquisition..................51Corno & Snow's Aptitude Complex.......................................................52Wlodkowski's Time Continuum Model of Motivation..........................55Kurt Lewin's Experiential Learning Model ...........................................58McCarthy's 4MAT with C-BAM................................................................59

Learning Styles .....................................................................................................62General Concept of Learning Styles.....................................................62Kolb's LSI Based on Experiential Learning Model.............................63Learning Environments............................................................................65

v

Human-Computer Interaction and User Interfaces.........................................66Related Research Studies...................................................................................70

Chapter 3 - Methodology.................................................................................................80Statement of the Problem....................................................................................80

Research Questions .................................................................................80Hypotheses ................................................................................................81

Design.....................................................................................................................83Population Sampling Strategies........................................................................85

Relative Level of Computer Expertise...................................................85Type of Computer Operating System....................................................85

Instrumentation......................................................................................................89Kolb LSI ......................................................................................................90Guglielmino' Self-Directed Learning Readiness Scale.....................91Bersch/Barrett Personal Computer Competency Inventory ..............92

Data collection procedures .................................................................................94Data Recording..........................................................................................95Data Processing........................................................................................95Data Analysis.............................................................................................96

Definition of terms .................................................................................................99Assumptions, Limitations, Delimitations ........................................................ 101

Chapter 4 - Findings...................................................................................................... 102Description of Sample....................................................................................... 102

Demographics ........................................................................................ 102Socioeconomic Status.......................................................................... 103

Characteristics of Sample by Key Independent Variables......................... 106Learning Style ........................................................................................ 106Type of Computer User Interfaces Preferred .................................... 107

Dependent Variables ........................................................................................ 109Self-Directed Learning Readiness ..................................................... 110Personal Computer Competency........................................................ 110

Hypotheses ......................................................................................................... 114Goal 1 Hypotheses Related to Self-Directed Learning

Readiness and Motivation........................................................ 115Goal 2 Hypotheses Related to Learning Style ................................. 138Goal 3 Hypotheses Related to Personal Computer Operating

System Interface......................................................................... 142Other Findings .................................................................................................... 157Summary ............................................................................................................. 160

Chapter 5 - Discussion.................................................................................................. 162Discussion of Hypotheses................................................................................ 164

Goal 1 - Self-Directed Learning and Motivation............................... 164Goal 2 - Learning Style......................................................................... 168Goal 3 - Type of Operating System Interface.................................... 169

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Developing a Model of Learning to Use Personal Computers.................. 172Computer Competency Learning Model ........................................... 175General Questions................................................................................. 196

Summary ............................................................................................................. 204Conclusions and Implications.............................................................. 204Conclusions and Recommendations ................................................. 211Final Comments ..................................................................................... 218

References ...................................................................................................................... 219

Appendix A - Letter & Participant Agreement .......................................................... 229

Appendix B - Instruments.............................................................................................. 232General Questionnaire...................................................................................... 236Personal Computer Competency Inventory.................................................. 242Optional Additional Questions......................................................................... 244

Appendix C - Learning Aids for Self-Directed Learning......................................... 245Computer Literacy Competency Model ......................................................... 246

List of Figures

Figure 1. The types of computer literacy......................................................................15

Figure 2. Dynamic view of computer literacy ..............................................................17

Figure 3. Information Processing Model.....................................................................41

Figure 4. Gagn�'s hierarchy...........................................................................................43

Figure 5. Situational training methods.........................................................................48

Figure 6. Consciousness and competence learning model ....................................49

Figure 7. Five Stages of Skill Acquisition....................................................................51

Figure 8. A schematic conceptualization of aptitude for learning in relation toeducational performance.....................................................................................53

Figure 9. Time Continuum Model of Motivation..........................................................56

Figure 10. Lewinian experiential learning model ......................................................58

Figure 11. CBAM and 4MATª.......................................................................................59

Figure 12. Concerns-Based Adoption Model .............................................................61

Figure 13. Kolb Learning Style Inventory....................................................................64

vii

Figure 14. Research Participant Quadrant..................................................................87

Figure 15. Overview of Research Design....................................................................88

Figure 16. Overview of Data Analysis ..........................................................................97

Figure 17. Hypotheses, Instruments & Statistical Analysis ......................................98

Figure 18. Kolb Learning Styles of Participants...................................................... 106

Figure 19. Self-Rating of Participants........................................................................ 111

Figure 20. Mean PCCI Scores by Self-Rating of Competence ............................ 112

Figure 21. Summary of Findings for Hypothesis 1.4 .............................................. 137

Figure 22. Summary of Hypotheses Results............................................................ 161

Figure 23. An Integrated Model of Learning ........................................................... 174

Figure 24. Transition #1 - Beginning of the learning process.............................. 176

Figure 25. Transition #2 - During the learning process ........................................ 182

Figure 26. Transition #3 - At the end of the learning process.............................. 190

Figure 27. Variables that Impact on Acquiring Personal ComputerCompetency........................................................................................................ 196

List of Tables

Table 1. Age of participants in the study................................................................... 103

Table 2. Occupation of participants in the study...................................................... 104

Table 3. Educational level of participants in the study........................................... 105

Table 4. Income level of participants in the study ................................................... 105

Table 5. Learning style of participants in the study................................................. 106

Table 6. Percentage of computer operating system preferences ofparticipants in the study with all types of computers currently used......... 107

Table 7. One type of computer currently used the most......................................... 108

Table 8. Percentage of computer preferences by type of interface with onetype of computer currently used the most...................................................... 109

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Table 9. Self-directed learning readiness by Learning Style of participantsin the study with mean scores on the SDLRS .............................................. 110

Table 10. Self-reported level of competence of participants in the study withmean scores on the PCCI................................................................................. 111

Table 11. Analysis of variance of PCCI scores by level of expertise................... 113

Table 12. Analysis of variance of PCCI scores by gender .................................... 114

Table 13. Self-reported level of competence of participants in the study withmean scores of time spent in self-directed learning.................................... 115

Table 14. Preferred group for learning about computers by level of computerexperience........................................................................................................... 116

Table 15. Participants' mean PCCI Scores by preferred learning group........... 117

Table 16. Analysis of variance of PCCI scores by preferred group forlearning a personal computer.......................................................................... 117

Table 17. Self-reported level of competence of participants in the study withmean scores on the Self-Directed Learning Readiness Scale(SDLRS) .............................................................................................................. 119

Table 18. Analysis of variance of SDLRS scores by level of expertise .............. 119

Table 19. Participants reasons for learning a personal computer ....................... 121

Table 20. Mean scores of PCCI by primary reason for learning to use apersonal computer............................................................................................. 122

Table 21. Analysis of variance of PCCI scores by primary reason forlearning a personal computer.......................................................................... 123

Table 22. Analysis of variance of PCCI scores by type of motivation.................. 124

Table 23. Level of prior experience with different types of computing devicesby level of self-reported personal computer expertise................................ 126

Table 24. Analysis of all human sources of assistance used at the earlystages of learning............................................................................................... 127

Table 25. Analysis of all human sources of assistance used at the currentstage of learning................................................................................................. 128

Table 26. Frequency of primary human sources of assistance used in theearly stages......................................................................................................... 129

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Table 27. Frequency of primary human sources of assistance used at thecurrent stage ....................................................................................................... 130

Table 28. Analysis of all non-human sources of assistance used....................... 131

Table 29. Frequency of primary non-human source of assistance used............ 132

Table 30. Analysis of variance of hours spent using the computer by level ofexpertise .............................................................................................................. 133

Table 31. Analysis of variance of PCCI scores by typing speed.......................... 134

Table 32. Analysis of variance of PCCI scores by computer user groupmembership ........................................................................................................ 135

Table 33. Analysis of variance of PCCI scores by computer user groupattendance........................................................................................................... 135

Table 34. Analysis of variance of PCCI scores by computer ownership ............ 136

Table 35. Mean Scores on PCCI by Learning style of participants in thestudy ..................................................................................................................... 138

Table 36. Analysis of variance of PCCI scores by learning style......................... 139

Table 37. Analysis of variance of PCCI scores by active versus reflectivelearning style....................................................................................................... 140

Table 38. Analysis of variance of PCCI scores by abstract versus concretelearning style....................................................................................................... 141

Table 39. Observed frequency of preference for specific user interface bylearning style....................................................................................................... 142

Table 40. Mean number of applications by level of competence and type ofcomputer operating system preferred ............................................................ 144

Table 41. Analysis of variance of number of applications and computerinterface ............................................................................................................... 145

Table 42. All sources of assistance used by preferred computer interface........ 146

Table 43. All sources of assistance used by learning style preferences............ 147

Table 44. Primary source of assistance by computer interface preference ....... 149

Table 45. Primary source of assistance by learning style preference................. 150

Table 46. Preferred group for learning by computer interface preference......... 151

x

Table 47. Preferred group for learning by learning style preference .................. 152

Table 48. Initial learning strategies by preference for type of computerinterface ............................................................................................................... 154

Table 49. Initial learning strategies by learning style preference ........................ 154

Table 50. PCCI scores by preferred initial learning strategy ................................ 155

Table 51. Analysis of variance of PCCI scores by initial learning strategy ........ 156

Table 52. Problems experienced while learning a personal computer bylevel of experience............................................................................................. 158

Table 53. How software manuals are used by participants' level ofexperience........................................................................................................... 159

Abstract

In the past decade, many adults have undertaken the task of learning to use personal

computers, employing individual learning styles, resulting in varying levels of success. Past research

projects have been conducted with learners in organized computer classes; however, there has

been little research on the efforts of adults learning personal computers on their own. This

exploratory research study assessed the impact of learning style, readiness for self-directed learning

and the type of operating system interface (graphical or text) on the acquisition of personal computer

competency. Approximately half of the participants in the study were professionals and educators

from throughout the nation; the other half were from Alaska.

Over 194 participants filled out four instruments: Kolb's Learning Style Instrument (LSI);

Guglielmino's Self-Directed Learning Readiness Scale (SDLRS); a Personal Computer Competency

Inventory (PCCI); and a general questionnaire which asked questions about strategies for learning to

use (not program) a personal computer. A smaller number (31) answered open-ended responses to

more in-depth questions about their learning experiences. The data was analyzed by learning style,

by type of computer interface preferred, and by level of personal computer competency. An

integrated model of a developmental learning process was presented.

One of the findings suggests that Divergers (favoring concrete experience and reflective

observation) have more difficulty in gaining personal computer competency. Of the ten hypotheses

submitted, all but two were supported by the data analyzed by statistical measures. The two rejected

hypotheses found no relationship between learning style and preference for type of computer user

interface. The hypotheses that were supported found that: self-directed learning strategies were

employed at least 70% of the time; competent users had a slightly higher level of self-directed

learning readiness than beginners; intrinsic motivation led to higher levels of personal computer

competency; a foundation for learning , an active learning style, and an abstract learning style all lead

to higher levels of personal computer competency; competent users of graphical interfaces used

more types of applications than competent users of text interfaces; and there was more variability in

preferred learning strategies by the type of user interface preferred than by learning style.

1

CHAPTER 1 - INTRODUCTION

Rapid Technological and Social Change

Many authors have commented on the recent and rapid changes in our

society (Toffler, 1970; Naisbitt, 1982) and the relationship between technological

change and social change (Daniels, 1979; Mesthene, 1981; Mowshowitz, 1976).

The computer has been at the center of the technological and social changes that

have occurred during the last half century and will continue to be a factor into the

next (Evans, 1979; Weizenbaum, 1976; Bell, 1976).

Personal Computer Revolution

The personal computer has become a dynamic factor in recent technological

change (Nichols, 1986; Cross, 1981; Zemke, 1985). In the 1980s, the cost of

personal computers has fallen and their power has increased, providing an

opportunity for adults to have access to these new tools in their homes as well as in

the workplace. This increase in the use of personal computers has also placed

new demands on adults as learners, to discover how to make the best use of this

technology.

Adults have employed a variety of strategies to gain competence in using

personal computers with varying levels of success: classes, user groups, CAI

software, books and magazines, consultants and self-directed learning (Ludden,

1985; LaPlante, 1986). Self-education appears to be the most commonly

employed strategy for adults to gain competence in using a personal computer.

However, this method is not without its frustrations, especially if the learner has no

prior experience to relate to this new knowledge (Zemke, 1985). The manuals that

come with the hardware and/or software are the primary resource used in self-

2

directed learning, along with a problem-solving, trial and error approach. These

manuals, however, may not support the adult learning process if they do not

address the needs of the learner (Knowles, 1983; Gerver, 1984), or individual

learning styles (Galagan, 1987). There is a need to study the processes that adults

employ to gain competence in using a personal computer.

The need for lifelong, self-directed learning skills has become escalated

because of the emergence of the information age (Zemke & Kramlinger, 1982;

Toffler, 1980; Scully, 1987b). The half-life of information in most professions is less

than 10 years (Knowles, 1975), which demands a new paradigm of learning: one

less devoted to the memorization of facts (product-orientation) to one more

dedicated to a process of inquiry and access to resources (Cross, 1981; Knowles,

1975). The personal computer has become an essential catalyst and tool in the

lifelong learning process, facilitating the access to information and providing an

environment to support inquiry and creativity (Moursund, 1984). Technological

change and the knowledge explosion make lifelong learning not only increasingly

necessary, but also increasingly possible (Cross, 1981).

Learning as an Individual Activity

Robert Gagn� (1967) reviewed then current learning theorists and their

implications for independent learning. He stated that most modern learning

theorists "consider learning to be a change that takes place inside the learner" and

that "most common forms of learning involve discovery" (p.29). He further stated:

Learning is an individual act, a set of events which take place entirely within thelearner. In fact, it is a highly idiosyncratic event, and depends very much on thenature of the learner, particularly on his own past learning. . . .Learning may take placein a social environment, but fundamentally is a process that takes place within theindividual. (p.30)

3

Chen� (1983) pointed out that the activity and process of learning is

autonomous. That is, "learning is the work of an independent agent (nobody, in

fact, learns in place of somebody else)" (p.44).

There is a new paradigm of education that is beginning to appear in the

literature, which is called the "constructivist paradigm" (Blais, 1988). The following

is a summary of this view of knowledge:

Constructivism does not say that knowledge is something that learners ought toconstruct for and by themselves. Rather it says that knowledge is something thatlearners must construct for and by themselves. There is no alternative. Discovery,reinvention, or active reconstruction is necessary. (p.217)

This body of knowledge has been developing since Piaget described

children actively constructing knowledge by putting things into relationships: it is

currently also known as "constructivist education" or "holistic constructivism"

(Poplin, 1988, p.402). "Constructivist education is based on a theory that explains

learning as a process of construction from within the individual, rather than one of

internalization or absorption from the environment" (Kamii, 1982, p.1). This theory

states that "learning is a process whereby new meanings are created (constructed)

by the learner within the context of his or her current knowledge" (Poplin, 1988,

p.404). These new meanings are the result of the "transformations that occur

between the new experience to be learned and all other previous and current

learning experiences." This process is described as a "spiral" of learning, where

knowledge is built on experience.

4

Self-Directed Learning Research

In recent years, a major emphasis on research in adult education has been

focused on self-directed learning (Brockett, 1985b, 1985c; Brookfield, 1984b,

1985b; Hall-Johnsen, 1985; Guglielmino, 1977; Long & Agyekum, 1983; Penland,

1977, 1979; Sabbaghian, 1979; Tough, 1971).

In a relatively short period of time, the research on self-directed learning has provideda great deal of information about how people go about learning the things they needor want to know. But it has raised far more questions than it has answered. Certainly,it appears to be a very productive avenue of inquiry. . . . If we wish to know how tohelp people become self-directed learners, we need to know in some detail whatproblems they are encountering, what kinds of help they need, and how theyevaluate their learning projects. So far, most pioneer researchers on self-directedlearning have left what happens during the learning project virtually unexploredterritory. Whether one wants to know how to facilitate learning or how to presentinformation to adults, more in-depth study of how learning actually takes place ineveryday settings is a necessity, one that should receive first priority in the 1980s.(Cross, 1981, p.199)

Research Purpose

In the spirit of the direction recommended above by K. Patricia Cross, one of the

foremost experts on adults as learners, there is a need for an in-depth study of

adult, self-directed learning, particularly of how learning proceeds with the use of

personal computers, to develop a better understanding of the process of teaching

oneself how to use a personal computer, and better techniques for learning how to

use computers on a self-directed basis.

Therefore, the general purpose of this research project is to gather in-depth

information from a cross-section of learners by computer type and level of

experience and to explore models of self-directed learning about personal

computers and the role of individual differences in the learning process.

5

The goals of this research project are:

To explore the role that readiness for self-directed learning has on the

acquisition of personal computer competency

To explore the role of learning style as a factor in the process of learning to

use a personal computer.

To develop an understanding of the impact that the type of computer

operating system (graphical vs. text user interface) has on the learning

process of learners with different learning styles

Design

This descriptive research project used four different quantitative instruments:

Kolb's Learning Style Inventory (LSI); Guglielmino's Self-Directed Learning

Readiness Scale (SDLRS); a Personal Computer Competency Inventory (PCCI),

developed by the author and another doctoral student in Anchorage, Alaska; and a

questionnaire developed by the author which inquired about computer learning

strategies, personal computer ownership and use. In addition, there were a series

of qualitative responses to some open-ended questions based on Raymond

Wlodkowski's (1985) Time Continuum Model of Motivation to inquire more deeply

into different phases of the learning process.

The quantitative data were subjected to appropriate statistical measures by

a statistical analysis software package; the qualitative data were organized by a

data base management computer program for content analysis of the responses.

6

Significance of the Research

As more adults become involved in the process of selecting and using

personal computers for learning, working and problem-solving, there will be an

increased need for knowledge about the successful learning strategies of

competent computer users. The current cohort of computer users could be

classified as innovators, early adopters, or early majority in reference to their

adoption of this innovation (Rogers, 1962). As more people (perhaps classified as

late majority and laggards in relation to this adoption) need to gain computer

competency skills for economic reasons, more knowledge about the learning

process will help select successful strategies which could enhance the motivation

to learn and reduce frustration and resistance to change.

There have been no doctoral dissertations which describe the self-directed

learning process of gaining competence in learning to use a personal computer. It

will be shown that, with a large majority of adults using this method to learn how to

use their personal computers, knowledge about this process would contribute to

both the theory and practice of self-directed learning in the information age.

There is also a need to explore the role of the computer interface on the

strategies that adults use to gain competence in learning to operate a personal

computer. Computer software is written according to the capabilities or limitations

of a specific operating system. There are currently two generic types of user

interfaces in the personal computer market today: the Text (or Character) User

Interface (TUI) follows the established method of communicating with a personal

computer by typing commands from a keyboard, using a text-only screen; and the

Graphical User Interface (GUI) which is most well-developed on the Apple

Macintosh at the current time. Most major computer manufacturers "now agree that

7

future users will operate computers by means of mice and menus, windows and

icons, WYSIWYG (What You See Is What You Get) representations of characters

and graphics, point-and-click commands, and bit-mapped screens" (InfoWorld,

1989, p.42).

This interface evolution has been occurring for more than two decades in the

development of personal computers, and is currently accelerating because of two

primary reasons: the computer hardware capabilities have reached the minimum

processing speed and memory capacity necessary to make it happen; and "users

have said a resounding yes to the ease of use and learning afforded by the

graphical user interface"(Ibid). As computer manufacturers further refine the

development of these new graphical user interfaces for all computer systems, there

will be a need to develop theoretical dialogue on both the development of such

interfaces and on the implications of their implementation for learning and

cognition.

8

CHAPTER 2 - LITERATURE REVIEW

The review of the literature focused on these areas: the current context of

technological change and computer literacy; general theory of the process of

learning, including adult, self-directed learning; psychological processes and

models of learning; and learning styles.

An exhaustive search of the literature was begun with several books on

adult learning (Brookfield, 1985c, 1986; Cross, 1981; Knowles, 1975; Tough,

1971). The bibliographies for these books were used to identify other books and

applicable articles from adult education journals and research publications.

Electronic searches were performed in Dissertation Abstracts, Books in Print, ERIC,

Psychological Abstracts, ABI/Inform, The Computer Database and Microcomputer

Index.

No doctoral research has been conducted on the topic of adult, self-directed

learning and personal computers, although one dissertation studied "computer

user groups and the role they play in the computer learning activities of adults"

(Ludden, 1985, p.i). There have been several studies conducted which

incorporated adult learning about computers using different learning styles. In

particular, two studies (Barrie, 1984; Mruk, 1984) focused on adult learning about

computers in traditional classroom settings and both used the Kolb LSI in their

design.

There is one dissertation in progress (Bersch, 1990) which is exploring, with

Apple computer owners in Alaska, issues of adult learning, motivation, learning

problems and resources, the degree of self-directed vs. organized computer

learning, and personal computer competencies.

9

This is the only dissertation to date which explores how adults teach

themselves how to use personal computers, and specifically whether learning

style, as measured by the Kolb LSI, differentiates the strategies used, or what

impact the type of computer operating system being learned (graphic vs. text

interface) has on these self-directed learning strategies.

Context for Change - Personal Computers in Society

Rapid change, facilitated by the recent pervasive proliferation of personal

computers throughout society, is the one of the main forces both mandating and

facilitating social change (Nichols, 1986). The computer has become the catalyst

for change in the '80s, placing more adults in more intense learning situations than

any other innovation in such a short period of history. The pressure for adults to

use personal computers forms an urgent need to develop an understanding of the

acquisition of personal computer competency.

The accelerating pace of change in our society is placing a greater

emphasis on the need for self-directed learning (Rogers, 1969; Knowles, 1975;

Cross, 1981). Knowles (1975) has called self-directed learning survival: the

survival of individuals, and also the survival of the human race (p.16). Carl Rogers

(1969) pointed out the need for people who can adapt to this rapidly changing

environment:

The aim of education must be to develop individuals who are open to change. Onlysuch persons can constructively meet the perplexities of a world in which problemsspawn much faster than their answers. The goal of education must be to develop asociety in which people can live more comfortably with change than with rigidity.(p.304)

Kidd (1973) pointed out that the purpose of any kind of education "is to make

the subject a continuing, 'inner-directed,' self-operating learner" (p.47). The

10

ultimate aim of instruction is "to shift to the individual the burden of pursuing his

own education" (Gardner, 1963, p.12).

Technology and Social Change

Technology has several major impacts on society (Lauer, 1973, pp. 106-

108):

1. It increases our alternatives and creates new opportunities, causing a change in values.

2. It alters our interaction patterns, such as with automobiles, telecommunications, etc.

3. Technological developments have a tendency to create new social problems.

We often see technology as a solution to our social problems, rather than

their cause. There is a great unresolved debate over whether technological

change is the result of social change, or whether social change results from

technological change. Most of the changes that are described above might

indicate the latter position: that technology causes change. Indeed, Daniels

(1979) discussed the concept of the "social lag" whereby technology "changes

society by changing our environment, to which we, in turn, adapt. Between the

change and the adaptation, however, there is always a lapse of time, the social lag.

. . .The social history of technology, then, is the story of institutions trying to catch up

with technological realities" (Daniels, 1979, p.162).

However, one author has disputed that claim and presented a valid

argument in favor of technological development being influenced by various social

changes taking place in society, and has asserted that technological innovation

reflects and responds to felt social needs. Historian George H. Daniels has

believed that "the direction in which the society is going determines the nature of its

technological innovations" (1979, p.162). He also believed there is a technological

lag, "a chronic tendency of technology to lag behind demand" (p.166). Society

11

must be ready to accept and use the technological changes that science has

developed: "Technological possibility continues to lie fallow in those areas where

institutional and political innovation is a precondition of realizing it" (Mesthene,

1981, p.108).

The Information and Learning Society

Even more dramatic than the changes in technology is the shift that has

occurred in our society over the last 30 years, from an industrial society, with the

majority of the work force devoted to the production of "things" to an information

economy, with a majority of people working with "information" in one form or

another (Bell, 1976; Toffler, 1980). The demands of constant change are requiring

that learning become a lifelong activity, creating a society devoted to learning.

K. Patricia Cross (1981) discussed the future directions for the learning

society and presents some assumptions about who will need to be learning and

under what circumstances: lifelong learning has become a necessity for survival in

most professions, where the half-life of skills is less than 10 years (Knowles, 1975)

(in that time, people become half as competent as when they completed their

formal training for the profession because of new developments, techniques or

knowledge). Those who lack the motivation for lifelong learning will be severely

handicapped in maintaining a decent standard of living. Learning is habit forming;

the more people practice it, the more their skills and motivation will increase

(Cross, 1981, p.48). Every experience should become a learning experience,

taking advantage of every resource, both in and out of educational institutions.

Education is no longer just for children; the primary focus of learning at childhood

should be on the skills of inquiry, and learning after schooling is over should focus

on "knowledge, skills, understanding, attitude and values required for living

12

adequately in a rapidly changing world" (Knowles, 1975, p.16). Heimstra (1980)

pointed out that there is currently an increase in self-actualization activities which is

contributing to increased learning.

Computer Literacy/Competency

Computer applications allow for individualization of the learning process

more than any other technological revolution in education and "we have only just

begun to appreciate how to use the computer in the pursuit of knowledge"

(Mowshowitz, 1976, p.104). Our increasingly technological society and the rise of

personal computing has created the necessity for universal computer

literacy/competency.

There are a variety of definitions of computer literacy, a term often used but

which generates as much confusion as understanding, when compared to other

forms of "literacy" (Goddard, 1983).

Clearly, we need to identify some relationship between people and computers thatlies somewhere between mere nodding acquaintance and the total knowledge andfamiliarity that comes with intimacy. This middle ground between acquaintance andintimacy has come to be widely known as "computer literacy." In some respects theterm is a helpful one, but there have been often greatly varying definitions of itsmeaning and...there are serious limitations in the implied analogy between printliteracy and computer literacy. (Gerver, 1984, p.62)

A conference was sponsored by the National Science Foundation in

December, 1980 (Seidel, Anderson & Hunter, 1982), which provided a forum for

opposing views on computer literacy and brought early research implications and

instructional expertise to bear on the issue. The participants at that conference

were not in agreement over whether computer programming was an essential

computer literacy skill.

"Defining computer literacy is like aiming at a moving target," says Andrew Molnar ofthe National Science Foundation. . . . Although there is considerable agreement thatthe computer is a tool and that literacy involves using it to accomplish one's purposes,

13

there is much disagreement as to whether or not the ability to program is essential tothis practical use. (Goddard, 1983, p.6)

However, there is essential agreement that computer literacy is job-specific,

varying with the circumstance.

In the winter of 1982-83, the Alaska State Department of Education

conducted a Statewide Computer Literacy Study, in which all current definitions

were reviewed and synthesized. The workgroup on citizen computer literacy

recommended the following definition:

Computer Literacy for the State of Alaska means developing all of our humanresources in an information and communications-based society. A society thatfosters computer literacy is one that affords its citizens opportunities to acquirecomputer literacy skills, knowledge and attitudes appropriate to individual needs.(Alaska Department of Education, 1982, p.v.)

Some of the experts in the field of computer education (Luehrmann &

Peckham, 1983; Bork, 1982) advocated universal training in computer

programming. Others (Moursund, 1982; Hunter, quoted in Goddard, 1983) have

discounted the need for programming the computer, preferring to regard the

computer as a tool. "Computer literacy probably will decreasingly involve

programming" (Goddard, 1983, p.22). Virtually every definition of computer literacy

includes the use of pre-programmed applications software as an essential skill. At

the present time, the most universally used applications programs are word

processing, data base management, electronic spreadsheet, graphics and

telecommunications programs.

Computer literacy appears to have at least three components:

the ability to use a computer as a tool, the ability to manipulate a computer beyondthat of the casual end-userÑan ability that can be acquired either by learning to usean applications package or actually learning to programÑand enough knowledge ofthe computer's capabilities to make intelligent decisions regarding its social andpolitical use." (Goddard., pp.22-3)

14

Daniel Watt (in Goddard, 1983, p.9) defined computer literacy as more of a "lifelong

process of acquiring a culture" than the "mastery of a well-defined body of subject

matter." This reference to a "lifelong process" is especially appropriate to a model

of adult computer competency.

Most of the literature on computer literacy is addressed to the needs of K-12

education. "[I]nformation about computers and adult learning is sparse and

fragmented" (Gerver, 1984, p.xv). In recent years, a body of literature and training

has emerged, targeted at computer trainers (Barbezette, 1987; Masie & Wolman,

1989). Masie and Wolman, in particular, have published a handbook for computer

trainers that outlines many factors to improve the training and increase the support

of adults learning to use personal computers, including adult learning theory,

thinking styles and a lesson planning model adapted from Madeline Hunter. They

identified two different types of computer use:

Procedural use - a finite series of very specific, linear procedures with tasks performedby following specific keystrokes - This is the group that sticks to one application andone way of doing things.

Navigational use - approached with a broader understanding of core procedures andfunctions which can be applied to multiple situations - This is the group thatexperiments with different programs and methods.

According to Masie & Wolman, when learning a new system, people may

work procedurally at first, eventually moving on to navigational use once

comfortable with the system. For users to teach themselves, there needs to be an

inclination toward navigational computer use. In addition, those who teach

themselves need to be motivated to "not only learn, but to learn independently"

(p.130).

Below is a model of different levels of computer literacy as defined for adults

in a major study sponsored by the United States Government (Mruk, 1984, p.49):

15

LearnerStatus Learner's World Learning Tasks/Activities Learner Experience

Pre-LiteracyThe "Uncharted Worldof the Raw Beginner"

(naive encounter with the computer)

Discovery of LearningNeed

(selection of learningenvironment)

Affective Reaction

(range - technophobiato excitement)

incr

easi

ng d

egre

e of

trai

ning

req

uire

d

ComputerAppreciation

The Cognitive World of"Intellectual Understanding"

(the computer as an object)

Cognitive Mapping

(basic concepts, understanding ofcomputers)

Forming an opinion

(range - resistance toconviction)

incr

easi

ng o

pera

tiona

l fam

iliar

ity w

ith c

ompu

ters

incr

easi

ng p

erso

nal e

xper

ienc

e w

ith c

ompu

ters

UserAwareness

The Functional World ofthe End User

(utility value of thecomputer)

Structured Interaction

(practice at using thecomputer manuals)

Acquiring a Tool

(range - awarenessto comfort)

ComputerCompetence

The World of PersonalComputing

(entering the computerculture)

Speaking theLanguage

(basic programming ability)

Making the ComputerOne's Own

(range - novice toprogrammer)

ComputerSkill/Fluency

The World of the Expert Mastery Becoming TrulyBilingual

(establishing computerintimacy) (computer as means and end) (range - professional

to wizard)

F igure 1 . The types of computer literacy: A developmental model(Mruk, 1984, p.49)

Mruk's model focuses on three different dimensions of computer literacy,

which have five different levels. As he has explained,

The first dimension of becoming computer literate concerns the "world" of thelearner, or how one is oriented toward the computer while actually in the learningsituation. Next, each type of literacy involves a specific set of major learning tasks andactivities. Mastering these learning objectives leads to the acquisition of a particularkind of computer skill. Third, the person's experience of learning tends to varyaccording to the type of computer skill that he or she is mastering, as each majorgroup of skills has its own character and priorities. (Mruk, 1984, p.31)

While learning to program a computer may be considered a component of

computer literacy, for the purposes of this study, a vast majority of adults learn to

16

use applications software as a tool rather than programming a personal computer.

Therefore, this study will focus on learning to use a computer as a tool through

applications software packages.

"The second aspect of the computer learning process involves the teaching

or learning pathways to computer skills. It is necessary to understand how older

learners acquire computer skills in order to develop practical teaching and learning

suggestions to help them" (Mruk, 1984, p.43). Mruk identified four different models

which are typical of ways that adults acquire computer literacy:

The Professional Model: "a comprehensive, long-term, theoretically

oriented approach that is characterized by a conceptual and programming

emphasis" (p.45).

The Academic Model: "a reasonable well structured, course oriented

educational approach to computer science and uses" (p.46).

The Structured Workshop Training: "practically oriented, time limited

and structured. . . .it may well be that more computer literate adults acquired

their skills through the structured workshop than any other format" (p.47).

Informal Learning Format: "individually oriented learning environment. .

. .Instructional techniques such as how-to manuals, casette recordings,

computer assisted instruction and multi-media methods" (p.48).

17

INITIATION PHASE

USERAWARENESS

COMPUTER APPRECIATION

COMPUTER COMPETENCE

COMPUTER SKILL/ FLUENCY

Training Route(Focus on Applications)

Academic Route(Focus on Concepts)

interactive stages

Integration WithConcepts

Integration WithApplications

incr

easi

ng p

rogr

amm

ing

abili

ty

incr

easi

ng l

earn

er t

ime

Figure 2. Dynamic view of computer literacyEducational pathways of computer literacy

Mruk, 1984, p.61

The picture above illustrates a developmental progression of gaining

computer literacy. "[T]he chart indicates that each type of literacy is connected to

the others in an interdependent, progressive way. . .the way teaching and learning

time is used in the educational process also suggests that becoming computer

literate is a developmental learning process. The more time one invests, the further

he or she can move through the structure" (Mruk, p.52). Mruk found that the

learning process was "both discontinuous (i.e., having relatively distinct types of

skills that are learning ends in themselves), and progressive (developmental

stages of increasing computer literacy)" (p.53).

18

Mruk's study focused on the "average adult learner," not the computer

professionals, which will also be the focus of this study. As the chart illustrates, the

professional usually takes the professional or academic route to computer

competence and fluency. The average adult learner will take the training or

informal learning route to user awareness. This study will focus primarily on the

informal learning activities to become competent in using a personal computer.

A common complaint of new users of microcomputers, indicates that the

learning aids provided with the systems are not designed in congruence with the

way adults learn (Knowles, 1983; Galagan, 1987). Knowles' (1983)

"Memorandum to the Personal Computer Industry" outlined his frustration with

learning to use his Apple:

The problem arises from the fact that software producers and manual writers arecomputer engineers or have been trained by computer engineers. They understandhow the machine works but have no idea about how adults learn. Consequently, theirsoftware and manuals are geared to teaching us how the machine works rather thanhelping us learn how to use the machine to perform the real-life tasks we buy it toperform for us. (p.12)

A recent issue of the Training and Development Journal addressed the

issues of computers and training. According to Galagan (1987), "Learning is a

process that has both profited and suffered from its entanglement with computer

technology" (p.73). She added that "the technology determines the style and

structure of learning while the instructor and student are left to adapt" (p.74).

According to MIT's Sherry Turkle (Rhodes, 1986b) and other learning theorists,

more attention should be paid to appropriation styles (the way people assimilate

and master computers) and cognitive styles (preferred ways to assimilate and

master learning).

Karl Albrecht has been addressing the problem of thinking-style and

computers for several years. He is quoted by Galagan (1987):

19

In computer learning, the left-brain/right-brain, concrete-abstract orientation seems tobe important. There seems to be a big difference between deductive learners andinductive learners. . . .This style difference becomes important when the learnerapproaches the collateral printed materials that accompany software. The inductivelearner wants to skip the big picture and start with specific procedures while thedeductive learner wants to understand the theory behind the software before goingon to specifics.

Highly technical peopleÑthe ones designing the softwareÑmost often aredeductive thinkers. . . .They tend to assume that everyone wants to be taught thatway. That's why software manuals have a deductive structure. (p.74)

Elisabeth Gerver (1984) wrote one of the few books on computers and adult

learning. She reported that her experience using a variety of computers was

similar to Knowles': unable to master the manual on how to operate the machine,

she resorted to asking someone more knowledgeable (pp.12-13). She pointed out

that the language or vocabulary of computing is often a barrier for many adults.

Ron Zemke (1985) pointed out some other difficulties that adults have in learning a

computer. They often do not have prior knowledge or a frame of reference to

associate with the knowledge being learned, "no correlate in their personal

knowledge base" (p.106). He called this process "Velcro learning," comparing it to

"cat hairs sticking to a wool skirt," where new learning needs to attach to some prior

information in the brain. "Under these circumstances, learning style, cognitive style,

and all that literature about individual prescriptions and approaches become a

major design consideration, a major variable to be accounted for in the computer-

literacy learning formula" (Zemke, 1985, p.107).

In the following advice on "teaching" adults to use a computer, there is

further support for the notion that "telling" is never as effective as "doing," because

of the need to provide that experiential base upon which to "hang" the verbal

information:

Have people do it! I was propelled into teaching people to use computers when afterattending my first hands-on lesson at a conference. The leader spent the first 60minutes of the 90-minute session talking about what we were about to learn. Peopleunderstand after they have done itÑ and not before. I did not learn this after onelesson, though. In the first programming session in which I taught (Logo), I spent the

20

first 30 minutes explaining in great detail the 10 most frequent errors and how to solvethem. I was going to save (protect?) my teacher-students from every error I had made.In the next 90 minutes each student made many, if not all, of the errors, I had talkedabout. Worse, when I explained and helped them to solve their errors, they all actedas if they were hearing this for the first time. In fact, they were. Though they hadwritten down my every word and listened carefully, they did not hear and could nothear until they had done it, until each had hooks on which to hang the newknowledge. (Mintz, 1990, p.159)

Mruk (1984) identified three basic problems that older learners must deal

with in acquiring computer skills:

1. computers are incredibly new as inventions go. . . .

2. the word "computer" is almost synonymous with "speed." . . .[T]he field moves sofast that by the time something is researched and published, significant newinformation has already been discovered.

3. the acquisition of computer skills is a highly complex type of human activity that isvery difficult to study and understand. Many of the particular learning activitiesinvolved are cognitive and experiential rather than merely behavioral. However, suchphenomena are not well suited to laboratory study, which is the preferred method inAmerican psychology. (pp.26-27)

To address the need for training a large number of adults to use personal

computers, a large training industry has grown up around the personal computer

industry. Dataquest, a market research firm, has estimated that "the training

industry will capture $3 billion of the $14 billion spent on personal computers by

1986. This money will be spent on classes, software, books and magazines"

(Ludden, 1985, pp.1-2).

Most of the research that has been conducted about adults learning to use

their computers has been within the context of organized classes or institutional

learning (Smith, 1982; Barrie, 1984; Mruk, 1984). The adult learners in these

studies were usually enrolled in some type of organized computer class with an

instructor. Gerver (1984) pointed out a major problem with organized computer

classes, due to the diverse goals of the participants. "It may very well be that this

discrepancy between the diverse needs of the adult learners and the relative

uniformity of provision is one of the sources of the highly variable rate at which

21

adult students have persisted in computing courses" (p.68). In addition, she

mentioned the problem of finding qualified instructors to teach adult learners: "In

computing courses for adults, however, the highly specific needs of the audience

are not the only potential headache. . . .[T]here are few adult educators who are

already qualified to teach such courses and few computer experts who are skilled

in teaching adults" (p.69). As mentioned earlier, the "experts" may not have a

compatible style for facilitating this learning process.

One effective method of learning how to use computers has been through

cooperative models, such as computer user groups. "Problem solving and finding

answers to questions are fundamental techniques used in learning. If learning is

considered to be the process of building knowledge and developing skills, then

learning was definitely the primary purpose for which computer user groups were

formed" (Ludden, 1985, p.136). Computer user groups, because their design

adheres more closely to andragogical principles, provide opportunities for learning

that are not usually provided by educational institutions. "One lesson taught by

computer user groups is that people want continuous support in their efforts to

learn, particularly when the learning involves a new technology"(Ibid., p.186).

The rapid development of new technology in our society means that people canexpect to learn and adapt to technological innovations throughout their lives.Computer user groups offer a model that can be used by adult and communityeducators to aid individuals in learning to use new technologies. (Ludden, 1985,p.188)

In one of the few doctoral research projects to date on adult learning and

computers, Ludden (1985) surveyed 91 members of the Northeast Indiana IBM-PC

Club. One of the questions on the survey asked what sources were used to learn

about computers. Of the respondents, 98% used "self-education" as one source of

learning about their computers, with 45% selecting that choice as their primary

source of learning (ranked first of all choices). The second choice in the overall

22

(81%) and primary (19%) source of learning were books and magazines (ranked

second of all choices). The third ranking choice for primary source of learning (9%

each) was a tie between college classes and user group meetings. The subject of

Ludden's study, computer use groups, ranked third at 63% of all methods used to

learn about computers. These statistics are comparable to other studies of adult

learning projects (Penland, 1977; Tough, 1971) where 80% of adult learning

projects are planned by an "amateur" (Gross, Tough & Hebert, 1977).

The Touche Ross Enterprise Group surveyed 526 companies with sales

between $1 million and $75 million (LaPlante, InfoWorld, 1986). "The leading

cause of dissatisfaction with microcomputers in smaller and emerging corporations

is the amount of time required for training," although it was also noted that "a

majority of the respondents Ñ 76 % Ñ say they taught themselves how to operate

microcomputers." Only 11% used a consultant and 9% took a course to learn how

to use their computers. These statistics are also comparable to the other research

on adult learning mentioned above.

Business Week (1982) reported on a survey conducted by Booz, Allen, and

Hamilton which considered adult attitudes toward computers. Some interesting

findings from this study are that

level of education affects receptivity to computers less than whether or not onelearned to type, and that people who are dissatisfied with how they use their time aremore likely to be receptive than others. . . .The Business Week article ends with thecomment that one cannot learn to use a computer in the abstract; one must ratherpick a real business problem and solve it with a computer. (quoted in Goddard, 1983,pp.10-11)

Recently, there have been changes in computer technology which may

contribute to more successful learning experiences. Computer hardware has

become more sophisticated, allowing more complex operating systems which

allow a more "transparent" user interface. Software has become easier to use and

23

software companies have developed much more readable program

documentation. There is also a large amount of materials available to assist the

learner, from audio or video tutorials to printed materials (books, periodicals,

training aids).

Learning to use a personal computer may be having an impact on learning

beyond the acquisition of a new tool. The idea of computing is process which is

also at the heart of any learning activity, and the impact of learning to use a

computer may have unseen consequences. "The principal impact of the computer

on scientific research and scholarship derives from the modes of inquiry which are

possible only with its use. We have acquired a new methodology having

capabilities we are ill-equipped to appreciate at this early stage in its development"

(Mowshowitz, 1976, p.118). Personal computing may be revolutionizing lifelong

learning.

Three strategies have been identified for employing personal computers in

adult continuing education (Sheckley, 1986; Heermann, 1986) with "the personal

computer as a tool, a resource and a teaching machine" (p.8).

1. As a teaching machine to help learn new material (examples include drill-and-practice, tutorials and self-assessment programs);

2. As a learning tool to promote formation of new ideas (such as word processors,data-base programs, and spreadsheets);

3. As a learning resource to access information (for example, computer networks, on-line data-base services, and computer bulletin boards). (Sheckley, 1986, p.95)

Using the computer as a teaching machine, sometimes known as CAI

(Computer-Assisted Instruction) or CBL (Computer-Based Learning) falls under a

different paradigm of learning than is covered by this study. There is a large and

growing body of literature on the use of the computer as teacher. This study will

focus on learning to use the computer as a tool and a resource to aid in self-

24

directed learning. "The central thesis here. . . is that microcomputers as learning

resources and learning tools represent a particularly appropriate technology to

enhance continuing education" (Sheckley, 1986, p.95).

Using a microcomputer as a learning tool and for access to learning

resources is particularly appropriate for what Kolb (1984) terms the higher-order

integrative learning tasks characteristic of adult learning (Dede, 1981).

Microcomputers could eventually transform the definitions of teaching and learning

by taking over education's traditional role of information dissemination. Jerold

Apps (1982) suggests that if microcomputers in continuing education can be

"liberated from primary reliance on the passive transmission of information,

participants could spend more time on the meaning of new input, on problems and

issues, on ethical concerns, and on matters that require creative deliberation"

(Sheckley, 1984, p.100).

Christopher Dede (1987, p.20) described the evolution of instructional

devices with not only the microcomputer but also other technological devices such

as optical discs for digital storage. With the fusing of various information

technologies (telephone, television, radio, printing press, computer and copier),

their increased power will create new types of applications which will have

dramatic impacts on learning. He identified the next generation of educational

software as cognition enhancers: empowering environments, hypermedia, and

microworlds.

An empowering environment uses the classical division of labor to assign

routine mechanical tasks to the computer which frees the person for higher-order

tasks. Gerver (1984, p.49) referred to the "use of computers to reduce inauthentic

learning" or handling those tasks that are not necessary to achieve a particular

25

learning objective, suggesting that this may be "the greatest role microcomputers

play since it alleviates such tedious activities as memorization or hunting for

information. With microcomputers as learning tools, adults spend more time in

creative integrative learning and idea formation and less time on calculating

statistical formulas, computing spreadsheet totals, and retyping manuscripts"

(Sheckley, 1984, pp.97-8).

Dede's (1987) concept of an empowering environment includes "cognitive

audit trails" where people could review a sequence of actions, looking for patterns.

Groups of people could work more effectively together through connected work

stations or tools that structure the dynamics of meetings. A primitive empowering

environment is beginning to be used in education: "a word processor with spelling

checker, thesaurus, typing tutor, and graphics tool is the beginning of an

empowering environment for writing" (p.22). There is evidence to show that an

unconscious shift in style is taking place when people use these new technologies

to accomplish traditional tasks, such as writing. "In a world of intelligent

empowering environments, the way we accomplish many tasks may alter" (Ibid.).

"Hypermedia is a framework for non-linear representation of symbols (text,

graphics, images, software code) in the computer" (Ibid.) storing information by

association in patterns similar to how information is stored in memory. Hypermedia

can serve as an externalized associational memory (as with idea processors or

software like Guide or Hypercard on the Macintosh) or as "intermedia" which allows

browsing through information that is "linked, cross-referenced and annotated."

Whereas a word processor is linear, hypermedia is associational, perhaps

increasing comprehension, styles of remembering and knowledge transfer.

26

Microworlds allow the user "to explore and manipulate limited artificial

realities" which will allow learners to translate "abstract, formal knowledge into

real-world situations" (Dede, 1987). The use of computer simulations, "surrogate

travel" and "surrogate experiences" can be extremely motivating, especially as the

computer becomes linked to videodiscs and key variables in the experience can be

manipulated, increasing the potential for more "learning by doing."

Cognition enhancers will change the relationship between student and

teacher, school and society; allow more coordinated participation of society's five

primary educational agents (schools, family, community, media and workplace);

provide access to a variety of data, courseware, tools and training; and facilitate a

major increase in productivity within learning environments. A mature, technology-

intensive educational approach would have long-term effects on cognitive style,

personality and social skills (Turkle, 1984), enabling more equitable access to

educational resources, and preparing learners for the future. According to Dede

(1987), "the real wave of technological change in schools is just beginning" (p.24).

"Availability of computer learning tools might even increase participation in

continuing education programs by promoting self-directed opportunities for idea

formation"(Sheckley, 1986, p.97) which is consistent with the adult learning

research that documents adults' preference for planning and controlling their own

learning projects (Tough, 1971).

The personal computer has a dynamic potential for changing the way adults

learn. Mruk (1984) presented three different metaphors of change: the computer

as invention, revolution, and transformation (p.9). He pointed out that "the process

of acquiring computer skills is a complex activity. Even the most basic

understanding involves dealing with information related to the fields of computer

27

science, psychology and education" (p.4). The focus of his study, and this research

study, is the adult learner, for which a growing body of literature is developing,

although, he noted, "the field of computer literacy education and training for adults

is so new that researchers and educators are still struggling with establishing the

most basic definitions and goals. . . the kind of detailed psychological research that

we expected to find simply does not yet exist for adult learners" (p.7).

The computer is having a dramatic impact on all levels of education, from

pre-school through graduate school, as one school district staff developer noted:

In my work in staff development, I have never seen teachers become so excited soconsistently about new intellectual content as I have since I began working in the fieldof computer education. Not only is the computer a powerful new tool for curriculumdevelopment, but it also provides the opportunity for teachers to become willinglearners and to use that experience to take a new look at how children learn. Theissues of control of the learning process, evaluation, pacing, and grouping are nodifferent for students than for the teachers themselves. Reflecting on their ownlearning allows teachers to think about their students' learning experiences, theirneeds, their strengths, and the variety of their learning styles.

For me, the computer has become a tool for new learning unlike any other I haveexperienced. With it I can move in any one of a thousand directions. The possibilitiesseem endless, limited only by time and imagination. The computer is a window intothe future, opening to new learning, new thoughts, and new knowledge. And, noless important, it is a window on the past, a reminder of what it is like to be six or eightor fourteen, beginning to grasp the mystery of some new domain. (Russell, 1983,p.107)

Computers and other electronic media are now accepted as performing an

increasingly important role in the learning process at all age levels. However, the

key variable in implementing these new learning technologies is the willingness to

learn and to change established patterns, for both teachers in the classroom and

adults pursuing their own learning projects. Becoming competent with a personal

computer is placing adults back into intense learning situations which creates a

need to understand both the adult learner and the processes of learning to make

these activities more effective.

28

Process of Learning

General Definition of Learning

The term learning has been used to describe a product, a process or a

function (Smith, 1982). As a product, the emphasis is on the outcome or results of

a learning experience. As a process, the emphasis is on what takes place during a

learning experience. As a function, "the emphasis is on certain important aspects

(like motivation) which are believed to help produce learning" (Smith, 1982, p.34).

Within the context of this study, all three uses of the term will be addressed, with

more emphasis on process and function.

Hilgard and Bower (1966) defined learning as "a process through which a

person by his or her own activity becomes changed in his or her behavior"(quoted

in Aker, Spaulding, Adams & White, 1984, p.24). Learning is defined as a complex

activity, which includes acquisition of skills and knowledge as well as changes in

attitudes and values (Verner & Davison, 1982). "The act of learning is a process

rather than a product. In other words, learning is the process through which an

individual acquires the facts, attitudes or skills that produce changes in behavior"

(Aker, Spaulding, Adams & White, p.4).

There is a distinct difference between learning, training, education and

development. Learning is a concept that encompasses the other three. Training

implies learning related to a specific job; education refers to learning in a broader

sense for some future role or job; development refers to the general growth of an

individual (Nadler, 1982, p.7). Teaching is what we do to others; learning is what

we do to ourselves (Nadler, 1982, p.3).

29

Domains of Learning

Aker, Spaulding, Adams and White (1984, p.24) identified three different

domains of learning: cognitive, psycho-motor and affective. The cognitive domain

refers to changes in what we know: the acquisition of information, increasing levels

of understanding up through inductive and deductive reasoning, problem solving

and evaluation. Learning in the psycho-motor domain refers to neuro-muscular

responses that are changes in what we can do. Learning in the affective domain

(attitudes, values, feelings, beliefs, emotions) refers to changes in how we feel.

Learning to use a personal computer involves learning in all three domains:

cognitive (knowing what to do), neuro-muscular (primarily the ability to use a

keyboard and other input devices), and affective (positive attitude and motivation to

learn).

A concept of central importance here is that changes in one domain are usuallydependent upon or affected by changes in the other domains. . . one who is deeplyinterested in a subject can usually acquire and assimilate knowledge about thatsubject more readily than one who has no particular interest in the area. In otherwords, the acquisition of knowledge and cognitive skills affects our attitudes andattitudes affect our ability to receive, accept and process new information. (Aker,Spaulding, Adams & White, 1984, p.24)

Nature of Learning

There are two different philosophies about learning: cognitive and

behavioral. Some theorists believe that learning results from cognition, that the

learner's perception is the most important factor in the learning process. Others

emphasize the importance of the environment on learning, based on the

association of stimulus and response.

The key difference between these two views of learning centers on whether learningis a change in perceptual organization or a change in stimulus-response connections,and whether reinforcement is necessary for the occurrence of learning. . . both typesof theories can be considered since no single theory has proved adequate toaccount for all forms of learning. (Verner & Davison, 1982, pp.4-5)

30

Conditions of Learning

According to Gagn� (1970), there are certain internal conditions that must be

present in order for learning to occur successfully: (Verner & Davison, 1982, p.6)

- the learner must be motivated to learn

- the learner must able to concentrate on the learning task (attentional set)

- the learner must be in a state of developmental readiness to learn

- the learning environment must be conducive to learning in ways

acceptable to the learner.

Adult Learning

The field of adult learning has been commonly called andragogy (Knowles,

1975, 1978, 1980), a term that has been established in the literature as

qualitatively different from the education of children (pedagogy) (Knowles, 1975,

1980; Cross, 1981). Three main notions stand out regarding adult orientation to

learning: immediacy of application, efficiency and the pragmatic application of

adult learning (Danis & Tremblay, 1985). An adult's past experience fosters a

learning process that focuses on "modifying, transferring, and re-integrating

meanings, values, strategies, and skills, rather than forming and accumulating as in

childhood" (Smith, 1982, p.41).

Robert Smith (1982, pp. 47-48) described six optimum conditions important

for adult learning:

1. They feel the need to learn and have input into what, why, and how they will learn.

2. Learning's content and processes bear a perceived and meaningful relationship topast experience and experience is effectively utilized as a resource for learning.

3. What is to be learned relates optimally to the individual's developmental changesand life tasks.

4. The amount of autonomy exercised by the learner is congruent with that requiredby the mode or method utilized.

31

5. They learn in a climate that minimizes anxiety and encourages freedom toexperiment.

6. Their learning styles are taken into account.

Independent Learning and Independent Study

As detailed earlier, Gagn� (1967) stated that "learning can, and often does,

take place in the absence of a teacher" (p.30) and that the learner is "in a

fundamental sense responsible for his own learning" (p.31). At a conference on

independent learning held in 1967, Jourard found the fact that independent

learning was then problematic to be most peculiar, "because man always and only

learns by himself.Ê.Ê.Ê.Learning is not a task or a problem; it is a way to be in the

world. Man learns as he pursues goals and projects that have meaning for him.

He is always learning something" (Jourard, 1967, p.80).

Self-Directed Learning

Cross (1981) described the adult learning force as a pyramid of learners,

with a broad base of self-directed learners (which includes almost everyone);

about one third of the population participates in some type of organized instruction,

while a small percentage pursue college credit. Tough used the metaphor of an

iceberg to describe adult learning efforts, with the majority of the efforts submerged

below the surface; most of the attention of professional adult educators has been

mostly focused on the top one-fifth, that of professionally-guided learning, which

has been visible (Brookfield, 1984a, p.34). "Tough proposes that the massive bulk

of the iceberg, up to 80% of an adult's learning efforts, consists chiefly of self-

planned learning and is ignored by professionals in the field" (Brookfield, 1984a,

p.34).

32

Knowles (1975) wrote one of the earliest works on the topic of self-directed

learning and identified three reasons for its success: pro-active learners learn

better than reactive learners, with greater purpose, motivation and retention; self-

directed learning is consistent with adults' psychological development toward

taking increasing responsibility over their lives; and many new developments in

education require learners to become responsible and to take the initiative for their

own learning.

According to Stephen Brookfield (1986), there are two forms of self-direction

in learning:

1. The various techniques of self-direction, which include "specifying goals,

identifying resources, implementing strategies and evaluating

progress" (p.47).

2. An internal change of consciousness. Brookfield (1985c) pointed out that

"self-directed learning is predicated on adults' awareness of their

separateness and on their consciousness of their personal power"

(p.14).

In fact, Brookfield (1985c) further stated that "self-directed learning is

concerned much more with an internal change of consciousness than with the

external management of instructional events" (p.15).

Ralph Brockett (1985b) identified three major branches of research on self-

directed adult learning:

1. Adult Learning Projects (Tough, 1971; Penland, 1977) which "addressed

the frequency and nature of learning projects undertaken by adults"

(Brockett, 1985b, p.16)

33

2. Using qualitative research methodologies to build "a greater

understanding of the meaning of self-directed learning" (Brockett,

1985b, p.16)

3. Quantitative studies using the Self-Directed Learning Readiness Scale

(SDLRS)

"The first of these directions has been the descriptive research emerging

from the use of the learning projects interview schedule and its various

modifications. These studies. . .have contributed to a better understanding of the

frequency of self-directed learning among various adult samples" (Brockett, 1985c,

p.56). Allen Tough (1971) performed one of the first major research projects of this

type and discovered that as much as 80% of all learning is directed by the learner

(Gross, Tough & Hebert, 1977).

Stephen Brookfield (1985a) claimed that quantitative approaches have

tended to predominate the study of self-directed learning to date. However, many

researchers are, in fact,

beginning to turn their attention to the kinds of questions that lend themselves toqualitative analysis. This would seem to be consistent with the observations byKnowles (1973) that as a research area evolves, it moves through several stages.With regard to self-directed learning, the recent years have witnessed a decided shiftin direction from the descriptive learning projects approach toward methodologiesemphasizing explanation and prediction as well as the development of groundedtheory. Indeed, self-directed learning at this time is an excellent example of aresearch area where qualitative and quantitative approaches have been used toexplore distinct pieces of the puzzle and are contributing to a much greaterunderstanding than would probably be possible where research is limited to the useof a single methodological paradigm. (Brockett, 1985c, p.57)

One criticism (Brookfield, 1984b, p.63) of recent quantitative research into

self-directed learning using the SDLRS is that the emphasis has been placed upon

the "quantity of self-directed learning, largely to the exclusion of any assessment of

its quality or effectiveness" (p.63). Even the learning projects research, with its

34

emphasis on the number of hours or percentage of time spent in self-directed

learning, does not account for the quality of effectiveness of such learning. Brockett

(1985c, p.57) saw a shift toward research that combines both qualitative and

quantitative methodologies which would contribute to greater understanding in

certain types of studies than from the use of a single paradigm.

The third major type of self-directed learning research has focused on the

relationship between self-directedness and a range of psychosocial variables,

primarily using the Self-Directed Learning Readiness Scale (SDLRS)

(Guglielmino, 1977).

[T]he SDLRS is an attempt to measure the extent to which individuals perceivethemselves to possess skills and attitudes often associated with the readiness toengage in self-directed learning. In this way, the SDLRS has helped to move self-directed learning research beyond description toward a greater understanding of therelationship between self-directedness and certain personological variables. . . [T]heSDLRS has led to some important strides in self-directed learning research. (Brockett,1985c, p.56)

Brockett pointed out the important contribution of the SDLRS to self-directed

learning research, saying that it "is a measure of perceived readiness, not of actual

self-directed learning behavior" (Brockett, 1985b, p.16). Research using the

SDLRS shows a positive relationship between high scores on self-directed

learning and self concept (Sabbaghian, 1979); creativity and originality as well as

a right hemisphere style of learning (Torrance & Mourand, 1978); higher number of

self-planned projects (Hassan, 1981); and as a predictor of success in continuing

education (Savoie, 1979).

The primary difference between pedagogical and andragogical education is

the attitude of the learners. Self-directed learning does not mean learning in

isolation or in a social and intellectual vacuum (Brookfield, 1984b). Knowles

(1975, p. 22) indicated that there are occasions where self-directed learners may

35

realize the need to enter a classroom situation, but with a searching, probing frame

of mind, exploiting the resources for learning.

According to Smith (1982), "the great share of self-directed learning tends to

be concerned with skills acquisition" (p.91). The best time to use self-directed

learning is when motivation is strong and the subject matter is not overly difficult.

Some possible obstacles to self-directed learning are obtaining appropriate

resources, devising useful procedures and getting feedback (p.93). There are

times when self-directed learning is not appropriate or efficient, and can be

downright frustrating, such as when initially learning a foreign language (p. 91), or

a computer (Mruk, 1984, p.48).

Self-taught adults are able to specify their learning goals only once they havemastered certain knowledge or skills. . . .This principle implies that the educatorsshould not ask the adult learners to assess their own needs, to set their objectives, toplan their own process and to evaluate their learnings until the learners have had timeto familiarize themselves with the basic aspects of their field of learning. Thiscontradicts some actual practices which, for example, make a precocious use of pre-established self-directed learning contract. (Danis & Tremblay, 1985, p.133)

The best time to select a self-directed learning methodology is when the

motivation to learn is high and the person already knows a bit about what is to be

learned, to be able to begin planning and implementation activities (Smith, 1982,

p.91). There are several issues related to self-directed learning which are

important here: empowerment, learning how to learn and motivation.

Education as the transformation and empowerment of persons is a theme

that has shown up recently throughout the literature (Stanage, 1986; Rhodes,

1986a, 1986b; Larson & Roberts, 1986; Block, 1987). Carl Rogers pointed out the

emphasis in humanistic education on "empowering individuals to make decisions

and choices for themselves" (Robinson, 1985, p.101).

36

Larson and Roberts (1986) defined empowerment as "the sense of control a

person has over his or her life" (p.52). While not directly related to adult learning,

their research points out that the computer can be a catalyst for mutual support and

empowerment among learning-disabled students: "the opportunity to learn and

share skills on the computer provides benefits in self-esteem which carry over to

social needs. The power of the computer in addressing social needs is not

inherent in its hardware, but lies in the fact that it provides an interesting, engaging

activity around which the learning and sharing of skills occur" (p.55).

Block (1987) has said that empowerment is a state of mind and that the

deepest purpose of training is to empower people: to give them choice and control

over their work lives. ASCD's Lewis A. Rhodes linked the concept of

empowerment with current technology, pointing out that these new tools can

"empower individuals increasingly frustrated by their lack of control over their own

job destinies" (Rhodes, 1987, p.11).

Another aspect of technology and empowerment relates to the fact that we alloperate with two levels of goalsÑthe possible and the desirable. Most of us spendour daily work lives within the limits of the possibleÑwhat we think we can accomplish.Technology can expand the limits of the possible, thus bringing us closer to thedesirable. (Rhodes, 1986, p.98)

Learning to use a personal computer may increase feelings of

empowerment, in that learners have more choice and control over their lives.

In a study on "empowering parents through computer literacy training," in

fact, David Dik (1984) found that through the process of learning basic computer

skills, parents were developing a different relationship with their children: that as

they gained computer literacy skills, "they became empowered to enter into the

teaching and learning process." (p.1) Dik defined empowerment as "taking charge

of one's life through the process of gaining new skills and confidence" (p.2) and

developing self-worth. He found that many different learning activities could be

37

planned that would lead to the same results, but the computer was a "very

convenient way to enhance this exciting, positive change." (p.5)

Another issue in self-directed learning is the area of mathetics, or "learning

how to learn" (Smith, 1982; Kasworm, 1982). "Learning how to learn, becoming

an effective self-directed learner, is one of the main elements in the adult learning

process (Kidd, 1973; Knowles, 1975, 1980; Smith & Haverkamp, 1977; Tough,

1971). Self-directed learning is a process in which individuals take the initiative,

with or without the help of others, to gain certain definite knowledge and skills

(Knowles, 1975; Cross, 1981; Smith & Haverkamp, 1977). This learning is usually

focused on problem-solving and related to practical topics and how-to-do-it

projects (Tough, 1971; Penland, 1977)" (Caffarella & Caffarella, 1986, p.45).

An analogous way in which computers can contribute to adult learning is by making itpossible for people to learn how to learn. . . [T]his is clearly territory which is ripe forexploration and development. (Gerver, 1984, p.49)

The final issue in adult learning, which will be addressed in the next section

on psychological learning models (Corno & Snow, 1986; Wlodkowski, 1985), is

motivation. Maslow (1971) defined motivation in terms of a hierarchy of needs:

from physiological or survival needs; safety needs; love, affection and

belongingness needs; esteem needs; to the need for self-actualization (see also

Knowles, 1980, p.28). Other descriptions refer to either intrinsic (internal to the

learner) or extrinsic (external to the learner) motivation. In an early study of the

motivation of adult learners, Houle (1961) classified three different subgroups in

terms of their reasons for learning: goal-oriented learners who use learning to

achieve certain objectives; activity-oriented learners who participate for the sake of

the activity of learning and not the objective; and learning-oriented learners who

pursue learning for its own sake (Cross, 1981, pp.82-83).

38

The relationship of motivation to self-directed learning is also important to

this study because, according to one study, "the self-taught adults' motivation

arises from curiosity, interest or challenge. . . . self-taught adults' motivation

increases as their competence is recognized and as they are invited to transmit

their knowledge or skills to others" (Danis & Tremblay, 1985, p.132). One issue

also of importance is not only how motivation increases, but also how it decreases

and ways that adults maintain enthusiasm for learning.

A very common problem in self-directed learning is wavering motivation. Theabsence of a major goal (e.g., to get a degree or a certificate) and of the externaldirection and reinforcement that good instruction or a compatible group of peers canprovide can be keenly felt. Initial enthusiasm may erode rapidly if a project provesunexpectedly difficult or if other commitments require that it be set aside.Knowledgeable learners expect cycles in motivation and plateaus in achievement.(Smith, 1982, p.103)

Learning Models

"In order to understand the variables affecting learning and their

interrelationships, it is convenient to adopt a model of learning" (Wager, 1982, p.3).

A model is not in itself reality, but it is a representation of the reality of those who

developed it (Nadler, 1982, p.4). "All of us are constantly 'developing models' as

we try to make sense out of the everyday world around us" (Ibid.). Lippitt (1973)

noted that "any model is valuable when it improves our understanding" (p.30). As

Nadler further stated, "A good model can help the user to understand what is

essentially a complicated process. Underlying a good model is some theory, and

both are interrelated" (p.5).

There are many benefits of models for the user, among which are: to explain

various aspects of behavior; to integrate what is known through research and

observation; to simplify complex human processes; and to guide observation

(University Associates, 1980, p.171).

39

The construct of mental models, as the way individuals organize their

thinking about a body of knowledge, has attracted a lot of interest recently (Foss &

DeRidder, 1987; Norman, 1982; Johnson-Laird, 1983). One explanation of a

mental model is provided by Norman (1982):

people's views of the world, of themselves, of their own capabilities, and of the tasksthat they are asked to perform, or topics they are asked to learn, depend heavily onthe conceptualizations that they bring to the task. In interacting with the environment,with others, and with the artifacts of technology, people form internal, mental modelsof themselves and of the things with which they are interacting. These modelsprovide predictive and explanatory power for understanding the interaction. (p.1)

When people interact with a system, they formulate mental models of how

that system works; that model may not be accurate, will be incomplete, but is

functional because it enables the user to predict the operation of the target system.

The model will be continually modified as the person interacts with the system. The

construction of a mental model will be limited by the person's technical background

and previous experiences with similar systems (Norman, 1982, p.2). People

usually do not have an accurate understanding of an entire system. Norman

(1982) concluded "that most people's understanding of the devices they interact

with is surprisingly meager, imprecisely specified, and full of inconsistencies, gaps,

and idiosyncratic quirks" (pp.2-3). Too often, there is little relationship between the

system as designed, the material presented in the instruction manuals and the

mental model of the user. The concept of individuals constructing mental models is

important to understanding how adult learners attempt to conceptualize the

systems with which they interact.

In addition to a learner's own mental model of how a system works, there are

a number of models that have been developed which lend some conceptual clarity

to the complex process of human learning that is affected by many variables.

There are several different models of learning which form the conceptual

framework for this study: Corno & Snow's (1986) Aptitude Complex for Learning;

40

Wlodkowski's (1985) Time Continuum Model of Motivation; John Carroll's (1963)

Model of School Learning; Gagn�'s (1962) Hierarchy of Learning; Barbazette's

(1987) Situational Training Methods; Gullander's (1974) model of consciousness

and competence; Dreyfus and Dreyfus' (1986) stages of learning; Kolb's (1984)

adaptation of Lewin's Experiential Learning Model; and McCarthy's (1980) 4-MAT

system which is an adaptation of Kolb's model. Another important model to explain

learning is the Information Processing Model, based on research into sensory,

short-term and long-term memory.

Elements from each of these models will be incorporated into a new model

or grounded theory about how adult learners teach themselves how to use

personal computers. The following is an explanation of these models and their

significance to this research project.

Information Processing Model

There is currently a lot of research into the underlying mechanism of brain

function, human memory, and how people learn. This field, which capitalizes on

recent developments in computer science, provides a model of the human mind as

information processor. "The mind is seen as doing many things that a computer

does; the mind accepts or encodes information, manipulates and transforms,

stores, and retrieves it" (Wolfe & Robbins, 1987, p.11). Memory can be defined as

"an active mental process by which information is coded, stored, retrieved, and

integrated into previously stored information." (Wolfe & Robbins, 1987, p.11)

Figure 3 presents a schematic representation of the movement of information

through the different types of memory.

41

STIMULUS

SENSORY MEMORY

SHORTTERM

MEMORY

LONGTERM

MEMORY

RETRIEVED

NOT TRANSFERRED TO NEXT STAGEAND THEREFORE FORGOTTEN

REHEARSED

Figure 3. Information Processing ModelWolfe & Robbins, 1987, p.6

Each of the types of memory has unique characteristics. Sensory memory

can take in massive amounts of data rapidly through our senses but only holds that

data for a very short period of time. Only a small amount of the information that

comes in to our sensory memory passes into our short-term memory (STM), which

is a process controlled by the attention that we pay to the sensory input. Short-term

memory holds the recently experienced information temporarily (much like the

Random Access Memory of a computer) and is often called the working memory.

The STM has a short duration, less than 20 seconds (Peterson & Peterson, 1959)

and a limited capacity, approximately seven items (Miller, 1956). To remember

more items, the mind often chunks the information together. "The difference

between novices and experts in a field appears to be that experts, because of their

great experience, tend to organize information into bigger chunks. Novices work

with isolated bits of information" (Wolfe & Robbins, 1987, p.14).

42

Through the process of rehearsal, the mind can hold information in STM for

longer periods of time and can also transfer this data to long-term memory (LTM).

The process of transferring the information in STM to LTM is often referred to as

learning although the computer equivalent would be "storage." The mind stores

this information through a variety of meaningful patterns, associations or chunks;

the better the information is organized when it is stored, the easier it will be to

retrieve. "The more connections we have, the more ways we have to retrieve

information." (Wolfe & Robbins, 1987, p.16) There is much research being

conducted today into how the long-term memory is organized by the brain, and

there is some indication that emotion and images are a vital part of this process.

Perhaps the importance of moving images and sound should have been anticipated.After all, we've learned more about the brain in the last 10 years than in all the years ofprevious research. Current research indicates that emotion is not only involved inmemory but is also the basis on which our memory is organized. If we can affect alearner's emotions, then we will certainly have a significant impact on learning. Imagerecall is our most reliable and generally accessible memory, and moving images andsound are a particularly effective way to elicit emotionÑand that's exactly what goodmultimedia should provide. (Koetke, 1990, p.353)

Gagn�'s Hierarchy of Learning

Robert M. Gagn� (1962) was the first learning theorist to systematically

propose a hierarchy of learning, or a pyramid of tasks which must be completed

prior to mastery of a skill. The higher order skills require mastery of the lower level

skills. Gagn�'s (1962) work also shows that "there is a more efficient and less

efficient sequence which can be arranged for the learning of a procedural task, and

that this sequence involves learning one subtask before the total task is

undertaken" (quoted in Zemke & Kramlinger, 1982, p.272). In the context of this

study, Gagn�'s hierarchy is important for its emphasis on an appropriate sequence

to the learning task, and to identify a process that successful learners follow in

order to successfully use a personal computer. Below in Figure 4 is an illustration

of Gagn�'s bottoms-up principle (Ibid.):

43

Problem SolvingRule ApplyingConcept FormingDiscrimination MakingTerminology UsingChaining (Procedure Following)Response GeneratingStimulus Recognizing

Most Complex

Least Complex

Figure 4. Gagn�'s hierarchy

Following is a brief explanation of each one of these stages:

Stimulus Recognizing: the simplest form of cognition, or perception of a

change in the environment which precipitates the learner's action.

Response Generating: the action that the learner should take based on

a cue from the stimulus. A response assumes that the stimulus has

been learned. Therefore, this type of learning is usually paired as

stimulus-response (S-R).

Chaining (Procedure Following): as the next step, the action of

eliciting a series of responses. Zemke & Kramlinger (1982) defined a

procedure as "a task composed of two or more steps that follow each

other in a definite sequence" (p.204).

Terminology Using: describing the action being taken, or being able to

use the vocabulary associated with a skill. This is the borderline skill

between the purely behavioral nature of the lower skills and the

cognitive components or higher level skills of a task.

44

Discrimination Making: the addition of variables or changeable

conditions which require the learner to distinguish between similar

stimuli and generate appropriate responses.

Concept Forming: the addition of the ability to generalize from an

incomplete or changing set of variables, or a more abstract level of

learning. This stage involves seeing patterns among the variables.

Rule Applying: understanding "the logical rules that govern the

technology of a task" (Zemke & Kramlinger, p.207), which includes

laws or principles that are implicit in every task to be learned. The

learner must make a series of "if. . .then" decisions made up of a

number of variables. "Rule following means taking the most

appropriate action for the circumstances within the context of a

complex process" (p.208).

Problem Solving: what you do when the rules or logical systems don't

apply. The main characteristic of problem solving is projecting a

hypothesis. Some characteristics of this level are: (Zemke &

Kramlinger p.210).

- Different high performers do it differently.

- There are many unpredictable elements in the task.

- The performer is expected to define, fix, create, or re-examine the

rules.

One of the problems in learning to use a personal computer is that too often,

the learner is required to immediately perform at the higher level of problem-

solving, without the requisite more basic skills as identified in Gagn�'s hierarchy.

As will be shown later, the Kolb Learning Style Inventory gives insight into the

45

problem-solving style of a learner, and could provide insight into methods that

could used by learners of personal computer applications.

Caroll's Model of School Learning

John Carroll (1963) looked at "the degree of learning attained by an

individual as a function of both personal and environmental factors. Factors such

as motivation and aptitude will affect the time a person spends in a learning task.

Factors related to the type and quality of materials will affect the time a person

needs in order to learn" (Wager, 1982, p.3).

Degree ofLearning = Ä ( )time actually spent

time actually needed

At the simplest level of the Carroll model, the degree of learning is a function

of the time actually spent at learning relative to the time actually needed for mastery

of a learning task (Block, 1971).

Next, Carroll defined those variables that affect how much time a person

spends in learning, which accounts for both the time allowed for learning and the

perseverance of the learner.

time spent = time allowed ¥ perseverance

The second set of variables Ñ for time needed Ñ consists of the student's

aptitude for learning, the quality of instruction and the student's ability to

understand the instruction. The relationship here is inverseÑthat is, the greater the

value of these three variables, the less the time needed.

time needed = aptitude ¥quality ofinstruction ¥

ability tounderstand( )

46

The variables are then substituted into the original equation to reflect

Carroll's hypothesis that "an increase in any of the values on the top of the

equation will increase the time spent and increase the degree of learning.

Likewise, an increase in the value of the variables in the denominator will decrease

the time needed and increase the degree of learning that will occur" (Wager, 1982,

p.5).

Degree ofLearning = Ä ( time allowed ¥ perseverance

aptitude ¥quality ofinstruction ¥

ability tounderstand

)This model is significant to this research because it focuses on the time a

person spends in learning and the impact of certain variables on the degree to

which the learning is effective. While the original model was developed for use in

instruction, it has some implications for adult, self-directed learning, especially as

this research attempts to find ways to improve the quality of the self-instruction

related to learning a personal computer. Prior research also shows that time is an

important factor in acquiring skill in using a personal computer (Mruk, 1984).

Barbazette's Situational Training Methods

Training consultant Jean Barbazette (1987) developed a decision-making

model (see Figure 5) for determining the methodology to be used in achieving a

certain type of objective. This two-by-two matrix places the type of objective (to do

or to know) on one axis, and the maturity of the learner on the other. Using an

example that fits into this study, if the objective is to do something (as in the case of

using a personal computer) and the learner maturity is high, then the

recommended method of training is through experience, which is learner-directed,

involving the steps of doing, generalizing concepts from experience and applying

concepts to new experience. This process is essentially Kolb's (1984, p.21)

47

Experiential Learning Model. In the case of the learner of low maturity, if the

objective is to do something, then the demonstration model is recommended, with

telling, showing and practicing as methods that are instructor-directed.

OB

JE

CT

IVE

ToDO

ToKNOW

LEARNER MATURITYExperienceEducation

Attitude

High Low

Style 3

EXPERIENCE

Style 1

DEMONSTRATE

Style 2

LECTURE/TEST

Style 4

CASE STUDY

1. Do

2. Generalize concepts from experience

3. Apply concepts to new experience

(Learner Directed)

1. Tell

2. Show

3. Practice

(Instructor Directed)

1. Tell them

2. They tell you what they know

3. Exercise/case study

(Instructor Directed)

1. Case study or experience

2. Generalize concepts from experience

3. They tell you

(Learner Directed)

Figure 5. Situational training methods

This model points out the importance of the learner's level of maturity with

three factors: experience, education and attitude. Barbazette noted that the

maturity level applies to each learning situation or objective: a person may have a

high level of maturity learning one type of objective, while having a low level with

another. Therefore, the decision on methodology is based on the specific situation

and learners can move from low maturity methods to high as experience,

education, or attitude increases for a specific objective.

48

Consciousness and Competence Model

Gullander's (1974) model of consciousness and competence is a cognitive

model of learning. Whenever anyone learns anything, there are four stages to that

process:

1. Unconscious Incompetence

2. Conscious Incompetence

3. Conscious Competence

4. Unconscious Competence

The model, illustrated in Figure 6, is often represented on a two-by-two

matrix, with consciousness (unconscious and conscious) on one axis and

competence (competence and incompetence) on the other.

COMPETENCECompetence Incompetence

CO

NS

CIO

US

NE

SS Unconscious

Conscious

Transition 1

Awareness &Motivation to

Learn

Step 1

UnconsciousIncompetence

"Why?"

Step 2

ConsciousIncompetence

"What?"

Step 3

ConsciousCompetence

"How?"

Transition 2

Learning how to learn

Step 4

UnconsciousCompetence

"If?"Transition 3

DevelopingAutomaticity& Intuition

Figure 6 . Consciousness and competence learning model

49

This model describes one view of the steps or stages that people go through

in any learning activity. When beginning a learning project, the learner is in a state

of unconscious incompetence. ("You don't know what you don't know!") Through

the first transition that I define as developing awareness and motivation to learn,

the learner moves to the second step, that of conscious incompetence, where the

learner realizes the gap in his/her learning. This is similar to Knowles' (1980) use

of a competency model to create motivation for learning.

Through a second transition, that I call learning how to learn, the learner

gains skills and a basic level of competence, usually through some kind of

intentional training or learning activity, to move to the third stage in this model,

called conscious competence. Learners who are at stage three are acutely aware

of the steps and procedures involved in the task learned and are in an awkward

stage in the learning process.

Through a third transition which I call developing automaticity and intuition,

the learner is able to have enough experience with the skill to move into the fourth

stage of learning, unconscious competence.

This model is important to this research project because it outlines a

cognitive, step-by-step process of learning. Of special concern in this study will be

the transitions between each of the stages in this process as it relates to learning to

use a personal computer:

¥ What raises a person's awareness and motivates him/her to learn to use a

personal computer? (transition #1)

¥ What process is most effective in "learning how to learn" a personal

computer? (transition #2)

50

¥ Are there particular techniques that are effective for developing

automaticity and intuition in learning to use a personal computer?

(transition #3)

Dreyfus & Dreyfus Five-Stage Model of Skill Acquisition

Dreyfus & Dreyfus (1986) identified five stages which are roughly analogous

to Gullander's four stages: novice, advanced beginner, competent, proficient and

expert. This model was developed to compare the stages of human learning with

the attempts of computer scientists to create intelligent machines.

Perspec-Skill Level Components tive Decision Commitment1. Novice Context-free None Analytical Detached

2. Advanced Context-free None Analytical Detached beginner and situational

3. Competent Context-free Chosen Analytical Detached under-and situational standing and de-

ciding. Involvedin outcome.

4. Proficient Context-free Exper- Analytical Involved under-and situational ienced standing. De-

tached deciding

5. Expert Context-free Exper- Intuitive Involvedand situational ienced

Figure 7. Five Stages of Skill AcquisitionDreyfus, & Dreyfus, 1986, p.50

The beginning stages rely on rules and ignore context. As more skill is

acquired, application becomes more situational. At the level of competence,

decision-making is conscious, rule- and goal-bound, reducible to a set of elements.

At the level of proficiency, there is a tendency to see whole patterns without having

to break them down into component parts. As the level of expert is reached,

decision-making becomes unconscious and intuitive and does not rely on rules.

51

"Competent performance is rational; proficiency is transitional; experts act

arationally" (Dreyfus & Dreyfus, 1986, p.36).

This model also supports the sequential progressive nature of the learning

process and also confirms the last stage of Gullander's model (unconscious

competence), and the role of intuition in the later stages of the learning process.

"The distinction between the detached, rule-following beginner and the involved,

intuitive expert is crucial" (Dreyfus & Dreyfus, 1986, p.50). This skill model

"represents a progression in the sense that a typical learner's best performance in

a particular type of situation will initially stem from novice rule-following, then from

the advanced beginner's use of aspects, and so on through the five stages. If the

performer is talented, ultimately his best performance will result from the intuitive

use of similarity and experience, and he will perform as an expert"(p.35).

Corno & Snow's Aptitude Complex

Corno & Snow (1986) described a model of learning (presented in Figure 8)

that consists of an aptitude complex for performance in a particular educational

situation comprised of three components:

1. Cognition: The learner's intellectual abilities and prior knowledge,

"viewed as enabling cognitive skills and competencies" (Corno &

Snow, 1986, p.606)

2. Conation: The learner's cognitive and learning styles, which are "viewed

as propensities for processing information in certain ways that

develop around particular ability-personality intersections" (Ibid.).

3. Affection: The learner's academic motivation and related personality

characteristics, "enduring affective-emotional dispositions" (Ibid.).

52

An aptitude is generally a potential for achievement, a natural or acquired

adaptation, or a tendency toward quickness of understanding, a prediction that

further training will produce increased performance of a skill. Aptitude is seen as a

"broad, multivariate concept and yet also a simplifying one for theory construction,

under which many aspects of individual differences can be subsumed" (Ibid.).

COGNITION CONATION AFFECTION

INTELLECTUALABILITIES AND

PRIORKNOWLEDGE

COGNITIVEAND

LEARNINGSTYLES

ACADEMICMOTIVATION AND

RELATEDPERSONALITY

CHARACTERISTICS

APTITUDE COMPLEXfor performance in a particular educational situation

Purposive Striving and Control of Learning

QUALITY OF LEARNING ACT

QUANTITY OF LEARNING ACT

LEARNER ENGAGEMENT

LEARNER ACHIEVEMENT

Figure 8. A schematic conceptualization of aptitude for learning inrelation to educational performance

Corno & Snow, 1986, p. 618

53

From this aptitude complex comes "purposive striving and control of

learning" which leads to both the quality and the quantity of the learning act. Both

the quality and the quantity of the learning act lead to learner engagement, which

ultimately leads to learner achievement. As the figure is constructed, the

underlying cognitive skills have primary impact on the quality of learning, whereas

the personality predispositions which relate to motivation for learning have a major

impact on the quantity of learning, in particular, the persistence or level of effort

toward achieving the learning goal. The learning style, or the propensity to process

information in a certain way, is based on both intellectual ability and personality

characteristics, which thus "oversees and controls particular learning activities"

(Corno & Snow, 1986, p.619).

An individual's achievement in any learning activity will be the result of

interaction between this aptitude complex and the demands of the educational

tasks and the situation or context of the learning activity.

Most of the research on individual differences among learners (aptitudes) is

related to instruction rather than to self-directed learning. "The term aptitude

signifies some aspect of the present state of an individual that is propaedeutic to

some future achievement in some particular situation" (Corno & Snow, 1986,

p.605).

Corno and Snow developed a "Taxonomy of Adaptive Teaching" which

addressed both Inaptitude Circumvention and Aptitude Development. This study

seeks to determine the learning activities selected by learners with different

aptitudes for learning, to determine the successful learning activities of expert

computer users and to determine the frustrations or blocks to learning of the

novices and to see if patterns emerge for different learning styles.

54

Corno and Snow (1986) addressed strategies to develop different aptitudes

for learning.

Development of motivation to learn is closely linked to the successful development oflearning-to-learn skills, including metacognitive action control and the awareness thatone is learning through one's own effort and skills (Corno & Rohrkemper, 1987). Putdifferently, it involves development of a "way" to learn in consonance with a "will" tolearn, to aspects of learning that are in mutual interaction (Corno, Collins, & Capper,1982). (Corno & Snow, 1986, p.624)

Wlodkowski's Time Continuum Model of Motivation

As indicated by the last model, motivation is an essential component of an

aptitude complex for learning. Without motivation, there is no learning (Walberg &

Uguroglu, 1980), although there have been few research projects that thoroughly

examine the relationship between adult motivation and learning (Wlodkowski,

1985). There are presently few scientific methods to understand how motivation

affects learning and achievement. The closest direct measure of motivation is the

effort spent (Keller, 1983), which supports Corno & Snow's notion in the previous

model that motivation affects the quantity of learning. Wlodkowski (1985, p.61)

provided a comprehensive model of enhancing adult motivation to learn by looking

at factors that are important at certain time periods in the learning process.

55

Figure 9. Time Continuum Model of MotivationWlodkowski, 1985, p.61

According to this model, presented in Figure 9, every learning sequence can

be divided into a beginning, a middle, and an end. Wlodkowski believed that

"There are effective things that can be done during each of these phases to

enhance learner motivation" (p.60).

In the Time Continuum Model of Motivation there are three critical periods in anylearning sequence or process during which particular motivational strategies will havemaximum impact of the learner's motivation.

1. Beginning. When the learner enters and starts the learning process.

2. During. When the learner is involved in the body or main content of the learningprocess..

3. Ending. When the learning is finishing or completing the learning process. (p.60)

For each of these critical periods, there are two major factors of motivation that serveas categories for strategies that can be applied with maximum impact during thoseperiods of time.

56

Beginning:

Attitudes. The learner's attitudes toward the general learning

environment, instructor, subject matter, and self.

Needs. The basic needs within the learner at the time of learning.

During:

Stimulation. The stimulation processes affecting the learner via the

learning experience. (p.61)

Affect. The affective or emotional experience of the learner while

learning.

Ending:

Competence. The competence value for the learner that is a result

of the learning behavior.

Reinforcement. The reinforcement value attached to the learning

experience of the learner. (p.62)

As mentioned earlier, a critical focus of this research is the motivation of

adult learners who learn to use personal computers, and ways to increase that

motivation at each phases of the learning process.

57

Kurt Lewin's Experiential Learning Model

Concreteexperience

Observations andreflections

Formation of abstractconcepts and generalizations

Testing implicationsof concepts in newsituations

(Kolb, 1984, p.21)

Figure 10. Lewinian experiential learning model

Kurt Lewin developed techniques of action research and the laboratory

training method based on feedback processes. He saw that learning, change and

growth were best facilitated through an integrated process that began with a here-

and-now concrete experience, followed by data collection and observation of that

experience to form abstract conceptualizations or "theories" which serve as guides

for action in creating new experiences. This iteration (shown in Figure 10)

becomes a continuous process of action toward goals and evaluation of

consequences, integrated into an effective, goal-directed learning process (Kolb,

1984). The Lewinian model is one of the theoretical foundations of the Kolb

Learning Style Inventory, which will be used in this study.

58

McCarthy's 4MAT with C-BAM

Figure 11 presents a model developed by Bernice McCarthy, who studied

many learning style models but "settled on the work of David Kolb as an umbrella

descriptor of the learning process and the different ways people learn" (Guild &

Garger, 1985, p.52). McCarthy has taken the Lewin/Kolb experiential learning

model and developed a teaching model that takes learners through all four of

Kolb's learning styles in any learning activity. The model below superimposes the

Concerns-Based Adoption Model (CBAM) over Kolb's model, to show the levels of

concern that people have at various stages in the adoption of any innovation.

Sensing/Feeling

DoingWatching

Thinking

1's

2 's3 's

4 's

Und

erst

andi

ngIn

tern

aliz

ing

Ope

ratio

naliz

ing

Eva

luat

ing 0 Awareness

1 Informational

2 Personal(Meaning level)

Analysis forprofessionaluse

3A Firsttry"Cookbook"approach

3B Unique adaptation"Buffet" approach

4 Consequence

5 Collaboration Initialexperienceas catalyst

Abstract Conceptualization

Reflective Observation

Concrete Experience

Act iveExperimentation

3 Management

Figure 11. CBAM and 4MATªMcCarthy, 1982, p.25

59

This model emphasizes the sequential nature of the learning process, and is

used by teachers for lesson planning. In addition to the four learning styles based

on the four individual orientations toward learning (explanations of the four types

will follow), McCarthy also addressed theories of right-left brain functioning. The

4MAT system moves through a natural learning progression in sequence, with

each learning mode taught with both right and left brain processing techniques.

This lesson planning guide then divides a skill into eight separate learning steps,

one for each learning style and both right/left brain techniques.

This model also integrates the Concerns-Based Adoption Model (CBAM)

(Hord, Rutherford, Huling-Austin & Hall, 1987; Loucks & Hall, 1977) into this

sequential learning model. CBAM research on teachers adopting innovations

"hypothesizes that individuals move through different stages of concern as they

gain more experience with an innovation" (Wedman & Strathe, 1984, p.15).

The C-BAM model is a theory of change that is developmental and

sequential: the user must move through each stage in order in the adoption of an

innovation. McCarthy has superimposed the CBAM model over her 4MAT model to

show the sequential nature of the adult learning/staff development process, and its

integration with the four different modes of learning. Figure 12 below outlines both

the stages of concern and the levels of use of an innovation as defined by this

model.

60

Stages of Concern

0 Awareness Unconcerned about the innovation1 Information Concerned about the general characteristics of the

innovation2 Personal Concerned about the relationship between one's role

and the demands of the innovation3 Management Concerned about the time, organization, and

management of the innovation4 Consequences Concerned about the impact of the innovation on

student outcomes5 Collaboration Concerned about working with others using the

innovation6 Refocusing Concerned about something better than the innovation

Levels of Use of the Innovation: Typical Behaviors

0 Nonuse No action is being taken with respect to the innovationI Orientation The user is seeking out information about the innovationI I Preparation The user is preparing to use the innovationIII Mechanical Use The user is using the innovation in a poorly coordinated

manner and is making user-oriented changesIVa Routine The user is making few or no changes and has an

established pattern of useIVb Refinement The user is making changes to increase outcomesV Integration The user is making deliberate efforts to coordinate with

others in using the innovationVI Renewal The user is seeking more effective alternatives to the

established use of the innovation.

Figure 12. Concerns-Based Adoption ModelLoucks & Zigarmi, 1981, p.5

McCarthy has shown how Kolb's learning style can be integrated with

change theory (C-BAM) and applied both to learning in the classroom with children

and to adult learning situations. The model is sequential and developmental and

has a similar format to the Consciousness and Competence model discussed

earlier. This research will try to incorporate appropriate aspects of each of the

models illustrated above into a comprehensive model of how adults learn to use

personal computers. Some of the components to be included will be time (Carroll),

motivation factors (attitudes, needs, stimulation, affect, competence and

reinforcement from Wlodkowski), experiential learning stages (Lewin, Kolb),

cognitive factors and learning styles, all compared to empirical data gathered from

the subjects of this research project.

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Learning StylesThe more one understands the self as learner, the better equipped one is to learnand to take advantage of the myriad educational offerings that are now available. Thelearning-style concept represents one potential bridge and gateway to suchunderstanding. (Smith, 1982, p.78)

General Concept of Learning Styles

The study of learning style is complex, since learning style involves

cognition, conceptualization, affect and behavior. (Guild & Garger, 1985). There

are basic patterns in personality which "influence many aspects of personal and

professional behavior. In general they are called personality styles. When they

affect learning, we refer to learning styles" (Ibid., p.3). Carl Jung (1921) presented

one of the first major theories of both individual difference in human psychology as

well as typical differences (quoted in Guild & Garger,1985, p. 3), and his work on

personality styles forms much of the foundation for the current learning style theory.

There are several different learning style inventories and approaches to the

concept of individual differences. Below is an overview of the various approaches

to style (Guild & Garger, 1985, p.9):

Measures of style

COGNITION (perceiving, finding out, getting information)sensing/intuition: Jung, Myers-Briggsfield dependent/field independent: Witkinabstract/concrete: Gregorc, Kolb and McCarthyvisual, auditory, kinesthetic, tactile: Barbe and Swassing, Dunn and Dunn

CONCEPTUALIZATION (thinking, forming ideas, processing, memory)introvert/extrovert: Jung, Myers-Briggsreflective observation/active experimentation: Kolb and McCarthyrandom/sequential: Gregorc

AFFECT (feelings, emotional response, motivation, values, judgments)feeler/thinker: Jung, Myers-Briggseffect of temperature, light, food, time of day, sound, design: Dunn and Dunn

BEHAVIOR - manifestations of all of the above-mentioned characteristics

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For this particular study, I have selected the Kolb LSI because if reflects both

the cognitive and conceptual components of learning style and it is based on an

experiential learning model.

Kolb's LSI Based on Experiential Learning Model

David A. Kolb (1984) developed a Learning Style Inventory (LSI) which

measures two major differences in the way people learn: how they perceive or

grasp experience and information (concrete vs. abstract; sensing/feeling vs.

thinking); and how they process or transform experience and information (active vs.

reflective; doing vs. watching).

The LSI was designed to measure learning styles as a predictor of behavior

consistent with experiential learning theory. The latest version of the LSI (1981,

revised in 1985) consists of 12 sentences to complete with four phrases to be rank-

ordered with numbers from 1 to 4. Each of the choices corresponds to one of the

four learning modes. The LSI is a self-reporting instrument that is both normative,

allowing comparisons between individuals; and ipsative, where the "strength of

each learning style category is expressed, not in absolute terms, but in relation to

the strength of the respondent's other learning style preference" (Sewall, 1986,

p.56).

Experiential Learning Theory draws primarily on the work of John Dewey,

Kurt Lewin and Jean Piaget. Other contributors to experiential learning theory are

Jerome Bruner, Carl Jung, Erik Erikson, Carl Rogers, Fritz Perls, Abraham Maslow,

Paulo Freire, and Ivan Illich. "Experiential learning theory offers. . . the foundation

for an approach to education and learning as a lifelong process that is soundly

based in intellectual traditions of social psychology, philosophy, and cognitive

psychology" (Kolb, 1984, p.4).

63

There are several different forces that shape learning styles: psychological

type, educational specialization, professional career, current job demands and

adaptive competencies.

At one extreme there are those basic past experiences and habits of thought andaction, our basic personality orientation and education, that exert a moderate butpervasive influence on our behavior in nearly all situations. At the other end of thecontinuum are those increasingly specific environmental demands stemming from ourcareer choice, our current job, and the specific tasks that face us. (Kolb, 1984, p.98)

(CE)Concrete Experience

ActiveExperimentation

(AE)

ReflectiveObservation

(RO)

Abstract Conceptualization(AC)

Accomodators Divergers

Convergers Assimilators

Grasping viaAPPREHENSION

Grasping viaCOMPREHENSION

Tranformationvia EXTENSION

Transformationvia INTENTION

Figure 13. Kolb Learning Style Inventory

The Kolb LSI assesses individual orientations toward learning, measuring

the relative emphasis on the opposing tensions between abstract-concrete and

active-reflective polarities. Each learning style is a combination of the two adjacent

learning orientations.

Learning Environments

Fry (1978, quoted in Kolb, 1984) developed the concept of the learning

environment, in which any learning experience can have degrees of orientation

toward each of the four learning modes. These environments are labeled affective

(CE), perceptual (RO), symbolic (AC) and behavioral (AE), connoting the "overall

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climate they create and the particular learning skill or mode they require" (Kolb,

1984, p.197).

Affectively complex learning environments emphasize experience,

simulation, teacher as role model, personalized feedback from teacher and peers,

expressions of feelings, values, opinions in discussion format. Learners with high

CE scores like this mode because of its activities oriented toward "real-life"

situations, and their need to be self-directed and autonomous. Theoretical reading

assignments are considered a hindrance to learning.

Perceptually complex learning environments focus on understanding

something, identifying relationships between concepts, defining problems,

gathering data, researching a question, without measurement against rigid criteria.

Teachers serve as nonevaluative process facilitators and provide expert

interpretation. Learners with high RO scores like this mode because of teacher

guidance/expertise/lectures, and performance judged by external criteria.

Information focused on getting some job done is not considered helpful.

Symbolically complex learning environments deal with abstract concepts,

removed from the present, solving a problem with a right or wrong answer,

applying rules or concepts via memory. Teachers are taskmasters, sources of

knowledge, who judge and evaluate the success of learning, and make all

decisions regarding content and process. Learners with high AC scores like case

studies, reading theory, thinking alone. These learners find elements of the

affective and behavioral environments counterproductive to their learning.

Behaviorally complex learning environments emphasize active application

of knowledge or skill to a practical problem, without a right or best answer, where

information is focused on completing a project. The learner manages the time

65

allotted and makes choices about next steps. The teacher acts as coach or advisor

primarily at the learner's request, and success is measured by results of the task.

Learners with high AE scores like small-group discussions, projects, homework

problems, judging one's own work, and application to practical problems.

Hindrances to learning include lectures, teachers who are taskmasters and work

evaluated as right/wrong.

Human-Computer Interaction and User Interfaces

One growing area of cognitive research is the study of human-computer

interaction (Olson, 1987). Three different aspects of the interaction between

people and computers have been studied: the characteristics of computer system

itself, the person's mental and motor activity interacting with the software, and the

person's cognitive capacity (perception, short- and long-term memory). The goal of

this body of research is to learn how to make software easier to learn and use.

One of the major recent developments in computer technology has been the

increased power and capacity of the computer hardware, which allows both more

sophisticated applications and easier to use human interfaces. The traditional

method of communicating with the computer has been through the process of

typing commands into a keyboard. This method of communicating with the

computer has been classified as a command-line or text user interface. Computer

users must remember a whole set of commands for controlling the computer and

using the various functions of the operating system. Some current text user

interfaces are CP/M, MS-DOS, DOS 3.3 and ProDOS on the Apple II series of

computers.

As computers become more accepted as tools in most professions, it may

become important to use learning style measures in order to select the most

66

appropriate type of application or computer. There is some evidence in the popular

computer press (Harriman, 1988) that the basic differences between the two most

popular types of computers (IBM and Macintosh) may have a lot to do with the way

the brain works, which she referred to as hemisphere specialization. The

characteristics of the left brain are associated with the MS-DOS operating system

(verbal, abstract, sequential, analytical, successive) while the characteristics of the

right brain are associated with the Macintosh (graphic, concrete, multiple, holistic,

simultaneous).

While it's difficult to prove that different people react to either machine according to theirrelative brain-hemisphere dominance, it's certainly something you might consider whenmatching people and machine, and when asking them to switch from one way of computing toanother.

These correlations also bring up one additional inescapable point: not everyone willbe better off with a non-sequential graphic interface. While some criticize IBM forfueling the fires of chaos by supporting a non-graphic alternative to OS/2 (at least fornow), it's certainly valid to offer choices so that each person can match his own stylewith that of his computer. (Harriman, 1988, pp.209-210)

As computers became more powerful, newer graphical user interfaces were

developed which allowed users to communicate with the computer through a

pointing device, such as a mouse, selecting commands from a series of pull-down

menus. Xerox Corporation performed some of the early research into the graphical

user interface (GUI) at its Palo Alto Research Center in the 1970s. The GUI

became available to a larger number of computer users in 1984 with the

introduction of the Macintosh and its unique graphical user interface. Since that

time, there has been major developmental work to develop graphical interfaces for

most computers. There is a version of a GUI for the Apple IIGS as well as most

IBM-compatible machines (with the appropriate processor). With the computer

industry moving in this direction, it is important to look at the impact of the GUI on

learning strategies.

67

Apple Computer, Inc. (1987) provided the following definition of a human

interface:

A human interface is the sum of all communication between the computer and theuser. It's what presents information to the user and accepts information from the user.It's what actually puts the computer's power into the user's hands.

According to their guidelines, the primary advantage for the learner is

consistency. There is no need to reinvent the interface with each application. The

GUI provides ease of use, mistakes are less likely, and the tools don't keep the

user from the task. Apple's user-centered approach to their system (and their

assumptions about the people who use computers) is summarized below:

¥ People aren't trying to use computers Ð they're trying to get their job done.

¥ Users are instinctively curious.

¥ Users desire control over their environment.

¥ People are skilled at manipulating symbolic representations.

¥ People are productive, effective and imaginative when the environment is enjoyableand challenging.

From these assumptions about computer users, the company went on the

define ten principles on which they based the graphical user interface and which

are a set of guidelines for Macintosh software developers. These guidelines, and

some justification, are summarized below:

1. Use Metaphors from the Real World¥They're quickly understood.¥They take advantage of people's direct experiences with their immediateworld.¥ Concrete metaphors allow users to apply a set of expectations to computerenvironments.

2. Direct Manipulation\¥User's want to feel that they're in charge of the computer's activities.¥People expect their physical actions to have physical results.

3. See and Point¥Recognition, not recall. See-and-point, instead of remember-and-type.¥Users select actions from alternatives presented on the screen.¥The average user is not a programmer and is not familiar with usingcommand-line interfaces.

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4. Consistency¥Effective applications are both consistent within themselves and consistentwith one another.¥Consistency allows users to transfer a general set of skills from oneapplication to another.

5. WYSIWYG ("What you see is what you get")¥What you see on the screen is what you get in a printed version.¥No secrets from the user, no abstract commands without immediatefeedback.

6. User Control¥The user, not the computer, initiates and controls all actions.In other words, the user "acts", the computer "reacts."¥In risky situations the computer provides warnings, but the user ultimatelydecides whether an action should proceed or not.

7. Feedback and Dialog¥Always keep the user informed of what's going on.¥Provide immediate feedback . Use visual effects, sound, and/or brief anddirect messages expressed in a user's vocabulary, not a programmer's.¥ Reduce complex activities into small, simple steps.

8. Forgiveness¥Users learn best through exploration. They make mistakes when theyexplore; forgive them.¥Allow users to generally reverse their actions. Inform them whenever theywon't be able to.

9. Perceived Stability¥Users are most comfortable in an environment that appears to stay the same.¥Use consistent graphical elements and visual "landmarks."¥Unavailable objects aren't mysteriously missing - they're grayed out.

10. Aesthetic Integrity¥ Stress attractive displays, visual clarity, simplicity, consistency.¥ Visually confusing displays detract from effectiveness.¥ Different "things" should have distinct and different appearances.¥ Use the skills of a graphic designer to take advantage of the visually richpossibilities.

In addition to these ten principles, Apple also outlined certain concepts to

incorporate into all software:

¥ Modelessness Ð a given action always has the same results, irrespective of pastactivities.

¥ The event loop Ð allow the user to do anything at any time.

¥ Reversible actions Ð always provide a way out.

¥ The screen is central Ð it depicts all human-computer interactions.

¥ Plain language Ð communicate in concise, simple terms which are direct andunambiguous. Use the skills of a writer.

69

These instructions for the Macintosh's software design are at the center of

the design of applications for its graphical user. In addition to research on learning

style, there is a need for research into the impact of the graphical user interface on

learning strategies of adult learners.

Related Research Studies

There have been some mixed results from other research related to the

impact of learning styles on gaining competency in learning computers. There

have been few research studies conducted which integrate adult learning,

computers and learning style.

The only dissertation found to address adult learning about computers and

learning style using the Kolb LSI was conducted by Mary Cone Barrie (1984)

through the University of Toronto. Her study examined "the relationship between

learning style as identified by Kolb's Learning Style Inventory (LSI)(1976) and the

learning performance of adults enrolled in a 14-week course introducing them to

the use of a computer" (Barrie, 1984, p.14). It should be noted that the adults were

enrolled in two different courses, learning to program in either COBOL or BASIC

computer languages. Students were given the Kolb LSI at the beginning of the

course and Barrie used the final grade at the end of the course as an indicator of

success which, she admitted, was a "gross measure of the quality and quantity of

what an adult learns in a course" (p.79). Her conclusions were that "the LSI does

not differentiate people in ways that relate to a short-term learning situation. While

the LSI might be descriptive of certain psychological orientations, it is not predictive

of learning behavior measured by course grade" (p.75).

One interesting fact which she did not elaborate on in great detail, was the

significantly high proportion of Convergers enrolled in the course and whether that

70

reflected learner self-selection on the basis of learning style. This is the learning

style which is high in AE (Active Experimentation) and AC (Abstract

Conceptualization).

The Kolb LSI has been used to classify scores of various professions. Kolb

(1984, p.85) pointed out that "early educational experiences shape individual

learning styles; we are taught how to learn." His research has shown a

correspondence between LSI scores and certain undergraduate majors (p.86) and

professions (p. 89). Other research documented by Kolb has shown a relationship

between job demands and learning style. Primarily due to an inadequate research

base, there has been some criticism (Sewall, 1986) of using learning style

instruments for facilitating career planning. However, as an assessment of persons

already established in specific professions, the Kolb LSI appears to provide insight

into the adaptive competencies of various learning styles with various job demands

(Kolb, 1984, p.187).

Kolb and Smith's (1986) User's Guide for the Learning Style Inventory,

shows that computer programmers and data processing professionals score about

mid-range on the abstract conceptualization (AC) scale with little difference

between active or reflective styles (p.101); this finding could mean that either

computer-related jobs require higher levels of abstract learning skills or that people

with those types of skills prefer to work in computer-related jobs.

One of the most in-depth studies of adult learning about personal computers

was conducted by Christopher Mruk (1984, p.27). He tried to deal with the growing

problem of helping the average adult to acquire the basic kinds of computer skills

that are necessary to cope with the computerization of America. His goal was to

71

"help prevent the creation of a new social and economic disability, and the

unwanted formation of what we can only call the technological poverty line!" (p.3).

The three fundamental components of his study included: What was learned

(computer literacy); how it was learned (computer education); and who was

learning (the adult learner). The first objective concerned understanding the

learning levels, or what kinds of tasks, activities and goals are most important in

acquiring computer skill. The second area involved understanding the teaching

context, or the educational aspect of the process. The third research activity aimed

at describing the adult's experience of learning to operate a computer, or the

individual factors that were involved (pp.27-28).

Mruk also chose participants in two different computer classes (one for

"regular" college students and one for "adult" learners). He gave the Kolb LSI to the

participants in his study. He found:

Learning style, or the way that one habitually processes information, may be a factorthat affects acquiring computer skills. There are two possible ways that learning stylecould be active in this process. One way is that certain types of material may be morecompatible with specific styles. For instance, someone who is conceptually orientedwould probably do better with academic rather than experiential teaching approaches.This possibility is difficult to confirm because of measurement problems. However,there is some evidence which suggests that making an occupational choice is relatedto the way that one learns. (Kolb, 1976)

The assessment of learning style is another one of those areas of educationalpsychology that is new and needs further verification. However, Kolb's instrumentmay be especially well suited to the adult learner. The test is simple to administer,easy to score, and is based on experiential and developmental theories of learning,both of which apply to the adult. . . .Our findings do support the view that certainstyles of learning impact on computer learning rates for non-technical older learners.(Mruk, 1984, p.63)

Although the study was too small to provide a comprehensive analysis of the

relationship between learning style and degree of computer skill attained, he found

some:

interesting indications of possible correlations between learner style andperformance, at least for computer skills in the academic model. The most reliablefinding seems to be a negative relationship between performance and one particular

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learning style. Almost 56% of those who were "Divergers" were rated poorly by theirteachers. Further, none of the 14 computer professionals demonstrated this learningstyle. At the same time, Divergers made up the second largest learning group forboth the adult and regular learners. In other words, there is good reason to suggestthat learning to acquire basic computer skills of this type represents special difficultyfor one of the largest learner groups in the study. (Mruk, 1984, p.82)

It should be noted that the Divergers have the learning style that is high in

CE (concrete experience) and RO (reflective observation), which is the opposite

learning style from the unusually high proportion of learners enrolled in the

computer courses in Barrie's study.

Mruk made a number of interesting observations and recommendations

regarding adult learning and personal computers. In summary, he observed that

adults want hands-on, practical experience; that an introductory course reduced

anxiety about the computer, although beginners often had higher expectations of

results than could be achieved; that motivation was the key to acquiring basic

computer skills; that adults learn computers more effectively through a non-

threatening approach; that adults prefer learning to use applications software over

computer programming; that learning time (hands-on experience and practice) was

related to how effectively the skills were acquired; that learners need to build a

frame of reference to relate the computer to their own experience; that having

access to computers at home (an economic implication) was the best learner

support; that learners are often given bad advice about computer training; and that

a self-assessment instrument would be useful for decision-making. He also

remarked on the inefficiency of individually-oriented learning environments and the

need to develop a "non-threatening, supportive and self-paced learning

atmosphere that can accommodate a wide range of learning styles and needs"

(p.48).

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Several other research projects have recently been conducted with other

groups of adults learning computers. The Bank Street College of Education

(Sheingold, 1990) is in the process of completing a survey of over 600 teachers

across the nation who were using computers for instruction, both in classrooms and

in labs. Virtually all of the participants in this study were self-taught, taking

advantage of all kinds of assistance for learning. These were sophisticated users

who were very comfortable with computers (85% owned their own computers).

When asked what barriers they currently faced in implementing computers into

their instruction, the primary barrier was a lack of time to develop lessons to use the

computers.

In a long-term phenomenological study of computer users, Reva Shapiro

(1989) described the process of learning to use early home-based personal

computers, which she summarized as mega-frustration. She described this

process as one of moving contexts, with unique features:

One distinctive feature of this innovation was that users did not learn how to use justone technology. They were required to integrate different forms of technology andmatch resources; people, equipment and information. Ironically, while the PCsymbolized the "Age of Information," consumers did not have all the information theyneeded to get their units "up and running." (p.1)

Another problem was the inability of novice users to differentiate between

the machine's malfunctions and their own incompetence. "Learning how to use a

PC was a trial-and-error process. Users learned as much from their mistakes as

their successes" (p.11).

The users reported a "Catch 22 experience with manuals and other documentationthat accompanied their PCs. The manuals did not make any sense before one usedthe computer, but to use the PC, one had to understand the manual. Previousexperience with computers and electronic equipment did not always ease thetransition; nor was prior knowledge easily applicable to present challenges." (p.10)

Shapiro discussed the role of the guru and the helping relationships that

were set up in the early days of personal computers, especially through user

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groups. She found a lot of role reversal of age and wisdom, with adult users

seeking help from their own kids or younger user group members. Once the

learners gained a certain level of competency, they felt obligated to share new

information or materials with the gurus, or to assume this guru role with other

novice computer users.

Dona Kagan (1988) reviewed the six studies that were conducted during the

previous four years which examined "either personality traits associated with

achievement in computer courses or the nature of computer skills as cognitive

tasks" (p.49). One of the research questions related to the kind of personality most

conducive to learning to use computer; Kagan found the tendency to be "Type-A

individual (time-urgent, ambitious, relatively hard-driving) gave students in

computer literacy courses an edge early in the semester, but it diminished as the

course progressed" (p.51). She also found that the tendency to be compulsive

(perfectionistic and attentive to detail) was a personality factor that was significant

early in the computer literacy course. Personality traits play a significant role in

achievement in these courses because some students may be anxious and

intimidated at the beginning of the course. "However, as the literacy course

progresses, students may relax and realize it is not as difficult as they had

anticipated. . .computer literacy courses may be both easier and initially more

intimidating to students" than programming courses (Kagan, 1988, p.51).

The computer enables different methods for reaching educational goals,

such as graphic representation in place of verbal statement (Taylor & Cunniff,

1988), providing effective and powerful alternatives for learners. The research

being conducted at Columbia University by Taylor and Cunniff seeks to gain a

better understanding of the interaction of computing and learning, and is

specifically focused on a comparison of graphic versus textual representation of

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concepts. They hypothesized (and verified) that "graphic representation of some

concepts is superior to textual representation for at least some learners" (p.361).

Prior to the development of symbols and writing, human beings primarily

communicated through graphic representation. With the invention of the printing

press in the mid-15th century, and the ease with which print materials could be

disseminated, text has become the primary method of formal communication. As a

result of the limitation of the early printing press to print symbols only, the

technology gradually limited most printed communication to purely text, not

because if was more effective but because it fit the capabilities of the technology.

As a consequence, "we have become a culture whose dominant mode of

communication in formal settings is print, a culture where education is almost

synonymous with mastery of textual material" (p.363). However, a majority of our

informal communication today is through the visual medium (television and video),

creating a dichotomy between formal and informal communication, "between how

we typically learn in schools and how we learn elsewhere."

Today, the more powerful computers give us a medium to balance the

presentation of material with more graphic images which can enhance (and

sometimes replace) written descriptions. Graphic representation provides the

concreteness needed by some learners to help them grasp abstract concepts, and

graphic tools make it easier to understand the complex actions of a computer

(Taylor & Cunniff, 1988, p.366).

Taylor and Cunniff have argued that "educators can and should use the

computer to provide alternative ways of representing knowledge" (p.371), and in

particular, "using the computer to implement graphics as an alternative to the

traditional textual representation of concepts." Although their research looked

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specifically at graphical computer programming environments, their conclusions

could be applied to a variety of learning environments where computers could be

used. Their results "suggest that a full understanding of the effect of computing on

education can only come from rigorous and extensive research exploring the

relationships between learning and the alternative forms of representation the

computer offers."

In 1989, Microsoft Corporation and Zenith Data Systems commissioned a

year-long research project by the firm of Temple, Barker and Sloan (1990). The

results of that study showed that there is increased worker productivity with the

graphical user interface (GUI) compared to those using the character user interface

(CUI). Using clinical tests, attitudinal surveys and focus groups with both new and

experienced computer users, "the study found clear evidence of seven benefits of

the graphical environment over traditional character-based systems and a strong

user preference for graphical systems."

In general, the research concluded that GUI users "work faster and more

accurately, master more capabilities, require less training and support, are better

able to self-teach and explore and report lower levels of frustration and fatigue."

Specifically, researchers determined that:

¥ On the average, experienced graphical system users completed 35 percent moretasks than character-based system users completed in the same periods of time.

¥ Experienced graphical users correctly completed a higher proportion of theexercises they attempted: 91 percent versus 74 percent completed by character-based users.

¥ Together, these results mean that experienced graphical system usersaccomplished 58 percent more correct work than character-based system usersaccomplished in the same amount of time.

¥ After spending two days learning microcomputer applications, graphical systemnovices rated their frustration at 2.7 out of 10, while traditional system beginnersregistered a much higher frustration level, 5.3 out of 10.

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¥ Graphical system novices also attempted 23 percent more tasks than charactersystem novices when given new tasks to perform.

A recent research project presents another side to the debate over graphical

versus character or text user interface. In the January, 1990, issue of Academic

Computing, Marcia Peoples Halio reported a significant difference in the quality of

student writing completed in freshman composition courses at the University of

Delaware using two different computer systems. Compared to students using the

IBMÊPC, Halio reported that those students who wrote their papers on the

graphical Macintosh tended to have poorer grammar, punctuation and syntax as

well as less weighty subject matter. She attributed some of these differences to the

"playfulness" of the Macintosh and its small screen and she asked, "Can a

technology be too easy, too playful for young immature writers to use?" The furor

that resulted in the academic community from that article began with the non-

scientific nature of Halio's study and has spawned a variety of research projects to

further study this phenomenon, now dubbed "the Delaware effect." The debate

ranges from the expansion of rhetoric into more graphic forms of expression to the

concern about form over content in the composition process. With the release of

Windows 3.0 by Microsoft in May, 1990, and the movement of the computer

industry toward more graphical user interfaces, the next few years should provide a

plethora of studies in this area. I hope my study adds another dimension to this

burgeoning body of literature.

78

CHAPTER 3 - METHODOLOGY

Statement of the Problem

This study focused on a diversity of adult learners who used different types

of personal computers to study a variety of factors related to the acquisition of

personal computer competency, to see if learning style makes a difference in

learning strategies and competency; to see if the type of computer makes a

difference in learning strategies and competency; to see what motivates adults to

learn a personal computer; to see what factors are important in the learning

process; and to see if a model can be built of a common sequence or process in

learning to use a personal computer.

The overall goal of this study is to test a set of hypotheses related to the

three main goals of the study and to further develop research questions which

explore the role of readiness for self-directed learning as measured by the Self-

Directed Learning Readiness Scale; to study the factors related to individual

differences in the learning process as determined by the Kolb Learning Style

Inventory; and to study the impact of preference for computer interface on the

strategies used to gain personal computer competency.

Research Questions

The literature review has provided a wealth of questions related to adult

learning about personal computers.

¥ What are the strategies used to learn how to use a personal computer?

¥ Is there a predominant learning style preference for a certain type of

computer?

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¥ Does motivation to learn independently, as measured by the SDLRS, affect

the level of computer literacy?

¥ Is there any pattern that emerges in those strategies by learning style? by

type of operating system? by SDLRS score?

¥ Is there a difference in the process/strategy people use to learn how to use

a personal computer based on:

- personal/professional need to learn (motivation)

- competencies/skills to be acquired

- individual learning style

- prior experience

¥ Do all learners go through the same basic process in becoming competent

in using a personal computer?

¥ Do any other "success" factors emerge from the data?

¥ Is readiness for self-directed learning a factor in achieving relative

competence in using a personal computer?

Hypotheses

The following hypotheses which guided the data analysis are divided by the

three main goals of the study. A .05 level of statistical significance was the basis for

supporting or rejecting the hypothesis using Fisher's Protected Least Significant

Difference (PLSD) test on the ANOVA.

80

Goal 1: To explore the role of that readiness for self-directed learning has on the

acquisition of personal computer competency.

H 1.1: Competent computer users spend more than 70% of the time

learning their computers using self-directed learning strategies.

H 1.2: Competent computer users will have a higher level of self-directed

learning readiness than beginning computer users.

H 1.3: Computer users with intrinsic motivation to learn how to use a

personal computer have a higher relative level of personal computer

competency than those with extrinsic motivation to learn.

H 1.4: Computer users with a foundation for learning will have a higher level

of personal computer competence

Goal 2: To explore the role of learning style as a factor in the process of learning to

use a personal computer.

H 2.1: Computer users with an active learning style will have a higher

relative level of personal computer competence than those with a

reflective learning style.

H 2.2: Computer users with an abstract learning style will have a higher

relative level of personal computer competence than those with a

concrete learning style.

Subsidiary Question: What is the relationship between learning style and

readiness for self-directed learning?

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Goal 3: To develop an understanding of the impact that the type of computer

operating system (graphical vs. text user interface) has on the learning

process of learners with different learning styles

H 3.1: Computer users with a concrete learning style preference will favor

the Graphical User Interface.

H 3.2: Computer Users with an abstract learning style preference will favor

the text-based user interface.

H 3.3: Competent users of graphical user interface computers will use more

types of applications than competent users of text-based systems.

H 3.4: The type of computer learned has a greater impact on learning

strategies than the learners' preferred learning style.

Design

This study falls under the general rubric of descriptive research, which

includes both exploratory and phenomenological designs. Under an exploratory

design, the goal is to determine the characteristics of a certain phenomenon.

Under a phenomenological design, the goal is to determine the characteristics of a

certain phenomenon. In this exploratory research, the participants will be

considered partners in the research, not "subjects." The concept of "control" is

antithetical to this type of research.

This descriptive research project used four different quantitative instruments:

Kolb's Learning Style Inventory (LSI); Guglielmino's Self-Directed Learning

Readiness Scale (SDLRS); a Personal Computer Competency Inventory (PCCI),

developed by the author and another doctoral student in Anchorage, Alaska; and a

82

questionnaire developed by the author which inquired about computer learning

strategies, personal computer ownership and use. In addition, there were a series

of qualitative responses to some open-ended questions based on Raymond

Wlodkowski's (1985) "Time Continuum Model of Motivation" to inquire more deeply

into different phases of the learning process.

Between March and December, 1989, 194 people responded to all of the

questionnaires, with 31 of them completing the optional essay questions.

Participation was solicited from several sources: educators attending the Alaska

Association for Computers in Education annual conference in March, 1989;

attendees at the National Educational Computing Conference (NECC) annual

conference in June, 1989; graduate students and faculty at The Fielding Institute's

annual Summer Session, 1989; participants in adult computer classes at the

University of Alaska Fairbanks, School of Career and Continuing Education, in the

fall of 1989; and employees of the Fairbanks North Star Borough School District.

Two types of data were collected to enrich the data analysis and to further

support the development of hypotheses for further study. The quantitative data

provided opportunity for some interesting comparisons between different groups of

participants. The qualitative data were essential for developing a model or

grounded theory. In this case, both types of data contributed to a better

understanding of the phenomena under study.

83

Population Sampling Strategies

Relative Level of Computer Expertise

Participants were divided between beginners (B) and experienced or

competent (C) users of personal computers. The criteria for determining the level

was:

- The subjects' self-report of their level of expertise

- Their relative scores on the Personal Computer Competency Inventory

Type of Computer Operating System

As mentioned earlier, there is some evidence in the computer press that

certain types of computer systems are more "user friendly" or are easier to use.

This proposition has certainly been exploited by Apple Computer in its advertising.

There has also been some discussion recently about the basic differences

between Macintosh and MS-DOS systems as they relate to right- and left-brain

preferences (Harriman, 1988). Therefore, it was important to include learners of

both types of computer systems in this study. The participants were also classified

into groups by the type of computer operating system they preferred or used most

often: text user interface (T) or graphics user interface (G).

T users preferred computers running Apple II DOS 3.3 or ProDOS, MS-DOS

(IBM-Compatible), or CP/M operating systems.

G users preferred computers running Apple IIGS, Macintosh, Atari or

Windows operating systems.

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Efforts were made to find a cross section of users in four categories, based

on the participants' perceived level of their own competence (beginning or

competent user) and their preference for either the graphical user interface (GUI) or

the text/command-line user interface. This resulted in four main groups:

BT - Beginning users of text user interface operating systems

BG - Beginning users of graphics user interface operating systems

CT - Competent users of text user interface operating systems

CG - Competent users of graphics user interface operating systems

There were a few combination users, especially among the experienced set,

who may have started out with a text-based system in the early days of personal

computing (1978-84) and moved on to a graphical interface when those systems

came on the market (1984-present). Of particular interest was the subjects'

preference for one type of system over another and for what purposes those

systems were selected or used.

A group of four instruments were given to a cross section of learners from

each category of participant group. The great majority of the participants in this

study were between the ages of 21 and 55. No attempt was made to secure a

random sample, since the purpose of this research is to provide initial insights into

the learning styles of specific targeted groups of computer users. There was an

initial goal of 100 people to complete the four written instruments. Below is a chart

which represents the goals and actual number of participants in each category.

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Beginning Users (B)Competent Users (C)

Typ

e of

Com

pute

r O

pera

ting

Sys

tem

GraphicUser

Interface(G)

TextUser

Interface(T)

Relative Level of Computer Expertise

CG BG

CT BT

Goal for minimum number of participants to complete instrument: 100Goal for minimum number of participants to complete optional additional questions: 16

MacintoshAtari

Apple IIGSWindows

Apple II(DOS 3.3 & ProDOS)

MS-DOSCP/M

Goals for each quadrant: 25 completed instruments (n) 4 qualitative responses (q)

Actual numbers noted

n=84q=11

n=24q= 7

n=60q=10

n=26q= 3

n=108q= 18

n=86q=13

n=144, q=21 n=50, q=10

Actual number of participants to compete questionnaire: (N):194Actual number of participants to compete optional additional questions (Q): 31

Figure 14 . Research Participant Quadrant

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Adult Self-Directed Learning, Personal Computer Competency and

Learning Style: Models for More Effective Learning

Components of Study

LearnerCharacteristics

PersonalComputerCompetency

Learning Style Self-DirectedLearningReadiness

LearningStrategies

Instrument GeneralQuestionnaire

PCCIBersch/BarrettPersonalComputerCompetencyInventory

LSIKolb LearningStyleInstrument

SDLRSSelf-DirectedLearningReadinessScale

OptionalAdditionalQuestions

Type ofDataGathered

QuantitativeDemographicData

Quantitative(Singleresponse onlist of 60competencies)

Quantitative(12 questionswith forcedresponses to 4words/phrases

Quantitative(58 LikertScaleresponses)

QualitativeResponses toopen-endedquestions

Type ofScoresProduced

Single itemindicators

Total Scoreplus tenclusters ofcomputerapplicationsskills

Two polarityscores (AE-ROand AC-CE)plus one offour LearningStyles

Total Scoreplus eight Self-DirectedLearningFactors

Analysis ofresponses byquestion

Final Synthesis and Summary of Findings:

1. Report findings related to hypotheses

2. List of questions found to warrant further study

3. Graphic model of learning process

4. Suggested methods for "learning how to learn" personal computers

Figure 15. Overview of Research Design

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Instrumentation

Each of the participants was asked to complete the following instruments

(copies of the last two instruments are included in Appendix B):

1. Guglielmino's SDLRS - to asses readiness for self-directed learning

2. Kolb LSI - to assess learning style

3. Bersch/Barrett Personal Computer Competency Inventory (PCCI) - to

assess a comparative level of computer skills of the subjects

4. General Demographic Information Questionnaire: age, sex, educational

level, occupation, type of computer(s) learned/used, computer

ownership (number, kind, when purchased), years using a PC,

subjects' assessments of their level of expertise (based on Mruk's and

Dreyfus's 5 levels), how they fall on the BT, BG, ET, EG user quadrant.

An additional set of open-ended questions were included along with the

instruments. These questions probe areas of motivation, current applications,

learning strategies and problems and provide qualitative data for analysis.

More than one inventory was selected because I wanted to gather as much

data as possible about adults who learn to use personal computers. The SDLRS

alone would not give any indication of preference for concrete vs. abstract or active

vs. reflective learning. The LSI alone would not give any measure of a person's

preference for self-directed vs. other-directed learning. Neither of the instruments

would give an indication of the level of personal computer use.

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Kolb LSI

The Kolb Learning Style Instrument (LSI), revised in 1985, consists of 12

simple sentence-completion items. In the new format, the language has been

simplified; there is improved reliability with good internal reliability on all six LSI

scales.

For each item, the respondent rank-orders the four responses to the

question with a number from 1 to 4. Each column is totaled, providing a raw score

for each learning style:

Column 1 = Concrete Experience (CE)

Column 2 = Reflective Observation (RO)

Column 3 = Abstract Conceptualization (AC)

Column 4 = Active Experimentation (AE)

These raw scores (ranging from 12 to 48) are then used to calculate a

combination score:

AC - CE = the preference for Abstract Conceptualization over Concrete

Experience

AE - RO = the preference for Active Experimentation over Reflective

Observation

Specific information on the reliability scores of the 1985 version of the LSI is

available in Kolb & Smith's User's Guide for the Learning Style Inventory (1986,

pp. 95-101).

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Guglielmino' Self-Directed Learning Readiness Scale

The SDLRS is a self-report questionnaire using a Likert-type scale, which

asks for responses to 58 statements regarding learning preferences and attitudes

toward learning. The instructions for administration ask that respondents not be

told the name or exact purpose of the scale to avoid possible response bias. Once

the items are answered, then 17 items are selected for one treatment (reversed)

and the rest of the items are simply totaled. The total score is a range between 58

and 290. The average score for adults is 214 with a standard deviation of 25.59.

High scores indicate persons who prefer to determine their own learning

needs, and plan and implement their own learning. As the instrument explains,

"this does not mean that they will never choose to be in a structured learning

situation. They may well choose traditional courses or workshops as part of a

learning plan."

The instrument was developed by Lucy M. Guglielmino (1977, p.4) through a

three-round modified Delphi Survey with a panel of authorities on self-direction in

learning. Some of the participants were: Arthur Chickering, Patricia M. Coolican,

Cyril O. Houle, Malcolm S. Knowles, Allen Tough. She estimated a reliability of

.87. In addition to the overall score, there are eight factors of self-directed learning

identified: openness to learning opportunities, self-concept as an effective learner,

initiative and independence in learning, informed acceptance of responsibility for

one's own learning, love of learning, creativity, future orientation, and ability to use

basic study and problem solving skills.

One critique of the instrument (Brockett, 1985b) indicates a bias in the test

toward more academic types of learning "related to schooling and/or learning

acquired through books and study skills" (p.21) and may not be appropriate for

90

older adults or for those with low levels of formal education. This criticism may not

apply to the context of this study, since a large majority of the participants have

college degrees or are enrolled in formal university courses and the ages did not

coincide with those in Brockett's study.

Bersch/Barrett Personal Computer Competency Inventory

As part of her dissertation for a doctorate in Adult Education through Florida

State University, Gretchen T. Bersch (1990) conducted a research project which

developed a "workable personal computer competency model" which she

validated with a panel of experts. She sent out the model along with an extensive

questionnaire on the acquisition of knowledge and skills in the use of personal

computers, and the obstacles encountered and resources used in acquiring

computer competency. With the exception of these two research projects, this

inventory has only been used informally with adults in Alaska; it was used again in

this research project to provide some relative measure of the skill level of the

participants in this study. A copy of this questionnaire is in Appendix B.

General Questionnaire

A general questionnaire was also developed, to collect data on other factors

considered as basic information about the participants and their learning process.

Questions were drawn from several sources, including Ludden (1985), Bersch

(1990), and a study conducted by the Boston Computer Society (Allerbeck, 1988).

The entire series of instruments, including this questionnaire, were pre-tested with

a group of Fielding graduate students and faculty members at a cluster meeting in

November, 1988. The questions were further revised with the assistance of

Fielding research faculty and committee members in February, 1989. A copy of this

questionnaire is in Appendix B.

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Optional Additional Questions

In addition to the demographic questionnaire, a set of open-ended questions

was developed which probed in-depth into the learning process. Questions were

asked about motivation to learn, the sequence of their learning activities, the

problems encountered in learning and helpful resources for gaining competency.

A copy of these questions is included in Appendix B.

Validity

The validity of the PCCI and the General Questionnaire was established

through several procedures:

1. The items of the questionnaire were designed to accurately reflect both

the hypotheses and the research questions stated in the problem statement.

Content validity was checked by linking each of the survey questions to the

research questions. The PCCI items were checked with a panel of experts by

Gretchen Bersch, co-developer of the instrument. Content validity was further

substantiated by references to the literature review.

2. Face validity was field tested by submitting the questionnaires to at least

five other adults knowledgeable about computers, to determine if the questions

were appropriate, if the instructions clear, if the format and sequence were easy to

follow and if the order of administering the instruments would bias the results. The

questions on the General Questionnaire were revised, and the order of the

separate instruments in the package was adjusted following the field test.

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Data collection procedures

Permission was granted by the University of Alaska, School of Career and

Continuing Education to get participants from introductory computer classes.

Permission was also granted by the Fairbanks North Star Borough School District

to survey staff members involved with computers.

A package of materials was developed for each participant, which included:

- a letter of introduction (see Appendix A)

- a permission slip containing name and address plus signature. This form

was coded with a unique number which was the only link between the

questionnaires and the identity of the participants (see Appendix A)

- the questionnaire packet, coded with unique number, in following order

- Kolb LSI

- SDLRS (called a Learning Style Assessment to avoid possible

response bias)

- General Questionnaire

- Personal Computer Competency Inventory (see Appendix B)

- Optional Additional Questions, coded with unique number (see

AppendixÊB)

- a return envelope and a smaller envelope with instructions for placing the

permission form inside to insure confidentiality

The instruments were not mailed. Rather, participation was requested of two

computer conference attendees, University course participants, school district staff,

and word of mouth. People were handed the questionnaire packet (all in one

93

envelope) and a letter of permission which they were asked to read and sign.

Once the letter of permission was signed, it was placed in another envelope which

was kept separate from the set of instruments.

Data Recording

The instruments were completed with paper and pencil, which formed the

basis for entry into a computer data base. The pre-assigned unique number was

written on each set of instruments and the cover letter of permission, and was the

key field in the data base. Responses were coded and entered in the data base.

Separate data bases were developed for each instrument, with the capability of

scoring each of the instruments independently. The General Questionnaire file

was used to summarize the information of the other instruments through a "look-up"

procedure for scores in the other files.

Data Processing

Six separate data base files were developed:

1. General data on each subject, including all demographic data and

summaries of SDLRS, LSI, BBPCC - This data base will be linked to

each of the other separate data files by the coded key field

2. A separate data base with names and addresses of subjects, linked by

code to the master file (to insure anonymity)

3. SDLRS scores

4. Kolb LSI scores

5. Bersch/Barrett Personal Computer Competency (PCCI) Inventory scores

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6. Qualitative answers to questions regarding motivation and learning

strategies.

The qualitative data (optional additional questions) were entered into the

data base management program for each participant, with the response to each

question entered into a separate field in the record. In addition, the participants'

learning style, PCCI score and SDLRS score appeared in this data base through

its "lookup" capabilities from the General Questionnaire file. All of the responses to

each question were exported to a text file, along with the respondent's summary

scores on each of the three instruments, and printed out for review and analysis.

Data Analysis

From the data base management program, the numerical data were

exported into a text file to be set up for statistical analysis. Quality control and a

random check of data was performed to insure that the data were accurately

exported. The data were analyzed by two different software packages: MicroSoft

Excel 2.2, which provided overall summary totals, means and standard deviations

by total group and by four contrasting pairs (beginning vs. competent, active vs.

reflective, abstract vs. concrete, graphical vs. text). In addition, the data were also

entered into StatviewÊSE+Graphics for the rest of the statistical analysis

procedures.

95

C G BG

C T BT

ParticipantGroups

General Questionnaire

Personal ComputerCompetency Inventory

n=100+

Self-Directed LearningReadiness Scale

Kolb LearningStyle Inventory

Optional AdditionalQuestions

n=16+ Responses to questions -by computer type- by learning style

Suggestions to aid learners of different types of operating

systems

Graphic model of self-directed learnng

strategies by sequenceof learning process

Qua

ntita

tive

Dat

a A

naly

sis

Qua

litat

ive

Dat

a A

naly

sis

Synthesis of all findings as they relate

to hypotheses.

actual n=194

actual n=31

Figure 1 6 . Overview of Data Analysis

96

Hypothesis GQ PCCI LSI SDLR Variables StatTest

H 1.1: Competent computer users spend morethan 70% of the time learning their computersusing self-directed learning strategies.

#17 IND=Level of PCCDEP=% Time SDL

Mean& St.Dev.

H 1.2: Competent computer users will have ahigher level of self-directed learning readinessthan beginning computer users.

TotalScore

IND=Level of PCCDEP=SDLRS Score

ANOVA

H 1.3: Computer users with intrinsic motivationto learn how to use a personal computer have ahigher relative level of personal computercompetency than those with extrinsic motivationto learn.

#3 &4

TotalScore

IND=Source ofMotivationDEP=PCCI Score

ANOVA &Chi-Square

H 1.4: Computer users with a foundation forlearning will have a higher level of personalcomputer competence

#9, 5,6, 10,31,30,15,16,21

TotalScore

IND=PriorExperience,Sources ofAssistance, Hoursspent, typingspeed, user group,PC ownershipDEP=PCCI Score

ANOVA &ChiSquare

H 2.1: Computer users with an active learningstyle will have a higher relative level of personalcomputer competence than those with areflective learning style.

TotalScore

AE-RO

IND=Learning StyleDEP=PCCI Score

ANOVA

H 2.2: Computer users with an abstract learningstyle will have a higher relative level of personalcomputer competence than those with aconcrete learning style.

TotalScore

AC-CE

IND=Learning StyleDEP=PCCI Score

ANOVA

Subsidiary Question: What is the relationshipbetween learning style and readiness for self-directed learning?

FourStyles

TotalScore

IND=Learning StyleDEP=SDLRS Score

ANOVA

H 3.1: Computer users with a concrete learningstyle preference will favor the Graphical UserInterface.

#33 AC-CE

IND=Learning StyleDEP=InterfacePreference

Chi-Square

H 3.2: Computer Users with an abstract learningstyle preference will favor the text-based userinterface.

#33 AC-CE

IND=Learning StyleDEP=InterfacePreference

Chi-Square

H 3.3: Competent users of graphical userinterface computers will use more types ofapplications than competent users of text-based systems.

#8 #Apps

IND=LevelExpertiseDEP=# ofApplications

2-WayANOVA

H 3.4: The type of computer learned has agreater impact on learning strategies than thelearners' preferred learning style.

#10,18,14

TotalScore

AC-CEAE-RO

IND=Learning StyleIND=Type ofInterfaceDEP=Strategies

ANOVA Chi-Square

97

Figure 17 . Hypotheses, Instruments & Statistical Analysis

98

Definition of terms

Adult: Anyone over the age of 21 years of age and not attending

secondary school, or anyone who has assumed adult-like responsibilities such as

marriage and parenthood (Long, 1983, p.268)

Personal Computer: The smallest category of desktop computer,

designed for individual use (as contrasted with larger mini- or mainframe

computers which are designed for use by more than one person at a time). Most of

the personal computers used by participants in this study were: IBM PC or

compatibles, Macintosh and Apple II

Personal Computer Competency: The knowledge, skills and attitudes

which have been acquired, as rated on the self-reported PCCI (Personal Computer

Competency Inventory)

Self-Directed Learning: Malcolm Knowles (1975) is most identified with

this concept, which he has defined as "a process in which individuals take the

initiative, with or without the help of others, in diagnosing their learning needs,

formulating learning goals, identifying human and material resources for learning,

choosing and implementing appropriate learning strategies, and evaluating

learning outcomes" (p.18). Allen Tough (1967) coined the term "self-teaching" to

describe an adult learning behavior whenever an individual "may decide to act as

his own teacher, and assume the primary responsibility for planning, initiating, and

conducting the learning project" (p.3). Brookfield (1984a) defined independent

adult learning as "that learning which occurs independently of the formal education

system and which is characterized by learner responsibility for the direction and

execution of learning" (p.26).

99

Intrinsic Motivation: that type of motivation which comes from within the

person: personal curiosity, general career advancement, entertainment,

recreation, or simply to save time and effort. The decision to learn how to use a

personal computer is basically the learner's own free choice.

Extrinsic Motivation: that type of motivation which comes from outside

the person: mandatory work requirements, organizational expectations, keeping

up with one's children, the requirements of school work or the need to develop a

learning tool. The decision to learn how to use a personal computer comes from

others: one feels one is forced to learn.

100

Assumptions, Limitations, Delimitations

This study is descriptive in nature and is not intended to examine causality

or correlation. It is based on two assumptions about how people learn (Kolb, 1986,

p.11):

- People learn from immediate, here-and-now experience, as well as from

concepts and books

- People learn differently; that is, according to their preferred learning styles.

This research is also based on some assumptions about adult self-directed

learning: (Knowles, 1975, pp.20-21)

¥ human beings grow in capacity (and need) to be self-directing as an essentialcomponent of maturing

¥ the learner's experiences become an increasingly rich resource for learning

¥ each individual has a somewhat different pattern of readiness from other individuals

¥ the natural orientation to learning is task- or problem-centered, and learningexperiences should be organized as task-accomplishing or problem-solving learningprojects

¥ learners are motivated by internal incentives, such as the need for esteem, thedesire to achieve, the urge to grow, the satisfaction of accomplishment, the need toknow something specific, and curiosity

Volunteers will be used as participants in the study, which may result in a

focus on those learners who are enthusiastic or otherwise biased toward using

computers.

101

CHAPTER 4 - FINDINGS

This chapter first describes the characteristics of the 194 participants in the

study, before turning to a presentation of the principal findings. Following a

description of these demographic variables, the findings are organized by the three

main goals of the study. First, there is a discussion on self-directed learning of the

beginning and competent computer users, and the impact of motivation and self-

directed learning readiness. Next, I explore the impact of learning style on the

acquisition of personal computer competency. Finally, I investigated the role of the

personal computer operating system interface on learning strategies in comparison

with the learning style variables.

Description of Sample

Demographics

There were 194 participants in the study: 122 women (63%) and 72 men

(37%). Ninety-four percent of the participants were White (n=182); the other 12

participants were Black (n=4), Asian (n=1), Hispanic (n=3), Native America (n=2)

and other (n=2). More than half (56%, n=108) of the participants lived in Alaska,

with the balance residing throughout the United States and Canada. The

participants were primarily between the ages of 30 and 50, as presented in

TableÊ1. The median age of all respondents was 42.

102

TABLE 1

Age of participants in the study

Age n %

< 30 15 7.8

30 - 40 52 26.9

40 - 50 90 46.6

50 - 60 30 15.5

>60 6 3.1

193** 99.9*

* Does not equal 100 due to rounding errors** The age was not given in one case

Socioeconomic Status

The participants were primarily educators, from primarily middle to upper-

middle income levels. In Table 2, we note that over half (52.6%) were educators,

primarily teachers (37.5%). In addition, 18.8% classified themselves as

Professional/Technical; many of these people worked as either managers or

psychotherapists.

103

TABLE 2

Occupation of participants in the study

n %

Teacher 72 37.5

Educational Administrator 29 15.1

Professional/Technical 36 18.8

Student 11 5.7

Clerical worker 9 4.7

Officer/Manager 9 4.7

Farming/Fishing/Forestry 2 1.0

Artist/Writer 2 1.0

Homemaker 1 .5

Crafts/Trades/Construction 1 .5

Other 20 10.4

192** 99.9*

* Does not equal 100 due to rounding errors** The occupation was not given in two cases

Table 3 indicates that nearly two-thirds of the participants hold a graduate

degree, a fact reflected in the relatively high median income revealed in Table 4

(over half report an income in excess of $60,000 per year).

104

TABLE 3

Educational level of participants in the study

n %

High school diploma or GED 2 1.0

Some College 20 10.3

Bachelor's Degree 52 26.8

Graduate Degree 120 61.9

194 100.0

TABLE 4

Income level of participants in the study

n %

$0 - $20,000 9 4.7

$20,000 - $40,000 37 19.5

$40,000 - $60,000 43 22.6

$60,000 - $80,000 43 22.6

$80,000 - $100,000 32 16.8

over $100,000 26 13.7

190 99.9*

* Does not equal 100 due to rounding errors** The income was not given in four cases

105

Characteristics of Sample by Key Independent Variables

Learning Style

The four Kolb Learning Styles of participants in the study were somewhat

evenly distributed, as presented in Table 5. There was a very even preference for

the active learning style (51.5%, n=100) compared to the reflective learning style

(48.5%, n=94.), although there was a distinct preference for the abstract learning

style (57.7%, n=112) over the concrete learning style (42.3%, n=82).

TABLE 5

Learning style of participants in the study

n %

Divergers - Concrete Experience & Reflective Observation 36 18.5

Assimilators - Abstract Conceptualization & Reflective Observation 58 29.9

Convergers - Abstract Conceptualization & Active Experimentation 54 27.8

Accommodators - Concrete Experience & Active Experimentation 46 23.7

194 100.0

Divergers

Assimilators

Convergers

Accommodators

18.5%

29.9%27.8%

23.7%

Figure 18 . Kolb Learning Styles of Participants

106

Type of Computer User Interfaces Preferred

Table 6 presents the brands of all computers currently used by the

participants in the study. The choice of the preferred computer user interface was

determined by two items on the General Questionnaire: a question that asked

which interface they preferred (or to indicate if they had used only one type of

computer). For those who checked that they had used only one type of system,

earlier questions were consulted, to determine the type of operating system being

used. Many of the more competent participants used more than one type of

computer. Table 6 indicates the type of user interface preferences with all types of

computers used.

TABLE 6

Percentage of computer operating system preferences of participants in the studywith all types of computers currently used

Graphical Text Total

IBM or Compatible Used 40.0% 60.0% 100.0%(38) (57) (95)

Macintosh Used 89.0% 11.0% 100.0%(97) (12) (109)

Apple II Used 56.4% 43.6% 100.0%(53) (31) (84)

Other Computer Used 43.3% 56.7% 100.0%(13) (17) (30)

Of the 194 participants in the study, 55.7% (n=108) were classified as

preferring the graphical user interface; 44.3% (n=86) were classified as preferring

the text user interface. Participants were asked to indicate which computer they

used most. Table 7 presents the one computer that the participants indicated they

107

used most; Table 8 presents the same information classified by their preference for

the graphical or text user interface.

108

TABLE 7

One type of computer currently used the most

Type of Computer Most Used Total % of Participants

IBM or Compatible 66 35.7%

Macintosh 76 41.1%

Apple II 35 18.9%

Other Computer 8 15.5%

All participant preferences 185** 100.0%

** The single computer preference was not given in nine cases

TABLE 8

Percentage of computer preferences by type of interfacewith one type of computer currently used the most

Graphical Text Total

IBM or Compatible Most Used 19.7% 80.3% 100.0%(13) (53) (66)

Macintosh Most Used 98.7% 1.3% 100.0%(75) (1) (76)

Apple II Most Used 27.9% 72.1% 100.0%(12) (31) (45)

Other Computer Most Used 37.5% 62.6% 100.0%(3) (5) (8)

All participant preferences 55.7% 44.3% 100.0%(103) (82) (185)**

** The single computer preference was not given in nine cases

As Table 8 reflects, of the 66 participants who used an IBM or compatible,

almost 20% preferred the graphical user interface. Of the 76 participants who

109

primarily used a Macintosh, only one person preferred the text user interface; and

almost 28% of those who primarily used the Apple II preferred the graphical user

interface.

Dependent Variables

There were two primary dependent variables identified in the study: the

level of self-directed learning readiness, as measured by the Self-Directed

Learning Readiness Scale (SDLRS) and the self-reported level of competence in

using a personal computer, as measured by the Personal Computer Competency

Inventory (PCCI).

Self-Directed Learning Readiness

The mean scores of all participants on the SDLRS was 244.48 (sd=23.01), a

rating much higher than Guglielmino's stated average score for all adults

completing the questionnaire (mean=214, sd=25.59). Table 9 presents the mean

SDLRS scores of participants classified by their Kolb Learning Style.

110

TABLE 9

Self-directed learning readiness by Learning Styleof participants in the study with mean scores on the SDLRS

Learning Style n % Mean Score sdon SDLRS

Diverger 36 18.5% 244.47 20.84

Assimilators 58 29.9% 239.55 20.03

Convergers 54 27.8% 250.28 24.21

Accommodators 46 23.7% 246.61 18.03

All Participants 194 100.0% 244.48 23.01

No significant difference was found in readiness for self-directed learning

between these learning styles

Personal Computer Competency

The participants were asked to rate their own current level of expertise at

using a personal computer. Figure 19 presents the number of people in each self-

rating category of expertise using a personal computer. The distribution shows the

predominance of the participants in this study who rated themselves as

"competent" at the mid-point of this scale.

111

01 02 03 04 05 06 07 08 0

Beginners ( 1 )

Advanced Beginners

( 2 )

Competent ( 3 )

Proficient ( 4 )

Experts (5)

1 1

3 9

8 0

4 9

1 5

Figure 19 . Self-Rating of Participants

These self-ratings were then compared to the participants' scores on the

Personal Computer Competency Inventory. Table 10 presents their estimates of

their own competence and the mean scores of each group on the PCCI. For the

purposes of this study, those who rated themselves "Beginner" or "Advanced

Beginner" were classified in the "Beginner" category for data analysis (25.8%,

n=50); all the others rated themselves either "Intermediate/Competent,"

"Proficient/Power User," or "Expert" and were classified in the "Competent"

category for data analysis (74.2%, n=144).

112

TABLE 10

Self-reported level of competence of participants in the study withmean scores on the PCCI

Self-reported level n % Mean Score sdof Competence on PCCI

Beginner 11 5.7% 12.6 7.6

Advanced Beginner 39 20.1% 24.6 6.3

Intermediate/Competent 80 41.2% 34.9 8.7

Proficient/Power User 49 25.3% 48.0 7.8

Expert 15 7.7% 57.2 3.8

All Participants 194 100.0% 36.6 13.7

0

1 0

2 0

3 0

4 0

5 0

6 0

Beginners Advanced Beginners

Competent Proficient Experts

Figure 20 . Mean PCCI Scores by Self-Rating of Competence

An analysis of variance was calculated, using the self-report level of

competence with the total scores on the PCCI. Table 10 presents the mean scores

on the PCCI scale by level of expertise. An analysis of variance found these

differences to be significant at the .0001 level (f=106.16). The ANOVA also found

113

significant differences between every level of competence. Table 11 presents the

results of the Analysis of Variance.

TABLE 11

Analysis of variance of PCCI scores by level of expertise

Source of Variation Sum of Degrees of Mean FSquares Freedom Square Value

Between groups 24991.6 4 6247.9 106.16*

Within groups 11123.74 189 58.86

Total 36115.34 193

* p < .0001

Demographics - The factors related to gender, educational level and age

as they related to the level of personal computer competence (as well as self-

directed learning readiness) were also studied. There were 122 woman and 72

men in the study, and an analysis of variance of the PCCI scores and gender

revealed statistically significant differences. The mean PCCI score of men was

41.347 (n=72, sd=13.958), whereas the mean PCCI score for women was 33.762

(n=122, sd=12.753). Table 12 presents the analysis of variance for the PCCI

scores of men and women.

114

TABLE 12

Analysis of variance of PCCI scores by gender

Source of Variation Sum of Degrees of Mean FSquares Freedom Square Value

Between groups 2604.914 1 2604.914 14.925*

Within groups 33510.426 192 174.533

Total 36115.34 193

* p < .001

To see if readiness for self-directed learning had a gender bias, I also

computed an analysis of variance of SDLRS scores based on gender, which

yielded no significant differences. In fact, the mean SDLRS score for the women in

the study was 246.975 (n=122, sd=22.087) and the mean score for men was

241.986 (n=72, sd=19.481). The participants in this study follow the national trend

for gender differences in computer competence based on computer usage. I found

no significant differences in either PCCI scores or level of self-directed learning

readiness by either the educational level or age groups of the people in this study.

Hypotheses

The hypotheses in this study are organized according to the three main

research goals: to explore the role that readiness for self-directed learning has on

the acquisition of personal computer competence; to explore the role of learning

style as a factor in the process of learning to use a personal computer; and to

understand the impact of the type of computer operating system interface on the

process of learning to use a personal computer.

115

Goal 1 Hypotheses Related to Self-Directed Learning Readiness and

Motivation

H1.1 - Competent computer users spend more than 70% of the time

learning their computers using self-directed learning strategies.

The literature on self-directed learning supports the hypothesis that most

adult learning projects are self-directed. In the General Questionnaire, the

following question was asked:

Self-directed learning activities are those which you plan by yourself, using help andsubject matter from a variety of friends, experts, and print or non-print resources. (Byway of contrast, organized learning activities are workshops, courses, seminars andthe like.)

Think of the total amount of time that it has taken you to learn how touse your personal computer. Place an "X" along the line that mostclearly represents the percentage of that time spent in self-directedlearning activities:

To test this hypothesis, the independent variable was the self-reported level

of personal computer competence (Beginning or Competent). The dependent

variable is the percentage of time spent in self-directed learning. Table 13

presents the mean scores and standard deviations of the two groups.

TABLE 13

Self-reported level of competence of participants in the study withmean scores of time spent in self-directed learning

n % Mean % of sdTime

Beginning Users 50 25.8 62.8% 28.8%

Competent 144 74.2 77.3% 18.5%

All Participants 194 100.0 73.5 22.5%

116

Table 13 clearly shows that the competent users in the study spent more

than 70% of their time learning their personal computers in self-directed rather than

organized learning activities and the hypothesis is sustained.

There is further data to support this hypothesis on the amount of time spent

in self-directed learning. In one question on the General Questionnaire,

participants were asked to rate how they preferred to learn about personal

computers: on their own, in a small group, with a friend or relative or in a formal

class structure. Table 14 presents the preferences stated for these methods by all

of the participants, as well as those choices broken out for beginners and

competent users. Table 15 presents the mean PCCI scores for those people

selecting each learning method. Table 16 presents the analysis of variance of

preferred learning strategies to PCCI score.

117

TABLE 14

Preferred group for learning about computers by level of computer experience

Preferred Method of Learning All sources Beginning Users Competent Usersn % n % n %

On your own? 78 40.2% 11 22.0% 67 46.5%

With a friend or relative? 43 22.2% 13 26.0% 30 20.8%

In a small group? 41 21.1% 15 30.0% 26 18.1%

In a formal class structure? 19 9.8% 7 14.0% 12 8.3%

Other 13 6.7% 4 8.0% 9 6.3%

Total 194 100.0% 50 100.0% 144 100.0%

TABLE 15

Participants' mean PCCI Scores by preferred learning group

Preferred Learning Group All sources PCCI Scoren % mean sd

On your own? 78 40.2% 42.7 11.7

With a friend or relative? 43 22.2% 31.8 13.1

In a small group? 41 21.1% 33.8 13.7

In a formal class structure? 19 9.8% 31.3 13.1

Other 13 6.7% 32.3 15.1

Total 194 100.0% 36.6 13.7

118

TABLE 16

Analysis of variance of PCCI scores by preferred group for learning a personalcomputer

Source of Variation Sum of Degrees of Mean FSquares Freedom Square Value

Between groups 4982.42 4 1245.605 7.562*

Within groups 31132.92 189 164.724

Total 36115.34 193**

* p< .0001

These tables show that over 40% of all the participants in the study preferred

to learn on their own and that those who preferred to learn in this way had a

significantly higher level of personal computer competency than those participants

who preferred small groups or formal class structures.

An analysis of variance was also calculated, with the same learning

preference as the independent variable and the level of self-directed learning

readiness as the dependent variable. There were no significant differences found

between the groups based on the SDLRS scores, although those who preferred to

learn on their own had slightly higher scores (mean=247.8, sd=23.1) than the

people who preferred to learn in a formal class structure (mean=239.2, sd=21.6).

As will be shown in the next hypothesis, the readiness for self-directed learning, as

measured by the SDLRS, had little relationship to the level of personal computer

competency as measured by the PCCI, for the participants in this study.

119

H1.2 - Competent computer users will have a higher level of self-

directed learning readiness than beginning computer users.

This hypothesis is based on the assumption that those participants in the

study with higher levels of competence in using a personal computer would have

greater readiness for self-directed learning as reflected in higher scores on the

SDLRS. To test this hypothesis, the independent variable was the self-reported

level of personal computer competence (Beginning or Competent). The dependent

variable is the SDLRS score. Table 17 presents the mean scores and standard

deviations of the two groups on the SDLRS. Table 18 presents the analysis of

variance of the difference in the SDLRS scores between the two groups.

TABLE 17

Self-reported level of competence of participants in the study withmean scores on the Self-Directed Learning Readiness Scale (SDLRS)

n % Mean SDLRS sdScore

Beginning Users 50 25.8 238.7 21.0

Competent 144 74.2 246.5 23.4

All Participants 194 100.0 244.5 23.0

120

TABLE 18

Analysis of variance of SDLRS scores by level of expertise

Source of Variation Sum of Degrees of Mean FSquares Freedom Square Value

Between groups 2238.401 1 2238.401 4.3*

Within groups 99940.052 192 520.521

Total 102178.454 193

* p < .05

Table 17 shows that the competent users in the study had slightly higher

mean scores on the SDLRS. An analysis of variance of this difference is presented

in Table 18. The difference between the two groups was statistically significant at

p<.05, and the hypothesis is supported, although the relationship is weak.

H1.3 - Computer users with intrinsic motivation to learn how to use a

personal computer have a higher relative level of personal computer

competency than those with extrinsic motivation to learn.

The reasons for learning may be an important indicator of the success in

learning to use a personal computer because intrinsic motivation to learn the

computer appears to be related to higher PCCI scores for people in this study.

In one question on the General Questionnaire, participants identified all of

the reasons for learning. Table 19 reflects all reasons and the primary reasons that

the participants chose to learn to use a personal computer, organized by intrinsic

and extrinsic motivators. Table 20 shows the list of primary reasons for learning a

computer, listed in the order of the mean scores on the PCCI for all those

participants who chose that factor as their primary reason for learning. Table 21

121

presents the analysis of variance of the primary reason for learning and the PCCI

score.

TABLE 19

Participants' reasons for learning a personal computer

All reasons Primary Reasonn % n %

Intrinsic reasons for learning

personal curiosity 166 85.6% 52 26.9%

save time and effort 141 72.7% 37 19.2%

general career advancement 117 60.3% 22 11.4%

entertainment/recreation 81 41.8% 2 1.0%

Extrinsic Reasons for learning

school work/learning tool 151 77.8% 49 25.4%

organizational expectation 82 42.3% 9 4.7%

mandatory work requirement 59 30.4% 9 4.7%

keep up with kids 41 21.1% 2 1.0%

Other reasons for learning 33 17.0% 11 5.7%

Total 193 100.0%

Missing data 1

122

TABLE 20

Mean scores of PCCI by primary reason for learning to use a personal computer

Primary reason for learning n % PCCI sdmean

* Entertainment/recreation 2 1.0% 43.5 3.5

* Personal curiosity 52 26.9% 41.8 13.9

* General career advancement 22 11.4% 39.2 12.1

Organizational expectation 9 4.7% 37.4 13.7

School work/learning tool 49 25.4% 35.7 14.9

* Save time and effort 37 19.2% 32.5 9.8

Mandatory work requirement 9 4.7% 32.1 14.1

Other 11 5.7% 29.4 16.2

Keep up with kids 2 1.0% 26.0 2.8

Total 193 100.0% 36.6 13.7

* Intrinsic reasons for learning

123

TABLE 21

Analysis of variance of PCCI scores by primary reason for learning a personalcomputer

Source of Variation Sum of Degrees of Mean FSquares Freedom Square Value

Between groups 3248.329 8 406.041 2.278*

Within groups 32793.06 184 178.223

Total 36041.389 192**

* p < .05

These three tables show that such intrinsic motivation as personal curiosity

and general career advancement as the primary reason for learning to use a

personal computer led to a significantly higher level of personal computer

competency than such extrinsic motivators as mandatory work requirements,

saving time and effort, school work/learning tool or other reasons for learning.

The degree of personal choice exercised in deciding to use a personal

computer did not seem to have a significant effect on the level of personal

computer competence. Fully two thirds (67.5%, n=131) of the participants in the

study stated that their decision to learn how to use a personal computer was a 5 at

the highest end of the scale between no choice (forced to learn) as 1 and my own

free choice as 5.

To determine the degree to which the participants in the study exercised

intrinsic or extrinsic motivation to learn their computers, two motivation scores were

calculated by the computer data base in which all of the data were entered. Two

numbers were generated for each participant: the total number of intrinsic

motivators and the number of extrinsic motivators selected, up to a maximum of four

124

points on each scale. The degree of choice exercise was also evaluated: if a

person expressed little or no choice (from 1 to 3 on the five-point scale) then an

additional point was added to the "extrinsic" score; if a person expressed free

choice (either 4 or 5 on the five-point scale) then an additional point was added to

the "intrinsic" score. Each score had a maximum value of five points. The scores

for both internal and external motivation were compared, and the larger of the two

numbers determined whether the participants' motivation was internal or external.

Of the 194 participants, only 18% (n=35) expressed more external

motivation to learn than internal. The mean PCCI scores of those with internal

motivation was 37.9 (sd=13.9); the mean PCCI scores of those with external

motivation was 30.7 (sd=10.9).

TABLE 22

Analysis of variance of PCCI scores by type of motivation

Source of Variation Sum of Degrees of Mean FSquares Freedom Square Value

Between groups 1496.725 1 1496.725 8.301*

Within groups 34618.615 192 180.305

Total 36115.34 193**

* p < .01

The people with intrinsic motivation to learn a personal computer had higher

scores on the PCCI than those with extrinsic motivation to learn. An analysis of

variance of this difference is presented in Table 22 and shows that the difference

between the two groups is statistically significant at the .0044 level, and the

hypothesis is sustained.

125

H1.4 - Computer users with a foundation for learning will have a

higher level of personal computer competence.

This hypothesis is based on the assumption that those participants in the

study with a more adequate foundation for their learning would have as a

consequence higher scores on the PCCI. To test this hypothesis, various

dimensions of the independent variable, foundation for learning, were studied

separately: prior experience, sources of assistance, number of hours spent on the

computer, typing speed, participation in computer user groups, and personal

computer ownership. We shall discuss each of these dimensions in turn.

A. Prior Experience - Participants in the study were asked about their

level of prior experience with different types of devices: mini or mainframe

computers, memory typewriters, dedicated word processors, typewriters, hand

calculators and other types of computing devices. Each device was rated on a 5-

point Likert scale with 1=None, 3= A little, and 5= A lot. Table 23 shows the mean

and standard deviations of the beginning and competent users in the study, and

the significance of the difference between the means (an ANOVA was performed

for each variable separately).

126

TABLE 23

Level of prior experience with different types of computing devicesby level of self-reported personal computer expertise

Device Beginners sd Competent sd pMean Mean

Mini or mainframe computers 1.30 .69 1.95 1.20 .0006

Memory typewriters 1.73 1.01 1.76 1.19 n.s.

Dedicated word processors 1.36 .96 1.56 1.15 n.s.

Typewriters 4.10 1.09 4.00 1.11 n.s.

Hand calculators 3.62 1.07 3.99 .96 .0023

Other types of computing devices 1.50 1.16 1.78 1.37 n.s.

Table 23 shows that there is little difference between the beginning and

competent users in the study regarding their prior experience with different types of

computing devices, with the notable exception of mini or mainframe computers and

hand calculators. A majority of the experience of all users was with typewriters and

hand calculators.

The findings on prior experience lend support to the hypothesis that higher levels

of prior experience with at least other types of computers and with hand calculators

provide a foundation for learning about personal computers, which leads to higher

levels of personal computer competency.

B. Sources of assistance - There were several questions on the

General Questionnaire regarding both people who gave assistance at both early

and present phases of the learning process, as well as all sources of assistance

used. Table 24 shows the frequency of the responses to each of the choices of

who helped the participants at different phases of the learning process.

127

TABLE 24

Analysis of all human sources of assistance used at the early stages of learning

early beginners competent usersn % n % p

friend 16 32.0% 48 33.3% n.s.

family member 15 30.0% 24 16.7% 0.04

colleague(s) at work 22 44.0% 66 45.8% n.s.

computer dealer 8 16.0% 29 20.1% n.s.

consultant 3 6.0% 15 10.4% n.s.

instructor in computer class 20 40.0% 68 47.2% n.s.

user group member(s) 3 6.0% 16 11.1% n.s.

myself 37 74.0% 116 80.6% n.s.

others 6 12.0% 22 15.0% n.s.

Table 24 shows that at the beginning of the learning process, when

identifying all sources of human assistance, beginners used family members

significantly more than the more competent users. There was no other statistically

significant difference between beginning and competent users based on all

sources of human assistance during the early stages of the learning process.

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TABLE 25

Analysis of all human sources of assistance used at the current stage of learning

now beginners competent usersn % n % p

friend 21 46.7% 51 35.4% n.s.

family member 12 26.7% 23 16.0% n.s.

colleague(s) at work 20 44.4% 82 56.9% n.s.

computer dealer 5 11.1% 35 24.3% n.s.

consultant 4 8.9% 22 15.3% n.s.

instructor in computer class 17 37.8% 37 25.7% n.s.

user group member(s) 0 0.0% 30 20.8% 0.0008

myself 34 75.6% 125 86.8% n.s.

others 4 8.0% 20 14.0% n.s.

Table 25 shows that at the current stage of the learning process, when

identifying all sources of human assistance, competent users relied on computer

user group members significantly more than the beginning users. There was no

other statistically significant difference between beginning and competent users

based on all sources of human assistance during the current stage of the learning

process.

129

TABLE 26

Frequency of primary human sources of assistance used in the early stages

beginners competent usersn % n %

friend 3 6.7% 9 6.5%

family member 7 15.6% 6 4.3%

colleague(s) at work 8 17.8% 27 19.4%

computer dealer 1 2.2% 2 1.4%

consultant 0 0.0% 3 2.2%

instructor in computer class 12 26.7% 19 13.7%

user group member(s) 0 0.0% 3 2.2%

myself 11 24.4% 65 46.8%

others 3 6.7% 5 3.6%

Total 45 100.0% 139 100.0%

Table 26 shows that at the beginning of the learning process, when

identifying the primary sources of human assistance, beginners relied on family

members and instructors in computer classes more than the more competent users

did. The competent users primarily relied more on themselves in the early stages

of the learning process.

130

TABLE 27

Frequency of primary human sources of assistance used at the current stage

beginners competent usersn % n %

friend 6 14.3% 4 2.9%

family member 3 7.1% 2 1.4%

colleague(s) at work 10 23.8% 17 12.2%

computer dealer 1 2.4% 4 2.9%

consultant 0 0.0% 5 3.6%

instructor in computer class 10 23.8% 6 4.3%

user group member(s) 0 0.0% 2 1.4%

myself 12 28.6% 93 66.9%

others 0 0.0% 6 4.3%

Total 42 100.0% 139 100.0%

Table 27 shows that at the present time, when identifying the primary

sources of human assistance, beginners relied on friends, colleagues at work and

instructors in computer classes more than the more competent users did. The

competent users again relied primarily more on themselves at this stage of the

learning process.

These last four tables show some significant differences between the types

of human assistance used by people at different stages of the learning process and

at different levels of competence, to provide support for this hypothesis. There is

even more of a difference in forms of non-human assistance, as shown in

TablesÊ28 and 29.

131

TABLE 28

Analysis of all non-human sources of assistance used

Beginners Competent Usersn % n % p

Classes for college credit 25 50.0% 88 61.1% n.s.

Non-credit workshops or short courses 18 36.0% 83 57.6% 0.0082

Conferences 4 8.0% 65 45.1% 0.0001

User group meetings 6 12.0% 42 29.2% 0.0152

Software tutorials on the computer 31 62.0% 103 71.5% n.s.

Books and magazines 19 38.0% 111 77.1% 0.0001

Hands-on experimenting with thecomputer 48 96.0% 135 93.8% n.s.

Software Manuals 35 70.0% 132 91.7% 0.0001

Family & Friends 35 70.0% 87 60.4% n.s.

Television shows about computers 3 6.0% 21 14.6% n.s.

Audio or Video Training Tapes 5 10.0% 25 17.4% n.s.

Other 4 8.0% 14 10.0% 0.0193

As illustrated above, the competent users employed significantly more non-

human sources of assistance: non-credit workshops, conferences, user group

meetings, books, magazines, software manuals, and other sources of assistance.

132

TABLE 29

Frequency of primary non-human source of assistance used

beginners competent usersn % n %

Classes for college credit 8 16.3% 15 10.7%

Non-credit workshops or short courses 3 6.1% 4 2.9%

Conferences 0 0.0% 3 2.1%

Software tutorials on the computer 1 2.0% 3 2.1%

Books and magazines 1 2.0% 9 6.4%

Hands-on experimenting with the computer 14 28.6% 68 48.6%

Software Manuals 10 20.4% 21 15.0%

Family & Friends 10 20.4% 11 7.9%

Other 2 4.1% 6 4.3%

Total 49 100.0% 140 100.0%

Table 29 highlights the primary source of non-human assistance for learning

about personal computers. Almost half of the competent users relied on hands-on

experimenting with the computer as their primary source of assistance. The

beginning users did not have as clear a primary choice, with hands-on

experimenting the first choice of a little more than a quarter of that group. This

wider variety of resources for learning contributes to a broader foundation for

learning, further supporting the hypothesis.

C. Number of hours spent using a computer - One question on the

General Questionnaire asked the participants how many hours they used a

personal computer in an average week. The mean amount of time spent in using a

computer for the beginning computer users was 10.8 hours a week (n=48, sd=15);

the mean amount of time spent in using a computer for the competent users was

18.3 hours a week (n=142, sd=12). Table 30 presents an analysis of variance for

133

the level of computer competence (beginning versus competent) and the amount of

time spent in using the computer in an average week. These differences are

significant at the .001 level.

TABLE 30

Analysis of variance of hours spent using the computer by level of expertise

Source of Variation Sum of Degrees of Mean FSquares Freedom Square Value

Between groups 2041.093 1 62041.093 12.0*

Within groups 31941.461 188 169.901

Total 33982.554 189**

* p <.001

*** The number of hours was not given in four cases.

The competent users in the study spent significantly more time using their

computers in an average week. Sufficient time is another factor in the foundation

for learning a personal computer, providing further support for the hypothesis.

D. Typing speed - One question on the General Questionnaire asked the

participants for their own estimate of their typing speed. The speeds were grouped

in five categories, from 1 (very slowÑless than 20 wpm) to 5 (fastÑover 60 wpm).

Table 31 presents the results of an analysis of variance comparing participants'

self-reported typing speed with their relative level of personal computer

competence.

134

TABLE 31

Analysis of variance of PCCI scores by typing speed

Source of Variation Sum of Degrees of Mean FSquares Freedom Square Value

Between groups 913.948 4 228.487 1.258

Within groups 33777.801 186 181.601

Total 34691 190**

** The typing speed was not given in three cases

There was no significant difference between groups relating typing speed to

personal computer competence. Therefore, at least for the people in this study,

typing speed does not support that part of this hypothesis: that being a good typist

is part of the foundation for gaining personal computer competency.

E. Participation in Computer User Groups - Two questions on the

General Questionnaire asked whether the participants were members of a

computer user's group or had ever attended any user group meetings. Of all the

participants, only 20% (n=39) were members of a computer users group, although

36.1% (n=70) had attended at least one user group meetings. Table 32 presents

the analysis of variance of computer user group membership and personal

computer competence. The mean PCCI score of those who were members of

computer users groups was 47.462 (n=39, sd=10.728), whereas the mean PCCI

score of those who were not members was 33.839 (n=155, sd=12.983).

135

TABLE 32

Analysis of variance of PCCI scores by computer user group membership

Source of Variation Sum of Degrees of Mean FSquares Freedom Square Value

Between groups 5782.68 1 5782.68 36.603*

Within groups 30332.66 192 157.983

Total 36115.34 193

* p <.0001

Table 33 presents the analysis of variance of user group attendance and

personal computer competence. The mean PCCI score of those who had attended

computer users group meetings was 45.90 (n=70, sd=11.27), whereas the mean

PCCI score of those who were not members was 31.315 (n=124, sd=12.04).

TABLE 33

Analysis of variance of PCCI scores by computer user group attendance

Source of Variation Sum of Degrees of Mean FSquares Freedom Square Value

Between groups 9518.306 1 9518.306 68.711*

Within groups 26597.034 192 138.526

Total 36115.34 193

* p < .0001

These findings support the role of computer user groups, either through

membership or participation in meetings, as part of a foundation for gaining

personal computer competency, further supporting the hypothesis.

F. Relationship of Computer Ownership and Personal Income to

Personal Computer Competence - There has been some concern expressed

136

about the "gap" occurring between those who have access to computers and those

who don't, based on socioeconomic groups. Therefore, issues related to personal

computer ownership and household income were explored. Almost 88% of the

participants in this study owned at least one personal computer. The median

annual household income of the computer owners was between $60,000 and

$80,000; for non-computer owners it was $40,000 - $60,000 per year. However,

there was only a slight statistically significant difference in the level of personal

computer ownership based on income levels. There was no difference in the level

of personal computer competence and income. However, there was a statistically

significant difference between personal computer ownership and personal

computer competence. The mean PCCI scores of computer owners was 37.7

(n=170, sd=13.382) whereas the mean PCCI score of non-owners was 28.7 (n=24,

sd=13.4). Table 34 presents an analysis of variance of the PCCI scores based on

computer ownership.

TABLE 34

Analysis of variance of PCCI scores by computer ownership

Source of Variation Sum of Degrees of Mean FSquares Freedom Square Value

Between groups 1713.913 1 1713.913 9.566*

Within groups 34401.427 192 179.174

Total 36115.34 193

* p < .01

Based on this analysis of variance, computer ownership is another part of a

foundation for becoming competent using a personal computer, further supporting

the hypothesis

137

Summary: The finding on typing speed was the only surprising finding in

all of these analyses. With the exception of experience with mini or mainframe

computers and hand-held calculators, prior experience did not seem to have any

effect on the PCCI score. The competent users used more sources of assistance

than beginning users, both human resources and all sources. The competent

users spent more time using the computer than the beginning users. The

participants who either were members of computer users groups or who had

attended any user group meetings had a higher level of personal computer

competence. Computer ownership has a positive impact on personal computer

competency. Therefore, the hypothesis related to the importance of a broad

foundation for learning a personal computer is sustained.

Component Result Stat Test p

A. Prior Experiencemini or mainframe computers and hand calculatorsonly

Supported ANOVA <.001

B. Sources of AssistanceFamily members, User Group MembersNon-credit workshops, Conferences, User GroupMeetingsBooks & Magazines, Software Manuals

Supported ANOVA various

C. Number of Hours Spent Using a Computer Supported ANOVA <.001

D. Typing Speed NotSupported

ANOVA N/A

E. Participation in Computer User Groups Supported ANOVA <.0001

F. Computer Ownership Supported ANOVA <.01

Figure 21. Summary of Findings for Hypothesis 1.4

138

Goal 2 Hypotheses Related to Learning Style

There are two main hypotheses related to learning style, looking at the

relationship between personal computer competence and the preference of both

the abstract-concrete and the active-reflective scales of the Kolb LSI. In addition,

there was one subsidiary question, with no hypothesis stated, regarding the

relationship between learning style and readiness for self-directed learning

readiness as measured by the SDLRS.

Initially, an analysis of variance was calculated using the learning styles

category as the independent variable, and the PCCI score as the dependent

variable. Table 35 presents the mean scores and standard deviations on the

Personal Computer Competency Inventory for each of the four learning styles.

Table 36 presents the results of the analysis of variance.

TABLE 35

Mean Scores on PCCI by Learning style of participants in the study

n % PCCI sdmean score

DivergersConcrete Experience & Reflective Observation 36 18.5 28.5 13.2

AssimilatorsAbstract Conceptualization & Reflective Observation 58 29.9 37.5 14.1

ConvergersAbstract Conceptualization & Active Experimentation 54 27.8 40.8 12.8

AccommodatorsConcrete Experience & Active Experimentation 46 23.7 36.5 12.1

194 100.0 36.6 13.7

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TABLE 36

Analysis of variance of PCCI scores by learning style

Source of Variation Sum of Degrees of Mean FSquares Freedom Square Value

Between groups 3364.923 3 1121.641 6.507*

Within groups 32750.418 190 172.371

Total 36115.34 193

* p < .001

The analysis of variance presented in Table 36 shows a significantly lower

score on the PCCI for the Divergers (concrete and reflective) than for all of the other

learning styles at the .0003 level. The hypotheses, however, are not presented by

the four separate learning styles but by preferences on the abstract-concrete and

active-reflective scales. Therefore, additional statistical tests were performed.

H2.1 - Computer users with an active learning style will have a higher

relative level of personal computer competence than those with a

reflective learning style.

The mean PCCI score for active learners was 39.05 (n=100, sd=12.6); the

mean PCCI score for the reflective learners was 33.95 (n=94, sd=14.4).

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TABLE 37

Analysis of variance of PCCI scores by active versus reflective learning style

Source of Variation Sum of Degrees of Mean FSquares Freedom Square Value

Between groups 1261.856 1 1261.856 6.951*

Within groups 34853.484 192 181.529

Total 36115.34 193**

* p < .01

The analysis of variance is presented in Table 37 and shows a significant

difference in the PCCI scores based on preference for active versus reflective

learning style and is statistically significant at the .0091 level. Therefore, the

hypothesis is sustained.

H2.2 - Computer users with an abstract learning style will have a

higher relative level of personal computer competence than those with

a concrete learning style.

The mean PCCI score for abstract learners was 39.13 (n=112, sd=13.6); the

mean PCCI score for the concrete learners was 33.09 (n=82, sd=13.1).

141

TABLE 38

Analysis of variance of PCCI scores by abstract versus concrete learning style

Source of Variation Sum of Degrees of Mean FSquares Freedom Square Value

Between groups 1731.947 1 1731.947 9.671*

Within groups 34383.394 192 179.08

Total 36115.34 193**

* p < .01

The analysis of variance is presented in Table 38 and shows a significant

difference in the PCCI scores based on preference for abstract versus concrete

learning style and is statistically significant at the .0022 level. Therefore, the

hypothesis is sustained.

Subsidiary Question - What is the relationship between learning style

and readiness for self-directed learning?

There is nothing in the literature that relates the level of self-directed

learning readiness to different learning styles. One question of this research, for

which there is no stated hypothesis, is whether learning style is a factor in

readiness for self-directed learning, or if there is one learning style that appears to

have significantly higher SDLRS scores than any of the others. Table 9 presented

earlier showed the mean SDLRS scores and standard deviations of each learning

style group in this study. The information presented does not indicate a

relationship between learning style and readiness for self-directed learning, since

there were only 11 points difference between the highest mean score (Convergers

at 250.3) and the lowest group (Assimilators at 239.6). The analysis of variance did

not show significance at the p<.05 level.

142

Goal 3 Hypotheses Related to Personal Computer Operating System

Interface

One important factor in this study was the relationship between the preferred

personal computer interface (graphical or text) to the acquisition of personal

computer competence. One of the first issues to be addressed was if there was a

preference for a particular type of computer interface by learning style. Table 39

presents the the observed frequency of the preference for a specific user interface

with the learning style preferred. Hypotheses 3.1 and 3.2 both refer to this table.

TABLE 39

Observed frequency of preference for specific user interface by learning style

Graphical TextLearning Style Preference n % n %

Reflective 51 47.2 43 50.0

Active 57 52.8 43 50.0

Total Active vs. Reflective 108 100.0 86 100.0

Abstract 66 61.1 46 53.5

Concrete 42 38.9 40 46.5

Total Abstract vs. Concrete 108 100.0 86 100.0

This table shows that of those participants in the study who preferred the graphical

user interface, there is a slightly higher percentage of active (over reflective)

learners and a much higher percentage of abstract (over concrete) learners. Of

those participants in the study who preferred the text user interface, there is no

143

difference between the active or reflective learning style and only a slightly higher

percentage of abstract (over concrete) learners.

H3.1 - Computer users with a concrete learning style preference will

favor the Graphical User Interface.

As noted in Table 39, of the 82 participants with the concrete learning style,

42 (51.2%) preferred the graphical user interface and 40 (48.8%) preferred the text

user interface. This difference was not significant at p<.05. The hypothesis is not

sustained.

H3.2 - Computer Users with an abstract learning style preference will

favor the text-based user interface.

As noted in Table 39, of the 112 participants with the abstract learning style,

66 (58.9%) preferred the graphical user interface and 46 (41.1%) preferred the text

user interface. This difference was not significant at p<.05. The hypothesis is not

sustained.

H3.3 - Competent users of graphical user interface computers will use

more types of applications than competent users of text-based

systems.

Table 40 presents the average number of applications that people at

different levels of competence use. To determine the number of applications used,

the responses on the PCCI were analyzed, to determine the different types of

applications possible. For the purposes of this study, up to 14 different applications

were possible, including programs for word processing, simple paint-type graphics,

drawing, graph-generation, computer-assisted drafting/design,

telecommunications, data base management, personal finance or checkbook

144

management, electronic spreadsheet, simulations, music performance or

composition, idea processing, statistical analysis, and project management. This

score did not include computer programming, since this hypothesis was simply

looking at number of applications used.

TABLE 40

Mean number of applications by level of competence and type of computeroperating system preferred

All Users Graphical Users Text UsersLevel of Competence n mean n mean n mean

Beginners (1) 11 1.6 3 1.7 8 1.5

Advanced Beginners (2) 39 4.1 21 4.7 18 3.4

All Beginning Users 50 3.5 24 4.3 26 2.9

Competent (3) 80 6.5 42 7.4 38 5.6

Proficient (4) 49 10.2 30 10.7 19 9.3

Experts (5) 15 12.9 12 12.9 3 12.7

All Competent Users 144 8.5 84 9.4 60 7.2

Total All Users 194 7.2 108 8.3 86 5.9

As Table 40 reflects, those who prefer the graphical user interface use more

applications than those who prefer the text user interface, although as the level of

expertise rises, the average number of applications becomes virtually the same.

Table 41 presents a two-factor analysis of variance of the number of applications

by the computer interface preferred and the level of competence.

145

TABLE 41

Analysis of variance of number of applications and computer interface

Source of Variation Sum of Degrees of Mean FSquares Freedom Square Value

Interface (A) 124.51 1 124.51 14.02*

Level of Competence (B) 823.67 1 823.67 92.73**

Interaction A*B 5.71 1 5.71 .64

Error 1687.74 190 8.88

* p < .001 level ** p < .0001

Table 41 presents the analysis of variance between number of computer

applications used and both the computer interface preferred and the relative level

of computer competence. There are significant differences in the number of

applications used by either the interface or the level of competence and therefore

the hypothesis is sustained. However, there does not appear to be any significant

interaction between the two independent variables (interface or level of

competence) as they affect the dependent variable, namely, the number of

applications used.

H3.4 - The type of computer learned has a greater impact on learning

strategies than the learners' preferred learning style

To analyze this issue, there were several questions on the General

Questionnaire that related to learning strategies. These comparisons include

sources of assistance, both all sources and primary source; preferred learning

group; and preferred initial learning strategies.

146

Table 42 presents all sources of assistance used, by type of computer

interface preferred. Table 43 presents the same sources of assistance, however,

presented by learning style preference.

TABLE 42

All sources of assistance used by preferred computer interface

All sources of assistance All sources Graphical Interface Text Interfacen % n % n %

Hands-on experimentingwith the computer 183 94.3% 102 94.4% 81 94.2%

Software Manuals 167 86.1% 97 89.8% 70 81.4%

Software tutorials on the computer 134 69.1% 76 70.4% 58 67.4%

Books and magazines 130 67.0% 79 73.1% 51 59.3% *

Family & Friends 122 62.9% 70 64.8% 52 60.5%

Classes for college credit 113 58.2% 68 63.0% 45 52.3%

Non-credit workshops or courses 101 52.1% 63 58.3% 38 44.2% *

Conferences 69 35.6% 46 42.6% 23 26.7% *

User group meetings 48 24.7% 30 27.8% 18 20.9%

Audio or Video Training Tapes 30 15.5% 24 22.2% 6 7.0% **

Television shows about computers 24 12.4% 16 14.8% 8 9.3%

Other 18 9.3% 12 11.1% 6 7.0%

Total of types of assistance 1139 683 456

Average number of sources 5.9 6.3 5.6

* p<.05 ** p<.01 *** p<.001

Table 42 shows that there were statistically significant differences between

users of graphical and text user interfaces on four learning strategies: books and

magazines, non-credit workshops or courses, conferences and use of audio or

147

video training tapes, with graphical interface users scoring significantly higher on

all four scores.

TABLE 43

All sources of assistance used by learning style preferences

All sources of assistance Concrete Abstract Reflective Activen % n % n % n %

Hands-on experimentingwith the computer 76 93% 107 96% 89 95% 94 94%

Software Manuals 66 80% 101 90% 80 85% 87 87%

Software tutorials on the computer 58 71% 76 68% 68 72% 66 66%

Books and magazines 48 59% * 82 73% 59 63% 71 71%

Family & Friends 50 61% 72 64% 61 65% 61 61%

Classes for college credit 45 55% 68 61% 55 59% 58 58%

Non-credit workshops or courses 37 45% 64 57% 48 51% 53 53%

Conferences 23 28% 46 41% 27 29% 42 42%

User group meetings 16 20% 32 29% 18 19% 30 30%

Audio or Video Training Tapes 9 11% 21 19% 15 16% 15 15%

Television shows about computers 5 6% * 19 17% 12 13% 12 12%

Other 7 9% 11 10% 10 11% 8 8%

Total number of resources 440 699 542 597

Average number of resources 5.4 6.2 5.8 6.0

* p<.05 ** p<.01 *** p<.001

Table 43 shows that only two sources of assistance were significantly

different between concrete and abstract learners: use of books and magazines

and television shows about computers were both higher among the latter. There

was no statistical difference between all sources of assistance for active or

reflective learners. Table 42 and Table 44 present one contrast between the

148

graphical and text user interfaces and the strategies favored by preference for a

certain learning style. The source of learning about computers most frequently

chosen for all participants, regardless of type of computer operating system or

learning style, was hands-on experimenting with the computer. Those users

preferring the graphical interface used more sources of learning (an average of 6.3

different sources) compared to the text interface users (an average of 5.6 difference

sources). By learning style, the major exception noted is the use of software

manuals. Over 90% of the abstract learners used software manuals compared to

80% of the concrete learners, although this was not a statistically significant

difference. The abstract learners also used books and magazines more often

(73%) that the concrete learners (59%), which was statistically significant. From

these two tables, we can see that there were more statistically significant

differences in all types of learning resources between users of graphical and text

user interfaces, which lends support for this hypothesis.

Table 44 and Table 45 present the primary source of learning to use a

personal computer, both by computer interface and learning style preferences.

149

TABLE 44

Primary source of assistance by computer interface preference

Primary source of assistance All Users Graphical Interface Text Interfacen % n % n %

Hands-on experimentingwith the computer 82 43.4% 59 55.1% 23 28.1%

Software Manuals 31 16.4% 15 14.0% 16 19.5%

Classes for college credit 23 12.2% 9 8.4% 14 17.1%

Family & Friends 21 11.1% 8 7.5% 13 15.9%

Books and magazines 10 5.3% 6 5.6% 4 4.9%

Non-credit workshops or short courses 7 3.7% 4 3.7% 3 3.7%

Software tutorials on the computer 4 2.1% 1 0.9% 3 3.7%

Conferences 3 1.6% 1 0.9% 2 2.4%

Total 189 100.0% 107 100.0% 82 100.0%

Missing data or other 13 5 8

150

TABLE 45

Primary source of assistance by learning style preference

Primary source of assistance Concrete Abstract Reflective Activen % n % n % n %

Hands-on experimentingwith the computer 34 43.0% 48 43.6% 31 34.1% 51 52.0%

Software Manuals 15 19.0% 16 14.6% 14 15.4% 17 17.4%

Classes for college credit 8 10.1% 15 13.6% 15 16.5% 8 8.2%

Family & Friends 7 8.9% 14 12.7% 16 17.6% 5 5.1%

Books and magazines 5 6.3% 5 4.6% 5 5.5% 5 5.1%

Non-credit workshops or shortcourses 4 5.1% 3 2.7% 2 2.2% 5 5.1%

Software tutorials on the computer 1 1.3% 3 2.7% 1 1.1% 3 3.1%

Conferences 2 2.5% 1 0.9% 2 2.2% 1 1.0%

Total 79 100.0% 110 100.0% 91 100.0% 98 100.0%

Missing data or other 6 7 8 5

As the information presented in these tables reflects, hands-on learning was

also the primary source of learning for 43% of the participants. However, 55% of

those participants preferring the graphical user interface chose this method as their

primary source of learning, compared with only 28% of those participants preferring

the text user interface. Fewer graphical users chose college credit courses (8%) as

their primary source of learning, compared with 17% of the text users. The major

difference by learning style was the statistically significant difference on the Chi-

square between the reflective and active learners. Only 34% of the reflective

learners chose hands-on experimenting as their primary source of learning

compared with 52% of the active learners. Active learners relied less on classes,

family and friends for their primary source of learning than the other learning styles.

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Tables 44 and 45 lend further support to the hypothesis that there is more of

a difference in primary learning strategies by computer interface than by learning

style preference.

When asked how participants preferred to learn (on their own, in a small

group, with a friend or relative, or in a formal class structure), there were a few

differences noted by learning style and type of computer interface. Table 46

presents the preferred learning group by computer interface. Table 47 presents the

same information by learning style preference.

TABLE 46

Preferred group for learning by computer interface preference

All sources Graphical Interface Text Interfacen % n % n %

On your own? 78 40.2% 49 45.4% 29 33.7%

With a friend or relative? 43 22.2% 21 23.2% 22 25.6%

In a small group? 41 21.1% 25 19.4% 16 18.6%

In a formal class structure? 19 9.8% 10 9.3% 9 10.5%

Other 13 6.7% 3 2.8% 10 11.6%

Total 194 100.0% 108 100.0% 86 100.0%

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TABLE 47

Preferred group for learning by learning style preference

Concrete Abstract Reflective Activen % n % n % n %

On your own? 30 36.6% 48 42.9% 34 36.2% 44 44.0%

With a friend or relative? 16 19.5% 27 24.1% 25 26.6% 18 18.0%

In a small group? 20 24.4% 21 18.8% 20 21.3% 21 21.0%

In a formal class structure? 9 11.0% 10 8.9% 11 11.7% 8 8.0%

Other 7 8.5% 6 5.4% 4 4.3% 9 9.0%

Total 82 100.0% 112 100.0% 94 100.0%100 100.0%

As reflected in Table 47, 40% of all participants indicated their preference for

learning on their own. Neither of the tables reflect statistically significant

differences on the Chi-square at p<.05, although there was more of a difference

between user interfaces than learning style. More than 45% of those preferring the

graphical user interface chose this grouping compared with 34% of the text users;

44% of the active learners preferred learning on their own, compared with 36% of

the reflective learners; 43% of the abstract learners expressed this preference

compared to 37% of the concrete learners.

One item on the General Questionnaire asked what the participants liked to

do first when learning a new program. Their responses followed a pattern similar

to the previous items. Table 48 presents the preferred initial strategy by type of

computer interface preferred. Table 49 presents the same information by learning

style preference.

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TABLE 48

Initial learning strategies by preference for type of computer interface

All participants Graphical Interface Text Interfacen % n % n %

Turn on the computer andstart experimenting? 64 33.3% 45 42.5% 19 22.1%

If available, work through thetutorial disk? 44 22.9% 20 18.9% 24 27.9%

Watch a demonstration? 29 15.1% 16 15.1% 13 15.1%

Read the manual? 27 14.1% 15 14.2% 12 14.0%

Talk to a friend or colleagueabout the program? 14 7.3% 8 7.6% 6 7.0%

Take a private lesson? 10 5.2% 1 0.9% 9 10.5%

Other 3 1.6% 0 0.0% 3 3.5%

Take a class? 1 0.5% 1 0.9% 0 0.0%

Total 192 100.0% 106 100.0% 86 100.0%

Missing 2 2 0

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TABLE 49

Initial learning strategies by learning style preference

Initial Strategies Concrete Abstract Reflective Activen % n % n % n %

Turn on the computer andstart experimenting? 23 28.8% 41 36.6% 29 30.9% 35 35.7%

If available, work through thetutorial disk? 20 25.0% 24 21.4% 21 22.3% 23 23.5%

Watch a demonstration? 15 18.8% 14 12.5% 14 14.9% 15 15.3%

Read the manual? 9 11.3% 18 16.1% 15 16.0% 12 12.2%

Talk to a friend or colleagueabout the program? 5 6.3% 9 8.0% 6 6.4% 8 8.2%

Take a private lesson? 5 6.3% 5 4.5% 7 7.5% 3 3.1%

Other 2 2.5% 1 0.9% 2 2.1% 1 1.0%

Take a class? 1 1.3% 0 0.0% 0 0.0% 1 1.0%

Total 80 100.0% 112 100.0% 94 100.0% 98 100.0%

Missing 2 0 0 2

These data provided some of the most interesting contrasts between groups.

While one third of all participants chose as their initial strategy, Turn on the

computer and start experimenting, there are significant differences between the

users of graphical and text user interfaces. Over 42% of the graphical users chose

this item as their initial strategy, compared with only 22% of the text users. In fact,

the first choice of the text users was to work through a tutorial disk, if available. A

slightly higher percentage of abstract users (36.6%) chose this method compared

with the concrete users (28.8%); 35.7% of the active users preferred this method

compared with 30.9% of the reflective users, although the differences are not

significant. It should be noted that reading the manual placed fourth for all groups

except the abstract learners and the reflective learners, where this strategy placed

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third. Only one person in the entire study chose to take a class as his or her initial

learning strategy! Since these tables show that there is a greater difference in

preferred initial learning strategy by user interface than by learning style, the

hypothesis is further supported.

To see if the initial strategies chosen had any impact of gaining personal

computer competency, Table 50 presents the mean PCCI scores of each group

based on initial strategy chosen, in order of the mean PCCI scores. Table 51

presents an analysis of variance of PCCI scores of those who chose these different

initial learning strategies.

TABLE 50

PCCI scores by preferred initial learning strategy

n % PCCI PCCIMean sd

Turn on the computer and start experimenting? 64 33.3% 43.59 11.48

Read the manual? 27 14.1% 39.22 13.11

If available, work through the tutorial disk? 44 22.9% 34.16 11.68

Talk to a friend or colleague about the program? 14 7.3% 33.14 13.52

Watch a demonstration? 29 15.1% 32.59 14.02

Take a class? 1 0.5% 28.00 n/a

Take a private lesson? 10 5.2% 20.50 11.75

Other 3 1.6% 19.00 8.19

Total 192 100.0%

Missing 2

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TABLE 51

Analysis of variance of PCCI scores by initial learning strategy

Source of Variation Sum of Degrees of Mean FSquares Freedom Square Value

Between groups 7805.13 7 1115.019 7.354*

Within groups 27897.239 184 151.615

Total 35702.37 191**

* p < .0001

** There were no responses in 2 cases.

Based on this analysis, there are two initial learning strategies which may

result in higher levels of personal computer competency than other learning

strategies: turning on the computer and experimenting, and reading the manual.

The people who preferred those strategies had statistically significant higher

scores on the Personal Computer Competency Inventory. Those who read the

manual showed statistically higher PCCI scores than those who watched a

demonstration or took a private lesson; those who relied on hands-on

experimenting had statistically higher PCCI scores than those who watched a

demonstration, worked through a tutorial disk or took a private lesson.

Summary - The quantitative data presented above shows conclusively that

there is fundamentally a greater difference in learning different computer systems

than in just a matter of learning styles. There are some indicators that the graphical

user interface allows certain types of learning strategies that the text user interface

doesn't. For example, the hands-on experimenting method works when the

options can be selected from a menu. The qualitative data, however, points to a

major difference in learning strategies between operating systems. To quote one

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typical response to the question on all sources used to learn the computer, "I learn

on the Mac by experimenting on my own. I ask for help and attend classes to learn

the IBM software." Several other respondents commented that their responses

would be different, depending on the type of computer they learned. In addition,

one participant mentioned that she learned the computer in a very different way

than she learned everything else in her life.

Based on the data presented in Tables 49, 50 and 51, and the data

presented in Table 44 and Table 48, in my opinion, the hypothesis is sustained;

that is, the type of computer being learned has more impact on the learning

strategies used than the difference in learning style.

Other Findings

There were several other findings which were not specifically related to

hypotheses, but which lend further support to an understanding of how adults gain

competence in using a personal computer. Of particular interest are the problems

that different groups experienced while learning to use their computers, and the

way these learners used computer manuals as they were learning.

Table 52 presents the responses of participants for a series of questions

related to the types of problems experienced when learning to use a personal

computer. The range of choices was from 1 (never a problem) to 5 (very frequently

a problem). Table 52 presents the mean score for all participants and the mean

scores for the beginning and competent users in the study.

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TABLE 52

Problems experienced while learning a personal computer by level of experience

All sources Beginners Competentmean sd mean sd mean sd

Problems with computer hardware 2.52 0.92 2.44 0.94 2.55 0.92

Resources too expensive 2.99 1.32 3.17 1.31 2.93 1.32

Difficulty with software commands 2.82 1.11 3.34 1.05 2.65 1.08 ***

Difficulty with operating system 2.62 1.06 3.24 0.99 2.42 1.00 ***

Manual hard to read 3.49 1.19 3.83 1.31 3.38 1.13 *

Trouble getting help 2.82 1.21 3.08 1.25 2.73 1.19

Experts were not knowledgeable 2.34 1.14 2.15 1.15 2.41 1.13

Resources not available 2.64 1.25 2.60 1.35 2.65 1.22

Lack of available courses/workshops 2.58 1.33 2.57 1.35 2.58 1.33

Not enough time because of otherresponsibilities 3.72 1.20 4.20 1.03 3.55 1.22 ***

* p<.05 ** p<.01 *** p<.001

There were four different "problems" where the beginning users had

significantly higher ratings, based on the Analysis of Variance: difficulty with

software commands, difficulty with operating system, manual hard to read and not

enough time because of other responsibilities. There were no significant

differences between any learning style on these variables. There was only one

item (manual hard to read) which showed any statistical difference between the

users of graphical and text interfaces.

Table 53 below presents the responses of participants for a series of

questions related to the the way software manuals were used. The range of

choices was from 1 (never) to 3 (sometimes) to 5 (always). Table 53 presents the

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mean score for all participants and the mean scores for the beginning and

competent users in the study.

TABLE 53

How software manuals are used by participants' level of experience

All participants Beginners Competentmean sd mean sd mean sd

I read most or all of it before I start 2.25 1.19 2.16 1.18 2.28 1.19

I skim it to see what the program does 3.70 1.02 3.58 0.92 3.74 1.05

I only consult the manual if I have aproblem that I can't solve 3.19 1.21 3.47 1.10 3.11 1.23

Table 53 reflects the inclination of the participants to thoroughly read

computer manuals less than half of the time, preferring instead to skim the manual

more than half of the time and then to consult the manual only if there is a problem

that cannot be solved. There were no significant differences between beginning

and competent users on this question. These data will be discussed further in the

next chapter.

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Summary

In summary, of the 10 hypotheses submitted, all but two were supported by

the data analyzed by statistical measures. The two rejected hypotheses found no

relationship between learning style and preference for type of computer interface.

From the hypotheses that were supported, I found that self-directed learning

strategies were employed at least 70% of the time; that competent users had a

slightly higher level of self-directed learning readiness than beginning users; that

intrinsic motivation led to higher levels of personal computer competency; that a

foundation for learning, an active learning style, and an abstract learning style all

are associated with higher levels of personal computer competency; that

competent users of graphical interface computers will use more types of programs

than the competent users of text interface computers; and that the type of computer

operating system interface has a greater impact on learning strategies than

individual learning style.

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Hypothesis Result Stat Test p

H 1.1: Competent computer users spend more than 70%of the time learning their computers using self-directed learning strategies.

Supported Mean & St.Dev.

H 1.2: Competent computer users will have a higher levelof self-directed learning readiness than beginningcomputer users.

Supported ANOVA <.05

H 1.3: Computer users with intrinsic motivation to learnhow to use a personal computer have a higherrelative level of personal computer competency thanthose with extrinsic motivation to learn.

Supported ANOVA &Chi-Square

<.05

H 1.4: Computer users with a foundation for learning willhave a higher level of personal computercompetence

Supported ANOVA & ChiSquare

various

H 2.1: Computer users with an active learning style willhave a higher relative level of personal computercompetence than those with a reflective learningstyle.

Supported ANOVA <.01

H 2.2: Computer users with an abstract learning style willhave a higher relative level of personal computercompetence than those with a concrete learningstyle.

Supported ANOVA <.01

Subsidiary Question: What is the relationship betweenlearning style and readiness for self-directedlearning?

NoRelationship

ANOVA N/A

H 3.1: Computer users with a concrete learning stylepreference will favor the Graphical User Interface.

NotSupported

Chi-Square N/A

H 3.2: Computer Users with an abstract learning stylepreference will favor the text-based user interface.

NotSupported

Chi-Square N/A

H 3.3: Competent users of graphical user interfacecomputers will use more types of applications thancompetent users of text-based systems.

Supported 2-WayANOVA

<.001

H 3.4: The type of computer learned has a greater impacton learning strategies than the learners' preferredlearning style.

Supported ANOVA &Chi-Square

various

Figure 22 . Summary of Hypotheses Results

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CHAPTER 5 - DISCUSSION

The main purpose of this exploratory study has been to assess the impact of

learning style, readiness for self-directed learning, and personal computer

operating system interface (graphical [GUI] and text, or command-line) on the

acquisition of personal computer competency. The study used the Kolb Learning

Style Instrument (LSI) to identify learner preferences for grasping (or figurative

representation) of experience through either abstract conceptualization or concrete

experience; and for transformation of that experience through either reflective

observation or active experimentation. The level of personal computer competency

was assessed by the participants through 1. assessing their own competency on a

five-step scale from Beginner to Expert, and 2. their relative scores on a self-report

Personal Computer Competency Instrument (PCCI). The PCCI assessment

identified generic skills in using applications software with a limited assessment of

computer programming skills. All 194 participants are classified on each of four

contrasting pairs: abstract or concrete learning style; active or reflective learning

style; graphical or text user interface; and beginning or competent computer user.

The participants in this study are highly educated (more than half of the

participants have earned graduate degrees), white, middle-class, middle-aged and

a majority (64%) are women. More than 50% of the participants have household

incomes in excess of $60,000, reflecting a higher than average socioeconomic

level, even given Alaska's higher income levels.

The sample's mean score (244) on the Self-Directed Learning Readiness

Scale (SDLRS) reflected a group with a much higher level of readiness for self-

directed learning than the mean score of all adults (216) that Guglielmino (1977)

identified. Perhaps the weak relationship between the SDLRS score and personal

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computer competency is due to the fact that the SDLRS scores were uniformly high

for all groups (beginning vs. competent, by learning style or by type of user

interface), and therefore no group deviated from the group mean SDLRS score by

more than six points. Since all of the participants in this study are computer users,

this may imply that computer users have a higher level of self-directed learning

readiness than the average population.

Not only is this a group with higher levels of self-directed learning readiness,

they are knowledgeable about their own learning processes, are highly motivated

adult learners in general, and in particular have highly intrinsic reasons for learning

to use a personal computer. The fact that 88% of the participants in the study own

at least one personal computer at the time of the study is perhaps another factor

that influenced their acquisition of personal computer competency. Even with the

high level of education and motivation, these participants still expressed varying

levels of problems related to their experience in learning to use their computer

systems.

This study focuses on the participants' preference and/or use of the

graphical or text user interface. Many participants (37%, n=68) use only one type

of system, and so they are classified by the only system they use. It should be noted

that those who use only one type of computer have significantly lower mean PCCI

scores than those who use more than one system and whose preference for either

the graphical or the text interface is based on their own experience. Learning more

than one computer or operating system appears to contribute to a better

understanding of computers and a higher level of competence.

Only two participants had no preference for a particular interface. Their

preference was based on the particular task they had to perform, which infers that

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they judge different operating systems to work better for different tasks. For the

purposes of this study, these respondents are placed in the group represented by

the computer they used more often (one in each interface group). It should be

noted that for those people who used both the text and the graphical user interface,

there is a distinct preference for the graphical interface.

Discussion of Hypotheses

The effectiveness of the PCCI for assessing general competence in using a

personal computer is shown by the significant relationship (p=.0001) between the

participants' indication of their own level of expertise (on a five-point scale from

beginner to expert) and their relative scores on the PCCI. These participants

appear to have a fairly accurate estimate of their own personal computer

competence which further validates the use of the PCCI score as the primary

dependent variable in this study.

Goal 1 - Self-Directed Learning and Motivation

Regardless of the expense and effort devoted to organized computer

training programs, this study further validates the assumption that self-directed

learning is the primary method that most adults use to gain competence in using a

personal computer. There appears to be no substitute for the "discovery learning,"

inductive, experiential approach, based on the strategies they prefer. Self-directed

learning may not be efficient, but it appears to be a most effective method of

learning.

The hypotheses related to self-direction and motivation in learning are all

sustained, although the significance (p=.0394) of self-directed learning readiness

of competent users over beginners is only weakly supported. There may be

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statistical significance, but the scores are so close that there was no practical

significance of this finding. The percentage of time that the competent users spent

in self-directed learning (77%) is consistent with previous studies that have been

conducted on adult learning projects, and supports a study cited earlier concerning

learning to use personal computers in small businesses (LaPlante, 1986).

This study does not consider personality factors, such as persistence in the

learning process, which the SDLRS cannot adequately measure. Because

differences are small between the groups on the SDLRS, that measure does not

appear to give any insight into the relationship between readiness for self-directed

learning and personal computer competence.

Time appears to be an important factor in learning to use a personal

computer, both as a factor in gaining competence, and as a problem in learning.

Competent users spent significantly more time using the computer than beginning

users. In addition, all groups indicate that the Number One problem in learning

their computer is lack of sufficient time because of other responsibilities.

Another important factor is computer ownership and socioeconomic status.

As noted above, the participants in this study have relatively high incomes. While

there is no relationship between household income and personal computer

competence, there is a significant relationship between income and computer

ownership. It has also been shown that the participants who own computers have

statistically higher PCCI scores than the non-owners. The implications of this

finding reflect the concern that those adults with lower income are at a

disadvantage for gaining enough access to computers to be able to learn computer

skills needed to succeed in our information society. Economically disadvantaged

adults are falling farther and farther behind in computer competency. In the long

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run this problem may be lessened because of the numbers of personal computers

being placed in public school classrooms; however, there will be an interim period

where older adults will be at a disadvantage compared to recent graduates.

The need of beginners for more structured, organized learning activities is

also consistent with self-directed learning theory. As learners become more

competent in a particular knowledge or skill, they are more able to assume

independence in the learning process. Competent users employ a greater variety

of learning strategies than do beginners. It is not clear whether the number of

strategies contributed to competence, or the competent users felt more confident in

using a variety of strategies (or a combination thereof). One recommendation for

new learners of personal computers is to use many different types of learning

strategies.

The motivation to learn a personal computer may have more impact on the

acquisition of personal computer competency than any other factor studied. A

reason for learning and its relationship with the participants' life tasks appears to

be a very important factor. If the computer can help pursue a life passion (a

professional task or personal interest that can be better accomplished with a

computer), then the motivation will be more intrinsic. Those who chose personal

curiosity and general career advancement as the primary reasons to learn to use a

computer had significantly higher mean scores on the PCCI. This would indicate

that when the motivation is intrinsic, the learning efforts are more effective, further

supporting Corno and Snow's (1986) aptitude complex model which illustrates the

positive relationship between motivation and the quantity of the learning act (the

amount of time committed to learning).

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In looking at the primary reasons for learning to use a personal computer, it

is interesting to note that the mean PCCI scores of those who chose save time and

effort are below the mean for the entire sample. The time-saving features of the

computer may be negated by the amount of time it takes to become productive.

Since the PCCI score relates to the number of applications learned, perhaps this

reason alone (saving time and effort) is insufficient in light of the time commitment

required for learning, which could have a negative impact of such motivation. If a

learner has narrow goals (such as word processing only) and is able to achieve

those objectives, there will be no further motivation to continue beyond that level.

On the other hand, if the primary motivation is personal curiosity or general career

advancement, then the amount of time committed to the learning process would

have a positive effect, with the experience further reinforcing the willingness to

keep on learning. Activity that leads to success leads also to more motivation to

continue using the computer.

Prior experience with most office automation devices seems to have little

impact on successfully learning to use a personal computer. Long experience with

typewriters does not lead to higher levels of personal computer competency; even

typing speed makes no difference on this measure Typing proficiency may make a

difference in the initial decision to learn a computer, but that issue was not

addressed in this study. Only experience with mini- or mainframe computers and

hand calculators seems to have a significant impact on personal computer

competency.

Reliance on self in learning to use a personal computer is first in order of

priority for all groups. It ranks above all other forms of human assistance, on all

scales, and for all levels of learners. This preference is further support for the

importance of self-directed learning in gaining personal computer competency.

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Other people may help, especially colleagues at work, but in the final analysis, it is

the individual who must recreate this knowledge through a "discovery learning"

process.

Further support is found in the selection of all sources of assistance in

learning to use a personal computer. The primary choice of all users was hands-

on experimenting, followed closely by software manuals, software tutorials, books,

and magazines, which are all tools for self-directed learning.

When asked the preferred social group for learning a personal computer, on

your own was the choice of two out of five participants. The PCCI score of that

group is also significantly higher than all of the others who chose different

groupings. Only one in 10 preferred to learn in a formal class structure.

Goal 2 - Learning Style

In general, the participants in this study prefer the abstract over the concrete

learning style. There is a slight difference in the number of active learners over

reflective learners. However, the difference in personal computer competence

between those groups may reflect the success of active learning strategies over

reflective strategies when learning to use a personal computer. In an analysis of

the items on Kolb's instrument, the characteristics of the active learner (active,

practical, hard work, results-oriented, try things out, and practice) reflect the favored

practices in learning a personal computer, according to the responses to items on

the General Questionnaire. The characteristics of a reflective learner (watching

and listening, observing, carefulness, taking time before acting) do not appear to

be strategies favored by successful learners of personal computers.

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Learning style appears to have an impact on the learning strategies as well

as the self-reported level of personal computer competency for the people in this

study. Divergers, or learners who preferred the reflective observation and concrete

experience learning styles, had significantly lower PCCI scores than all of the other

learning styles. Active learners had significantly higher scores than reflective

learners. Abstract learners had significantly higher scores than concrete learners.

This finding is similar to prior research that has been conducted, where

assimilators and convergers (the abstract learners) had more success in computer

programming classes, as reflected in the grades they received in the courses

(Barrie, 1984).

Learning style is a factor in adopting certain learning strategies which lead

to personal computer competency. From the comments of some participants as

they completed the instruments, the computer has forced them to "stretch" their

learning style, with one person commenting that she learned to use the computer

very differently than she learned everything else in her life.

Goal 3 - Type of Operating System Interface

One of the major findings of this study is the impact of the computer interface

on the strategies used to gain competence with personal computers. The graphical

user interface allows much more hands-on experimenting and discovery learning

than the text user interface.

The type of computer interface preference, compared to the actual computer

used, provides some interesting comparisons. Of roughly half of the participants

who currently use an IBM-Compatible computer, fully 35% preferred the graphical

over the text user interface that is standard with IBM or compatible computers. Of

the more than half of all participants who currently use a Macintosh, 90% prefer the

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graphical user interface over the text. If participants used both computers, there is

an overwhelming preference for the graphical user interface, which should further

encourage the development of this type of interface for all computers.

The two hypotheses relating a preference for type of computer interface by

learning style showed no significant relationship. The concrete learners in this

study show no significant preference for the graphical interface, and the abstract

learners do not overwhelmingly prefer the text user interface. This is not surprising

when the reasons for buying a certain type of computer are examined. Virtually

none of the participants indicated that their choice of a computer system was based

on their learning style. The most frequent reasons for buying a personal computer

were based on compatibility, software, price, and ease of use. Quite a few

participants chose their first computer based on what they had at work or school or

the system a friend recommended.

The difference in learning strategies based on learning style is not as great

as the difference in learning strategies based on user interface. A majority (55.1%,

n=59) of the graphical users prefer hands-on experimenting as the primary source

of assistance in learning their computers; only 28.1% of the text users (n=23)

choose this method as their primary choice. On this same item, the greatest

difference based on learning style is between the active learners (52%, n=51) and

the reflective learners (34.1%, n=31), and this finding appears to be consistent with

learning style theory. Perhaps the reason for the greater difference in strategies

based on computer interface is the intuitive nature of the graphical interface; an

icon/mouse/menu-based system allows for more experimentation than the text

system, which relies more on memorization and recall of commands than on

intuition and recognition.

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This difference is further supported by the initial learning strategies selected.

When asked what they liked to do first when learning a new program, 42.5% (n=45)

of the graphical users selected turn on the computer and start experimenting. This

was the second choice of the text users (22.1%, n=19); that group's first choice

was If available, work through a tutorial disk (27.9%, n=24). The difference

between learning styles on this item was not as extreme. The participants who

preferred this strategy (33.3%, n=64) and the participants who initially read

manuals had significantly higher mean PCCI scores than those participants who

chose other initial learning strategies.

One of the concepts of learning that warrants further research is the impact

of the graphical user interface on the ability of users to develop a concrete mental

model of how a computer works. Perhaps the graphical nature of the computer (the

pictures, icons, metaphors that the computer displays) does some of the work of the

human mind, developing a model for the user, cutting through some of the

confusion caused by abstract concepts and creating more understanding in

concrete terms and images.

For example, the concept of subdirectories in the DOS world is often a

difficult one to explain. However, the same concept implemented in the graphical

user interface is a "folder" in which to store files. The concept of different types of

files is represented by different pictures or icons that visually represent the diverse

kinds of files that the computer can store.

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Developing a Model of Learning to Use Personal Computers

In addition to the quantitative data presented so far, a series of open-ended,

qualitative questions were asked as Optional Additional Questions. While

originally intended as questions for a structured interview, these questions were

provided to every participant and 16% (n=31) chose to respond to them in writing.

The responses were initially entered into a computer data base by respondent, and

then printed out by all answers to each individual question. The questions were

divided into sections, first by Wlodkowski's (1985) Time Continuum Model of

Motivation which addresses the motivational factors during three different stages of

the learning process (beginning, during, and end). An additional set of general

questions was asked regarding both useful metaphors, difficult concepts, mental

models, and advice to others on learning to use a personal computer. Finally, two

questions were asked requesting some self-reflection about the learning process.

The purpose of these additional questions is to help develop a model of

successful learning strategies by learning style and other learner characteristics.

This model will be useful to other people who might be having difficulties learning

to use their computers. It will integrate some of the learning models presented

earlier in the literature review.

We need to look at learning to use a computer as a developmental process.

That is, there is a sequence of activities that culminates in competency in using any

computer system; the exact sequence will be different for every person and this

difference may be affected by a person's learning style; the type of user interface

being learned has a definite impact on the learning strategies; and there are

specific stages that people go through in the learning process. These stages can

be broken down in four stages with three transitions between them:

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Stage #1 : Unconscious incompetence Ñ lack of awareness or motivation to

learn

Transition #1 .

Deciding to learn Ñ Key variable: the reason for learning Ñ "Why"

Motivational variables: "need and attitude"

Stage #2 : Conscious incompetence. Getting started Ñ the "How" Ñ Figuring out

"What's it all about?" Having a good first concrete experience

Transition #2 : Gaining confidence Ñ "Learning how to learn" Ñ

Motivational variables: "stimulation and affect"

Stage #3: Conscious competence. The computer has become useful, but all

activities are conscious and unnatural. These skills are not yet integrated

into the subconscious.

Transition #3 : Developing automatic and intuitive use Ñ rehearsal

"practice, practice, practice" -

Motivational variables: "competence and reinforcement"

Stage #4 : Unconscious competence. The technology becomes invisible; the

learner's energy is focused on the task that needs to be done, not on the

technology.

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Figure 23. An Integrated Model of Learning

The model shown in Figure 23 incorporates several existing models of

learning, which were presented in the literature review, into an integrated model

that covers many of the issues raised in this research. The focus of the study was

not only on how adults learned to use personal computers, but on generic learning

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strategies, with the goal of shedding some light on the developmental process of

learning. The model focuses on four stages or phases and the transitions between

each of those phases. Participants' responses to questions about learning

processes illustrated these transitions. Their responses provide a rich description

of motivational factors important to each transition, as it relates to learning to use a

personal computer.

Computer Competency Learning Model

The model presented in Figure 23 gives us an opportunity to compare the

experience of some of the participants in this study with the various learning

theories that are represented here. The model as presented represents a spiral or

an iterative, developmental process that is repeated for each new skill to be

learned. For clarity and purposes of discussion, at the end of the participants'

comments, the graphical representation of each stage of the process will be

presented to facilitate the presentation and discussion of both the theoretical and

practical implications.

Participants were asked to describe the sequence of learning activities that

they went through to gain competence in using a personal computer. To help with

this process, I provided a list of questions for their response. In order to synthesize

the learning theories, the participants' experiences will be used to illustrate the

various learning principles at each phase of the learning process.

The earliest stage of the learning process takes place before the decision to

learn how to use a personal computer. This earliest stage is characterized by a

lack of awareness about what or how to learn. At some point, all computer users

became aware of the personal computer and formed their own reasons for wanting

to learn. From the results reported earlier, motivation may be one of the most

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critical success factors. Thus, learners must go through some kind of transition,

which increases awareness and motivation to embark on a new learning project.

Transition 1: Beginning of the learning process

ConcreteExperience

Initial Experienceas "catalyst"

StimulusRecognizing

ResponseGenerating

ProcedureFollowing

AttitudeNeed

Developing Awareness& Motivation for

Learning

Beginning

ReflectiveObservation

UnconsciousIncompetence

ConsciousIncompetence

SensoryMemory

Short-TermMemory

Lewin/KolbExperiential Learning

Model

Gagn�'sHierarchy of Learning

Wlodkowski'sTime Continuum Model

of Motivation

Consciousness & Competence

Model

InformationProcessing

Model

Figure 24. Transition #1 - Beginning of the learning process

The initial learning experience is extremely important in relationship to the

learning theories illustrated in Figure 24, because that first try can serve as a

catalyst for subsequent learning. Finding personal meaning, meeting practical

needs, approaching the learning task with a positive attitude, and building

awareness are essential motivational factors. This is the stage where the learner

needs to recognize the stimulus of what is happening on the screen and decide

what actions to take in response to cues from the computer. In this early stage,

having a written set of procedures to follow is useful, since the knowledge and

skills have not been committed to long-term memory. One mistake many computer

instructors make is to teach computer vocabulary terms or to teach several different

strategies for accomplishing the same task, before students have any hands-on

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experience. These skills, terminology using and discrimination making, come

later. At the beginning stage of the learning process, it is important for the learner

to have hands-on experience with the computer. Based on this experience, they

will form their own mental models about how the computer works and build

motivation to continue learning.

At this stage of the learning process, those students using self-directed

learning can just turn on the computer, boot the program, start experimenting, and

observe what happens on the screen. If the program does not have an intuitive

interface, then going through a tutorial disk on the program is a good first activity.

Watching a short demonstration is useful for some people, and user groups or

computer dealers are good places to see programs in operation.

It is important to interact with a program at the start, to begin forming a

mental model of how the program works. With a new program of a familiar

application, it is useful to observe how it is similar to, or differs from other familiar

programs. In learning a totally new application, there is a dual need to understand

the application as well as the specific commands of the program. The learner

should make notes, especially of elementary procedures, such as how to start and

exit the program or open and save files.

Self-directed learners often suffer from a lack of structure that an instructor

provides. Most of us unconsciously set some sort of learning goals without

realizing it, and even an informal learning plan organizes activities to reduce

anxiety and increase productivity. Learners should go further and consciously set

stated realistic goals for learning so they can check their progress along the way

and be motivated by their successful accomplishments. Using a real task can

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enhance motivation because the learner can make progress in learning to use the

computer and get a job done at the same time.

Instructors of new users should not use or require detailed terminology, or

teach numerous disconnected commands. Beginning with concrete, hands-on

experience is best. It helps the learner become comfortable with the program

through step-by-step procedures. It is important for the learner to recognize the

stimulus the computer screen is providing, and what responses are appropriate.

Once a minimum level of comfort is reached, then the learner will have some

concrete experience upon which to relate the more abstract knowledge that can

subsequently be introduced. At this stage, it is important that learners be shown

the practical needs the application they are learning can address. The instructor's

positive attitude toward the subject is also extremely important at this beginning

stage. The most effective role an instructor plays at this stage is to provide

inspiration and structure for the learners.

The qualitative data contributed by the participants in this study lend further

support to the integrated theory just presented. The first question I asked was

related to the reasons that made the participants want to learn to use a personal

computer. Their responses were divided into several categories: professional

responsibilities, such as a job; personal curiosity, or excitement over a new thing;

and a specific task that could be done more efficiently on the computer. Some

people had very practical reasons:

The original reason for learning was word processing. I discovered through using acolleague's system that I am probably a better rewriter than writer. Editing evaluationsand reports improved the quality of my work.

Some wanted to be involved in something new and exciting:

I wanted to use a computer because it was a very exciting new tool for the educationcommunity in the early 80s, one that I could see would have a great impact on the waywe taught and managed our schools.

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One of the teachers wanted to learn the computer to help her students:

When I began learning to use the computer it was with the idea that I could helpstudents learn material that they could not learn by using a new and motivationalmode to teach them.

And a housewife had no good reason for learning except for the fun of it:

Even though I had the least reason for learning computers in our family (full-timemother), I was the one bitten with the computer bug and would invent tasks in order tojustify "playing" with the computer.

When asked to describe any specific needs they hadÑ tasks or jobs that

they wanted to accomplish by learning to use a personal computer Ñ their

responses could be divided into four specific skills: communication or word

processing to produce newsletters, write dissertations, or similar purposes;

financial management or bookkeeping to manage business affairs; data

management or statistical applications; and graphics. Several comments were

made about the computer's ability for better organization. For example:

It now provided me with more professional work and was organized. Being of Germandescent, I love organization.

I had to track every penny in order to do [a] child support increase suit against [my] ex-husband.

Initially, and all along in the process, I have had specific tasks to complete which havebeen made more dynamic by the use of the computer.

In describing their early attitudes toward learning to use a personal

computer at this early stage of the learning process, the responses were either of

fear and anxiety or excitement. Many of the respondents expressed frustration, a

topic specifically covered in a later question. Some examples of the negative

responses were:

Total panic. I was afraid to touch a computer. I was afraid I would touch a wrong buttonand erase material or jam the machine.

I first learned to use a personal computer because of a sense of embarrassment. I hadalways had a computer in my first grade classroom but had no idea how to use itexcept to boot simple programs for my students. I felt this was an injustice to my

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students and myself. I remember being intimidated by the computer. Afraid I'd breaksomething.

I was reluctant to begin with the computer because I feared it would add another timeconsumer to my already busy life interfering with my painting and sewing. (I was righttoo in thinking such thoughtsÑI seldom paint anymore as I use the paint programs onthe computer.)

I felt really dumb. I hated it when it controlled me. It wasn't as easy as demonstrated.You are only a victim if you let yourself be, soooooo. I was so mad and frustrated that Iplanned and plotted to understand the Macintosh.

Other people had very positive attitudes:

I constantly wanted to learn more.

I was excited about having a computer, curious, eager to learn and to master basicskills so I could use it at least as well as the dedicated word processor. That happenedvery quickly, and was very exciting.

When asked to describe any specific events or activities that raised their

awareness and motivation to learn to use a personal computer, they told many

interesting stories about specific experiences. I classify these experiences into the

following categories: doing specific tasks (not just practice); teaching someone

else (either teachers in the classroom or helping other adults); a life change event

spurred one person into learning; and working with peers who were also learning

to use the computer.

The type of computer or software being learned was often mentioned, such

as:

Changed from IBM to Mac.

Some people mentioned specific classes they took or described their own

self-directed learning activities.

About a year later in the lower 48, I took a short introductory course in computers,which turned out to be instruction in programming. Very interesting, but not thatuseful.

I was embarrassed to ask my fellow teachers to help me and I know now that they werejust as intimidated as I was. I had tried to take computer classes but they alwaysrequired time to work on a computer and I had neither the time nor access to a

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computer. I think what finally helped me take the first jump towards learning thecomputer was when I was given a printer and a copy of Printshop.I sat down one afternoon at school determined to make the program work for me. Iwas alone, just the computer, the manual and the software. I figured if I blew the disk Icould buy another. If I blew the computer I was in trouble. I must have sat at thecomputer for four hours never aware of the passing time. I was amazed at what I coulddo. It was so easy. The manual was easy to use and the program was even easier. Icouldn't quit making cards and signs. I made them for everything you can imagine.What a sense of power to have control of the machine which had intimidated me for solong. I quickly made arrangements to get my own home computer so I could continuelearning and share my new knowledge with my family.

Some people learned from and with their peers:

If you remember, documentation was a joke in those days, so I joined a computer clubin order to supplement the manuals and to connect with a network of experiencedusers. It proved to be an excellent way to learn of new software as well as to learnprograms themselves.

I worked with many people who embraced technology early on. I was encouraged toget with it. Saw the time saving for my peers.

One person even found learning the computer to be useful in making a

significant life transition:

Seeing colleagues and fellow graduate students produce documents and changes indocuments with ease. There were also life events occurring at that time whichenergized my motivation to learn a personal computer. This was primarily a divorceand single parenthood. Putting energy and time into the computer became initially anagitation and eventually was useful in my grieving the loss of that marriage.

In summary, at the beginning of the learning process, some of the most

important factors expressed were: access to learning resourcesÑ both manuals

and someone who could answer questionsÑ a positive mental attitude toward the

learning process, a specific need which the computer could help answer, and

enough time to devote to the learning process.

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Transition 2: During the Learning Process

ConsciousIncompetence

Short-TermMemory

Lewin/KolbExperiential Learning

Model

Gagne'sHierarchy of Learning

Wlodkowski'sTime Continuum Model

of Motivation

Consciousness & Competence

Model

InformationProcessing

Model

Long-TermMemory

ConsciousCompetence

During StimulationAffect

Learning How to Learn

TerminologyUsing

DiscriminationMaking

ConceptForming

AbstractConceptualization

Forming abstract concepts and

generalizations

Finding personal meaning

Figure 25. Transition #2 - During the learning process

The learning theories integrated in Figure 25 describe the stage where

learners need to form theories and develop an abstract conceptual understanding

of how the computer works. During this time the learner should move from the

primarily behavioral nature of the learning process to the more cognitive functions

or higher-level skills. At this point, it is appropriate to begin discriminating between

visual stimuliÑ even similar stimuliÑ and use some of the vocabulary associated

with computer use. The associations made during this stage will store knowledge

and skills in the long-term memory for later retrieval. During this transition phase, a

positive emotional tone (affect) is important to provide sufficient stimulation to form

cognitive links necessary for retrieval. Images and visual metaphors will contribute

to more successful information storage.

It is important for self-directed learners to allow sufficient uninterrupted time

for learning. Using real-life data to perform practical tasks will aid motivation.

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Having a cheat sheet or a minimum manual will help with the task of maintaining

important commands in short-term memory. Skimming the manual before starting

can give a sense of the scope of the material. Another useful skill is learning to

decode the way the information is organized in the manual, using the Table of

Contents or an Index. Learners should not try to memorize commands, but develop

a written record of each command that is looked up. When encountering a problem

that the manual will not solve, it is useful to explore assistance available, including

telephone numbers of dealers, and the software manufacturer. There are many

third-party book publishers who provide excellent resourcesÑ often better than the

original manual. These books often make access to the information easier

because their organization is more logical than the original software manuals.

Other useful learning aids include audio and video tapes that both demonstrate

and explain the command structure and applications.

Guided practice while on-site assistance is available is important, and

instructors should model activities that provide this opportunity Students should

have in hand a textbook or written materials to use as reference as they are

learning. At this stage the amount of stimulation is extremely important Ñnot so

much that the students are overwhelmed, and not so little that they are bored. The

affective learning environment must be positive with efforts made to help students

handle the inevitable frustration that will occur. Care should be taken never to

embarrass students in front of the class and to adjust the pace of the class for the

slowest member of the class (offer alternative assignments or additional written

instructions for enrichment for those students who learn more quickly). One

important function of an instructor at this point is as coach or guide, helping

learners avoid major problems, but not totally avoiding mistakes. Another

important function is also guiding the learners to refine their conceptual knowledge

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of how the computer works and help them develop skills in "learning how to learn"

the computer. The goal of this stage should be to help students gain sufficient

knowledge and skills to become independent and continue learning on their own.

This phase of the learning process could be described as the movement

from conscious incompetence to conscious competence, when the participant is

actively engaged in figuring out how the computer works. To further illuminate the

integrated theory presented above, the second set of questions on the qualitative

portion of this inquiry focused on the learning process itself. Both positive and

negative emotions describe the feelings that the respondents had while they were

learning the personal computer. Participants expressed a range of emotions from

fear, anger, incompetence, frustration, and loneliness, to excitement, pride, fun, and

amazement. Some comments were related to self-esteem:

At first my feelings were anxiety, caution, fear, incompetence, etc. In other words,not one of your basic self-esteem times! Gradually, due probably to a patientinstructor and lots of hands-on experience I was able to feel confident enough toreally use it without having to ask for help every five minutes.

While learning the personal computer, I often felt completely inadequate to the task.The basic vocabulary was unfamiliar. Even as I mastered the simple steps for entryinto the program, it seemed as if I was intimidated by the process. I had to force myselfto sit down and even turn on the computer; I knew that it was going to take intenseeffort to try to repeat each of the tasks learned in class. I found that I easily forgot the"layering" of tasks, and that was very frustrating. I was disappointed in myself if Icouldn't do what I set out to do, which happened pretty frequently.

Some commented about the quality of the manuals:

Excited, pleased, occasionally frightened when I thought I'd lost something, or thecomputer didn't seem to be working (have I broken it? how much will it cost to fix?),and frustratedÑwhy don't they have the answer to my question in my language in thismanual; what in the world does this manual mean?

Some commented on the time involved in learning:

Mostly my first feelings were of anxiety. I feared wrecking the computer. I dreadedthe time involved learning new tasks because uninterrupted time when I am not tiredis scarce. It seemed I would be on the verge of understanding enough to integrateeverything into a project and I would have to stop.

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Computing is addictive; one can spend hours in front of a CRT before realizing howmuch time has passed. I enjoyed problem-solving any errors that occurred; I likedtrying to figure out how to do something. I also had friends I could call for help.

Finally, the computer becomes just another tool:

I would say that working with computers, even if all you use is a word processor, isoften frustrating, and frequently exciting. But most of the time, today, the computer isinvisible. And that is how it should be in education. You shouldn't be aware of thepencil you are using, you should simply be able to use it.

Most of the participants said they experienced frustration while they were

learning to use their computers.

I was (am) frequently frustrated. It seems that I follow exactly what the directions tellme to do and the computer is stubborn and recalcitrant and refuses to do what I ask ofit. I can learn on my own about stuff and ideas but learning to use technology on myown has been just awful.

Of course! It was probably one of the most frustrating learning experiences I haveever had other than algebra which I never mastered (nor do I ever intend to!) Thefrustration was in not knowing what to do when something didn't work like I thought itwas supposed to. And it was frustrating to have to ask for help. And it was frustratingnot to know the right terminology. To handle it I took notes in class, reread thematerial, watched others do things, ask for help, and finally, held my breath andpunched buttons. When the computer failed to explode whenever I made a mistake Igrew braver. What kept me from giving up was really wanting to learn and having anexcellent understanding, patient (and handsome) instructor.

Experience frustration? Absolutely! I STILL experience frustration. I get so excited ifsomething goes wrong, that I panic and that stops me from thinking rationally.

Some of the suggestions for handling this frustration were calling someone

for help, doing relaxation exercises, walking away from the problem for a while,

taking a philosophical attitude about the process.

Just took deep breaths and reminded myself that learning takes time - "I like to learnattitude" is what kept me going.

One thing I have to remember when dealing with new technology is that we asindividuals know only a small segment of the whole picture. With knowledge doublingat such an accelerated rate we have to be selective with what we want to learn fully.

Determination kept me going. Anger kept me going. Frustration kept me going.

Yes indeed! How I handled it was to scream and then try again. I'm not gonna let anymachine defeat me and besides I was too intrigued with the possibilities forperforming wondrous works for me.

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And some people felt very little frustration:

I experienced almost no frustration learning to use the Mac. It was consistentlydelightful.

I'm sure there were times when I was frustrated because something wasn't happeningthe way it was supposed to. I stayed calm and tried to reason out the mistake. Was Ihitting the wrong key(s), or in the wrong sequence? I was usually able to figure it outby myself and that triumph was motivation enough to continue the learning process.

There were many lengthy descriptions of learning activities, a few involving

organized classes, but many describing hours (and hours) of reading the manual

and experimenting.

I have had a variety of learning experiences on personal computers. First, a familymember and an employee at my husband's office spent time with me. That was onone of the very early computers, which was replaced shortly with a more complex and"better" system! I never learned the "ins and outs" of one system, so each changewas very confusing. I would try to build on prior skills, but they were not applicable anymore. Since the office staff tried to keep me out of their hair, they usually completelyset the computer up as a typewriter whenever I had the need, and negated any needfor me to perfect skills. Then I tried learning from the instructional manual over at theoffice on weekends when no one was around, but I soon lost interest when the socialaspect was missing!

The whole area of computers became terribly frustrating and confusing forme. My earliest successes came from a combination of watching someone else dosomething for me; then I would try it and use them for problem-solving. But mygreatest success, particularly as relates to building confidence, has come from takingthe beginning class in personal computers. The learning disk that we were assignedto practice with was extremely helpful. Even though at times it seemed terribly basic,it allowed me to read and then practice skills at my own pace.

These disk tutorials seemed to be helpful for some people, but the manuals

continued to be frustrating:

I first took the Apple Tour with the disk. Then I practiced like they suggested. I readeverything they said to read. The manuals are not user friendly, the vocabulary isunfamiliar. I finally quit reading the manuals, although I was sure the answer was inthere somewhere!

Several people commented on the time they spent:

I bought the computer, took it home, uncrated it from the box, plugged it in, startedreading the manual, and stayed up all night several nights dealing with things that Inow wish that I had asked someone simply to show me.

Once I had Visicalc, I simply began to read the manual, practice examples and rereadthe manual. I stayed up all night many times and remember laughing and shouting outloud many times. I had nobody to rely on in any way, but the manual was thorough.

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The comment "stayed up all night till I worked it out" is particularly appropriate. I alsodo a lot of thinking about possible solutions. (I call it meandering). I'll muddle aproblem around in my head for a while before picking up a pencil and attempting asolution.

Others learned only what they needed at the time:

What I do is limp along using what I already know and then only learn the next stepwhen it becomes clear that mastering it will be more efficient for me than doingsomething the long way.

For many, self-directed learning was the most effective strategy, especially

for this concrete and active learner:

I find that almost everything I know about the computer, and I'm by no means anexpert, I learned on my own. I learn best when it's just the computer, the program, themanual, and me. Having someone show me does not have the lasting effect thatexploration does.

Stimulation was an important factor in Wlodkowski's model. When asked

what factors or activities kept them stimulated through the learning process, the

responses were primarily related to success, joy of learning, accomplishment of

real tasks, more efficiency and being able to help others.

Well, it is a constant struggle for me. I just cannot seem to learn from a book.Someone has to sit down and show me for me to learn. I get very confused because Ican't seem to get a good handle on the terminology and what it all means. But THIS iswhat keeps me stimulated. The darn thing is so much smarter than me. And when Ido master something, it makes my life so much (I mean work) easier, I can neverbelieve I lived without knowing it before!

Practical projects and specific goals were important.

My projects are real projects, something I need to do, and I want it to take the shortestamount of time possible. If the computer can save me time, it is the right tool for me.Having a specific goal and accomplishing it has been vital to my success.

The Holy Grail of Utility. A feeling I had that learning the next thing would make memore productive and efficient.

Success was an extremely important factor.

Successfully completing projects and discovering the amount of saved time reallyspurred me on. I realize the more I learn the more I can do and the more there is tolearn.

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The things that kept me motivated during the learning process included the high rateof success I had when trying something new, with visible results on the screen and onpaper as I printed. This is directly attributable to the power and ease of use of theMacintosh interface. It was also just plain fun to use the Mac.

The primary factor that motivated me to continue learning skills was success! Everytime something worked out for me, I was encouraged to keep trying. Every time Ifigured something out for myself, I was willing to tackle something harder. . . .Helpingsomeone else successfully has a very positive effect on my motivation for furtherlearning.

Many people commented on how helping others was very important to their

own learning process:

Just the simple success of being able to be of help to someone with a computer wasall I needed to encourage me to keep learning and trying.

Teaching others to use computers has also been good as I observe how they learn,listen to the information they share with me, help them through the tough times I havealready experienced, make mistakes with them, and am a friend and fellow computerperson as we learn together.

I think the reason I have continued to learn about the computer is because of thesense of power I still get when I use a program either for fun or work. I also loveputting that same power in the hands of others who are just beginning their journeyinto the world of personal computers.

For some, the computer itself was stimulation enough.

It was the technology itself, the raw power I sensed that kept me stimulated. Itspotential was all that I needed to remain amazed.

It's hard to specify factors that kept me stimulated through the learning process. Likesome cigarette smokers, I was addicted with my first keystroke. If I had to list onefactor, it would be the excitement generated by the incredible potential of thepersonal computer. I am referring to the potential, not of number crunching, but ofopening up minds to more of their power.

There were quite a few suggestions for learning resources, such as good

books, magazines, manuals, or individuals who had been helpful in the learning

process. Quite a few of the participants recommended the assistance of friends or

specific short courses that they had taken. Only a few recognized the helpfulness

of computer dealers. One of the respondents highly recommended the assistance

received on CompuServe, an electronic information service. Several respondents

complained about the resources available to learn the MS DOS operating system.

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I am prejudiced toward the Macintosh because it's easy to use and turns out a verynice product. I think initial short courses or workshops have been very helpful inspecific programs.

I only know how to operate programs. I know nothing about the MS DOS operatingsystem except that it is essential to operate my programs. I think I would like to take aclass to gain a basic understanding of this system but the classes are expensive. Iftime would allow, I might be able to get it through the manuals but it would takesoooooooooooo long.

In summary, the most powerful motivators for learning were success, access

to sufficient learning resources, practical applications, and sufficient time for

learning.

Transition 3 : At the End of the Learning Process (Mastery)

Short-TermMemory

Lewin/KolbExperiential Learning

Model

Gagn�'sHierarchy of Learning

Wlodkowski'sTime Continuum Model

of Motivation

Consciousness & Competence

Model

InformationProcessing

Model

Long-TermMemory

ConsciousCompetence

UnconsciousCompetence

Mastery Competence Reinforcement

RuleApplying

ProblemSolving

Testing new implications ofconcepts in new situations

Developing Automaticity& Intuition

ActiveExperimentation

Figure 26. Transition #3 - At the end of the learning process

The integrated learning theories shown in Figure 24, illustrate that during

this transition stage the learner becomes more comfortable with the specific skills

or knowledge, and begins to test the implications of these concepts in new

situations. The skills are becoming intuitive and automatic. The learner is able to

understand the logic or rules and is better able to respond to the variable needs of

the situation. At this point, the learner becomes more comfortable with problem

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solving when the computer does not seem to follow the rules. Competence and

reinforcement Ñthe success factor at this stage of the learning processÑ is the

motivation and enthusiasm to continue learning new skills. The links with long-

term memory have been forged through meaningful patterns and associations, and

the learner is able to retrieve knowledge or skill as needed. At the level of

unconscious competence, the skills become automatic, and the computer user

often has a difficult time putting these skills and procedures back into words. Most

adults are more comfortable working at this level.

The end of a learning project could be defined as the accomplishment of

specific learning goals, although most computer users feel that there will always be

something more to learn as long as they live. The self-directed learner should

focus on reinforcement activities that contribute to increased competence in using a

particular application. One strategy is practice and repetition of the skill until a

minimum comfort level is reached and the skill becomes automatic. This is the

stage at which there is decreasing dependence on those "cheat sheets" that the

learner used in the previous stage. At this point, new applications can be added or

variations developed from actively experimenting with the program.

Students should now work independently and reinforce the skills they have

been taught in order to gain confidence and competence. Organized instruction

should focus on applying rules in different situations and solving problems. The

most important role for the instructor at this stage is to provide resources for further

learning and insight into different strategies for problem-solving. Much of this time

should be spent on projects that are meaningful for the student, giving them the

competence and reinforcement to continue learning more about the computer.

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To gather supporting testimony for the last transition stage just presented, I

labeled this section of the questionnaire "End of the learning process," using the

terminology from Wlodkowski's last stage. Many of the respondents took issue with

that term, stating that they were nowhere near the end of their task of learning to

use their computer. Many of them recognized that the learning process would

never end.

In describing the activities that were used to reinforce learning, the

predominant response was repetition and practice, again with practical

applications. Some people reinforced their learning with practical applications:

I try to incorporate what I learn into either my professional or personal life.

I haven't needed any activities to reinforce my learning beyond regular use of thesystem. I'm still delighted when I master a new maneuver with the computer.

I view it as a tool, which I can use. [I] felt good to be able to use it, but primaryreinforcement was time saved and quality of product.

Once again, several people mentioned teaching someone else as

reinforcement for their own learning:

To reinforce my learning, I need to practice the skills fairly soon after learning them.For instance, I would try to put time in the same day of class, to reinforce what I hadlearned. I learn much better from practicing skills, in comparison to listening todirections or reading directions from a manual. I also reinforce my learning if I canshow someone else what I have learned, or if I can help someone with the learningprocess. Another positive learning factor is if I can relate the learning to practicalapplication.

With the Mac, when you figure out how to do something, you have an immediateintrinsic reward. You feel that you have increased your power to use this powerfultool, so you keep doing what you have learned.

Finally, learners gained skills in "learning how to learn" their computer, with

time as an important factor:

I have discovered that once I learn a type of software I can transfer that knowledge toother software packages that [gain] the same skill. Computer knowledge is alsotransferable from computer to computer and the more I learn the easier it is to transferand use other new computers and different software packages.

Mostly just spending time with my computer and figuring out things with trial and error.

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Time and patience are the key words in learning about a computer.

Some people actually find excuses to learn something new:

I continue to work with new pieces of software, to try to find better ways to accomplishtasks I set for myself. I look at materials that are not related to computers at all in anattempt to apply them to [my] computer. I find specific goals the most motivating. Itend to either set the goals for myself, or let someone else set deadlines which I willthen meet.

Following is an excellent suggestion for learning how to manage the

complexity of mastering new programs or trying to computerize new tasks:

I usually apply familiar programs to new tasks, new programs to familiar tasks.

When asked to describe the feelings they had when they achieved a certain

level of competence in using a personal computer, there were many feelings of

satisfaction expressed, including pride, awe, enjoyment, accomplishment, and

empowerment:

I have always had faith that I would eventually get things worked out with thecomputer. Knowing what a transformation the dedicated word processor made in mylife, I've always approached computer learning with the attitude that it's going to bewell worth the trouble. I still get excited when I work something new on the computerfor the first time. The results are immediate and very reinforcing.

My thoughts were those of some ego gratification through competence andachieving. The feelings are those of confidence, excitement, and some pleasure.The pleasure is that others often see me as computer literate and ask my advice abouttheir situations.

This concrete and active user expressed the feeling of empowerment that

came from the process of discovery learning:

I feel smart when people ask me to help them on the computer. I'm basically a visuallyoriented person (why I prefer the Mac) and the computers I work with are generallytext oriented, not one of my strengths. When I reach a certain level of competency Itry and apply my new knowledge to something totally incongruent to see if possiblyI've discovered something. There's an element of discovery that appears every time Iboot my computer and [this] is one of the driving forces behind my involvement withthe technology. Sometimes I'll turn on the computer to work on one project, begin bydoing something else just for a moment, and never find my way to the original project.I get carried away by the speed of the technology and the ability to do something Ican't do in any other manner.

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The respondents gave many examples of specific incidents that they

considered significant to their success, including problems as well as successes.

Some of those incidents involved being recognized for specific expertise, asked to

teach others, and involvement with specific tasks. One participant indicated that

her use of a laptop computer was a significant breakthrough, allowing her to work

comfortably but efficiently away from her desk and then transfer the material she

wrote over to her desktop computer. Others mentioned specific incidents:

Calling an experience "significant" is purely subjective; let me relate one of those "ah-ha" moments people who like Macs can relate to. In the middle of the night when Iwas working on the first Apple grant I wrote, I wanted to footnote references toenhance the credibility of my proposal. Fooling around with the menu bar, I saw the"footnote" command and tried it, only to discover that it not only worked, but it workedlike a charm. It numbered the footnotes, re-numbering every time I added a new one,and automatically split the screen to allow me to insert the reference. That was power!I didn't really have to "learn" anything, I just had to be willing to experiment to see howthe command worked.

The incidents most significant to my success include my initial choice of the Mac as apersonal computer, and my active use of the resources available on CompuServeInformation Services.

Others mentioned more general results from their learning which kept them

motivated:

Being able to learn quickly, comprehend what I learned, then put it into usesuccessfully.

Learning quicker than others is very motivating to me and drives me to continuelearning more.

In summary, this last stage of the learning process is achieving the learning

goals set in the first stage, feeling competent to continue learning independently,

and being able to actively experiment with this knowledge and skill in new

situations.

One essential factor in learning to use a personal computer is the

environment, both the organizational context, as well as the time period in which

the change takes place. When the personal computer was introduced to the

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general public, those early adopters had to deal with extreme frustration because

of the lack of documentation, less intuitive user interfaces, intermittent hardware

failures, and lack of a peer group. On the other hand, some early adopters became

local experts, and provided assistance to others. They found that as teachers they

often learned more than their students. There was the pioneer effectÑthe

determination to become competent in a new field. The primary motivation was the

intellectual challenge for a few of the participants. For others, learning to use a

computer facilitated other life tasks, such as (for one participant) a divorce, and for

many, career change or advancement.

From these findings, it appears that there are a variety of variables that

contribute to the acquisition of personal computer competency. These variables

include individual factors within the learner (cognitive, conative or affective

characteristics as identified by Corno & Snow). Socioeconomic factors (and

support from home and family) also contribute to variables related to motivation, the

learning climate, and the the time available for learning. The environment for

learning is related to support variables, such as learning climate and the access to

resources, and process variables, such as time, sequence, and strategies used in

learning. The "state of the art" in the computing environment, and the underlying

operating system, has a major impact on the quantity and type of computer

applications learned. The chart in Figure 27 identifies some of the relationships

that may exist between these variables, the environmental factors, and the outcome

of the learning process, which is personal computer competency.

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IndividualLearner

Socio-EconomicStatus(home & family support)

LearningEnvironment

ComputingEnvironment

Aptitude VariablesIntelligence (Cognative)

Learning Style (Conative)Personality Characteristics &

Motivation (Affective)

Support VariablesLearning climate

Access to resoutrcesOpportunity for learning

Process VariablesSequence of events

StrategiesTime Available for Learning

Content/Context VariablesApplication/Need/Task

User Interface

Outcomes

PersonalComputer

Competency

Figure 27 . Variables that Impact on Acquiring Personal ComputerCompetency

General Questions

In addition to questions regarding the stages of the learning process, I asked

several questions related to mental models or concepts that people found useful or

problematic in their learning. There were also questions related to advice for both

new users as well as writers of software manuals. The final questions dealt with

some personal changes and meta-learning that occurred as a result of the learning

process.

Metaphors, Models and Concepts

When asked what metaphors or mental models, if any, were formed about

the computer that helped with the learning process, a few participants suggested

models that may be useful to beginners (file cabinet, desktop, understanding

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through the use of icons). Several people mentioned that the lack of a metaphor or

familiar terminology made the learning process more difficult. One person even

used a metaphor as a recommendation for learning: "You have to be a 'velcro'

person not a 'teflon' person Ñ from Stan Pogrow (U of AZ)." Only one person drew

a little cartoon. Some people drew parallels with their own experience:

I think of a computer as an unruly kid. You want to focus the brilliance without killingthe kid. I also see computers as the ultimate challenge. If you can really understand it,really control it, you've proven your ability beyond a doubt.

I like the concept that AppleWorks uses for explaining some of the terminology usedin some programs. I literally pictured a file on my desk, files in the file cabinet andusing the keystrokes to move them from one place to the other. Gaining anunderstanding of the terminology used in word processing and in data entry hasmade me unafraid to tackle other programs of a similar nature but produced bydifferent companies. I have also lost my fear of computers.

Others relied on visual metaphors, some of which are provided by the

computer:

I definitely establish a general knowledge structure in my head while working with thecomputer. Everything is visual to me.

I think of the Mac as a way to engage my whole mind because I can use both myhands, can use pictures as well as words, and can experiment. I began as manypeople do, writing on paper first and then taking it to the computer. Now, I draftdirectly on the computer. This has had the effect of making me think more clearly; Ialso usually do not refer to the first draft when I write the second, and so on; in thefinal drafts, I do edit rather than totally re-write.

The desktop metaphor inherent [in] the Mac has been very useful, although I wouldnot be surprised if there were other graphic metaphors of equivalent or greater powerand usefulness.

Some found the lack of a mental model troublesome:

Hardest concept(s) to develop was a mental model (concepts, schemata) of how thesoftware system was organized and the development of a mental picture of what youdo, how you "branch" and why, the sequence of functions and subfunctions and therelationships between functions and commands. I don't think I ever mastered a goodunderstanding of the CPU. Certainly, I don't spend any time working with it!

As mentioned above, one of the concepts that was particularly difficult for

some people was understanding how the computer worked and understanding the

terminology. Some other difficult concepts included: relationships between cells in

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spreadsheets, computer languages, desk accessories (on the Macintosh), and MS-

DOS commands. Several people thought that the graphical interface was easier to

grasp than the text interface:

I found MS-DOS difficult to grasp. In comparison, the graphic interface is far easier.

You asked about difficulty in grasping a new idea Ñ I decided early on that I wanted atool to use rather than one to understand. I drive my car without knowing exactly howthe transmission works, and yet it gets me where I want to go. I have not had anydifficulty because I have not taken on the area of computer programming, which didnot serve any purpose I could foresee. With the advent of a computer like theMacintosh, the idea has been that people come first; and that people use tools, toolsthey don't necessarily need to understand. I like that.

Recommendations for Practice

Participants were asked to give some advice to new users for selecting a

computer system. An almost identical comment from at least eight of the

respondents was to "buy a Macintosh." Several suggested:

1. Decide what you will be using it for. Know what you want to do.2. Have friends or support help available.

The first advice would be to take your time and talk with others about the jobs youwant a computer and its software to do. Talk with others about how they decided.Generally, I think it best to decide on the functions first and then purchase hardwarewith perhaps 150% of the power necessary for what you think will be needed. Thismust include knowing the software that will be necessary to perform the functions andits hardware requirements.

My advice to beginners selecting a computer system would be to get all theinformation you can get from the most reliable sources. People with the bestintentions will try to sell you on what they know best, but a trained technician or aninstructor can better match your needs with your abilities.

When selecting a computer system, go with one that others you are workingaround/with are using. The support system is critical, particularly if you are not anindependent user. Also, pick a system that offers potential for growth.

Try to use it before you buy it Ñ ask others for information

Their advice for new users of personal computers about selecting software:

In general, plan for the future to the extent possible. Buy more capability than youneed now, if you can afford it. Find out from user groups what actually works best forpeople with similar needs.

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Be sensitive to the amount of learning you'll be required to do. Generally programsthat have steep learning curves (Word Perfect, Pagemaker) are better later in theacquisition process. In the beginning, use the KIS concept (Keep It Simple).

The more different methods you make available to yourself, the more likely you are tofind one that works well. Avoid using only one approach. If it fails, you are in trouble ifyou haven't thought ahead about a backup. Also, be aware that frustration is boundto occur, and occur often. Think of it as helping you learn, as yield signals, ifnecessary.

Selecting software depends on how you want to use your computer. Get softwarefrom good reliable companies - software that you can make as many copies as youlike. It is very frustrating to have to return a program disk to a company in order forthem to send you another original.

Here is some advice for new users of personal computers about

approaching the learning process:

Get a good teacher

I would advise approaching the learning process through taking a course. The schoolof hard knocks has hard knocks.

When I inherited this computer, I also inherited a large variety of disks from everyonewho had upgraded beyond this system. It is very confusing to me! Would that I hadonly one or two disks to worry about. This is certainly a case of "least is best"! You canadd to the system anytime. I'd rather learn a little well and then upgrade my skills andcomputer at the same time.

I would definitely recommend that beginners take a computer class such as this one. Iwish I had done it that way, rather than all the fooling around I did on early computers.It was clearly a case of "a little information is dangerous," and, I might add, veryconfusing.

Shop carefully. Take your time. Do the tutorials. Take a class. Do not let the manualsupset you.

Commit the time - learn as you go - you can get started without knowing everything.

If I had to give a new user of personal computers any advice it would be to marry acomputer professional. That may not be practical in the general case, but it sureworks.

Approaching the learning process: Join a user group and/or find a knowledgeablefriend to help you through the process, if you anticipate difficulty.

Be excited and eager - will help through difficult learning experiences.

1. It helps to have a little instruction - to get over the fear of using a computer.2. Get a book you can read, understand, and apply.3. Make friends with someone who knows computers.4. Stick with it - it eventually becomes old hat - it makes sense and subsequentlearning becomes faster and easier.

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The use of computer manuals was the topic of one question on the General

Questionnaire, as well as in the Optional Additional Questions. Many suggestions

were made to write in normal, everyday English, emphasize clarity, simplicity,

functionality and use a visual format. Some responses recommended color

coding, having a novice to write or test the manual (formative evaluation). Here is

some more specific advice for writers of software manuals:

I would enjoin writers of software manuals to submit their work to the "beta testing" ofnaive users, and respond to their concerns, before publishing the final product.

Better indexing, better indexing, better indexing. More pictures, more pictures, morepictures.

If I were to make any suggestions for the writers of instructional manuals, it would be toexplain vocabulary in the margin of the text when the computer terms are used. It isannoying to flip back to a vocabulary list every time you don't understand a term. Itseems as if certain words or terms could be in a contrasting color, indicating that theexplanation is in the margin. That certainly would have eased some of my frustration.It would also be helpful to have a kind of summary, or review, at the end of eachsection, helping to consolidate information and bring it into a frame of reference.

Having written several software manuals myself, I would ask that writers consult withusers during the writing process if possible. First time authors should refer to manualsthat are considered successful and not try to reinvent the wheel unless they arewilling to invest in the time and money required to field test the materials. This isparticularly true of material that is intended for use by non-computer people. I find itvery valuable to hand a manual to a neophyte and just watch them work with thesoftware and manual. It is often insulting, but you can really see where you've makebig mistakes.

Take Malcolm Knowles' advice about how adults learn. Have more well-done pictures.Collaborate with someone who has difficulty with technical stuff. Technical writerswho know this stuff make a great many assumptions about the users' understandingthat are often not warranted. Working with someone who finds it difficult will enablethem to explain it for the true layman.

The advice I'd give software manual writers would be to sit down with a novice andwrite the manual as the person learns the program. Write down all the instructions asyou give them and answer all the questions the user asks. Don't write the manual as ifthe person using it is an expert or can read your mind.

Self-Reflection

One question asked about how learning a personal computer has changed

the respondent's life. Several people commented upon the change made in their

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professional lives, and several people said that they now earned their living

through the computer.

Certainly increased my marketability in the job market. Provided an arena where Icould be a leader.

The computer has become a substantial element in my life. I have new friendships Iwouldn't have had without the Mac, I've earned more money (consulting) than I wouldhave without it, my life is better organized as a result of using it, I'm more productivewhen I write, and I have more fun, and more different kinds of fun, than I did beforethe Mac.

Other people noted significant internal changes:

I am more willing to admit ignorance.

Learning to use a personal computer has changed my life. It's opened up a new areaof entertainment, proved to save me time in business dealings, is an excellentenhancer and tool in the classroom and created in myself a knowledge that I have aplace in the future.

Still others wrote about their newfound productivity and efficiency:

The main thing the computer has done is taken the dread out of typing and writingand actually made it a pleasure.

It has made my business more efficient, it makes writing more fun as well as ofimproved quality. As an addiction, it is less damaging than most.

Others spoke of enhanced communication skills:

It really has changed my life. I find it far easier to write, to express myself in writing, andthat makes me far more effective in my work. It has given me confidence about myability to get things done, to effect a change, both from the direct experience oflearning to use the computer and seeing those results, and from the results of beingable to prepare documents which express my ideas clearly in a timely fashion, withoutundue effort. I am not an organized thinker or writer, as you may tell from my answersto these questions. Without the computer, the amount of scotch tape and littlepieces of paper and crossed out writing on any manuscript was most discouraging. Ithas also helped me to tolerate frustration without becoming overly discouraged.

I also have found the computer a tool that helps me express myself. I love to talk buttalk is very temporary. The computer is retrievable and my ideas and thoughts can bereread, shared, or changed with the stroke of a key.

For some people, the computer has opened up new worlds of learning:

Now I can type. Now I can get information from people and places thousands of milesaway. I have a window into the world of knowledge.

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I'm not sure that the effort I put into learning changed my life, but the resultantknowledge sure did.

For others, the computer helps overcome the tendency toward

procrastination:

Learning a personal computer has changed my life in a variety of ways. For one, I canno longer "duck" the responsibility of getting on with all the projects I said I was goingto computerize. I also am somewhat less intimidated by the computer. I find I caninsert the right disk and find what I want with minimal effort. That in itself is prettyrewarding!

My writing skills have improved tenfold because with the computer I spend more timeat it. The speed of the keyboard motivates me to write more that I ever did before. Iactually still dislike writing, but without the computer I never would have sat down andanswered these questions for you.

If there was one word to encapsulate the life changes that the respondents

experienced as a result of learning to use a personal computer, it would be

empowerment. These people found the personal computer to be a tool that could

greatly enhance their intellectual and professional lives, providing new models for

learning, working, and communicating.

The participants were then asked to discuss what else they learned about

themselves as they learned to use a personal computer. Some of their comments

about the learning process could be interpreted as an "internal change of

consciousness" (Brookfield, 1986) on the part of some of the respondents:

I have come a long way and could not exist comfortably in this world of technologywithout computers. They are the tool that has enriched my life, heightened my self-concept, and allowed me to communicate more freely and frequently with the peoplewho matter the most in my life.

That I can sit still and do something for hours on end.

That I am stubbornly insistent at learning things that are useful to me even when thelearning is difficult and frustrating.

Computers have given me a different perspective on myself. It is the first time in myadult life that I have been the "resident expert" on something other than stoves andfloor wax. It is a nice feeling.

It's given me confidence to attempt things which before I might have steered awayfrom because of a fear of the unknown.

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Quite a few people commented that one thing they learned about

themselves was that they really enjoyed learning, that they were "not ready for the

pasture yet." Others reflected on changes in their thinking:

As I have gotten older, my thinking processes have become much more logical; Ithink this is, in part, certainly due to my computer use.

I love the ability of computers to help me organize my life and time. I enjoy being ableto start with notes and end up with a finished product.

Others have learned a lot about how they learn:

What else have I learned about myself? I learned unfamiliar tasks slower than a lot ofpeople. I am 53-years old. I don't think I have a bad memory; I think I have a selectivememory. But I am terribly committed to the process, and I think that learning computerskills just requires a different kind of memory than I am used to relying on. I love tolearn new skills.

I have learned that my communication skills and learning skills are not perfect but theyhave improved since my old college days.

Past learning and concepts sometimes interferes with present learning.

Things I learned about myself: I have learned more about my writing style; I'velearned that I can organize my thoughts without hard copy; I've learned that I like totake a chance; I've learned that I can grasp concepts that relate to business andindustry and relate them to my work in education; I've learned that the computer is inmy control instead of the opposite; I've learned that children learn faster than adults inmost cases because they make fewer assumptions.

Others have overcome previous impediments to their learning:

I've learned that I can write. As a high school and college student I was often told that Icould not write, that my aptitude in math and science was high but that my ability towrite was so poor that I would avoid it at all costs. It was only my use of [a] wordprocessor with a built-in spelling checker that make me willing to commit words toscreen. My knowledge about personal computers and education have provided mewith an audience to talk to, and word processors with spelling checkers have mademe willing to make the attempt to communicate.

If I could summarize the "meta-learning" that is reflected in these responses,

it would be that the process of learning the computer has provided these people

with more insight into themselves as learners, with greater confidence in their own

abilities as learners. Not only has the personal computer become a useful tool for

learning, working and problem-solving, for some people, it is also a powerful tool

for self-reflection.

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The 31 participants who responded to the optional additional questions

enriched this study by providing insight into the process of learning a personal

computer. A few of them wrote short comments directly on the question sheet.

Some of them wrote many pages on their experiences, such as the delightful story

of the teacher in a rural Alaskan village and his self-directed discovery of the

computer he found by accident in his school (and didn't know what it was for

several months); or the housewife who became the resident expert in her school

district, teaching keyboarding to children and desktop publishing to adults,

designing curriculum guides, and making presentations at national conferences;

or the parenting skills trainer whose family gave her a used computer for Christmas

with the response, "You DO want to learn this, don't you?" Each of the 31

respondents had a different experience, most of them were primarily self-taught

and many of their answers reflected much more thought and insight than could

have been accomplished in an in-depth interview.

Summary

Conclusions and Implications

The process of learning to use a personal computer is primarily a

constructivist, discovery learning process; that is, learners must construct in their

own minds the knowledge and skills involved in using the computer for themselves.

There appears to be no practical alternative to the discovery learning process. In

addition, personal computers are becoming indispensable tools which often

empower learners to achieve results which were unanticipated when they

originally decided to use a computer.

I think it is important that the intellectual tools that adults use are compatible

with both the task they have to do and with individual thinking and learning styles.

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One dimension of style not addressed directly in this study was the issue of right

brain or left brain dominance. It would be interesting to replicate this study with

another style instrument to assess the impact of thinking styles and brain

dominance on the acquisition of personal computer competence. I think there may

be computer operating systems that fit better with certain thinking styles. There are

two ways that human beings represent concepts: symbolically, with numbers and

words, and graphically, through pictures and graphs. Recent research has shown

that each type of representation is processed in a different part of the brain. The

text user interface may fit with the left-brained thinking style, which is step-by-step,

analytical, using words and numbers. The graphical user interface may fit with the

right-brained thinking style: wholistic, graphic, multi-options.

A major focus of this study has been the impact of the graphical and text user

interfaces on the learning process. There were many comments in the qualitative

data that further supported the benefits of the Macintosh's user interface for the

ease of use and efficiency in learning. On balance, and from my experience

teaching adults to use both types of computer operating systems, I am not

convinced that the graphical user interface is for everyone, regardless of the

advertising "hype" that comes from Apple. Some people need a step-by-step

approach to using a computer (press this key, type this command, follow these

rules). The Macintosh user interface relies more on a conceptual process. The

learner needs to understand conceptually how the graphical user interface works

before they can really become productive. There are a whole variety of choices to

make, a whole series of "short cuts" in using clicks of the mouse that are not

explicitly stated and which, if performed by accident, can lead novice users into

more confusion. The fact that there are four different ways to eject a disk from the

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disk drive, rather than just one, provides almost too many choices for some people

who are learning to use their first computer system.

This should not imply that the Mac is harder to learn in the long run. I

believe that the initial learning curve is a little steeper on the Macintosh. There are

many special skills that a beginner needs to learn that seem to have nothing to do

with the task at hand: moving the mouse around; pulling down menus and

selecting items, especially now that we have nested menus (another menu that

opens when a selection is made); how windows workÑthe fact that they can

overlapÑand clicking the mouse in another window which might be hiding behind

it, bringing it to the foreground, etc.

Gagn� (1962) pointed out that following a procedure is a much less complex

skill than forming a concept, which Macintosh users need to do from the beginning

of the learning process. Novices with the graphical user interface (GUI) have more

initial difficulties in stimulus recognition and knowing what response to generate:

with the GUI there is much to look at on the screen and so many choices to make.

With MS-DOS, the traditional "C:>" prompt is usually the only thing showing on the

screen once the computer boots, and a simple "cookbook" approach can be

developed with exact keystroke commands.

One of the first contacts that the GUI learner has with that type of system is

the mouse or the pointing device that is used to manipulate objects on the screen.

Learning to manipulate the mouse is often an early frustration in learning to use the

GUI: when to click once, when to double click, what happens when you click in the

wrong place, how to correct mistakes. Learning to use the mouse is as much a

physical as a cognitive learning activity, requiring more practice than memorization.

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Fortunately, there are many resources which come with the computer to practice

these skills.

Although that initial learning curve can be more confusing for the GUI than

the text user interface (TUI), once the learner gets through that initial learning

curve, becomes comfortable with the mouse, and learns where to find the

commands on the menu bar, the subsequent learning activities become much

easier. This is especially important for the majority of computer users who will

never go beyond the competent stage, based on the number of applications that

they eventually use. As shown in Table 40 the competent graphical users (level 3)

of the sample used significantly more applications than the text users. I think that

this is a direct result of the ease of use inherent in the GUI, which allows more

exploratory learning once a learner has passed through the initial confusion stage.

I often tell my classes, "Once you learn your first Macintosh application, you have

learned 50%-60% of everything you need to know to learn every other Macintosh

application." As learners move past the competent phases, toward the level of

expert, the difference in the number of applications becomes less significant.

Learners gain the tools and competencies necessary to progress in any specific

learning environment. They have effectively learned how to learn to use their

computer system, regardless of the type of interface.

Therefore, it is important for new learners of graphical interfaces to

recognize that their learning strategies will need to change, to adapt to this new

way of interacting with a computer. This will be especially true of those people who

are competent at using the traditional text user interface. As IBM develops OS/2

and MicroSoft perfects Windows Version 3, and we see graphical user interfaces

becoming more common in the DOS world, there are different learning strategies

that will need to be employed. Some type of translation between the two different

207

paradigms would be useful: drawing parallels between tasks on the text system

and those same tasks on the graphical system (applying transfer theory or "velcro"

learning). Perhaps a side-by-side comparison of such a system would be useful. It

would be even more useful to set up situations where experienced learners could

develop their own structures to compare tasks on the familiar TUI with those on the

new GUI.

The same comparison strategies could be applied in learning to use a new

applications software package. For example, there are many tasks that can be

accomplished with a word processing program. However, most programs (at least

outside of the Macintosh world) use different keystrokes or methods to accomplish

these tasks. One useful method of learning might be to take skills already known,

and then compare these skills with the new program to be learned.

Appendix C includes examples of aids that could be useful for self-directed

learning about a personal computer. Appendix C includes a more detailed list of

competencies or tasks that can be accomplished with different generic types of

software packages. These competencies were derived, in part, from IBM's Guide

for Learning published in 1982 which provided suggested lesson plans for

introducing new learners to personal computer applications and to BASIC

programming. These same strategies could provide competent computer users

with additional tools for learning to use new software programs within the same

generic application they already know (i.e., a new word processing program or a

new spreadsheet).

I am not suggesting that no one should attend organized computer classes.

My data show that classes are a part of the learning process, and adult learning

theory as well as my own experience confirms the fact that when learning

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something brand new, a class is the most efficient way to get started. Without an

instructor (and at the level of unconscious incompetence), we often don't even

know what questions to ask. In addition, organized instruction provides those

opportunities for "teachable moments" (Havinghurst, 1948) that can enrich the

learning process. However, within the context of the entire learning process,

organized courses are a small percentage of the time spent in learning to use a

personal computer. Many novices can attest to the problem of having a "teachable

moment" without appropriate assistance available.

Computer manufacturers and software developers need to improve the

support materials that come with their systems. My data reveal that support

materials are important (manuals, books and magazines, self-paced disk-based

tutorials). However, users do not use software manuals all the time when they

learn. This reluctance to learn from printed materials may be related to preferred

learning style. However, I believe much of this resistance to using the printed word

for learning to use a personal computer is the manual's illogical organization (at

least for the person's learning style), or the lack of a good index. Context-sensitive

"Help" files are also important, but are usually so large that they require hard disk

storage.

One of the problems related to learning in a class environment is the

diversity of the group. The ideal course is a small group or one-on-one training,

which in organizational settings is neither practical nor cost-effective. However, the

training does not need to be lengthy. Perhaps an organization could have one

individual who worked close to new learners and could provide support on an as-

needed basis, not for a long time, but at a critical time in the learning process. In a

school, it could be a teacher who could provide suggestions to colleagues at

strategic times throughout the school year. In a business organization, there could

209

be an in-house "guru" who could provide suggestions for improvements to a

particular report. The problem of on-going support is critical in small businesses,

since large organizations can afford to provide the assistance of these people

through data processing support or some other MIS function.

The Michigan Department of Education (1990), in its report to the legislature,

stressed that training was the most critical factor to successful use of technology in

schools.

Learning to use the computer is much like learning to play the piano. You need tohave an instrument available, quality instruction, and time to practice. Teacherinteraction is essential to any training effort. Teachers who use computers more oftenand with a richer variety of software have higher skills and more positive attitudes.Practice, feedback, and coaching are essential even for highly motivated teachers.(p.4)

Much published discussion in the computer press focuses on the difficulty of

having regular employees who, on top of their regular responsibilities, must

provide computer assistance to their colleagues. Employees who provide on-site

assistance should be rewarded either with additional compensation, reduction in

regular responsibilities, or recognition. This reward could be an opportunity for

more in-depth learning, such as paid attendance at a computer conference or

technical workshop. For example, sending a few teachers to an educational

computing conference may have a greater impact on a school's overall level of

computer literacy than many other incentives, especially if these teachers are

expected to share what they learn with other members of the staff. A school district

that encourages staff members with other types of incentives, such as access to

some of the latest technology or a small budget for new software, can expect to see

indirect results of those teacher benefits in increased use of computers with

students.

210

Do children use computers more often because teachers have computers on

their desks? If we want students to use computers, then we need to have the

teachers become more comfortable in using computers. Following is a short

discussion of the process of learning computers, especially in education, from an

individual who has witnessed the whole development of integrating computers into

schools. Sally Sloan's (1990, p.3) comments seem most appropriate in light of the

results of this research project.

We seem to have come full circleÑone of many, I hope. In the early days, learning touse a terminal connected to a mini or mainframe was perceived as difficult. People didnot really comprehend what they were doing and the software was often ponderousto use. However, the limitations were so severe that the learning curve was, in reality,quite low. As the hardware and software became more powerful, that learning curvetook a sharp turn upward. Enter the early micros that were so limited as to make themseem "easy" for most people. Now we again see an upward turn of the learning curveas the micros gain in power, and the software becomes yet again more powerful andmore complex. We have immense power, barely dreamt of just a few years ago, yetwe suffer a major inhibitor to its use in schools. In my opinion, the culprit is not justlack of funds but lack of time. Teachers and students simply do not have the time inthe current structure of education to really learn to use those tools that couldultimately save them time and frustration. I am like a chain smoker when it comes tousing my computer; yet I too am frustrated by the lack of quiet time to learn moreabout the powerful applications I use regularly. . . . Perhaps the key is having anurgent need to learn. Mother necessity and all that. Perhaps with the massivechanges to the structure of schools currently in the wind, thoughtful educators cancause some time to be built in for staff and students to learn to make maximum use ofthese wonderful devices.

Conclusions and Recommendations

Recommendations for learning based on principles of self-directed learning

and motivation are:

¥ Self-directed learning is the primary method employed by adults learning

to use personal computers.

¥ There appears to be no substitute for the discovery learning, i.e., the

inductive experiential approach to learning.

211

¥ Computer users are more self-directed in their learning than the average

population.

¥ Intrinsic motivation leads to higher levels of personal computer

competency.

Recommendations based on findings related to learning style are:

¥ Learners with a reflective learning style will need to stretch their style to

adopt a more active approach to learning if they want to gain personal

computer competency. The most successful learning strategies are

those that are consistent with an active learning style (hands-on

experimenting, turning on the computer and just experimenting).

¥ An active learning style contributes to increased personal computer

competency.

¥ An abstract learning style contributes to increased personal computer

competency.

¥ Learning how to use a personal computer is an active, hands-on

experiential process

Recommendations based on findings related to personal computer

operating system interface are:

¥ Learning more than one type of computer operating system leads to higher

level of personal computer competency

¥ People who have experience with both types of user interfaces prefer the

graphical user interface.

212

¥ A graphical user interface facilitates the development of a concrete mental

model of how the computer works.

¥ A graphical user interface is more motivating because of the higher

potential for success.

¥ There is more variability in learning strategies based on computer

interface than on learning style.

Suggestions for learners are:

¥ Find an urgent reason to learn the computer through meaningful, practical

tasks.

¥ Find ways to support self-directed learning efforts through access to a

variety of learning resources.

¥ Allow sufficient, uninterrupted time for learning.

¥ Phase in the learning process. Don't try to learn everything at once.

¥ Self-directed learning does not mean learning in isolation: join a user

group or learn with a colleague.

¥ Learn how to de-code manuals.

¥ Organized courses should focus on learning how to learn and skills that

logically or intuitively can be transferred.

¥ Teach someone else. The teacher often learns more than the student.

¥ Learners with a reflective learning style will need to become more active

learners.

213

¥ The most successful learning strategies are those that are consistent with

an active learning style (hands-on practice, turning on the computer

and just experimenting).

¥ Apply familiar programs to new tasks, new programs to familiar tasks.

¥ If possible, select a computer and/or software that conforms to a more

intuitive user interface.

¥ A graphical user interface is much easier to learn in the long run but may

be more difficult to learn in the beginning, because learning to use a

GUI-WIMP (Graphical User Interface-Windows/Icons/Mouse/Pointer)

is more conceptual than procedural.

¥ Provide learners in lower socioeconomic status with access to computers

in long enough blocks of time to increase their computer competency.

Suggestions for the personal computer industry are:

¥ Computer manufacturers and software publishers should develop more

intuitive graphical user interfaces, recognizing that many people are

visual learners.

¥ Provide context-sensitive "help" files.

¥ Writers of manuals need to better test their materials on computer novices.

¥ Manuals should be well indexed and logically organized.

¥ Diagrams and screen displays in manuals are useful.

214

¥ Pay attention to how adults learn. Focus on process and context, not

learning isolated commands.

Recommendations for Future Research

This study should be replicated with a less highly-educated group, in

professions other than education. Educators may have different learning styles

and readiness for self-directed learning than the average population. A study of

computer users who are do not have bachelors degrees, for example, may provide

a different set suggestions or different strategies than illustrated by the participants

in this study.

There are many impediments to learning different types of systems, and

these should be researched thoroughly to provide more efficient and effective

learning strategies. As suggested by this study, learning strategies vary depending

on the type of computer system being learned. More research is needed to identify

these impediments and the strategies that work for a variety of learners.

This study should be replicated with another instrument to assess the impact

of right/left brain thinking styles on the acquisition of personal computer

competency. The preference for right/left brain thinking styles may provide further

insight into successful learning strategies. Right/left brain preference instruments

have been developed by Ned Herman, Katharine Benziger and Bernice McCarthy.

Further research needs to be conducted to determine the most appropriate

assessment instrument which will provide a different perspective on the impact of

cognitive styles on computer learning strategies.

This study should be replicated with an instrument that measures the impact

of learners' locus of control on the acquisition of personal computer competency.

215

Locus of control is the perception of control that a person has over a particular

situation (Kay, 1990). If a person feels powerless relating to technology, that would

be an external locus of control. If, however, a person felt that they could learn to

control the computer, that would be an internal locus of control.

Presumably, individuals who have an internal locus of control with respect tocomputers will make more of an effort to learn about computers than individuals whohave an external locus of control, simply because "internals" believe that their effortswill not be fruitless. (Kay, 1990, p.464)

An empirical study could be conducted, observing learners with different

learning styles approaching identical computer learning tasks. Learning style is

related to the activities learners prefer at different stages of the learning process.

Close observation of these learning activities may provide more insight into

strategies that are more effective based on an individual's learning style.

Another empirical study could be conducted, observing learners with

identical learning styles, learning different computer operating system interfaces.

By controlling for different learning styles, a study of learning strategies under

different operating systems could provide further insight into effective strategies that

support the selection of type of user interface. These observations could be

videotaped for in-depth analysis.

Other "style" instruments could be substituted for the Kolb LSI, such as the

Myers-Briggs Personality Type Indicator (Myers, 1962), or the Canfield Learning

Style Inventory (Canfield, 1974). The Myers-Briggs is one of the most popular

psychological inventories, and analysis of data based on Introvert/Extrovert,

Sensory/Intuitive, Thinker/Feeler and Perceptive/Judging would contribute further

knowledge about the impact of different personality types on the acquisition of

personal computer competency. Another study using the Canfield Learning Style

216

instrument is currently being conducted with farmers (Jerold Apps, personal

communication, June 9, 1990).

The open-ended questions in the Optional Additional Questions provided

rich insights into the learning process. These questions could be posed to more

people in a structured interview format. In an interview format, more information

can be gained through more probing questions, and more impromptu discussions

can be pursued.

The proposed integrated learning model could be tested further in an

experimental design to provide insight into its applicability to non-computer-related

learning. Through observations and in-depth interviews at different stages of the

learning process, the integrated learning model could be further refined and

examined for application beyond the personal computer content area.

New computer learners could be asked to keep a journal of their learning

experiences, and the journals could be analyzed for differences in learning styles

and strategies. There are many early learning experiences that are suppressed or

forgotten after the initial stages of the learning process. Learners could keep a

structured journal at different levels: at the cognitive level, or the

concepts/procedures that were easy/difficult to grasp; at the affective level, or the

feelings experienced; and at the behavioral level, or the actual strategies tried.

Final Comments

It has been my opinion that through the process of learning to use a

personal computer, adult learners can gain a better understanding of their own

learning processes. For some people, the process may awaken a spark or

217

capacity for independent learning that may have been unrealized. Perhaps the

process of learning to use a personal computer has the potential to enhance our

self directed learning skills as well as our self-esteem and confidence in our own

abilities as lifelong learners.

In the future, personal computers and interactive multimedia will provide a

whole new environment for self-directed learning, not just for learning about the

technology, but as a process to explore new bodies of knowledge. A computer

providing access to vast storehouses of visual as well as textual data, will be the

catalyst for a major change in adult, self-directed learning.

218

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