Personality and cognitive differences between online and

PERSONALITY AND COG, IT DIFFERENCES 3ETWEEN ONLINE AND CONVENTIONAL UNIVERSITY STUDENTS

by

Karel J. Stanz

Thesis

Submitted in fulfilment of the requirements for the degree

DOCTOR OF PHILOSOPHIA

LEADERSHIP IN PERFORMANCE AND CHANGE

in the

FACULTY OF MANAGEMENT

at the

UNIVERSITY OF

JOHANNESBURG

Promoter: Prof Christa Fouche

JANUARY 2005

STATEMENT

I certify that the dissertation submitted by me far the degree of DOCTOR of

PHILOSOPIA (Leadership in Performance and Change) at the University of

Johannesburg has not been submitted by me for a degree at another

faculty/university.

Karel J Stanz

DECEMBER 2004

ii

ACKNOWLEDGEMENTS

I would like to express my sincere gratitude and appreciation to the following

persons who greatly contributed to the successful completion of my studies:

Prof Christa Fouche, my promoter, for her skilful guidance. Her commitment,

professionalism, focus, optimism and sense of humour as well as her dedication

were a permanent source of encouragement, especially in the last two years from

New Zealand.

My deepest appreciation goes to my fellow students who became friends and

colleagues, allowed me to assist them (30 Doctoral and 22 Master's candidates) to

complete their studies and made this thesis a rich learning experience. Most of

them offered me support, encouragement, and were interested in this project.

Prof Johan Schepers, who supported, encouraged, showed interest and helped.

What a privilege to sit in the office next door to him!

Deo Strumpfer, former colleague and friend, for providing the initial inspiration for

my studies.

Ms Riette Eiselen, Head of Statcon (RAU), for effectively conducting the statistical

analyses.

My employer, RAU and the Department of Human Resource Management, for

supporting my studies. A special word of thanks to Prof Jos Coetzee!

To Charles Lutwidge Dodgson, alias Lewis Carroll, for the very appropriate

comments of his characters in Alice's Adventures in Wonderland (1865) and

Through the Looking Glass (1872).

Marietjie, my wife, for believing in me. Thank you for being there while I was

completing these chapters of my life. Madri, Carli, Danien "Cool, nou is daai boek-

ding klaar"!

Most important, my gratitude to God for His grace, companionship and

faithful guidance throughout the most difficult of times.

Karel J Stanz

December 2004

iii

I WANT TO DEDICATE THIS RESEARCH TO

MY FRIEND...

MY LOVE..

MY LIFE...

MY WIFE MARIETJIE

I KNOW I AM NOT THE BEST

BUT I TRY HARDER...

iv

ABSTRACT

PERSONALITY AND COGNITIVE DIFFERENCES BETWEEN ONLINE UNIVERSITY

STUDENTS AND CONVENTIONAL STUDENTS

By

Karel J Stanz

PROMOTER: Prof Christa Fouclgie

DEPARTMENT: Department of Human Resource Management

Faculty of Management

University of Johannesburg

DEGREE: D. Phil

DATE: January 2005

Background

The advances in information technologies have created an array of possibilities

for today's learners in institutions of higher education. Kaye (1989) predicted that

online education would ultimately emerge as a new educational paradigm, taking

its place alongside conventional (face to face) education as well as distance

education, and even changing the face of education in general.

Although online education is becoming a common component of higher

education, Wang & Newlin, (2000) confirm that relatively little is known about the

characteristics of learners who choose to enroll for courses in an online learning

environment. Schlosser and Anderson (1997) published a report entitled

Distance education: Review of the literature in which they did not cite a single

study on the characteristics of online learners.

v

What seem to remain unanswered out of the literature are the questions:

Who are the students who undertake and succeed in online learning? Are these

students different from students who take and succeed in traditional, face-to-face

classes?

The answers to these questions are critical for the future of higher education.

Literature Research

The primary objective of a literature oversew is to create a theoretical frame of

reference for the concept of online education. The two secondary objectives of

the literature overview are:

to discuss the role of online education in Higher Education. Such a

discussion will provide a background for understanding online education and,

specifically, distance education and how it impacts on higher education. A brief

overview of the history of distance education from the correspondence phase to

the current use of computer-mediated communication will be outlined. Also

briefly reviewed will be the theories underlying distance education, focusing on

those that have an impact on online education.

to review the research on distance- and conventional education.

Currently, research on distance education is relatively narrow and many studies

highlight a need for research to be conducted in the various areas of online

education (Russell, 2002; Charp, 1999). Merisotis and Olsen confirm this view

by concluding "...while a plethora of literature on the distance education

phenomenon is available, original research on distance education is limited."

(2000, p. 62).

Empirical Research Objective

The primary objective was to determine whether there are:

vi

Differences between online university students and conventional university

students as a function of personality factors;


students as a function of cognitive factors, and


students as a function of biographical factors.

The following secondary objectives were formulated:

Personality Differences

To determine personality differences for the two groups in respect of

(a) personality factors, (b) personality types, (c) locus of control, and

(d) interest.

Cognitive Differences

To determine cognitive differences for the two groups in respect of (a)

aptitude, (b) previous academic performance at school, (c)

matriculation achievement, (d) first-semester academic performance at

university, and (e) academic performance on the HRM course.

Biographical Differences

To determine biographical differences for the two groups in respect of

(a) gender, (b) age, (c) language, and (d) computer literacy.

Participants

The sample from which the primary and secondary data were obtained consisted

of first-year students at a large University in South Africa. The study population

consisted of first-year students enrolled for a compulsory Business Science

course, tested in 2001. Based on self-selection, 242 students voluntarily made

use of the online course while 323 students used the conventional course

offered. The ages of the students varied from 18 to 21 years, 91% of them 18

vii

years and younger. As far as gender was concerned, 51,9% were female; and

69,1% preferred English as the language of instruction.

The Measuring Instrument

In order to identify the personality and cognitive differences between online and

conventional students, the following measuring instruments were selected for use

in the current study:

Personality Differences

The 16 Personality Factor Questionnaire (16PF),

Jung's Personality Types ,

The Locus of Control Inventory (LCI), and

The 19 Field Interest Inventory (19 FII).

Cognitive Differences

The Senior Aptitude Tests (SAT),

Academic Performance at School,

General Average Matriculation Achievement,

The First Semester Academic Performance, and

Academic Performance on the HRM Course.

Biographical Questionnaire

Gender, Age, Computer Literacy, and Language.

The Research Procedure

The prescribed battery of psychometric tests was administered to the full intake

of first-year university students by the Career Counselling Division during their

first month at the university. Testing was compulsory for all first-year students

and took place over four days under strict supervision. A course was designed

for conventional classes supplemented with an online version of the same course

and students were allowed to choose freely to enrol either in online or in

•

viii

conventional sections of the course. Performances of the students in the first

semester as well as during the course being presented were collected as primary

data.

Statistical analysis

The primary and secondary data sets were subject to one-way multivariate

analyses of variance (MANOVAs using Hotellings' T2), followed by students' t-

tests. Estimated effect sizes were also calculated using coefficient eta. In order to

test hypotheses relating to biographical differences, cross tabulations were

calculated using the chi-square test. Cramer's V was also calculated as an index

of the strength of the association between • the biographical variables. All

calculations were done by means of the SPSS- Windows program of SPSS -

International. The analysis was conducted with the assistance of a Statistical

Consultation Service.

Conclusions and Recommendations

Very little empirical research has been conducted, certainly in the South African

context, but also internationally, in assessing differences between online and

conventional students. It is, however, reasonable to conclude that there is

insufficient evidence to support the expectation that there are significant

personality and cognitive differences between online and conventional students.

This is supported by studies done by Schlosser & Anderson (1997) and Moore

and Kearsley (1996). What makes any course good or poor is a consequence of

how well it is designed, delivered, and conducted, not whether the students are

face-to-face or at a distance (Moore & Kearsley, 1996).

This study focussed on one of the burning 'people' issues in South Africa, and

contributed to a better understanding of the kind of person who takes online

learning by assessing personality and cognitive differences between online and

conventional students. Insight into personality and cognitive differences enables

the effective management thereof, which in turn contributes to the success of

ix

educational institutions, by providing a framework for institutions of higher

education to understand, manage and facilitate online and conventional students.

Aided by this study, educators and course designers will be able to match the

needs and expectations of their online students more effectively. This will ensure

that, from a pedagogical perspective, the design of a flexible learning

environment within a technology-rich medium is not hampered by a lack of

understanding of the needs of learners. This information will allow institutions of

higher learning to increase the overall satisfaction of the learner in the online

environment. Lastly, it will make a contribution to ensuring that course design

does not become technology driven but, rather, allows technology to serve as a

resource in support of student needs.

Within the framework of this study the following suggestions for potential

research opportunities are made:

A comparative analysis should be carried out between students from different

South African Universities, from different faculties and registered for different

courses to give generalised findings.

Further research should include a comparison between students within the

context of South African higher education and other institutions that might

provide online education.

Given the findings of this study, there is still a large amount of effect size to be

explained. Individual characteristics such as learning styles and commitment

could be included.

"...and what is the use of a book," thought Alice, "without pictures or

conversation?

xi

TABLE OF CONTENTS

STATEMENT II

ACKNOWLEDGEMENTS III

ABSTRACT IV

TABLE OF CONTENTS XI

LIST OF TABLES XV

LIST OF FIGURES XVIII

CHAPTER 1: 2

PROBLEM STATEMENT, PURPOSE AND METHOD 2

1.1 INTRODUCTION 2

1. 1.1 Background to the problem 2

1.2 PROBLEM STATEMENT 7

1.3 PURPOSE OF STUDY 8

1.4 OBJECTIVES OF RESEARCH 8

1.4.1 Literature Review 9

1.4.2 Empirical Research Objectives 10

1.5 RESEARCH HYPOTHESES 1 1

1.5.1 Personality Differences 12

1.5.2 Cognitive Differences 1 2

1.5.3 Biographical Differences 13

I.6 SIGNIFICANCE OF THE STUDY 14

1.6.1 Theoretical Significance. 15

1.6.2 Methodological Significance 15

1.6.3 Practical Significance 15

1.7 NATURE OF THE STUDY 16

ix

1.8 DELIMITATIONS 18

1.9 LIMITATIONS 19

1.10 DEFINITION OF KEY TERMS USED 20

1.11 CHAPTER LAYOUT 23

1.12 CONCLUSION 25

CHAPTER 2: 28

LITERATURE RESEARCH 28

2.1 INTRODUCTION 28

2.2 THE ROLE OF ONLINE EDUCATION IN HIGHER EDUCATION 29

2.2.1 Distance Education 29

2.2.2 Distance Education vs Online Education 38

2.2.3 Online education compared to conventional classroom education 42

2.3 RESEARCH ON DISTANCE EDUCATION AND CONVENTIONAL EDUCATION 43

2.3.1 Course completion and dropout rate 44

2.3.2 Student outcomes, such as grades and test scores; 48

2.3.3 Attitudes and perceptions about learning through distance education 54

2.3.4 Who undertakes online courses? 55

2.3.5 Summary of literature findings 62

2.4 CONCLUSION 63

CHAPTER 3: 66

RESEARCH METHODOLOGY AND PROCEDURES 66

3.1 INTRODUCTION 66

3.2 RESEARCH HYPOTHESES 68

3. 2.1 Personality Differences 68

3. 2.2 Cognitive Differences 70


3.3 RESEARCH DESIGN 75

3.3.1 Quantitative Research vs Qualitative Research 77

3.3.2 Classifying the Research Design 79

3.3.3 Secondary data versus primary data 81

3.3.4 Choice of Research Design 82

3.4 SAMPLE 83

3.4.1 Sample Statistics 83

X

3.4.2 Descriptive Statistics for the Two Groups (Online and Conventional Students) 86

3.5 THE MEASUREMENT INSTRUMENTS 90

3.5.1 Personality Measures 91

3.5.2 Cognitive ability measures 93

3.5.3 Biographical Information 95

3.6 RESEARCH PROCESS 95

3.7 PROCEDURE OF DATA COLLECTION 96

3.8 STATISTICAL ANALYSES APPLIED [N THE RESEARCH 97

3.9 CONCLUSION 98

CHAPTER 4: 100

RESEARCH RESULTS 100

4.1 INTRODUCTION 100

4.2 STATISTICAL ANALYSIS 101

4.2.1 Differences in means between the two groups with regard to objective 1: Personality

Differences 102

4.2.2 Differences in means between the two groups with respect to objective 2: Cognitive

factors 118

4.2.3 Differences in means between the two groups with respect to objective 3" Biographical

Differences 135

4.3 SUMMARY OF MAIN FINDINGS 141

4.3.1 Personality Differences 142

4.3.2 Cognitive Differences 143


4.4 CONCLUSION 145

CHAPTER 5: 147

DISCUSSION OF RESULTS, CONCLUSION AND RELATED RECOMMENDATIONS 147

5.1 INTRODUCTION 147

5.2 PRESENTATION OF THIS RESEARCH 148

5.3 A SUMMARY OF METHODOLOGY 149

5.3.1 The Research Participants 149

5.3.2 The Measuring Instruments 150

5.3.3 The Research Procedure 151

5.3.4 Statistical Analysis 151

xi

5.4 DISCUSSION OF FINDINGS 152

5.4.1 Literature Research Objectives 152

5.4.2 Empirical Research Objectives 155

5.5 SIGNIFICANCE OF THE STUDY 164

5.5.1 Theoretical Significance 164

5.5.2 Methodological Significance 165

5.5.3 Practical Significance 165

5.6 THE MAIN CONTRIBUTION OF THE STUDY 166

5.6.1 Theoretical Value 166

5.6.2 Methodological Value 167

5.6.3 Practical Value 169

5.7 LIMITATIONS OF STUDY 169

5.7.1 Delimitations 170

5.7.2 Limitations 170

5.8 RECOMMENDATIONS 171

5.8.1 Recommendations from a Theoretical Perspective 172

5.8.2 Recommendations from a Methodological Perspective 172

5.8.3 Recommendations from a Practical Perspective /73

5.9 SUGGESTIONS FOR POTENTIAL RESEARCH OPPORTUNITIES 174

5.10 CONCLUSION 174

xii

LIST OF TABLES

TABLE 3. 1 AGE GROUP DISTRIBUTIONS FOR THE OBTAINED SAMPLE 84

TABLE 3.2 GENDER DISTRIBUTIONS FOR THE OBTAINED SAMPLE 84

TABLE 3.3 PREFERRED LANGUAGE FOR THE OBTAINED SAMPLE 85

TABLE 3.4 HOME LANGUAGE FOR THE OBTAINED SAMPLE 85

TABLE 3. 5 CROSS TABULATION : AGE GROUP FOR

ONLINE AND CONVENTIONAL STUDENTS 87

TABLE 3.6 GENDER CROSS-TABULATION FOR ONLINE AND CONVENTIONAL STUDENTS 87

TABLE 3.7 PREFERRED LANGUAGE CROSS-TABULATION FOR ONLINE AND

CONVENTIONAL STUDENTS 88

TABLE 3.8 HOME LANGUAGE CROSS-TABULATION FOR

ONLINE AND CONVENTIONAL STUDENTS 89

TABLE 3.9 CALCULATION OF MATRICULATION SCORES 94

TABLE 4.1 MULTIVARIATE TESTS OF SIGNIFICANCE FOR THE 16PF 103

TABLE 4.2 DESCRIPTIVE STATISTICS FOR THE I6PF 104

TABLE 4.3 T-TEST: INDEPENDENT COMPARISONS OF THE MEAN DIFFERENCE SCORES ON

THE I6PF 105

TABLE 4.4 MULTIVARIATE TESTS OF SIGNIFICANCE FOR THE JPT 107

TABLE 4.5 DESCRIPTIVE STATISTICS FOR THE JPT 108


THE JPT 109

TABLE 4.7 MULTIVARIATE TESTS OF SIGNIFICANCE FOR THE LCI 110

TABLE 4.8 DESCRIPTIVE STATISTICS FOR THE LCI 111

TABLE 4.9 T-TEST: INDEPENDENT COMPARISONS OF THE MEAN DIFFERENCE SCORES

ON LCI 112

TABLE 4.10 MULTIVARIATE TESTS OF SIGNIFICANCE FOR THE 19FII 113

TABLE 4.11 DESCRIPTIVE STATISTICS ON THE 19FII 115

TABLE 4.12T-TEST: INDEPENDENT COMPARISONS OF THE MEAN DIFFERENCE

SCORES ON THE 19FII 116

TABLE 4.13 MULTIVARIATE TESTS OF SIGNIFICANCE FOR THE SAT 119

TABLE 4.14 DESCRIPTIVE STATISTICS ON THE SAT 120


THE SAT 122

TABLE 4.16 MULTIVARIATE TESTS OF SIGNIFICANCE FOR ACADEMIC PERFORMANCE AT

SCHOOL 123

TABLE 4.17 DESCRIPTIVE STATISTICS ON ACADEMIC PERFORMANCE AT SCHOOL 124

TABLE 4.18T-TEST: INDEPENDENT COMPARISONS OF THE MEAN DIFFERENCE SCORES FOR

ACADEMIC PERFORMANCE AT SCHOOL 125

TABLE 4.19 DESCRIPTIVE STATISTICS ON GENERAL AVERAGE MATRICULATION

ACHIEVEMENT 127

TABLE 4.20T-TEST: INDEPENDENT COMPARISONS OF THE MEAN DIFFERENCE SCORES ON

GENERAL AVERAGE MATRICULATION ACHIEVEMENT 128

TABLE 4.21MULTIVARIATE TESTS OF SIGNIFICANCE FOR ACADEMIC PERFORMANCE IN THE

FIRST SEMESTER 129

TABLE 4.22 DESCRIPTIVE STATISTICS ON ACADEMIC PERFORMANCE IN THE FIRST

SEMESTER 130


ACADEMIC PERFORMANCE IN THE FIRST SEMESTER 132

TABLE 4.24 DESCRIPTIVE STATISTICS ON ACADEMIC PERFORMANCE ON THE HRM

COURSE 133


ACADEMIC PERFORMANCE ON THE HRM COURSE 134

TABLE 4.26 CROSS-TABULATION: DESCRIPTIVE STATISTICS ON GENDER 136

TABLE 4.27 CHI-SQUARE TEST: GENDER VS TYPE OF LEARNER

(ONLINE VS CONVENTIONAL) 136

TABLE 4.28 CROSSTABULATION: DESCRIPTIVE STATISTICS ON AGE 137

TABLE 4.29 CHI-SQUARE TEST: AGE GROUP VS TYPE OF LEARNER(ONLINE VS

CONVENTIONAL) 138

TABLE 4.30 CROSSTABULATION: DESCRIPTIVE STATISTICS

ON COMPUTER LITERACY 139

TABLE 4.31CHI-SQUARE TEST: COMPUTER LITERACY VS TYPE OF LEARNER (ONLINE VS

CONVENTIONAL) 139

TABLE 4.32 CROSS-TABULATION: DESCRIPTIVE STATISTICS ON PREFERRED LANGUAGE .140

TABLE 4.33CHI-SQUARE TEST: PREFERRED LANGUAGE VS TYPE OF LEARNER (ONLINE VS

CONVENTIONAL) 141

xiv

LIST OF FIGURES

FIG 1.1: RELATIONSHIP BETWEEN CHAPTERS 24

FIG 2.1 : CHAPTER 2 IN CONTEXT 28

FIG 2.2 : RELATIONSHIP BETWEEN THE THREE DOMAINS 42

FIG 3.1 : CHAPTER 3 IN CONTEXT 66

FIG 3.2 : THE REACH OBJECTIVES AND HYPOTHESES 67

FIG 3.3: RESEARCH PARADIGMS IN CONTEXT 766

FIG 3.4: STATISTICAL PROCESS FLOW CHART 98

FIG 4.1: CHAPTER 4 IN CONTEXT ... 1002

FIG 5.1: CHAPTER 5 IN CONTEXT 1003

XV

Chapter 1

Introduction

"Speak English!" said the Eaglet. "I don't know the meaning of half

these long words, and what's more, I don't believe you either!"

1

CHAPTER 1:

PROBLEM STATEMENT, PURPOSE AND METHOD

1.1 Introduction

The aim of this chapter is to serve as the introduction to this research and to

place the total investigation in context by providing a framework for the problem

being studied. A brief description of the subject of the study as well as the

research problem and specific questions generated by the problem are being

offered. The purpose, objectives and hypotheses are given as well as an

overview of the methodology (including e.g. design, the sampling strategy

selected, data gathering instruments and techniques of analysis). The value of

the research as well as the delimitations and limitations of the study are

discussed. Definitions of key concepts central to the study are also included.

1.1.1 Background to the problem

Advances in information technology have created an array of possibilities for

today's learners in institutions of higher education. Kaye (1989, p. 3) predicted

that online education would ultimately emerge as a new educational paradigm,

taking its place alongside conventional (face-to-face) education as well as

distance education, and even changing the face of education in general.

According to Van der Westhuizen (1999) these prophetic words are increasingly

reverberating through the halls of higher education institutions on the bandwagon

of online education, seemingly unstoppable since the introduction of the World

Wide Web (WWW) in 1993.

2

This impetus for institutional leaders to reassess their traditional methods of

educational delivery is highlighted by the statement by Samuel Smith, former

President of the Washington State University that: "We are in the midst of

another cultural and educational revolution that will shake our institutions to their

very foundations if we are not prepared for what lies ahead. The key to success

will be using new technologies to expand access to quality education and

enhance instruction" (Smith, 1999, p. 4).

This should be seen against the background of a worldwide and local increase in

the need for education and continuous education. This resulted in an explosion of

higher education institutions and programmes available throughout the world.

(Fehnel, 2002). According to Fehnel (2002) many countries, including South

Africa, have opened up their educational market places as a way of responding

to these growing pressures for access to higher education.

In reviewing the literature, it is clear that, to address this need, the use of online

learning is transforming the education industry and business educational

establishments (McFadzean, 2001b).

Horton (2000) confirms this by arguing that online education is part of the biggest

change in the way our species learns since the invention of the chalkboard, or

perhaps even of the alphabet. It is therefore not surprising that universities are

experiencing a huge demand for courses taught online and do not wish to be

swept aside by competitors in the commercial sector. According to Smith,

Ferguson and Caris (2001), providing online learning programmes for the large

number of learners who are unable to attend lectures on a daily basis is

becoming imperative, as the demand for such courses and the competition for

learners are huge. Hence institutions of higher education are putting pressure on

their faculties to provide opportunities for online learning (Smith, et al., 2001).

3

Smith et al. (2001) come to the conclusion that now is the time for tertiary

institutions in South Africa to harness the power of online learning. Follows

(1999) goes so far as to state that the online learning environment is the ideal

learning environment.

Many studies highlight the need for research to be conducted in the various

areas of online education (Russell, 2002 1; Charp, 1999). Past studies have

tended to compare outcomes in distance education with traditional face-to-face

courses (Schlosser and Anderson, 1994). Moore and Kearsley (1996) reviewed

distance education research over the previous 50 years and found no significant

differences between learning in the two environments.

1 Russell compiled a website of the significant difference phenomenon, citing more than 300

studies comparing distance education with conventional education.

4

Schlosser and Anderson's review of distance education research (1997)

indicates that learners learn equally well from courses presented through various

media and concluded that there is no inherent significant difference in the

educational effectiveness of the various media (Schlosser and Anderson, 1997).

These studies concluded that other factors might be more important than or

interact with the media in affecting educational outcomes for students. These

conclusions beg the question: "What other factors are pertinent?"

Studies examining the students themselves have been limited, and only largely

tentative conclusions can be drawn (Handson, Maushak, Schlosser, Anderson,

Sorenson and Simonson, 1997). Although online education is becoming a

common component of higher education, Wang and Newlin, (2000) confirm that

relatively little is known about the characteristics of learners who choose to enrol

for courses in an online learning environment. Schlosser and Anderson (1997)

published a report entitled Distance education: Review of the Literature in which

they did not cite a single study on the characteristics of online learners.

The questions that seem to remain unanswered out of the literature are:

Who are the students who undertake and succeed in online learning? Are

these students different from students who undertake and succeed in

traditional, face-to-face classes?

The answer to these questions is critical for the future of higher education.

Universities are prey to continual reductions in funding and are being forced to

contend with attracting learners in an increasingly competitive environment

(Littlejohn and Sclater, 1999; Fehnel, 2002). Online learning offers much promise

5

for attracting students to higher educational institutions in a modern and novel

way. This promise often comes with a high price tag. Although, according to

Horton (2000), the increasing cost of bricks and mortar is minimized, the virtual

classroom also carries costs in regular maintenance and upgrading of computer

hardware and infrastructure, and the development of instructional materials can

be very expensive. Should universities invest in online delivery systems?

According to Greene and Meek (1998), the amount of time spent by higher

educational professionals on technology and related issues will continue to

increase steadily. There is also an increasing need to raise awareness among

educators and course designers about the critical issues that impact on online

learning (Morgan, 1996). Educators and course designers will be able to match

the needs and expectations of their online students when they have answers to

the first of the two questions posed above, namely "Who are the students who

undertake and succeed in online courses?" According to Smith (1997), this will

ensure, from a pedagogical perspective, that the design of a flexible learning

environment within this technology-rich medium is not hampered by a lack of

understanding of the needs of learners. It will also ensure that course design

does not become technology-driven but allows technology to serve as a resource

in support of students' needs (Trapp, Hammond and Bray, 1996).

Various studies have shown that matching the teaching strategy to the needs of

students can contribute to more effective learning. (Rainey and Kolb, 1995; Hsu,

1996; Hartman, 1995; Galbraith, 1994). According to Galbraith (1994) by

recognising these unique characteristics, educators can utilise the information to

plan learning opportunities and strategies in such a way as to reach a more

diverse student population. This is confirmed by Westbrook (1999) who states

that such an approach will allow institutions of higher education to increase the

overall satisfaction of the adult learner in the online environment.

6

This study is therefore an attempt to investigate whether there are any

differences in personality and cognitive profiles between online and conventional

students.

1.2 Problem Statement

It has been shown in the previous section that online education is used in both

international and South African institutions of higher education. Many studies

indicate the need for research to be conducted in the various areas of online

education (Russell, 2002, Charp, 1999). There is also an increasing need to raise

the awareness of educators and course designers about critical issues

influencing online learning (Morgan 1996). As demands for lifelong learning

increase, so the demands on higher education to become more accessible and

more learner-driven will increase (Higher education's Role in the digital Age,

1999).

According to Mouton (2001), most of the methodological research has been

conducted in the United States. One obvious limitation, therefore, is the

applicability of these results to other contexts and especially to developing

countries. Methodological research in the area of online education has been

done in most countries, including some developing countries although its

relevance for current research practice is not obvious. It seems from the

literature that very little empirical research has been done in developing countries

and specifically in South Africa

However the question is not whether online education is an acceptable

alternative for teaching but who undertakes and succeeds in online courses.

7

Personality and cognitive differences between online and conventional students

in other countries may not be applicable in the South African context. Hence a

critical need exists to do research in this field in the South African context, within

which the research question is formulated as follows:

Are there personality and cognitive differences between online and

conventional students?

Emanating from the above problem statement, this study has the following

purpose:

1.3 Purpose of study

The purpose of this study is to provide information for the current deficit in

knowledge about possible differences between online university students and

conventional university students. This study is subdivided into a primary objective

and secondary objectives.

In the next section the research objectives for the study will be formulated.

1.4 Objectives of Research

The objective of this research can be visualized in terms of literary and empirical

objectives. Both the literary objective and the empirical objective, in turn, consist

of a primary objective and secondary objectives.

8

1.4.1 Literature Review

The literature review is divided into a primary research objective and secondary

research objectives.

1.4.1.1 Primary Objective of the Literature Review

The primary objective of the literature review is to create a theoretical frame of

reference for the concept of online education.

1.4.1.2 Secondary Objectives of the Literature Review

The secondary objectives of the literature review are:

1.4.1.2.1 To discuss the role of online education in Higher Education

This discussion will provide a background for understanding online education and

specifically distance education and how it impacts on higher education. A brief


the current use of computer-mediated communication will be outlined. Also

briefly reviewed will be the theories underlying distance education, focusing on

those impacting on online education.

9

1.4.1.2.2 To review the research on distance education and conventional

education


highlight the need for research to be conducted in the various areas of online

education (Russell, 2002; Charp, 1999). Merisotis and Olsen (2000, p. 42)

confirm this view by concluding "while a plethora of literature on the distance

education phenomenon is available, original research on distance education is

limited".

1.4.2 Empirical Research Objectives

The empirical research is also visualized in terms of a primary research objective

and secondary research objectives.

1.4.2.1 Primary Objective of the Empirical Research

The primary objective is to determine whether there are:

Differences between online university students and conventional

university students as a function of personality factors;


university students as a function of cognitive factors; and


university students as a function of biographical factors.

10

1.4.2.2 Secondary Objectives

The following secondary objectives are formulated:

1.4.2.2.1 Personality Differences

To determine personality differences for the two groups in respect of (a)

personality factors; (b) personality types; (c) locus of control; and (d) interest.

1.4.2.2.2 Cognitive Differences

To determine cognitive differences for the two groups in respect of (a) aptitude;

(b) previous academic performance at school; (c) matriculation achievement; (d)

first-semester academic performance at university; and (e) academic

performance on the HRM course.

1.4.2.2.3 Biographical Differences

To determine biographical differences for the two groups in respect of (a) gender;

(b) age; (c) language; and (d) computer literacy.

1.5 Research Hypotheses

The parameters of this study were formulated by hypotheses related to the

primary and secondary objectives. The detailed hypotheses will be outlined in

chapter 3.

11

1.5.1 Personality Differences

Four hypotheses are postulated relating to personality differences in the two

groups in respect of (a) the 16 Personality Factors Questionnaire (16PF); (b)

Jung's Personality Types' (JPT); (c) the Locus of Control Inventory (LCI); and (d)

the 19 Field Interest Inventory (19 FII).

Hypothesis, H1 1 : there is a statistically significant difference between the vectors

of means of the two groups in respect of the 16PF.

Hypothesis, H 2,: there is a statistically significant difference between the vectors

of means of the two groups in respect of the JPT.

Hypothesis, H3 1 : there is a statistically significant difference between the vectors

of means of the two groups in respect of the LCI.

Hypothesis, H4 ,: there is no statistically significant difference between the

vectors of means of the two groups in respect of the 19FII.

1.5.2 Cognitive Differences

Five hypotheses were postulated relating to cognitive differences in the two

groups in respect of (a) the Senior Aptitude Tests (SAT); (b) the academic

performance at school; (c) general average matriculation achievement (GAMA);

(d) first-semester academic performance at university; and (e) the academic

performance on the HRM Course.

12

Hypothesis, H 5,: there is a statistically significant difference between the vectors

of means of the two groups in respect of the SAT.

Hypothesis, H6,: there is a statistically significant difference between the vectors

of means of the two groups in respect of the academic performance at school.

Hypothesis, H7,:there is a statistically significant difference between the vectors

of means of the two groups in respect of GAMA (M-score).

Hypothesis, H8,: there is no statistically significant difference between the

vectors of means of the two groups in respect of academic performance in the

first semester.

Hypothesis, Hg,: there is no statistically significant difference between the

vectors of means of the two groups in respect of academic performance on the

HRM course.

1.5.3 Biographical Differences

Four hypotheses are postulated relating to biographical differences in the two

groups in respect of (a) gender, (b) age, (c) language, and (d) computer literacy.

Hypothesis H10 : there is a statistically significant association between gender

and online vs conventional students.

13

Hypothesis H11: there is a statistically significant association between age and

online vs conventional students.

Hypothesis H12: there is a statistically significant association between computer

literacy and online vs conventional students.

Hypotheses H13: there is a statistically significant association between preferred

language and online vs conventional students.

The a priori assumption of this study is that differences can be expected between

online and conventional students with respect to personality and cognitive

differences.

The next paragraph will highlight the significance of this study.

1.6 Significance of the study

In addressing the problem "Who undertakes and succeeds in online

courses?" the anticipated significance of this study will be threefold, namely

theoretical, practical and methodological. The current deficit in empirical data is

unfortunate because online learning and its inherent multimedia environment are

increasingly prevalent in the higher education environment. (Bentley, Appelt,

Busbach and Hinrichs (1997); Locatis and Wiesberg (1997); Porter (1997))

14

1.6.1 Theoretical Significance

The research will more comprehensively shed light on differences between


differences, and will serve as a benchmark and building blocks for future

research on the kind of person who undertakes online courses.

1.6.2 Methodological Significance

The methodological significance will support the value of quantitative methods in

assessing differences between online and conventional students with respect to

personality and cognitive differences. It ill furthermore provide a benchmark for

future research designs on differences between online and conventional courses.

1.6.3 Practical Significance

This study will contribute to one of the burning 'people' issues in South Africa. It

will lend support to the important role differences between online and

conventional students with respect to personality and cognitive differences plays

will provide a framework for institutions of higher education to understand

manage and facilitate differences between online and conventional students.

Educators and course designers will be able to match the needs and

expectations of their online students. This will ensure that, from a pedagogical

perspective, the design of a flexible learning environment within this technology-

rich medium is not hampered by a lack of understanding of the needs of learners.

(Smith,1997). This information will allow institutions of higher education to

increase the overall satisfaction of the adult learner in the online environment

15

(Westbrook, 1999). Lastly, this study will ensure that course design does not

become technology driven, but allows technology to serve as a resource in

support of the needs of students (Trapp, Hammond and Bray, 1996).

The next paragraph will briefly outline the nature of this study.

1.7 Nature of the study

Following is a brief outline of the research design, research process and

statistical procedures employed in this study. A more detailed discussion will

follow in Chapter 3.

1.7.1.1 Research Design

The study is quantitative in nature and aims to test hypotheses. The study

population consisted of first-year students enrolled for a compulsory business

science course at a large SA university. A non-probability sampling technique,

more specifically convenience sampling, was the method of sampling used in the

study. Based on self-selection, 242 students voluntarily made use of online

course while 323 students remained in the conventional class.

1.7.1.2 Research Process

With a view to reaching the objectives of the research, the process consisted of

the following steps:

16

Step 1: The prescribed battery of psychometric tests was

administered to the full intake of first-year university students by the

Career Counselling Division during their first month at the university.

Testing was compulsory for all first-year students and took place over

four days under strict supervision. The measuring instruments

selected for use in the current study will be discussed in detail in

Chapter 3. Performances of students in the first semester as well as

during the course being presented will be collected as primary data.

Step 2: A course was designed for conventional classes

supplemented with an online version of the same course and students

were allowed to freely choose to enrol in either online or conventional

sections of the course. Performances of the students in the first

semester as well as during the course being presented were collected.

Step 3: The data set was compiled consisting of all the data

collected in step one and two and were verified to ensure that it was

error free.

Step 4: The data were statistically analysed by the Statistical

Consultation Service (STATCON) with the SPSS programme. The aim

was to determine statistically the differences between the two groups

with respect to the stated hypotheses.

Step 5: The analysed information was interpreted and

recommendations were made for potential research opportunities.

17

1.7.1.3 Statistical Analyses


analyses of variance (MANOVAs using Hotellings's T 2), followed by Students' t-


test Hypotheses relating to biographical differences, cross tabulations were

calculated and the chi-square test was used. Cramer's V was also calculated as

an index of the strength of the association between the biographical variables.

All calculations were done by means of the SPSS- Windows programme of

SPSS - International. The analysis was conducted with the assistance of a

Statistical Consultation Service.

The next paragraph will set the foreseen limits of the study.

1.8 Delimitations

The following delimitations of the study were imposed:

Only students from one large South African university, one faculty and registered

for a compulsory first-year course were used. The sample was chosen because

the researcher was familiar with the online environment and was assisted by the

lectures presenting this specific course. Since a limited sample was used, the

results should be generalised to the population with caution.

The research focused on an online course in a specific South African higher

education context. Other institutions that might have provided online education

were not included or represented. The lecturer contracted for the presentation of

the course had taught and designed various other online courses.

18

The next paragraph will set the limitations of the study.

1.9 Limitations

The limitations of the study lay in its design, subjects and the nature of the online

course being presented. Each of the limitations will be elaborated on in the

following paragraphs.

The limitations in the design of this research were imposed by the quasi-

experimental nature of the study. Even if a random selection of students'

completed assignments had been possible, it would not have been possible to

separate those students who preferred online courses from those who preferred

conventional courses.

The second limitation relates to the subjects used for this research. Although a

relatively large sample of subjects was used, the course was compulsory. The

two student populations were not distinctly separate from each other. The online

students were a subset of the populations of students in the conventional face-to-

face environment. The relatively large sample did not allow control in terms of

limited interaction between the two groups. An additional concern was the rate of

participation of online students in the research. These could have biased the

results achieved in different unknown ways.

The third limitation relates to the nature of the online course. This was the first

time students were exposed to online education. The effectiveness of the

computer and software technology on a student's decision to opt for the online

course was also not included in this study.

19

For clarity of interpretation, the next paragraph will define relevant key concepts.

1.10 Definition of key terms used

It is acknowledged that a number of definitions for each of the following concepts

exists in the literature. However, there is a lack of agreement on the fundamental

definitions and because most definitions are determined by the purpose of the

author, the following concepts will be operationally defined and extensive

terminological discourse avoided. 2 (http://www.learningcircuits.orq/glossary.htm)

2 These definitions are available on the ASTD website and are used to promote consistency in

the online environment see http://www.learninqcircuits.orq/glossarv.htm

20

"Asynchronous learning: Learning in which interaction between instructors and

students occurs intermittently with a time delay. Examples are self-paced

courses taken via the Internet or CD-ROM, Q&A mentoring, online discussion

groups, and email.

Blended learning: Learning events that combine aspects of online and face-to-

face instruction.

Cyberspace: The nebulous "place" where humans interact over computer

networks; term coined by William Gibson in Neuromancer.

CAI (computer-assisted instruction): The use of a computer as a medium of

instruction for tutorial, drill and practice, simulation or games. CAI is used for both

initial and remedial training, and typically does not require that a computer be

connected to a network or provide links to learning resources outside of the

course.

Digital Divide: The gap that exists between those who can afford technology

and those who cannot.

E-learning (electronic learning): A term covering a wide set of applications

and processes such as Web-based learning, computer-based learning, virtual

classrooms and digital collaboration. It includes the delivery of content via

Internet, intranet/Extranet (LAN/WAN), audio- and videotape, satellite broadcast,

interactive TV, CD-ROM, and more.

Email (electronic mail): Messages sent from one computer user to another.

21

F2F (face-to-face): Term used to describe the traditional classroom

environment.

ILT (instructor-led training): Usually refers to traditional classroom training in

which an instructor teaches a course to a room of learners. The term is used

synonymously with on-site training and classroom training (c-learning).

Internet: An international network first used to connect education and research

networks, begun by the US government. The Internet now provides

communication and application services to an international base of businesses,

consumers, educational institutions, governments and research organizations.

Internet-based training: Training delivered primarily by TCP/IP network

technologies such as email, newsgroups, proprietary applications and so forth.

Although the term is often used synonymously with Web-based training, Internet-

based training is not necessarily delivered over the World Wide Web, and may

not use the HTTP and HTML technologies that make Web-based training

possible.

Synchronous learning: A real-time, instructor-led online learning event in which

all participants are logged on at the same time and communicate directly with

each other. In this virtual classroom setting, the instructor maintains control of

the class, with the ability to "call on" participants. In most platforms, students and

teachers can use a whiteboard to see work in progress and share knowledge.

Traditional classroom: The physical learning space where students and

instructors interact.

22

24/7: Twenty-four hours a day, seven days a week. In e-learning, used to

describe the hours of operation of a virtual classroom or how often technical

support should be available for online students and instructors.

Virtual classroom: The online learning space where students and instructors

interact.

WBT (Web-based training): Delivery of educational content via a Web browser

over the public Internet, a private intranet, or an extranet. Web-based training

often provides links to other learning resources such as references, email,

bulletin boards and discussion groups. WBT may also include a facilitator who

can provide course guidelines, manage discussion boards, deliver lectures, and

so forth. When used with a facilitator, WBT offers some advantages over

instructor-led training while also retaining the advantages of computer-based

training.

WWW (World Wide Web): A graphical hypertext-based Internet tool that

provides access to Web pages created by individuals, businesses and other

organizations". (http://www.learningcircuits.org/glossary.htm)

The next paragraph will outline the chapters of the study.

1.11 Chapter Layout

This dissertation consists of five chapters. Fig 1.1 depicts the relationship

between the various chapters. The same figure will be used at the beginning of

each chapter to indicate the role of the chapter in the context of the thesis. This

23

Chapter 2 Literature Researc

Chapter 5 Discussion and Conclusion

Chapter 4 Reporting of Empirical Results

Chapter 3 Research Design

ti

.......... • ..

Chapter 1 Introduction to the Research

thesis will conclude with a list of references and appendices. Each of the five

chapters will now be discussed briefly.

The first chapter serves as the introduction to this research and places the total

investigation in context by providing a framework for the problem being studied.

A brief description of the subject of the study as well as the research problem

and specific questions generated by the problem are given. The purpose,

objectives and hypotheses are given, as well as an overview of the methodology

(including e.g. design, the sampling strategy selected, data-gathering instruments

and techniques of analysis). The value of the research as well as the

delimitations and limitations of the study are discussed. Definitions of key

concepts central to the study are also included.

4..

i7(

Fig 1.1: Relationship Between Chapters

24

The second chapter is the literature research. The literature research maps out

the main issues in the field being studied. As such, an overview of previous

research on the topic and a summary of the status quo are also included.

The third chapter outlines the research methodology and procedures. The

research methodology is described comprehensively. The context in which and

purpose for which the collection of data took place, as well as the steps

according to which the data were gathered are clearly spelled out. This will be

followed by a detailed discussion on the descriptions of the participants, the

research design, the sampling plan, data collection procedures and measuring

instruments.

The fourth chapter outlines the results and includes the processing, analysis

and interpretation of the data in figures and tables.

The fifth chapter is entitled 'Conclusions and recommendations'. The review

of literature and the findings from the empirical methods are compared with each

other. In this chapter, all the conclusions and recommendations as well as further

interpretation and a summary of the study are presented. The discussion also

focuses on the future directions that research might take.

1.12 Conclusion

In this chapter a brief description of the subject of the study as well as the

research problem and specific questions generated by the problem are

presented. The purpose, objectives and hypotheses are given as well as an

overview of the methodology (including e.g. design, the sampling strategy

selected, data gathering instruments and techniques of analysis). The value of

25

the research as well as the delimitations and limitations of the study are

discussed. Definitions of key concepts central to the study are also included. In

the next chapter, Chapter two, based on a review of the literature the current

knowledge relating to online learning is discussed.

26

Chapter 2

LITERATURE RESEARCH

" What do you know about this business?" the King said to Alice.

"Nothing," said Alice.

"Nothing whatever?" persisted the King.

"Nothing whatever," said Alice.

"That's very important", the King said.

"Why," said the Dodo, "the best way to explain it is to do it."

"Curiouser and curiouser," cried Alice.

27


-4

Chapter 1 Introduction to the Study

/



CHAPTER 2:

LITERATURE RESEARCH

2.1 Introduction

The previous chapter presented a general background and orientation to the

study. The aim of this chapter is to review the literature relating to online

education. Figure 2.1 portrays the relationship of this chapter within the context

of this research. The literature review maps out the main issues in the field being

studied. As such, an overview of previous research on the topic and a summary

of the status quo are also included.

Chapter 2 Literature Research

✓ \

Fig 2.1 : CHAPTER 2 IN CONTEXT

28

Firstly an attempt will be made to create a theoretical frame of reference for the

concept of online education. It will necessitate the inclusion of the concept of

distance education because online education has, to a large extent, developed

from distance education.

An examination of the research on distance education and conventional

education follows. Finally, the need for assessing personality and cognitive

differences in online education is pointed out.

2.2 The role of online education in Higher Education

This section will provide a background for understanding online education, and

specifically distance education, and how it impacts on higher education. A brief


the current use of computer-mediated communication is provided. Also

addressed in this section are the theories underlying distance education,

focusing on those impacting on online education.

2.2.1 Distance Education

2.2.1.1 History of distance education

According to Richards (1992) the history of distance education includes various

methods of instruction starting with correspondence, home study, televised

courses, extension classes, video conferencing and online learning. What, then,

is distance education?

29

Distance education can be defined as any planned educational activity that

"takes place when a teacher and student are separated by physical distance, and

technology is used to bridge the instructional gap". (Willis, 1994) By 'technology'

in this context is meant audio, video data and print.

Ehrmann (1999) states that the first transformation in higher education started

when learners and scholars began to rely more and more on reading and writing

and less on oral exchange. By writing information down, teachers could reach

more students and the learner could access more teachers. The oral tradition of

lecturing was gradually replaced by reading and writing. Steward (1995) argues

that the printed word became the most obvious and dominant medium used to

transmit information, whatever the context.

Cantelon (1995) contends that by correspondence in higher education, distance

education can be traced back to the Chautauqua movement of the early

nineteenth century. The Chautauqua literary and scientific circle has enrolled at

least half a million readers and sponsored ten thousand reading articles

throughout the United States. According to Cantelon (1995) the Chautauqua

movement introduced learning by correspondence even before the School of

Technology. The Chautauqua University was responsible for the development of

correspondence courses (Chantau, 2000).

Cantelon's (1995) studies indicate that these early courses focused on the social

sciences in business and public administration and the humanities.

30

According to Richards (1992) the roots of distance education can be traced back

to at least the 1700s when advertisements were used to offer instruction by mail.

Holmberg (1986) quotes an advertisement in a Swedish newspaper (1833) that

offered instruction on composition. According to Schlosser and Anderson (1994),

Germany established correspondence study in the 1800s, while a Boston-based

programme was also emerging.

Ehrmann (1999) contends that the transition to the printed word as a means of

transmitting information was the first educational revolution and was followed by

the second revolution when students and scholars gathered together to share

facilities and resources. This 'campus revolution' (Ehrmann, 1999, p. 42) brought

significant changes to the learning process. Students become more a "mass of

learners waiting for experts to tell them what was important" (Ehrmann, 1999, p.

43). The need for teaching at a distance increased, as students could not afford

to move to campuses and gain access to the education provided there.

Increasingly more sophisticated technology was used to breach the instructional

gap. According to Schlosser and Anderson (1994) the first experimental

television teaching programmes were produced in the American Midwest in the

1930s. This phenomenon rapidly spread to various universities and colleges.

The proliferation was further facilitated by the emergence of satellite technology

in the 1960s, reaching a high in the 1980s with further cost-effective

improvements.

Steward (1995) describes variations in the use of television-based education.

The most basic variation used VHS tapes that allowed a synchronous delivery of

the classroom experience to students in the remote classroom. More

31

sophisticated variations are a single camera on the teacher, (multiple cameras)

with synchronous conference calls between classroom(s) electronically linked.

The emergence of computer-mediated education offered alternative ways of

connecting teachers and students. A new distance education frontier emerged.

Ehrmann (1999) describes this frontier as the third significant transformation of

education. The nature of this revolution involves the technologically altered

transmission of the printed word.

2.2.1.2 Distance Education Theories

According to Garrison (2000) theoretical enquiry is central to the vitality and

development of a field of practice because the theoretical foundations of a field

describe and inform the practice and provide the primary means to guide future

developments. Therefore theory is not limited to describing what is, but should

also help predict what will be or what could be.

Moore and Kearsley (1996, p. 197) define a theory as "a representation of

everything that we know about something". Expanding on this definition,

Garrison (2000, p. 3) argues that theory is a coherent and systematic ordering of

ideas, concepts and models with the purpose of constructing meaning to explain,

interpret and shape practice. This purpose is a particular challenge to distance

education as the technology and delivery methods have evolved rapidly in the

last decade of the 20 th century. To understand the challenges facing distance

education, it is essential to do a selected review of the most influential distance

education theories that also impact on online education.

32

Schlosser and Anderson's (1994) review of distance education research

indicates that distance education theory was largely undeveloped until the 1970s.

Since that time several theories have begun to emerge.

A more detailed description of these perspectives will be provided in the next

section.

2.2.1.2.1 Independent Study

The first influential theoretical contribution to distance education is that of pioneer

Charles Wedemeyer. Based on the philosophy of teaching and learning it

focused on independent study and learning. According to Garrison (2000) this

was not merely a change in terminology but also a shift from the correspondence

practice dominated by organisational and administrative concerns. The focus

clearly was on educational issues concerned with learning at a distance

(Wedemeyer 1971). Although the focus was on the individual rather than the

group, Wedemeyer (1971, p. 549) identifies characteristics and advantages of

independent learning based on "a democratic social idea!' of not denying anyone

the opportunity to learn. Garrison (2000, p. 5) argues that "consistent with the

principles of equity and access, independent study was also related to self-

directed learning and self regulation".

Wedemeyer (1971) made a distinction between teaching and learning tasks and

identified characteristics such as communication, pacing, convenience and self-

determination of goals and activities.

33

Wedemeyer's work is surprisingly relevant to the new era of distance education,

and is of particular importance to this study. He was an advocate of freedom of

choice for the learner, criticising reluctance to individualise and personalise

independent study courses. In this regard Wedemeyer questioned the "seeming

rigidity of the format and materials apparently deterring teachers and students

from more completely exercising their respective options". (Wedemeyer 1971, p.

551).

In fact it designated a new era in the development of distance education. It

helped to shape the structure of many distance education universities all over the

world. His "contribution to the establishment of the British Open University"

(Sherow and Wedemeyer 1990, p18) resulted in the BOU influencing "more than

30 open universities all over the world" (Peters 2002, p. 42).

2.2.1.2.2 Industrial Production Model

Otto Peters, another person linked to the historical development of the BOU,

postulated his theory of Industrial Production Model in 1967 in a study entitled

"Distance Teaching and Industrial Production: a comparative interpretation in

outline" (Peters, 1994). Garrison (2000) contends that his theory is the "most

coherent, rigorous and pervasive example of distance education theory to date".

In analysing the structure of distance education, Peters (1994) adopted the

processes of division of labour, mass production, formalisation and

standardisation, concentration and centralisation, economies of scale and

reduction of unit costs.

34

Peters (1994) describes the industrial approach as "rationalization ...(and)

objectification of the teaching process" (p. 111). Criticising this form of distance

education, the limitation is in "reducing the forms of shared learning, and keeps

learners away from personal interactions and critical discourse" (p. 16). This may

be the reason why Peters (1994) did not recommend this approach for all of

distance education: "it is a special way of conceiving distance education — and

nothing more" (p. 17).

2.2.1.2.3 Theory of Guided Didactic Conversation

Holmberg's theory of distance education is a type of communication theory and

helps to explain the effectiveness of teaching at a distance as it relates to the

sense of belonging and cooperation amongst learners (Holmberg, 1988). At the

core of this theory is the concept of "guided didactic conversation" (Holmberg,

1988, p. 115). This "conversation—like interaction" is based on both "real" and

"simulated' two-way communication (Holmberg, 1988, p. 116).

The seven background assumptions for this theory are centred on the philosophy

that distance teaching should "support student motivation, promote learning

pleasure and make the study relevant to the individual learner and his/her needs"

(Holmberg, 1988, p. 116).

Although conversation is the defining characteristic of this theory, Garrison

(2000) contends that it is still directed to the pre-produced course packages and

clearly originates from the industrial perspective. According to Keegan (1996, p.

98), the major part of the communication was envisaged to be "by postal

correspondence".

35

Holmberg made a substantial contribution to the field of distance education and

also contributed to making distance education materials recognisably different

from conventional textbooks (Van Der Westhuizen, 1999).

2.2.1.2.4 The Theory of Transactional Distance

Michael Moore "combines both Peters' perspective of distance education" and

"Wedemeyer's perspective of a more learner —centred interactive relationship" in

what has been known since 1986 as the "theory of transactional distance"

(Moore and Kearsley, 1996, p. 199). According to Moore and Kearsley (1996),

transactional distance is a pedagogical phenomenon caused by geographical

distance. To overcome it necessitates special arrangements being made based

on interaction and design.

'Interaction' refers to the dialogue (D) between the teacher and the learner and is

associated with the medium of communication. Dialogue may include either two-

way communication or Holmberg's internal didactic conversation. 'Design' refers

to all the elements of the course design (Structure S) that will address the needs

of the learners. Transactional distance therefore is a function of dialogue and

structure. The most distant programme has low dialogue and low structure (-D-

S), while the least distant programme has high dialogue and low structure (+D-

S). (Moore and Kearsley, 1996).

The greater the transactional distance the more responsibility the learner has to

assume. To accommodate this Moore adds another dimension — the concept of

'learner autonomy'. This personality characteristic seems to be associated with

different "capacities" and "abilities" of the learner. Moore seeks learner

36

autonomy in setting objectives, methods of study and evaluation (Moore and

Kearsley, 1996, p. 205).

Moore (Keegan, 1996) combined these results in a typology of educational

programmes of most independent study (with high distance and high autonomy)

to least independent study (with low distance and low autonomy). The challenge

is to match the programmes to the learners so that each learner exercises the

maximum autonomy and grows (Keegan, 1996).

From the above discussion it seems that there are several different viewpoints

regarding distance education. Keegan (1986) classified theories of distance

education into three main groups: theories of 1. independence and autonomy; 2.

industrialisation of teaching and 3. interaction and communication. According to

Keegan (1986), theories of independence and autonomy include theories of

independent study by Charles Wedemeyer and the theory of independent study

by Michael Moore. The theory of industrialisation of teaching was developed by

Otto Peters. Borge Holmberg's guided didactic conversation theory (1986) is of

particular relevance to online instruction and falls into the third category, that of

theories of interaction and communication.

Because of the interactive nature of the online classes included in this research,

it seems that the theories that are most relevant are those of Holmberg as well as

Keegan's theoretical framework.

Peters (2002, p. 13) concludes that there is "clearly a structural relationship

between distance education and online learning". The author warns that, "as we

enter the digital age of learning and teaching, this relationship should not be

forgotten and that the experiences gained in the past should be kept in mind'.

37

2.2.2 Distance Education vs Online Education

Online education is changing the landscape of learning like a tornado sweeping

through a wheat field (Galagan, 2000). It is no longer necessary to convince

anyone to make the transition from conventional to online education. John Cone,

Vice-President of Dell Learning at Dell Computer Corporation said, "Our

conversations today are not about 'Shall we do this?' They're about 'How shall

we do it?' (Galagan, 2000, p. 62). Farrington (1999, p. 47) confirms this

viewpoint with the observation: "Traditional institutions can be leaders or

spectators. The smart ones will choose the forme".

We know that we want to move online to make learning more scalable, flexible,

and focused on learners' needs. Now, the question is 'How do we reap the

benefits?" (Galagan, 2000).

It seems that learners no longer have a choice as to whether to get involved in

online education or not; they have to engage in it if they want to survive in the

ever-changing workplace of the information age which is increasing the demand

for self-directed adults (Hengstler, 2001) who can learn effectively in an online

education environment.

The following sub-sections will provide a detailed discussion on the role of the

Internet in education, followed by a comprehensive discussion on online

education. A comparison between online education and conventional classroom

education will also be made.

38

2.2.2.1 The role of the Internet in education

Technically the Internet can be defined as a wide area network that links one

computer to another. It can also be described as a collection of different

communication media including e-mail, newsgroups and the World Wide Web.

(Behrens, Olen and Machet 1999, pp. 181-182)

The development of the Internet evolved from technologies designed to fight

World War III. The basic protocols that allowed one computer to send an e-mail

to another originated in research intended to create a communication network

that could survive a nuclear attack. Universities doing defence research on this

network began finding more and more uses for it. The result was that the system

was opened to the public — the Internet.

Dysan (1997) contends that the Internet doesn't actually do much but is a

powerful tool for people. Horton (2000) goes further and states that now millions

can enjoy and profit from the internet's bounty and millions can contribute to it.

Training has obvious applications. Star (1998) predicts that institutions, long

involved in building "communities" on campus, will see themselves actively

building "virtual or electronic communities".

Today individuals can access thousands of databases, articles, books, research

and courses right from their own desks, whether at home, at work or while

travelling. This resource did not exist a mere ten years ago and one can well

imagine what the next ten years will bring.

39

2.2.2.2 Online Education

Several terms are used in the literature to refer to education that takes place via

the Internet. (Tennyson, 1980; Piskurich, 1993; Williams and Zahed, 1996; Hall,

1997; Davies, 1998; Driscoll, 1998; Follows, 1999; Santo, 1999; Berry, 2000;

Galagan, 2000; Garten, 2000; Horton, 2000; Lee and Owens, 2000; Kruse and

Keil, 2000; Wang and Newlin, 2000; Brown, 2001; Smith, Ferguson and Caris,

2001; Goldschmidt, 2001; Goldsmith, 2001; Van Tonder, 2001; Burrows, 2002;

McFadzean, 2001a; McFadzean, 2001b.)

Some writers call it Web-Based Training (WBT) (Hall, 1997; Driscoll, 1998;

Horton, 2000; Lee and Owens, 2000). Others call it computer-based training

(CBT) (Williams and Zahed, 1996; Brown, 2001). Tennyson (1980) calls it

"computer-based instruction (CBI)", Wang and Newlin (2000) refer to "web-based

classes", Berry (2000), Galagan (2000), Goldschmidt (2001), Van Tonder (2001)

and Burrows (2002) call it "e-learning", and Davies (1998), Follows (1999), Santo

(1999), Garten (2000) and McFadzean (2001a, 2001b) use the term "virtual

learning". "Online learning" (Goldsmith, 2001; Smith et al., 2001), "technology-

based training" (Kruse and Keil, 2000) and even "self-directed learning" (SDL)

(Piskurich, 1993) are other terms in use.

According to Horton (2000), computer-conveyed education today exists in

several forms and takes various names, the most common being computer-aided

instruction (CAI), computer-based education (CBE), computer-based instruction

and computer-based training (CBT). Generally it seems that the term CAI is

used in the educational institution context while CBT is used in the industrial

context.

40

The root of computer conveyed education can be traced back to World War II

when audiovisual education was used to train soldiers. The first widespread use

of computers occurred in the 1950s when Stanford University provided CAI to

elementary schools. The University of Illinois took this further and assisted in

developing the Plato system (Programmed Logic for Automated Teaching

Operations). Since then according to Horton (2000), a steady development and

refinement of technologies for delivering training has taken place. Although each

advance has made training easier and less expensive to develop and deliver, the

training has been limited to a single computer system.

Although there are some technical distinctions among these types of learning,

they all involve the use of computers as the dominant medium for delivering

instruction to learners. This study opts for "online education".

According to Van Der Westhuizen (1999), online education in South Africa is still

in its infancy and it appears that only a few universities have policies regarding

online education. According to Broere, Geyser and Kruger (2002), the current

policy drive in South Africa - to establish a single dedicated distance education

institution by merging UNISA and Technicon SA - seems to be against the

international trend towards online education. On the other hand, it appears that

South African universities are quickly catching up with world trends. At a recent

World Wide Web conference held at the University of Stellenbosch Business

School, more than 20 papers were presented dealing with e-learning.

Representatives of eight universities delivered 15 papers on e-learning.

41

One to Many One to One

Many to Many (One to One One to Many

Time/Place Independent

Time/Place Dependent

Mediated

Many to Many

Time/Place Independent

2.2.3 Online education compared to conventional classroom

education

Harisim (1989) argues that online education is a new domain although it overlaps

with distance education and face-to-face education. In figure 2.2 the relationship

between the attributes of the three domains are represented

Interactive

FIG 2.2 : RELATIONSHIP BETWEEN THE THREE DOMAINS

In Schlosser and Anderson's review of distance education, research indicates

that "students learn equally well from lessons delivered with any medium, face-

to-face or at a distance." Hundreds of media comparison studies have indicated,

unequivocally, that there is no inherent significant difference in the educational

effectiveness of media. Schlosser and Anderson go on to say that further

comparisons of the effectiveness of distance education methods were not

needed. The research indicates that students learning at a distance have the

42

potential to learn just as much and just as well as students taught traditionally

(Schlosser and Anderson, 1997).

Moore and Kearsley (1996) have reviewed distance education research that

goes back more than 50 years. The studies have compared grades, test scores,

retention and job performance of students who are taught at a distance with

those taught face-to-face. Moore and Kearsley reported that the usual finding in

these comparison studies is that there are no significant differences between

learning in the two different environments, regardless of the nature of the

content, the educational level of the students or the media involved. It is

reasonable to conclude that (1) there is insufficient evidence to support the idea

that classroom instruction is the optimum delivery method; (2) instruction at a

distance can be as effective in bringing about learning as classroom instruction;

(3) the absence of face-to-face contact is not in itself detrimental to the learning

process; and (4) what makes any course good or poor is a consequence of how

well it is designed, delivered, and conducted, not whether the students are face-

to-face or at a distance (Moore and Kearsley, 1996).

2.3 Research on distance education and conventional education



education (Russell, 2002; Charp, 1999). Merisotis and Olsen (2000 p. 62),

confirm this view by concluding "While a plethora of literature on the distance

education phenomenon is available, original research on distance education is

limited'. As this chapter will indicate, there is a good deal of research dealing

with distance education. From the literature it seems that most of the research

43

being done has focused on the effectiveness of online education compared to

traditional face-to-face education and has addressed a variety of issues. Three

broad measures of the effectiveness of distance education are usually examined

in research. These include:

Course completion and dropout rate

Student outcomes, such as grades and test scores; and

Attitudes and perceptions about learning through distance education.

However, as stated in Chapter 1, the question is not whether online education is

an acceptable alternative for teaching but one of Who undertakes online courses.

Relatively little is known about the characteristics of learners who choose to enrol

for courses in an online learning environment. This section will also provide a

review of research literature with specific reference to personality characteristics.

2.3.1 Course completion and dropout rate

Many of the studies that focus on students were typically looking at course

completion and dropout rate in distance education courses. Kembler (1995,

p.258) defined dropout as "anyone who enrols in a programme and does not

eventually complete it".

In a number of studies there was evidence that a higher percentage of students

participating in a distance-learning course tended to drop out before the course

was completed, compared with students in a conventional classroom. Knowles

44

(1999) confirms this viewpoint by concluding that the dropout rate for online

students is inexplicably high.

This is supported by studies done by McIntosh, Calder, and Swift(1977), Ostman,

Wagner and Barrowclogh (1988), Wu (1995), Jewett (1997), Hammond (1997),

Phelps, Wells, Ashworth, Hahn (1991), Cheng, Lehman, & Armstrong (1991),

Ostman, Carnavale (1999), Maki, Maki Patterson, and Whittaker (2000), White

and Weight (2000), Stinson and Claus (2000) and Terry (2001).

Ostman, Wagner and Barrowclogh (1988), investigated the reasons why

students drop out or remain in online course. The study was based on a survey

done on 942 distance education students in New Zealand in 1980 -1981.

Ostman el al. (1988) concluded that personal factors, social interaction, job

satisfaction and lastly institutional issues determined the reasons for students

either dropping out of or remaining in online courses.

Phelps, Wells, Ashworth, Hahn. (1991) investigated the effectiveness and costs

of distance education using computer-mediated communication, The study

compared an engineering course taught in a conventional classroom with one

taught through computer-mediated learning. Ninety five percent of the resident

students finished the course, compared to 64 percent of the computer-mediated

learning students.

Cheng, Lehman and Armstrong (1991) compared the performance and attitudes

prevalent in traditional vs computer-mediated conferencing classes. The study

found that students participating in computer-mediated learning had significantly

higher incompletion rates (32 percent) than the on-campus students (4 percent).

45

Wu's (1995) research focused on constructing predictive scales and formulas for

dropouts in open universities and colleges. Carnavale (1999) also confirmed that

students in online education had a higher dropout rate than those in face-to-face

courses. The research recommends various techniques to reduce the dropout

rate in online courses.

Hammond's (1997) study focused on a comparison of the learning experience of

Tele-course students in community and day sections. Students who registered

for a Sociology 101 course on campus were told that the course would, instead,

be taught by means of a one-way televised broadcast. The group's performance

with respect to attrition and grades was then compared with another group of

students from the community who took the same course, also via one-way

televised broadcast, but in their homes, not in a classroom on campus. Both

sections experienced a high percentage of students who did not complete the

course. (44% of the on-campus students, and 33% of the "off-campus" students).

Although both groups rated the course as good or excellent, a higher proportion

of on-campus students reported that they would not recommend the Tele course

to a friend.

Jewett, (1997) utilized students on the human computer interaction certificate

programme at Rensselaer Polytechnic Institute as participants in a case study.

The case study was on the benefits and costs of a joint industry/university-

designed programme featuring integrated delivery systems. In the study, one-

third of the students in a video-conferencing class received the grade of "I"

(incomplete), compared with only 15 percent in an on-campus course.

46

Maki, Maki and Patterson and Whittaker (2000) did research on learning and

satisfaction in online versus lectured courses of an Introductory Psychology

course. The authors found that the dropout rate for the online sections (13.9%)

was significantly higher than the dropout rate of the lecture section (3.8%).

Terry (2001) did research on the enrolment and attrition rate of online MBA

students. 200 students were given the option to complete all courses on campus

or online. Of the fifteen graduate courses offered the online courses averaged

higher enrolments than the campus-based courses. The average for online

courses was 34 students compared to 25 students in the campus-based courses.

Terry (2001) found that the attrition rate of students in online courses was higher

compared to that of campus-based students. The highest attrition rate (43%)

was found amongst the advanced course, Quantitative Analysis in Business.

Terry (2001) concluded that the higher attrition rate could be explained by

students not adjusting to the self-paced approach, the rigor of the study

(specifically the mathematics-based courses) and the lack of student and faculty

experience with online education.

Only in one study found in the literature was there evidence that a lower

percentage of students participating in a distance learning course tended to drop

out before the course was completed compared with students in a conventional

classroom. In studying the effects of electronic classrooms on learning English

composition, Stinson and Claus (2000) found that dropouts were non-existent

after the first two class sessions in the electronic rooms. In the traditional

classroom setting, dropout rates averaged 10%. According to Stinson and Claus,

(2000) with rare exceptions, students in the electronic rooms always handed in

papers on time. In the other classrooms, 20% of papers were habitually turned in

late.

47

Although this research does not adequately explain why the dropout rates of

distance learners are higher, White and Weight (2000) contributed to

understanding it by developing "The online teaching guide: a handbook of

attitudes, strategies and techniques for the virtual classroom". The authors argue

that the following reasons might explain why students leave the online courses:

Isolation, accelerated pace, competing responsibilities and technical issues.

2.3.2 Student outcomes, such as grades and test scores;

A substantial portion of research on distance learning seems to focus on student

outcomes such as grades and test scores. Schlosser and Anderson (1994)

emphasise this viewpoint by concluding that most studies have tended to

compare outcomes in distance education with traditional face-to-face courses. A

myriad such studies conclude that, regardless of the technology used, there is

no significant difference in the learning outcomes of online students and face-to-

face students (Russell (1999), Navarro, & Shoemaker, (1999), Hammond (1997),

Cheng, Lehman, & Armstrong (1991), Martin, & Rainey (1993), Johnson (2002),

Shachar (2002), Brown, & Liedholm (2002), Thomas (2001), Efendioglo & Murray

(2000), Redding (2000), Stinson and Claus (2000), Navarro & Shoemaker

(1999), LaRose, Gregg, & Eastin (2001), Gagne & Shepherd (2001), Johnson,

Aragon, Shaik, & Palma-Rivas (2000) and Souder (1993). Moore and Kearsley

(1996) reviewed distance education research over the previous 50 years and

found no significant differences between learning in the two environments.

Several examples illustrate this point.

A study done by Souder (1993) on the effectiveness of traditional versus satellite

delivery in three Management of Technology master's degree programmes,

48

compared the results of a take-home essay exam for students who participated

in a live broadcast televised graduate course in management of technology with

the results for students in the on-campus classroom. The students participating in

distance learning performed better than students in the conventional classroom

and had less inter-student variation. Term papers for the groups were also

compared, and no significant difference was found. With respect to homework,

the distance-learning students performed at a higher level.

In the study done by Hammond (1997) that focused on the comparison of the

learning experience of tele-course students in community and day sections, the

two groups' performance with respect to grades was compared. The off-campus

students had much higher grades than the on-campus students. Although both

groups rated the course as good or excellent, a higher proportion of on-campus

students reported that they would not recommend the Tele-course to a friend.

Cheng, Lehman, & Armstrong (1991) compared the performance and attitudes in

traditional and computer conferencing classes: one group was taught by

computer-mediated learning, another group of teachers was taught on campus,

and yet another group was taught through correspondence. The outcomes

measured included scores on achievement tests, time-on-task, student attitudes,

and dropout rates. The study found that students participating in the

correspondence course had significantly higher scores on achievement tests

than the on-campus students while scores were lowest for the students

participating in computer-mediated learning. The computer-mediated learning

and correspondence students spent more time-on-task than the on-campus

students

49

At high school level, a study done by Martin and Rainey (1993) compared the

attitude and achievement scores of students participating in an anatomy and

physiology course taught in a regular classroom with one delivered through

interactive satellite. The group taught via satellite had higher mean scores on an

achievement test than students in the classroom. However no significant

differences were found between the attitudes of either group toward the courses.

Johnson (2002) assessed the outcomes of two student populations in an

introductory biology course. ANOVA results indicated no significant difference in

means for reasoning post-test scores between online (M = 9.66) and on-campus

(M = 8.56) classes. Johnson (2002) categorized the students as concrete (zero to

three correct answers), transitional (four to seven correct answers), or formal

resonators (more than seven correct answers) and, by means of the Chi square

analysis, found a statistically significant difference between online and on-

campus students.

In a meta-analytic study Shachar (2002) investigated the differences between

traditional and distance learning outcomes. Eighty-six experimental and quasi-

experimental studies met the established inclusion criteria for the meta-analysis

(including data from over 15,000 participating students), and provided effect

sizes. This meta analysis clearly demonstrated that: (1) In 2/3rds of cases,

students taking courses by distance education outperformed their student

counterparts in the traditionally instructed courses; (2) The overall effect size d+

was calculated as 0.37 standard deviation units (0.33 <95% Confidence Interval

<0.40); and (3) This effect on 0.37 indicated that the mean percentile standing of

the DE group is at the 65th percentile on the traditional group (mean defined as

the 50th percentile).

50

Brown and Liedholm (2002) investigated online versus on-campus students in a

Principles of Microeconomics course. The study found that the virtual course

represented an inferior technology compared to the on-campus live

presentations. The on-campus students did significantly better than the virtual

students on the most complex material. The students in the virtual classes

performed significantly worse on the examinations than the on-campus students.

In a Master's of Education study, Thomas (2001) investigated the effect of

computer-based instruction on performance in physics. Students were assigned

to either the experimental group (computer-based/traditional instruction Web

course) or to a control (traditional face-to-face instruction) group. An analysis of

covariance (ANCOVA) revealed a significantly higher performance for the

experimental group than for the control group.

In another study looking at the differences between on-campus MBA students

and MBA students receiving tutored video instruction (TVI) in China, Efendioglo

and Murray (2000) found that TVI students scored slightly lower grades than did

the average on-campus MBA student. However, comparing each of the TVI

student classes directly against the on-campus class that was taped, the TVI

students did not perform as well.

Redding (2000) did a comparative analysis of online learning with traditional

classroom learning. The online group typically achieved the higher GPA mean in

each topic, and the higher cumulative GPA average mean. The standard

deviation and variance for this group indicate a consistently high-quality learning

51

of content. The online group was the more successful at cognitive learning as

measured by the end-of-course examinations.

In studying the effects of electronic classrooms on learning English composition,

Stinson and Claus (2000) found that students enrolled in the electronic rooms

had an average one-half grade higher than the students in the traditional

settings. According to Stinson and Claus (2000), with rare exceptions, students

in the electronic rooms always handed in papers on time. In the other

classrooms, 20% of papers were habitually turned in late.

In a comparison study between "Cyber learners" and "Traditional learners" in

Economics, Navarro and Shoemaker (1999) found that cyber learners performed

significantly better than traditional learners. The final exam mean score for the

Cyber learners was 11.3, while the mean score for the Traditional Learners was

9.8. With a t-test statistic of 3.70, this result was statistically significant at the

99% level.

In experimental research done by LaRose, Gregg, & Eastin, (2001) on an

audiographic tele-course, forty-nine subjects were recruited from a live lecture

class and randomly assigned to either the experimental (Web course) group or to

a control group that took the class in a traditional lecture section. Analysis of

covariance (ANCOVA) showed that the experimental group had test scores equal

to those of the control group after controlling for student gender, class level,

grade point average and attendance.

Gagne and Shepherd (2001) did a comparison between a distance and

traditional graduate Accounting class and found that the performance of students

52

in the distance course was similar to the performance of students in the on-

campus course. The students' evaluations of the course were similar, although

students in the online course indicated that they were less satisfied with the

instructor availability than the in-class students. According to Gagne and

Shepherd (2001), they did not find a difference between the multiple choice exam

format and the complex problem-solving exam format.

Johnson, Aragon, Shaik and Palma-Rivas (2000) did a comparative analysis of

learner satisfaction and learning outcomes in graduate online and face-to-face

learning environments. The researchers did not find a difference between the two

course formats in several measures of learning outcomes. The overall mean

rating of the face-to-face class projects was 3.47 (SD= .60) and the mean rating

for the online class projects was 3.40 (SD= .61). The difference in the project

ratings for the two groups was not significant. The student satisfaction with their

learning experience was slightly more positive for students in a traditional course

format, although there was no significant difference in the quality of the learning

that took place. Using a blind review process to judge the quality of the major

course projects, the ratings of the three independent reviewers showed no

difference in the quality of the projects across the two course formats. In

addition, the distributions of course grades for both the online and face-to-face

classes were, to a large extent, equally distributed.

In another study comparing cyber learning and traditional classroom learning,

Navarro and Shoemaker (1999) did not find a significant difference between the

two groups. Results from t-tests indicated that there were no significant

differences on six of the eight academic variables. The two groups achieved at

approximately the same level as measured by test scores for student learning. In

general, no significant differences were found in academic outcomes when cyber

53

learners were compared with traditional learners. Ninety per cent of the Cyber

learners believed that they had learned as much or more as they would have

done in a traditional classroom.

2.3.3 Attitudes and perceptions about learning through distance

education

There is a body of descriptive analysis and case studies that focuses on student

and faculty attitudes and perceptions of distance learning. The purpose of many

of these types of research is to develop recommendations to improve distance

learning.

One such study, Goodwin, Miklich, and Overall (1993), examined the perceptions

and attitudes of students and faculty toward computer-mediated learning and

courses broadcast over a local television channel. A majority of faculty observed

that the distance learning students were "more serious, accomplished and

articulate" compared to on-campus students. The distance-learning students also

had stronger analytical skills and written communication skills, and were more

"self-directed" than their on-campus counterparts. The students reported that

they chose distance education because of the flexible schedule, and nearly half

of the students noted that the instructional method also influenced their decision.

Students preferred not to commute and "enjoyed the luxury of not having to

commit to a specific class meeting time." Forty percent of the students reported

missing the face-to-face interactions and twenty-five percent missed the group

dynamics. One of the major recommendations of this study was "to explore

alternative ways to meet students' interaction needs" on a continuous basis.

54

In another descriptive study involving two-way interactive video Larson (1994),

graduate nursing students were surveyed about their satisfaction regarding the

technology. The majority of the respondents were very positive concerning

distance education and requested more distance learning opportunities. There

was, however, considerable dissatisfaction with the accessibility of the library.

Graduate education students participated in another study done by Bland,

Morrison and Ross (1992) to determine their perceptions of two-way interactive

video courses. Using written surveys, interviews and class observations, the

authors reported very mixed reactions from the students, which resulted in

several specific recommendations. In addition to recommending that the audio

quality needed to be improved, it was suggested that future training programmes

include information on successful strategies for instruction and use more

graphics. A toll-free phone number was proposed to contact the instructor.

2.3.4 Who undertakes online courses?

However, as stated in Chapter 1, the question is not whether online education is

an acceptable alternative for teaching but rather Who undertakes online

courses? Relatively little is known about the characteristics of learners who

choose to enrol for courses in an online learning environment, since web-based

technology and courseware are fairly new (Wang and Newlin, 2000). Schlosser

and Anderson (1994) published a report entitled Distance education: Review of

the literature, in which they did not cite a single study on the characteristics of

online learners.

55

Hanson et al. (1997), in a review of the distance education literature, called for

research to focus on the factors that contribute to differences in achievement.

The need is of the students themselves, particularly the psychological and social

attributes of the learner (Hanson, 1997, p. 31) and not the mode of delivery.

The next part of the literature research will focus on the paucity of research

currently being done.

Though relatively few studies have examined the personality characteristics of

students in online courses, there is pertinent literature on which to base the

expectation of personality and cognitive differences in online students. The

literature dealing with personality and cognitive differences is based on four focus

areas of inquiry to examine the experiences of students receiving some form of

online education: (1) student interest; (2) attitudes and learning behaviour of

students; (3) cognitive ability; and (4) personality measures such as the MBTI,

16PF and other demographic information.

The first focus area is a popular area of personality research and identifies typical

profiles of individuals in various professions. The study of interests has probably

received its strongest inputs from vocational and educational counselling

(Anastasi, 1961). According to Todd and Raubenheimer (1991), it seems only

logical that interest plays a major role. The more interested one is the more

motivated and enthusiastic one becomes. If a student's subject does not match

his interest or his course of study, the chance that he will not achieve success in

it is much higher than it would be if it did match. Although interest is not a

guarantee for academic success, it is essential for motivation and general

adjustment in a course.

56

These typical profiles are usually compared to the general population. These

research results are used in career counselling to provide guidance for selecting

a career that best suits the personality of the subject in terms of interest. With

respect to the differences between traditional and distance learning regarding

interest, there seems to be a definite lack of literature in this regard.

The second focus area seems to be a popular one, i.e. looking at the attitude and

learning behaviour of students. Gouws, Plug, Louw and Meyer (1993) define an

attitude as a relatively stable tendency to react in a certain manner towards

certain people, object or abstract causes. According to Van der Berg (1987), an

attitude towards study, therefore, is the student's orientation towards studying. It

also reflects the student's inclination and propensity for study.

There are a few studies, such as Westbrook (1999), which have looked at

student attitudes about Web-based or online delivery of instruction and students'

perceived level of learning. Westbrook found that students believed they learned

the same amount in the Web-based class as they typically did in a traditional

class. Students perceived that the Web-based class was more time-consuming

than was a traditional class. The students experienced significant increases in

the anticipated to actual level of student—to-instructor and student-to-student

amount of online interaction. In other words, the students interacted with each

other and with other students far more than they anticipated they would. Overall,

students in the Web-based classes reported that they learned as much in a

traditional class but had spent more time working on the course content than they

had expected when starting the course. They also spent more time interacting

with other students and the instructor than they had expected when starting the

course.

57

The study by Goodwin, Miklich, and Overall (1993) already reported in 2.3.3 par

two should also be seen in this context

The third focus area also seems to be a popular area of research, specifically

with regard to the traditional face-to-face students. Cognitive ability for the

current study represents the construct that traditional intelligence tests aim to

measure. Yen (2002) defines cognitive ability as a developed, general mental

ability to learn, to perform abstract thinking and to adapt to the environment.

Historically the interest in cognitive ability was based on the need of higher

education institutions to select students with academic potential. According to

Keogh and Becker (1973) this need is also enhanced by the diagnostic need to

early identify students likely to fail or underachieve. Cognitive ability (Intelligence

measurement) is well documented as one of the best predictors of academic

achievement, though most of the research being done focuses on on-campus

students. This is confirmed by a study on prediction of academic achievement:

the roles of cognitive ability and learning behaviour done by Yen (2002) who

found that the proportion of variation in academic achievement accounted for by

cognitive ability was 54%. According to Yen (2002), this was consistent with

past research (done by Ting and Robinson (1998).

Only one study found, (Correlates of Learning in the Virtual Classroom, Hiltz

(1993)), explored the relationship between academic ability and outcomes in the

virtual classroom compared to the traditional classroom. Using the Scholastic

Aptitude Test (SAT), the researchers found moderate to strong relationships in

overall outcomes. Students with high SAT scores signed on significantly more

often, spent more time online and sent more private messages, earned higher

final course grades online and were more likely to rate virtual classroom as better

than the traditional classroom.

58

The fourth focus area, personality, is also a popular area of research in the

traditional, face-to-face, environment. According to Roos (1984) researchers

found that non-intellectual factors can contribute up to 50% of academic success

and that personality contributes as much as 25% of the success. According to

Todd and Raubenheimer (1991), personality and its prediction is a complex

phenomenon, though it refers to the characteristic structure, combination, and

organization of behaviour patterns, thoughts and emotions that make each

individual unique. Personality, as defined by Herr (2001, p. 4), can be described

as 'the integrated and dynamic organisation of an individual's psyche, social,

moral and physical characteristics in interaction with the environment'. In a

literature review on traditional students, Todd and Raubenheimer (1991)

concluded that personality characteristics such as introversion and extroversion

seem to contribute to much of the research done on personality and achievement

and success. The question could be asked, then, whether personality factors

differ between online and traditional students. There seems to be no basis for

predicting that online students differ in the same ways as traditional students.

However, the study by Biner, Bink, Huffman, and Dean (1995) and Macgregor

and Donaldson (2000) support the prediction that there would be differences.

Both these studies support the prediction that there will be a relationship between

personality characteristics and achievement.

Biner et al (1995) identify personality correlates of student achievement by using

the 16PF as assessment instrument as a means of comparing students in a tele-

course with those in traditional courses. There were 164 tele-course students

and 271 traditional campus students included in the study. An analysis of

variance (2X16) and post hoc comparison t-tests was conducted. These tests

revealed significant differences in that tele-course students scored significantly

59

higher than traditional students on abstract thinking (Factor B+), emotional

stability (Factor C+), trusting (Factor L-), and controlling (Factor Q3+). The five

second-order factors were also calculated and the analysis of variance (2X5) and

post hoc comparison t-tests were conducted, revealing that tele-course students

were more dependent and conforming than traditional students. The result may

have been influenced by the difference in the average age 36.16 for the tele-

course group and 22.37 for the traditional students. Additional statistical analysis

of age had no significant effect on the results. (Biner et al., 1995)

According to Biner et al (1995), successful tele-course students tend to be

"resourceful and prefer to make their own decisions (p.57). In addition, "they are

not overtly concerned about following social rules or conventions, and may

actually disregard them all together in some circumstances" (p.57). Tele-course

students are also "introverted, self indulgent... and tend to meet their

responsibilities in an efficient, expedient manner, i.e. without being overtly

compulsive about completing tasks" (p.57). A negative relationship was found

between the introvert/extrovert dimension and course performance. This

indicated that the more introverted a student was, the better he or she performed

in a distance-education setting. Higher levels of expedience were associated with

higher grades in the tele-course group. This was in contrast to higher levels of

conscientiousness associated with higher grades in the traditional course group.

These results seem to indicate that different types of students thrive in different

types of academic environments.

As part of a doctoral study, Macgregor and Donaldson (2000) utilised the Biner

study in the online education environment as a base for answering the question:

60

Does personality matter when comparing students who complete traditional and

online courses? Macgregor and Donaldson (2000) concluded that the two

groups were very different and that "personality does matter" (p. 114).

Successful online students seem to be "more worrisome, serious, shy and non-

experimenting than students in traditional classrooms." (p. 114). Online students

tend to be "more introverted, accommodating and self-controlling" (p. 114)

compared to students in the traditional classroom. Online students also tend to

be more "cooperative, trusting and tough-minded' than students in the traditional

setting. Macgregor and Donaldson (2000) concluded that the study provides

enough justification for future research on personality characteristics of online

students.

One final example from the original research is a study by Powell, Conway and

Ross (1990) that attempted to identify specific student attributes associated with

student success in computer-mediated learning. 'Student success' was defined

by whether or not newly-enrolled students passed their first course using

computer-mediated learning. The following student characteristics were identified

as being correlated with success:

Students who rated themselves highly on various measures of

persistence;

Related to taking on new projects;

Married students;

Students who rated the consequences of not passing as serious;

Students who rated their chances of succeeding in their studies

higher than non-completers;

61

Students who did not need support from others to complete difficult

tasks and did not find it important to discuss course work with other

students;

Students with high literacy levels;

Students who rated themselves as well organized in terms of time

management skills and who said they generally had the time to do

what they intended to do;

Students who rated their formal and informal learning as high in

terms of preparing themselves for university studies; and

Female students.

2.3.5 Summary of literature findings

From the literature it seems that most of the research being done has focused on

the effectiveness of online education compared to traditional face-to-face

education and has addressed a variety of issues. Distance education research is

concentrated primarily on three areas which include:

•

Course completion and dropout rate;

Student outcomes, such as grades and test scores; and

Attitudes and perceptions about learning through distance

education.

Most of these studies conclude that, regardless of the technology used, distance

learning courses compare favourably with classroom-based instruction. For

example, many experimental studies indicate that students participating in

distance learning courses perform as well as their counterparts in a traditional

62

classroom setting. These studies suggest that the distance learning students

have similar grades or test scores, or have the same attitudes toward the course.

The descriptive analysis and case studies focus on student and faculty attitudes

and perceptions of distance learning. These studies typically conclude that

students and faculty have a positive view of distance learning. These examples

of experimental research are consistent with many other studies that indicate

students participating in distance learning courses perform as well as their

counterparts in a traditional classroom setting. In other words, distance is not a

predictor of learning.

Yet, despite the fact that there is an impressive amount of writing that concludes

that distance learning is viable and effective, the literature research revealed that

studies that examine the effect of individual differences in online education have

been grossly neglected. A review of the studies conducted indicates that several

learner characteristics have some effect on the success of the learner in a

distance education environment. Based on these studies the assumption can be

made that individuals differ in terms of personality, which in turn will influence

their respective choice of learning environment.

Hence, clearly there is a need to assess personality and cognitive differences in

online education.

2.4 Conclusion

In this chapter the current knowledge about online education according to the

secondary objectives of the literature research stated in Chapter 1, was

63

discussed. Emphasis was placed on creating a theoretical frame of reference for

the concept of online education.

This discussion provided a background for understanding online education and

specifically distance education and how it influences higher education. A brief


the current use of computer-mediated communication was outlined. Also briefly

reviewed were the theories underlying distance education focusing on those

impacting online education. From the review it is evident that there are several

different viewpoints regarding distance education.

This was followed by an examination of the research on distance education and

conventional education. Finally, the need for assessing personality and cognitive

differences in online education was highlighted.

64

Chapter 3

RESEARCH METHODOLOGY

AND PROCEDURES

"it sounded an excellent plan, no doubt, and very neatly and simply arranged.

The only difficulty was that she had not the smallest idea how to set about it ..."

65

-4


A



V


CHAPTER 3:

RESEARCH METHODOLOGY AND PROCEDURES

3.1 Introduction

This chapter outlines the research methodology and procedures. In the previous

chapter a review of the literature on online education was provided. From the

literature review it became clear that research into the effect of individual

differences on online education has been grossly neglected. Against this

background, the research reported in this thesis aims at testing the hypotheses

formulated in Chapter 1 in order to reach the overall goal of the study. Figure 3.1

portrays the relationship of this chapter to the context of this research.

N

17


FIG 3.1 : CHAPTER 3 IN CONTEXT

AP:

M School

(H6) (H7)

AP: 1st

Semester

(H8)

Gender Age Language Computer

Literacy

(H13)

The focus of the study is on possible differences between online university

students and conventional students. The research methodology is

comprehensively described. The rationale for the research design, the context in

which and purpose for which the data were collected, as well as the steps in

which the data were gathered are clearly spelled out.

Objective 1

Personality

Differences

Objective 2

Cognitive

Differences

AP: HRM

Course

(H9 )

Objective 3

Biographical

Differences

AP= Academic Performance M = general average matriculation achievement

FIG 3.2 : THE REACH OBJECTIVES AND HYPOTHESES

This will be followed by a detailed discussion of the descriptions of participants,

data collection procedures and measuring instruments.

-a

This research endeavours to reach the objectives of the study by testing the

hypotheses below as defined in Chapter 1 and depicted in Figure 3.2.

3.2 Research Hypotheses

A hypothesis is a statement used in research to help operationalise the research

question (Bailey, 1994; Mouton and Marais, 1994). It is presented as a

declarative statement of prediction. Bailey (1994, p.43) states that a hypothesis is

a proposition stated in a testable form which predicts a particular relationship

between two variables. It is an assumption made in order to draw out and test

empirical consequences. The two basic formats used are the null hypothesis and

the directional hypothesis. The null hypothesis is a statistical statement in which

it is postulated that no relationship or difference exists between the variables

being studied, while the directional hypothesis postulates that a relationship does

exists. Directional hypotheses will only be formulated if clear theoretical

evidence exists. Non-directional hypotheses will only be formulated where

theoretical evidence is contradictory or where theoretical evidence is not

available.

Based on the literature research, the following hypotheses were formulated for

purposes of this research.


Four hypotheses are postulated relating to personality differences for the two

groups in respect of (a) the 16 Personality Factor Questionnaire; (16PF), (b)

Jung's Personality Types (c) the Locus of Control Inventory (LCI); and (d) the19

Field Interest Inventory (19 Fl I).

Hypothesis, H1,: There is a statistically significant difference between the

vectors of means of the two groups in respect of the 16PF.

Rationale:

The directional hypothesis postulated is based on the findings of Biner et al

(1995) and Macgregor and Donaldson (2000) that online and conventional

students differ with respect to the 16PF.

Hypothesis, H2: There is a statistically significant difference between the vectors

of means of the two groups in respect of the JPT.

Rationale:

The directional hypothesis postulated is based on the findings of Biner et al.

(1995) and Macgregor and Donaldson (2000) who found that online students

tend to be more introverted than conventional students. The research of Todd

and Raubenheimer (1991) on traditional students should also be considered in

this context.

Hypothesis, H 3 : There is a statistically significant difference between the vectors

of means of the two groups in respect of the LCI.

Rationale:

The directional hypothesis postulated is based on the findings of Wang and

Newlin (2000) who indicated that online students exhibited a greater external

locus of control than their counterparts in conventional courses; and the findings

of Dille and Mezack (1991a, 1991b) that learners with an internal locus of control

are more likely to persist in distance education than those with an external locus

of control.

Hypothesis, H4: There is no statistically significant difference between the


Rationale:

The non-directional hypothesis is based on a definite lack of literature in this

regard.


Five Hypotheses were postulated relating to cognitive differences for the two

groups in respect of (a) the Senior Aptitude Tests (SAT); (b) academic

performance at school; (c) General Average Matriculation Achievement; (d) first-

semester academic performance; and (e) academic performance on the HRM

course.

Hypothesis, H5 , there is a statistically significant difference between the vectors

of means of the two groups in respect of the SAT.

Rationale:

Cognitive ability (Intelligence measures) is well documented as one of the best

predictors of academic achievement, though most of the research being done

focuses on on-campus students. There seems to be no basis for predicting that

online students differ in the same way as traditional students. Only one study,

Hiltz (1993), used the Scholastic Aptitude Test (SAT) and found moderate to

strong relationships between academic ability and outcomes in the virtual

classroom compared to the traditional classroom.

Hypothesis, Hs; There is a statistically significant difference between the vectors

of means of the two groups in respect of the academic performance at school.

Rationale:

The directional hypothesis is based on well-documented research that high

school achievement is one of the best predictors of academic achievement,

though most of the research being done focuses on on-campus students. The

research of Todd and Raubenheimer (1991) on traditional students should also

be considered in this context.

Hypothesis, H7 : There is a statistically significant difference between the vectors

of mean of the two groups in respect of GAMA (M-score).

Rationale:

The directional hypothesis is based on well-documented research that high

school achievement is one of the best predictors of academic achievement,

though most of the research being done focuses on on-campus students. The

research of Todd and Raubenheimer (1991) on traditional students should also

be considered in this context

Hypothesis, H8: There is no statistically significant difference between the

vectors of means of the two groups in respect of Academic Performance in the

first semester.

Rationale:

The non-directional hypothesis is based on contradictory findings in the literature

in this regard. Russell (1999); Navarro, & Shoemaker, (1999); Hammond (1997);

Cheng, Lehman, & Armstrong, (1991); Martin, & Rainey (1993); Johnson (2002);

Shachar (2002); Thomas (2001); Redding (2000); Stinson and Claus (2000);

LaRose, Gregg, & Eastin (2001); Gagne & Shepherd (2001) and Souder (1993)

found that there is no statistically significant difference. Brown, & Liedholm

(2002); Efendioglo, & Murray (2000); Johnson, Aragon, Shaik, & Palma-Rivas

(2000) and Navarro and Shoemaker (1999) found a statistically significant

difference in academic performance.

Hypothesis, Hg: There is no statistically significant difference between the

vectors of means of the two groups in respect of academic performance on the

HRM course.

The non-directional hypothesis is based on contradictory findings in the literature

in this regard. Russell (1999); Navarro, & Shoemaker, (1999); Hammond (1997);



LaRose, Gregg, & Eastin (2001); Gagne & Shepherd (2001) and Souder (1993)

found that there is no statistically significant difference. Brown, & Liedholm


(2000) and Navarro and Shoemaker (1999) found a statistically significant

difference in academic performance


Four Hypotheses relating to biographical differences in the two groups in respect

of (a) gender; (b) age; (c) language; and (d) computer literacy are postulated.

Hypothesis H 10 : There is a statistically significant association between gender


Rationale:

The directional hypothesis postulated is based on the findings of Powell,

Conway, and Ross (1990).

Hypothesis H 1 ,: There is a statistically significant association between age and

online vs conventional students.

Rationale:

The directional hypothesis postulated is based on the findings of Navarro, &

Shoemaker, (1999); Hammond (1997); Cheng, Lehman, & Armstrong, (1991);

Martin, & Rainey (1993); Johnson (2002); Shachar (2002); Thomas (2001);

Redding (2000); Stinson and Claus (2000); LaRose, Gregg, & Eastin (2001);

Gagne & Shepherd (2001); Souder (1993) and Powell, Conway, and Ross,

(1990) who did found significant associations between age and online vs

conventional students. Older students tend to choose online learning.

Hypothesis H12: There is a statistically significant association between computer

literacy and online vs conventional students.

Rationale:


Shoemaker, (1999); Hammond (1997); Cheng, Lehman, & Armstrong, who found

that online students tend to be more computer literate

Hypotheses H1 3 : There is a statistically significant association between preferred

language and online vs conventional students.

Rationale:


Shoemaker, (1999); Hammond (1997); Cheng, Lehman, & Armstrong.

The a priori assumption of this study is that differences can be expected between


profiles.

The remainder of this chapter provides the rationale for the research design. It

also documents the research instruments, the research process and the

statistical procedures applied to the data captured in this research.

In the next section the research design and supporting rationale will be

described.

3.3 Research Design

According to Creswell (2003), research approaches have multiplied to the point

that researchers have many choices in designing a framework for investigation.

Huysamen (1993) defines research design as "the plan or blueprint according to

which data are collected to investigate the research hypothesis or question in the

most economical manner". The research design defines what type of study will

be undertaken to provide acceptable answers to the research problem (Mouton,

2001). More simply stated, the research design is the plan, structure and

strategy of the researcher who seeks to obtain the answers to various questions

(Mouton and Marais, 1994).

Kerlinger (1986) postulates that a research design has two basic purposes, i.e. to

provide answers to research questions and to control variance. Research

designs are invented to enable the researcher to answer research questions as

validly, objectively, accurately and economically as possible. The reliability of

observations and inferences made during the empirical study is significantly

enhanced if the research design is meticulously planned and executed.

i OBJECTIVE ---...z....---

\ ,...... . .

DEDUCTIVE

CONTEXTUAL

I INDUCTIVE

Epistemology

DETACHED INTERACTIVE

Methodology

EMPIRICAL

INTERPRETATIVE

Fig 3.3: Research Paradigms In Context

In designing the research for this study the aim was to select a design that would

meet the goals of the research question. In this particular study the focus is on

possible differences between online university students and conventional

students. Thus the research questions dictate the research design.

The fundamental choices leading to the final research design are shown in

Figure 3.3. Following is a brief discussion on each of the characteristics in order

to put the study into perspective.

3.3.1 Quantitative Research vs Qualitative Research

There are two well-known and recognised approaches to research, namely the

quantitative paradigm and the qualitative paradigm (Schurink and Schurink,

2001).

According to de Vos (2001), there is a subtle difference in the way in which

qualitatively- and quantitatively-oriented researchers view the nature of research

designs. Quantitative researchers consult their list of possible designs and select

one while qualitative researchers develop their own designs as they go along,

using one or more of the available strategies or tools as a guide. According to

Schurink and Schurink (2001) the quantitative paradigm is based on positivism,

which takes scientific explanation to be nomothetic (i.e. based on universal laws).

Its main aims are to objectively measure the social world and to test hypotheses.

In contrast, the qualitative paradigm stems from an anti-positivistic, interpretative

approach, is holistic in nature and aims at understanding social life and the

meaning that people attach to everyday life.

Schurink and Schurink (2001) point out that qualitative and quantitative

researchers have different approaches to questions concerning ontology,

epistemology and methodology. In terms of ontology, the quantitative researcher

believes in an objective reality which can be explained, controlled and predicted

by means of natural (cause-effect) laws. Human behaviour can be explained in

causal deterministic ways, and people can be manipulated and controlled.

Qualitative researchers discard the notion of an external, objective reality. They

aim to understand reality by discovering the meanings that people in a specific

setting attach to it. To these researchers behaviour is intentional and creative

and can be explained but not predicted.

Quantitative researchers use deductive reasoning. In contrast, qualitative

researchers use inductive reasoning (Neuman, 1994). Quantitative research

takes universal propositions and generalisations as points of departure, whereas

qualitative research aims to understand phenomena within a particular context.

In terms of epistemology, the quantitative researcher sees him/herself as

detached from, not as part of, the object that s/he studies. The researcher can

therefore be objective, i.e. neither influence nor be influenced by study object.

By contrast, the qualitative researcher is subjective because of personal

interaction with the subject (the object of investigation).

In terms of methodology, the quantitative paradigm emulates the physical

sciences in that questions or hypotheses are stated and subjected to empirical

testing for verification. According to De Vos (2001), data analysis in the

quantitative paradigm involves the analyst breaking down the data into their

constituent parts in order to obtain answers to research questions, as well as to

test research hypotheses. De Vos goes on to state that the analysis of research

data does not in itself provide the answers to research questions. Therefore

interpretation of the data is necessary. Interpretation in this sense is defined as

the explanation or the quest for meaning. Kerlinger (1986), goes further by

stating that analysis means the categorisation, ordering, manipulating and

summarising of data to obtain answers to research questions. The purpose of

analysis therefore is to reduce data to an intelligible and interpretable form so

that the relations of research problems can be studied, tested and conclusions

drawn.

In contrast, qualitative methodology is dialectical and interpretative. During the

interaction between researcher and subject, the subject's world is discovered and

interpreted by means of qualitative methods.

Figure 3.3 summarises the crux of the distinction between qualitative and

quantitative research paradigms.

From the above it is clear that this study falls predominantly within the

quantitative paradigm, which best serves the purpose of this study.

3.3.2 Classifying the Research Design

The research design of the study can be classified as non-experimental and ex

post facto in nature. According to Kerlinger and Lee (2000, p. 558) "Non-

experimental research is systematic empirical inquiry in which the scientist does

not have direct control of the independent variable because the manifestations

have already occurred or because they are inherently not manipulable.

Inferences about relations among the variables are made, without direct

intervention, from concomitant variation of independent and dependent

variables."

In contrast Kerlinger (1986, p. 293) sees experimental research as the "ideal" of

science. In his words "an experimental design is one in which the investigator

manipulates at least one independent variable" and adds "In ex facto research

one cannot manipulate or assign subjects or treatments because the

independent variable or variables have already occurred, so to speak". He points

out that "the main reason for the pre-eminence of the controlled experiment is

that the researcher can have more confidence that the relations he discovers are

the relations he thinks they are, since he discovers them under the most carefully

controlled conditions of inquiry. The unique virtue of experimental inquiry, then,

is control".

Kerlinger (1986) contrasts ex post facto research with experimental research and

concludes that it would be unwarranted to infer that ex post facto research is

inferior to experimental research, especially in the social sciences context. It is

easy to say that ex post facto research is merely correlational. However, such a

statement would be an oversimplification. It is more important to get a balanced

understanding of the strengths and weaknesses of both kinds of research.

Kerlinger (1986) asserts that ex post facto research has three major

weaknesses:

The inability to manipulate independent variables;

The lack of power to randomise; and

The risk of improper interpretation.

In other words, compared to experimental research, ex post facto research lacks

control; this lack is the basis of the third weakness: the risk of improper

interpretation. The danger of improper and erroneous interpretations in ex post

facto research stems in part from the plausibility of many explanations of

complex events. However, when guided by proper hypotheses, the results of

such studies are more valid.

According to Kerlinger (1986), despite its weaknesses, much ex post facto

research must be done in the social sciences because many research problems

therein do not lend themselves to experimental inquiry. Many of the research

problems in social sciences lend themselves to controlled inquiry of the ex post

facto kind, which is also true for this study.

3.3.3 Secondary data versus primary data

3.3.3.1 Primary data

Primary data are obtained from a direct observation of the phenomenon under

investigation or are collected personally by the researcher (Struwig and Stead,

2001; Welman and Kruger, 2001, p. 142). To ensure that primary data are

collected, personal or telephonic interviews, self-administered questionnaires and

direct observation methods may be used (Struwig and Stead, 2001). The data

are collected by a researcher for a particular purpose (Welman and Kruger, 2001

p. 142).

3.3.3.2 Secondary data

Secondary data is information collected by individuals or organisations other than

the researcher (Struwig and Stead, 2001; Welman and Kruger, 2001). Data are

collected by someone else for another project and purpose (Struwig and Stead,

2001).

Ex post facto research, by its nature, relies on secondary data. Mouton (2001)

defines secondary data analysis as: "Using existing data (mostly quantitative),

secondary data analysis aims at re-analysing such data in order to test

hypothesis or to validate models " (p. 164).

Mouton (2001) points out that secondary data are amenable to statistical

analysis. The value of using secondary data is that it saves time and provides the

opportunity to re-analyse existing data and arrive at new conclusions. When

doing secondary data analysis, there is an opportunity to save on the cost of

doing research. However, the limitation is the inability to control errors inherent

in the data collected. Finally, the analysis is constrained by the original purpose

for collecting the data.

The nature of non-experimental design requires secondary data to be analysed.

The data collected in the current study had a common purpose which was to

measure the cognitive and personality profiles of students. In the context of the

present research, the data were considered to be secondary data.

3.3.4 Choice of Research Design

For the purpose of the present non-experimental research, ex post facto

research design is regarded as being appropriate to answer the research

problem. With the above in mind, research design was constructed to reflect the

following characteristics:

Quantitative research paradigm;

Ex post facto characteristics; and

Primary and secondary data will be used.

In the next section the Sample will be described.

3.4 Sample

The unit of analysis consisted of first-year students at a large University in South

Africa. As mentioned in Chapter 1, the sampling strategy was, in essence, non-

probability. The study population consisted of first-year students enrolled for a

compulsory Business Science course, tested in 2001. Based on self-selection,

242 students voluntarily made use of the online course while 323 students used

the conventional course offered.

3.4.1 Sample Statistics

The frequency distributions calculated for the sample are provided in the next

tables.

The age group is depicted in Table 3.1. The ages of the students varied from 18

to 21 years, with 91% 18 years and younger.

TABLE 3. 1

AGE GROUP DISTRIBUTIONS FOR THE OBTAINED SAMPLE

Frequency Percent Cumulative Percent

<18 101 19,8 19,8

18 362 71,1 91,0

19 36 7,1 98,0

20 6 1,2 99,2

20> 4 ,8 100,0

509

The gender is depicted in Table 3. 2. As far as gender is concerned, 54% were

female and 46% were male. Missing information accounted for 3,9%.

TABLE 3.2

GENDER DISTRIBUTIONS FOR THE OBTAINED SAMPLE

Frequency Percent Cumulative

Percent

Female 304 54,0 54,0

Male 259 46,0 100,0

The language preference is depicted in Table 3.3. The majority of students, 405

(71,9%) preferred English as the language of tuition.

TABLE 3.3

PREFERRED LANGUAGE FOR THE OBTAINED SAMPLE

Frequency

Percent Cumulative Percent

Alternate 158 28,1 28,1

English 405 71,9 100.0

The home language is depicted in Table 3.4. The majority of students were

English-speaking (314). One hundred and forty nine were Afrikaans-speaking,

and 24 spoke both English and Afrikaans. Only 43 had an African language as

lingua franca. Twelve spoke other languages, and 23 did not indicate their home

language.

TABLE 3.4

HOME LANGUAGE FOR THE OBTAINED SAMPLE


AFRIKAANS 149 26,5 26,5

AFRIKAANS/ENGLISH 24 4,3 30,7

Other African Language 2 ,4 31,1

Other Euro Language 1 ,2 31,3

CHINESE 1 ,2 31,4

German 3 ,5 32,0

ENGLISH 314 55,8 87,7

Any other language 2 ,4 88,1

FRENCH 1 ,2 88,3

GREEKS 3 ,5 88,8

ITALIANS 1 ,2 89,0

NAMA 1 ,2 89,2

NORTH SOTHO 7 1,2 90,4

PORTUGUESE 3 ,5 90,9

SOUTH SOTHO 8 1,4 92,4

SWATI / SWAZI 1 ,2 92,5

TSONGA 6 1,1 93,6

TSWANA/SETSWANA 12 2,1 95,7

VENDA 3 ,5 96,3

XHOSA 5 ,9 97,2

ZULU 16 2,8 100,0

3.4.2 Descriptive Statistics for the Two Groups (Online and

Conventional Students)

Cross tabulations were employed to compare the type of learners (online and

conventional) in terms of the four biographical variables: age group, gender,

preferred language and home language (see Tables 3.5 - 3.8).

The cross-tabulation of age against type of learner (online versus conventional)

is depicted in Table 3.5 and indicates that the respondents consisted of 99

learners who were younger than 18 years, 347 who were 18 years old and 46

who were older than 18 years.

The online group consisted of 39 (39.4%) of the 99 learners who were younger

than 18 years, 149 (42,9%) of the 347 learners who were 18 years old and 18

(50%) of the 36 learners who were 19 years old. The majority of students were

18 years and younger for both the online and conventional groups.

TABLE 3. 5

CROSS TABULATION : AGE GROUP FOR

ONLINE AND CONVENTIONAL STUDENTS

Age Online Conventional Total

<18 39 60 99

18 149 198 347

19 18 18 36

20 1 5 6

20> 1 3 4

Total 208 284 492

The cross-tabulation of gender against type of learner in Table 3.6 shows that

123 (42,1%) of women out of 292 formed part of the online group, as opposed to

110 (44%) men out of 250.

TABLE 3.6

GENDER CROSS-TABULATION FOR

ONLINE AND CONVENTIONAL STUDENTS

Gender Total

F M

Online 123 110 233

Conventional 169 140 309

Total 292 250 542

The cross-tabulation of preferred language against type of learner (online versus

conventional) is depicted in Table 3.7 and indicates that the respondents

consisted of 233 and 309 learners in the online and conventional groups

respectively.

In the online group 158 (67.8%) of the 233 learners indicated that English was

their preferred language, while 75 (32,2%) indicated that they would prefer

another language. In the conventional group 234 (75,7%) of the 309 learners

indicated that English was their preferred language, while 75 (24,3%) indicated

that they would prefer another language.

The majority of the students, 392 (72.3%), indicated that English was their

preferred language, while 150 (27,7%) indicated that they would prefer another

language.

TABLE 3.7

PREFERRED LANGUAGE CROSS-TABULATION FOR ONLINE AND

CONVENTIONAL STUDENTS

Preferred Language Total

Alternate English

Online 75 158 233

Conventional 75 234 309

Total 150 392 542

When comparing the demographic variables, it is clear that the obtained

frequencies correspond closely across the two groups. This implies that the two

groups were relatively homogeneous.

The following brief discussion of the measurement instruments puts the study

into perspective.

TABLE 3.8

HOME LANGUAGE CROSS-TABULATION FOR ONLINE AND

CONVENTIONAL STUDENTS


AFRIKAANS 149 26,5 26,5

AFRIKAANS/ENGLISH 24 4,3 30,7

Other African Language 2 ,4 31,1

Other Euro Language 1 ,2 31,3

CHINESE 1 ,2 31,4

German 3 ,5 32,0

ENGLISH 314 55,8 87,7

Any other language 2 ,4 88,1

FRENCH 1 ,2 88,3

GREEKS 3 ,5 88,8

ITALIANS 1 ,2 89,0

NAMA 1 ,2 89,2

NORTH SOTHO 7 1,2 90,4

PORTUGUESE 3 ,5 90,9

SOUTH SOTHO 8 1,4 92,4

SWATI / SWAZI 1 ,2 92,5

TSONGA 6 1,1 93,6

TSWANA/SETSWANA 12 2,1 95,7

VENDA 3 ,5 96,3

XHOSA 5 ,9 97,2

ZULU 16 2,8 100,0

3.5 The Measurement Instruments

Because the constructs of human behavioural science often involve human

attributes, actions and artifacts, it may appear to the lay person that these can be

appropriately measured by merely asking research participants about them

directly. However, for several reasons the reliability and validity of the

measurements obtained in this fashion would be questionable. Two of the most

important of these reasons include: (a) participants may have insufficient

knowledge about themselves or they may be unable to verbalize their innermost

feelings; (b) participants may deliberately provide incorrect answers with a view

to portraying themselves in a positive or negative light (Welman and Kruger,

2001).

Thus in order to collect both reliable and valid data, some form of measuring

instrument must be used. In the human sciences, 'measuring instrument' refers

to instruments such as questionnaires, observation schedules, interview

schedules and psychological tests (Mouton, 2001).

In terms of the measuring instruments referred to above, the researcher basically

has one of two options, either the use of existing instrumentation or the

development of new instruments designed specifically for the purpose of a

particular study. For the purpose of this study, the test scores of students tested

with a prescribed psychometric battery of tests for use by the Career Counselling

Division of the university are used. In order to identify the personality and

cognitive differences between online university students and conventional

students, the following tests were selected for use in the current study.

3.5.1 Personality Measures

3.5.1.1 16 Personality Factor Questionnaire (16PF)

The 16PF was originally developed by Raymond B. Cattell as a set of primary

and elementary factor scales whereby several other personality characteristics

and behavioural patterns could be predicted. The questionnaire contains 16

bipolar scales (called primary factors), five global factor scales and several

validity scales. Fifteen of the primary factors and five of the global factors

measure personality traits and the remaining factor measures cognitive ability or

reasoning ability. It is one of the most widely used tests of personality in the

world. The instrument not only allows the respondent's interests and abilities to

be examined, but also allows his or her personality to be taken into consideration

during occupational decision making (Conn and Rieke, 1994). Numerous validity

coefficients have been reported in respect of all 16 of the scales. Reliability

coefficients between 0,45 and 0,92 have been reported for the different scales by

means of the test-retest method (Conn and Rieke, 1994). The internal

consistency of the primary factors and validity scales range from 0,66 to 0,87.

3.5.1.2 Jung's Personality Types Questionnaire

Jung's Personality Types Questionnaire was constructed for the measurement of

personality in terms of Jung's personality theory. The questionnaire consists of

75 items and four scales: Introversion-Extroversion, Sensing-Intuition, Thinking-

Feeling and Judging-Perceiving. Numerous validity coefficients have been

reported in respect of Jung's Personality Types. Cronbach alpha coefficients

between 0,814 and 0,886 have been reported for the different scales (Du Toit,

1983).

3.5.1.3 Locus of Control Inventory

The Locus of Control Inventory (LCI), as designed by Schepers (1995), is based

on attribution theory and social learning theory. The LCI can be used for inter-

individual comparisons, as it is a normative instrument. A factor analysis of the

LCI Schepers (1995) identified the following sub-scales:

External control

The individual believes that outcomes are independent of his/her own

behaviour.

Internal control

The individual believes that outcomes are a consequence of his/her own

behaviour.

Autonomy

The individual practises an internal locus of control and prefers working

alone.

The questionnaire consists of 80 items, each in the form of a seven-point scale.

The Cronbach alpha coefficients for internal control, external control and

autonomy are 0,832; 0,841 and 0,866 respectively. Various South African studies

(De Kock & Roodt, 1995; Rieger & Blignaut, 1996; Le Roux et al., 1997; Bothma

& Schepers, 1997; Van Staden, Schepers & Rieger, 2000; Rothmann &

Agathagelou, 2000) have confirmed the validity coefficients.

3.5.1.4 19 Field Interest Inventory (19 FII)

The 19 Fll was constructed for the measurement of vocational interests of high

school pupils in Standards 8, 9 and 10 students and of adults in 19 broad fields

of interest. The 19FII also measures the extent to which a person is actively or

passively interested in the 19 fields and whether these interests are work- or

hobby-orientated. The results can be used for counselling and selection

purposes. Numerous validity coefficients have been reported in respect of all 19

fields. Reliability coefficients between 0,68 and 0,97 have been reported for the

different scales by means of the split half method (Fouche and Alberts, 1986).

3.5.2 Cognitive ability measures

3.5.2.1 Senior Aptitude Tests (SAT)

The SAT was constructed for the measurement of a number of aptitudes of

pupils in Standards 8, 9 and 10 (Grades 10, 11 and 12) and of adults. The

results can be used for counselling and selection purposes. It has also been

established that a fairly reliable IQ estimate can be obtained from the SAT scores

for pupils between 14 and 18 years of age. The reliability coefficients ranged

from 0,71 to 0,93 for standard 10 pupils. Numerous factor analyses of the SAT

together with other variables confirmed the construct validity of these tests. High

validity coefficients were obtained (Fouche and Verwey, 1991).

3.5.2.2 Academic Performance at School

Scholastic achievement has proved to be one of the best predictors of academic

performance. For the purpose of this study scholastic achievement refers to the

level that a matriculation learner attains in his/her subjects individually.

3.5.2.3 General Average Matriculation Achievement

TABLE 3.9

CALCULATION OF MATRICULATION SCORES

Academic symbol Numeric value per Numeric value per achieved in matric Higher Grade subject Standard Grade subject

A 5 4

4 3

C 3 2

2 1

1 0

F 0 0

(RADEMEYER and SCHEPERS, 1998)

The matriculation scores were calculated by assigning a numerical value to the

obtained matriculation symbols. The table 3.9 indicates the conversion of

matriculation symbols to numerical values.

3.5.2.4 Academic Performance in the first semester

First-semester academic performance at university was based on the average of

the first semester aggregate of each learner. The percentage of subjects passed

and the percentage of subjects failed for the two groups were also calculated.

3.5.2.5 Academic Performance on the HRM Course

Academic performance on the course was based on the semester marks, exam

and final marks of each learner.

3.5.3 Biographical Information

The biographical questionnaire included items requesting the respondents'

gender, age, computer literacy, and preferred language.

In the next section the statistical process will be described.

3.6 Research Process

With a view to reaching the objectives of the research, the process depicted in

the flow chart will be followed (See Figure 3.4).

The data collection procedure will be discussed in more depth in the next

sections.

STATISTICAL PROCESS FLOW CHART FOR COGNITIVE AND PERSONALITY DIFFERENCES

3.7 Procedure of Data Collection

DATA

COLLEC-

TION

DEVELOP

DATA SET

VERIFY DA TASET

STATIS-

TICAL

ANALYSIS

INTERPRET INFORMA-

TION

Data collected as survey information, collected as secondary data from the prescribed psychometric battery of tests administered for first year students and primary data collecting aditional biografical information based on the two groups

The dataset developed consisting of only the items from each measuring instrument

The dataset verified to ensure that it is correct.

The data in the dataset statistically analysed with the SPSS program with the aim to determine statistical differences between the two groups.

The analysed information interpreted and recommen-dations made for future research in online education.

Figure 3.4: Statistical Process Flow Chart

The prescribed psychometric battery of tests was administered to the full intake

of first-year university students at a South African University by the Career

Counselling Division during their first month at the university. Testing was

compulsory for all first-year students and took place over four days under strict

supervision. Due to incompleteness of some records, only 586 records could be

used in the sample. Absenteeism caused some of the records to be incomplete.

3.8 Statistical Analyses Applied in the Research

This section deals briefly with the statistical analyses employed in the study. In

order to test for differences between the means of the two groups (online and

conventional students) with regard to the personality and cognitive measures,

one-way multivariate analyses of variance (MANOVAs) followed by Students' t-

tests were conducted. A MANOVA determines whether mean differences

between groups on a combination of dependent variables are likely to have

occurred by chance (Tabachnick and Fidel!, 1996).

According to Cooper and Schindler (2001), MANOVA follows the model of

ANOVA where variance is partitioned into variance attributable to differences

among scores within groups and to differences of scores between groups.

Squared differences between scores and various means are then summed.

These sums of squares, when divided by the appropriate degrees of freedom,

provide estimates of the variance attributable to different sources. Cooper and

Schindler (2001) argue that Hotelling's T 2 test is analogous to a t-test or F test for

multivariate data. Sum of squares and cross-products (SSCP) matrices were

used. Significance levels of 0,05 were set for all the hypotheses tested.

Cohen (1988) argued that multivariate tests usually have lower power than

univariate tests. Therefore it was decided to proceed with Students' t-tests to

determine whether there are statistically significant differences between the

means of the two groups.

Estimated effect sizes were also calculated using coefficient eta. Effect sizes

indicate something different from significance levels (Rosenthal, Rosnow and

Rubin, 2000). Results that are statistically significant at conventional levels are

not necessarily 'practically significant' as judged by the magnitude of the effect

size.

3.9 Conclusion

In this chapter the research part of the study was discussed. This chapter

documented the research design, the instruments used, the research process

and the statistical procedures employed in the study. It was pointed out that the

research was designed in such a way that it could adequately answer the

research question in order to reach the objectives of the study. In the next

chapter the results of the statistical analyses will be discussed.

Chapter 4

RESEARCH RESULTS

" I'm glad they've begun asking riddles — I believe I can guess that," she added

aloud.

"Do you mean that you think you can find out the answer to it?" said the March

Hare.

"Exactly so," said Alice.

"Then you say what you mean," the March Hare went on.

"I do," Alice hastily replied; "at least — at least I mean what I say — that's the same

thing, you know."



N


A

4.

■ Chapter 4

Reporting of Empirical Results

CHAPTER 4:

RESEARCH RESULTS

4.1 Introduction

In Chapter two, various personality and cognitive characteristics influencing

online learning were described from both a theoretical and research point of

view. In order to investigate the personality and cognitive differences between

online and conventional students, Chapter Three focused on the design to

perform a successful empirical research experiment. Figure 4.1 portrays the

relationship of this chapter to the context of this research.

FIG 4.1: CHAPTER 5 IN CONTEXT

It led to the conclusion that this research design reflects:

a quantitative research paradigm,

with ex post facto characteristics, and

primary and secondary data will be used.

Several hypotheses were stated based on the psychometric battery completed

by all first year students as a point of departure. In order to understand the nature

of the findings, the sample and its characteristics were also discussed in Chapter

Three.

In this chapter the research results of the statistical analysis will be outlined in

terms of the design explained in Chapter Three. Each category of the findings

begins with a summarised description of what it focuses on. Tables describing

the findings follow, and the findings are then presented.

This chapter will be concluded by an interpretation of the results in terms of the

problem statement discussed in Chapter One, i.e. the personality and cognitive

differences between online and conventional students.

Following is a description of the statistical analysis.

4.2 Statistical Analysis

In order to test for differences between the means of the two groups (online and

conventional students) with regard to the personality and cognitive measures,

one-way multivariate analyses of variance (MANOVAs) followed by Students' t-

tests were conducted. Estimated effect sizes were also calculated using

coefficient eta. In order to test hypotheses relating to biographical differences,

cross-tabulations were calculated and the chi-square test was used. Cramer's V

was also calculated as an index of the strength of the association between the

biographical variables

Hotelling's T 2, descriptive statistics as well as the results of the t-tests, in respect

of (a) personality differences and (b) cognitive differences, are reported in Tables

4.1 to 4.25. Cross-tabulations and chi-square test in respect of biographical

differences are given in Tables 4.26 to 4.33.

In the following section one-way multivariate analyses of variance (MANOVAs)

followed by Students' t-tests with respect to personality differences are

discussed.

4.2.1 Differences in means between the two groups with regard to

objective 1: Personality Differences

In order to test Hypotheses H1 to H4 relating to personality differences, one-way

multivariate analyses of variance (MANOVAs) followed by Student's t-tests were

conducted on the two groups in respect of (a) the 16 Personality Factor

Questionnaire (16PF); (b) Jung's personality types; (c) the Locus of Control

Inventory (LCI); and (d) the 19 Field Interest Inventory (19 FII).

In the following section the results of the tests with respect to personality


4.2.1.1 Differences in means between the two groups with respect

to 16PF (Hi)

In order to test Hypothesis H1, which states that there is a statistically significant

difference between the vectors of means of the two groups in respect of the

16PF, one-way multivariate analyses of variance (MANOVAs using Hotelling's

T2) followed by Students' t-tests were conducted. The results of these analyses

are reported in Tables 4.1 — 4.3.

TABLE 4.1

MULTIVARIATE TESTS OF SIGNIFICANCE FOR THE 16PF

Value F Hypothesis Error df P Partial Eta

Df Squared

Hotelling's

Trace 0,045 1,306 16,000 464,000 0,189 0,043

Hotelling's V=0,045

F (16, 464) = 1,306; p = 0,189

The multivariate tests of significance presented in Table 4.1 show Hotelling's T 2

test F (16,464) = 1,306; p = 0,189. This test was compared to the F distribution

for interpretation. Since the observed significance levels were greater than p =

0,05 (p = 0,189) for the T 2 test, the null hypothesis was not rejected and the

alternative hypothesis HA was therefore not supported. This means that the

vectors of means of the two groups with respect to the 16PF did not differ

statistically significantly from one another.

Based on Cohen's argument (1988) that multivariate tests usually have lower

power than univariate tests, it was therefore decided to proceed with students' t-

tests to determine whether there are statistically significant differences between

the means of the two groups.

TABLE 4.2

DESCRIPTIVE STATISTICS FOR THE 16PF

N Mean Std. Deviation Std. Error Mean

Online Conventional Online Conventional Online Conventional Online Conventional

Sociability 209 277 5,20 5,11 2,063 1,799 ,143 ,108

Intelligence 208 275 4,54 4,17 1,801 1,716 ,125 ,103

Emotional stability 209 277 5,18 5,04 1,897 1,941 ,131 ,117

Dominance 208 275 5,44 5,67 2,035 1,936 ,141 ,117

Enthusiasm 207 275 6.22 6.17 2.105 2.112 .146 .127

Conscientiousness 207 275 5.70 5.30 1.694 1.687 .118 .102

Adventurous 209 276 5.83 5.77 2.040 1.931 .141 .116

Emotional

sensitivity

209 276 3.72 3.74 1.665 1.631 .115 .098

Aloof 209 277 4.55 4.83 1.776 1.636 .123 .098

Practical 209 275 4.39 4.24 1.852 1.869 .128 .113

Astute 209 277 5.40 5.26 1.746 1.835 .121 .110

Guilt feelings 209 275 4.80 4.81 1.873 2.026 .130 .122

Conservatism 209 275 5.41 5.57 1.848 1.773 .128 .107

Self-sufficient 209 277 4.60 4.64 1.990 2.000 .138 .120

Self-sentiment 209 274 5.52 5.63 1.952 1.722 .135 .104

Tense 209 275 5.22 5.34 1.819 1.878 .126 .113

The descriptive statistics for the 16PF are reported in Table 4.2. As can be seen

from Table 4.2, the two samples sizes differ, but the mean scores do not differ

statistically significantly for the online and conventional groups. Results indicate

that the lowest mean score difference (0.01), was obtained for Guilt feelings

(Online M=4,80 SD =1,873; conventional M=4,81 SD =2,026), while the highest

TABLE 4.3

T-TEST: INDEPENDENT COMPARISONS OF THE MEAN DIFFERENCE

SCORES ON THE 16PF

Levene's Test

for Equality of

Variances

t-test for Equality of Means

Dependent

variables

Equal

variances

F-

ratio p(F) t-value df p(t)

Mean

Differe

nce

Eta

Sociability Not 4,553 0,033 0,497

412,75 0,619 0,089 0,026

Assumed 0

Intelligence Assumed 2,254 0,134 2,281 481 0,023 0,368 0,125

Emotional stability Assumed 0,003 0,960 0,800 484 0,424 0,141 0,077

Dominance Assumed 0,531 0,467 -1,227 481 0,220 -0,223 0,039

Enthusiasm Assumed 0,357 0,551 0,221 480 0,825 0,043 0,124

Conscientiousness Assumed 0,015 0,901 2,563 480 0,011 0,399 0,052

Adventurous Assumed 0,043 0,837 0,329 483 0,743 0,060 0,07

Emotional sensitivity Assumed 0,153 0,696 -0,142 483 0,887 -0,021 0,089

Aloof Assumed 2,959 0,086 -1,801 484 0,072 -0,280 0,023

Practical Assumed 0,004 0,948 0,885 482 0,377 0,151 0,058

Astute Assumed 0,400 0,528 0,833 484 0,405 0,137 0,003

Guilt feelings Assumed 1,425 0,233 -0,066 482 0,947 -0,012 0,072

Conservatism Assumed 0,206 0,650 -0,984 482 0,326 -0,163 0,157

Self-sufficient Assumed 0,061 0,806 -0,178 484 0,859 -0,033 0,044

Self-sentiment Assumed 2,935 0,087 -0,684 481 0,494 -0,115 0,088

Tense Assumed 0,601 0,438 -0,716 482 0,474 -0,122 0,023

mean scores difference obtained for Conscientiousness was 0.3 (Online M=5,7

SD =1,694; conventional M=5,3 SD =1,687). A mean score difference of 0.37

(Online M=4.54 SD =1,801; conventional M=4,17 SD =1,716) was obtained for

intelligence.

The independent t-tests statistics for the 16PF are reported in Table 4.3 and

includes Levene's test of homogeneity of variance and eta.

As can be seen from Table 4.3, Levene's F-ratio is significant only for

"Sociability" F(ratio) = 4,553, p(F) = 0,033, indicating that the null hypothesis

cannot be rejected for the rest of the dependent variables, that is the variances

are homogeneous. The homogeneity assumption has therefore been met for all

the dependent variables except for Sociability.

The last five columns of Table 4.3 contain the t-test results and eta for the

dependent variables. The results indicate that except for Intelligence and

Conscientiousness, the group means of online and conventional students did not

differ statistically significantly.

The observed t-value for Intelligence was 2.281 (p= 0,023) and for

Conscientiousness 2.563 (p=,011). Therefore the null hypothesis of no

differences between the groups with respect to these two dependent variables is

rejected. This implies that the alternative hypothesis, H1, which states that there

is a statistically significant difference between the vectors of means of the two

groups in respect of the 16PF (Intelligence and Conscientiousness) is accepted.

This also implies that the alternative hypothesis, H1, for the other 16PF

dependent variables is not accepted.

The results were further explored by determining the effect sizes (see Table 4.3:

last column labeled "eta"). The estimated effect sizes were calculated using eta

and yielded very small effect sizes for Intelligence (eta = 0.125) and also for

Conscientiousness (eta = 0.052), because the values were smaller than 0.1

(Cohen 1988). A small effect is indicated if eta is 0.1, a medium effect equals

0.3, whereas a large effect is obtained if eta is 0.5 (Cohen 1988).


to Jung's Personality Types (JPT) (H2).

TABLE 4.4

MULTIVARIATE TESTS OF SIGNIFICANCE FOR THE JPT

Value

F Hypothesis Df Error df

Hotelling's

Trace 0,007 0,779 4,000 469,000 0,540

Hotelling's T2 =0,007

F (4, 469) = 0,779; p = 0,540

In order to test hypothesis H2, which states that there is a statistically significant

difference between the vectors of means of the two groups in respect of the JPT,

Hotelling's T2 followed by Student's t-tests were conducted. The results of these

analyses are reported in Tables 4.4 — 4.6.


test F (4, 469) = 0,779; p = 0,540. This test was compared with the F distribution


0,05 (p = 0,540) for the T2 test, the null hypothesis was not rejected and the


vectors of means of the two groups with respect to the JPT did not differ



power than univariate tests, it was therefore decided to proceed with Student's t-



The descriptive statistics for the JPT are reported in Table 4.5. As can be seen

from Table 4.5, the two sample sizes differ, but the mean scores do not differ


TABLE 4.5

DESCRIPTIVE STATISTICS FOR THE JPT

N Mean Std, Deviation Std, Error Mean

Online Cony Online Cony Online Cony Online Cony

Extroversion / 205 269 5,19 5,03 2,264 2,184 ,158 ,133

Introversion

Thinking / Feeling 205 269 6,468 6,539 1,742 1,763 ,1216 ,1075

Observation / 205 269 6,196 6,414 1,990 2,086 ,1390 ,1272

Intuition

Judging / 205 269 3,860 3,907 1,918 2,144 ,1340 ,1307

Perception

that the lowest mean score difference (0.047), was obtained for

Judging/Perception (Online M=3,860 SD =1,918; Conventional M=3,907 SD

=2,144), while the highest mean scores difference obtained for

Observation/Intuition was 0.218 (Online M=6,196, SD =1,990; Conventional

M=6,414 SD =2,086).

A mean score difference of 0,071 (Online M=6,478 SD =1,742; Conventional

M=6,539 SD =1,763) was obtained for Thinking/Feeling and of 0,160 (Online

M=5,19 SD =2,264; Conventional M=5,03 SD =2,184) was obtained for

Extroversion/Introversion.

The independent t-tests statistics for the JPT are reported in Table 4.6 and

include Levene's test of homogeneity of variance and eta.

As can be seen from Table 4.6, Levene's F-ratio does not differ statistically

significantly for the dependent variables, indicating that the null hypothesis

cannot be rejected, that is, the variances are homogeneous. The homogeneity

assumption has therefore been met for all the dependent variables.

TABLE 4.6

T-TEST: INDEPENDENT COMPARISONS OF THE MEAN DIFFERENCE SCORES

ON THE JPT

Levene's Test for Equality

of Variances t-test for Equality of Means

Equal t- Mean Eta

variances F-ratio p(F) value df p(t) Difference

Extroversion / Introversion Assumed 0,568 0,451 0,796 472 0,426 0,164 0,01

Thinking / Feeling Assumed 0,077 0,781 -0,435 472 0,664 -0,071 0,141

Observation / Intuition Assumed 0,331 0,565 -1,154 472 0,249 -,2189 0,056

Judging / Perception Assumed 2,364 0,125 -0,250 472 0,803 -0,048 0,034


dependent variables. The results indicate that the group means of online and

conventional students did not differ statistically significantly. The lowest observed

t-value obtained for Observation/Intuition was -1,154 (p= 0,249). Therefore the

null hypothesis of no differences between the groups with respect to the

dependent variables is accepted. This implies that the alternative hypothesis, H2,

which states that there is a statistically significant difference between the vectors

of means of the two groups in respect of the JPT, is not accepted.

Effect sizes, using eta, were also calculated for the four JPT subcategories. Very

small effect sizes were obtained ranging from 0,01 to a small effect size of 0,141,

thereby confirming the MANOVA results.


to the Locus of Control Inventory (LCI) (H 3)


difference between the vectors of means of the two groups in respect of the LCI,

Hotelling's T 2 followed by Student's t-tests were conducted. The results of these

analyses are reported in Tables 4.7 — 4.9.

TABLE 4.7

MULTIVARIATE TESTS OF SIGNIFICANCE FOR THE LCI

Value F Hypothesis Df Error df

P

Hotelling's 0,010 0,588 3,000 181 0,624

Trace

Hotelling's V= 0,010

F (3, 181) = 0,588; p = 0,624


test F (3, 181) = 0,588; p = 0,624. This test was compared to the F distribution for

interpretation. Since the observed significance levels were greater than p = 0,05

(p = 0,624) for the T 2 test, the null hypothesis was not rejected and the


vectors of means of the two groups with respect to the LCI did not differ


Again it was decided to proceed with Student's t-tests to determine whether there

are statistically significant differences between the means of the two groups.

TABLE 4.8

DESCRIPTIVE STATISTICS FOR THE LCI

N Mean Std. Deviation Std. Error Mean


extern 90 95 90.1298 91.3357 19.55249 18.52179 2.06101 1.90030

intern 90 95 158.3372 160.1799 18.70138 13.88546 1.97130 1.42462

auto 90 95 159.6960 158.5636 18.59522 17.18149 1.96011 1.76278

The descriptive statistics for the (LCI) are reported in Table 4.8. As can be seen

from Table 4.8 the two samples sizes differ although only by five, but the mean

scores do not differ statistically significantly for the online and conventional

groups. Results indicate that the lowest mean score difference (1,13), was

obtained for Autonomy (Online M=159.6960, SD =18.59522; Conventional

M=158.5636, SD =17.18149), while the highest mean scores difference obtained

for Internal was 1,84 (Online M=158.3372, SD =18,70138; Conventional

M=160.1799 SD =13.88546).

The independent t-tests statistics for the (LCI) are reported in Table 4.9. and


TABLE 4.9


SCORES ON LCI

Levene's Test for

Equality of

variances t-test for Equality of Means

Mean

F-ratio p(F) t-value df p(t) Difference Eta

external .262 .609 -.431 183 .667 -1.20583 0.00

internal 2.351 .127 -.764 183 .446 -1.84267 0.03

autonomy .092 .762 .430 183 .667 1.13242 0.03

As can be seen from Table 4.9, Levene's F-ratio does not differ statistically

significantly for the dependent variables, indicating that the null hypothesis

cannot be rejected, that is, the variances are homogeneous. The homogeneity




conventional students did not differ statistically significantly. The lowest observed

t-value obtained for "internal control" was -0,764 (p= 0,446). Therefore the null

hypothesis of no differences between the groups with respect to the dependent

Hotelling's

Trace

Hotelling's T2 = 0,036

F (21, 469) = 0,794; p = 0,729

0,036 0,794 21,000 469,000 0,729 0,034

variables is not rejected. This implies that the alternative hypothesis, H3, which

states that there is a statistically significant difference between the vectors of

means of the two groups in respect of the LCI, is not accepted.

Effect sizes, using eta, were also calculated for the three LCI subcategories.

Very small effect sizes were obtained for (eta = 0,00), (eta = 0,03) and (eta =

0,03), thereby confirming the MANOVA results.


to 19 Field Interest Inventory (H4)

TABLE 4.10

MULTIVARIATE TESTS OF SIGNIFICANCE FOR THE 19FII

Hypothesis Partial Eta

Value F df Error df Sig, Squared

In order to test Hypothesis H4, which states that there is no statistically significant

difference between the vectors of means of the two groups in respect of the

19FII, Hotelling's T 2 followed by Student's t-tests were conducted. The results of

these analyses are reported in Tables 4.10 — 4.12.


test F (21, 469) = 0,794; p = 0,729. This test was compared to the F distribution


0,05 (p = 0,729) for the T 2 test, the null hypothesis was rejected and the

alternative hypothesis HA was therefore supported. This means that the vectors

of means of the two groups with respect to the 19FII did not differ statistically

significantly from one another.

Again it was decided to proceed with Student's t-tests to determine whether there

are statistically significant differences between the means of the two groups.

The descriptive statistics for the 19FII are reported in Table 4.11. As can be

seen from Table 4.11, the two sample sizes differ, but the mean scores do not

differ statistically significantly for the online and conventional groups. Results

indicate that the lowest mean score difference (0.000) was obtained for Travel

(Online M=3,95 SD =2,062; Conventional M=3,95 SD =2,236), while the highest

mean scores difference obtained for Observation/Intuition was 0.58 (Online

M=4,15,SD =2,150; Conventional M=3,61 SD =1,997).

TABLE 4.11

DESCRIPTIVE STATISTICS ON THE 19FII



Fine Arts 212 287 3,96 4,01 1,829 1,826 ,126 ,108

Clerical 212 287 3,95 3,90 1,800 1,823 ,124 ,108

Social Work 212 287 4,45 4,19 2,098 1,965 ,144 ,116

Nature 212 287 4,15 3,61 2,150 1,997 ,148 ,118

Performing Arts 212 287 3,95 3,82 1,764 1,795 ,121 ,106

Science 212 287 3,73 3,64 1,753 1,868 ,120 ,110

Historical 212 287 5,57 5,70 1,883 1,866 ,129 ,110

Public Speaking 212 287 6,00 5,93 2,089 1,868 ,143 ,110

Numerical 212 287 5,02 4,97 1,958 2,005 ,134 ,118

Sociability 212 287 6,88 6,84 1,600 1,593 ,110 ,094

Creative Thought 212 287 4,58 4,29 1,795 1,907 ,123 ,113

Travel 212 287 3,95 3,95 2,062 2,236 ,142 ,132

Practical - Female 212 287 3,53 3,56 2,041 2,192 ,140 ,129

Law 212 287 7,78 7,78 1,278 1,204 ,088 ,071

Sport 212 287 7,39 7,52 1,455 1,433 ,100 ,085

Language 212 287 5,94 5,86 1,555 1,627 ,107 ,096

Service 212 287 4,35 4,37 2,155 1,804 ,148 ,106

Practical - Male 212 287 3,03 2,84 1,659 1,638 ,114 ,097

Business 212 287 3,48 3,59 1,764 1,707 ,121 ,101

Work Hobbie 210 282 5,10 5,23 1,297 1,315 ,090 ,078

Active Passi 210 283 5,86 5,92 1,824 1,786 ,126 ,106

TABLE 4.12


SCORES ON THE 19FII

Levene's Test for Equality of t-test for Equality of Means

Variances

F-ratio p(F) t-value df p(t)

Mean

Difference Eta

Fine arts Assumed ,135 ,714 -,270 497 ,787 -,045 0,107

Clerical Assumed ,197 ,658 ,349 497 ,727 ,057 0,036

Social work Assumed 1,771 ,184 1,400 497 ,162 ,256 0,123

Nature Assumed 2,463 ,117 2,915 497 ,004 ,545 0,237

Performing arts Assumed ,205 ,651 ,823 497 ,411 ,133 0,054

Science Assumed 1,371 ,242 ,539 497 ,590 ,089 0,025

Historical Assumed ,100 ,751 -,785 497 ,433 -,133 0,032

Public speaking Assumed 3,585 ,059 ,399 497 ,690 ,071 0,114

Numerical Assumed ,956 ,329 ,325 497 ,745 ,058 0,077

Sociability Assumed ,495 ,482 ,269 497 ,788 ,039 0,133

Creative thought Assumed 1,493 ,222 1,748 497 ,081 ,294 0,144

Travel Assumed 1,161 ,282 -,010 497 ,992 -,002 0,05

Practical - female Assumed 1,907 ,168 -,151 497 ,880 -,029 0,004

Law Assumed ,627 ,429 -,051 497 ,960 -,006 0,028

Sport Assumed ,264 ,607 -,951 497 ,342 -,124 0,02

Language Assumed ,802 ,371 ,564 497 ,573 ,082 0,077

Service Not 12,551 ,000 -,092

405,70 ,927 -,017 0.027

assumed 1

Practical - male Assumed ,111 ,739 1,319 497 ,188 ,197 0,066

Business Assumed ,685 ,408 -,687 497 ,492 -,108 0,038

Work hobbies Assumed 1,575 ,210 -1,055 490 ,292 -,126 0,065

Active passive Assumed ,049 ,826 -,375 491 ,708 -,062 0,006

The independent t-tests statistics for the 19FII are reported in Table 4.12. and


As can be seen from Table 4.12, Levene's F-ratio is significant only for "service"

F(ratio) = 12,551, p(F) = 0,000, indicating that the null hypothesis cannot be

rejected for the rest of the dependent variables; that is, the variances are

homogeneous. The homogeneity assumption has therefore been met for all the

dependant variables except for "service".


dependent variables. The results indicate that, except for Nature, the group

means of online and conventional students did not differ statistically significantly.

The observed t-value for Nature was 2,915 (p= 0,004). Therefore the null

hypothesis that there is a difference between the groups with respect to Nature is

accepted. This implies that the alternative hypothesis, H4, which states that there

is no statistically significant difference between the vectors of means of the two

groups in respect of the 19FII (Nature) is rejected This also implies that the

alternative hypothesis, H4, for the other 19FII dependent variables is accepted.

Effect sizes, using eta, were also calculated for the 19FII subcategories. Very

small effect sizes were obtained ranging from eta = 0,006 to small effect size of

eta = 0,237.

It is clear from the above statistical analysis that statistically significant

differences between the two groups with respect to only a few personality factors

do exist although very small effect sizes were obtained.

In the following section, one-way multivariate analyses of variance (MANOVAs)

followed by Student's t-tests with respect to cognitive differences are discussed.

4.2.2 Differences in means between the two groups with respect

to objective 2: Cognitive factors

In order to test Hypotheses H5 to Hg relating to cognitive differences, one-way

multivariate analyses of variance (MANOVAs) followed by Student's t-tests were

conducted on the two groups in respect of (a) the Senior Aptitude Tests (SAT);

(b) the academic performance at school; (c) general average matriculation

achievement (GAMA (M-score)); (d) first-semester academic performance at

university; and (e) the academic performance on the HRM Course.

In the following section, the results of the statistical tests with respect to cognitive



to Senior Aptitude Tests (SAT) (H 5)


difference between the vectors of means of the two groups in respect of the SAT,

one-way multivariate analyses of variance (MANOVAs using Hotelling's T 2)

followed by Student's t-tests were conducted. The results of these analyses are

reported in Tables 4.13 — 4.15.

TABLE 4.13

MULTIVARIATE TESTS OF SIGNIFICANCE FOR THE SAT

Hypothesis Partial Eta


Hotelling's ,036 1,714 10,000 470,000 ,075 ,035

Trace

Hotelling's T 2 = 0,036

F (10, 470) = 1,714; p = 0,075


test F (10, 470) = 1,714; p = 0,075. This test was compared with the F

distribution for interpretation. Since the observed significance levels were greater

than p = 0,05 (p = 0,075) for the T 2 test, the null hypothesis was not rejected and

the alternative hypothesis HA was therefore not supported. This means that the

vectors of means of the two groups with respect to the SAT did not differ






TABLE 4.14

DESCRIPTIVE STATISTICS ON THE SAT



Word analogy 206 276 20,24 19,55 2,915 3,570 ,203 ,215

Number

series 206 276 21,01 20,80 3,149 3,489 ,219 ,210

Verbal

reasoning 206 276 20,80 19,91 2,957 3,522 ,206 ,212

Pattern

completion 205 276 19,84 19,22 3,214 3,719 ,224 ,224

Word pairs 205 276 21,38 20,36 2,861 3,722 ,200 ,224

Figure

analogy 205 276 19,72 19,29 3,143 3,570 ,220 ,215

Non VERBAL 206 276 112,93 111,49 13,349 14,289 ,930 ,860

IQ

Verbal IQ 206 276 108,79 105,18 11,812 12,313 ,823 ,741

Total IQ 206 276 111,77 108,98 12,328 13,057 ,859 ,786

Descriptive statistics for the SAT are reported in Table 4.14. As can be seen

from Table 4.14 the two samples sizes differ, but it seems that some of the mean

scores do not differ statistically significantly for the online and conventional

groups. Results indicate that the lowest mean score difference (0.21), was

obtained for Number Series (Online M=21,01 SD =3,149; conventional M=20,80

SD =3,489), while the highest mean scores difference obtained for Verbal IQ

was 3.61 (Online M=108,79 SD =11,812; conventional M=105,18 SD =12,313).

A mean score difference of 2.79 (Online M=111,77 SD =12,328 conventional

M=108,98 SD =13,057) was obtained for Total IQ. A mean score difference of

1.44 (Online M=112,93 SD =13,349 conventional M=111,49 SD =14,289) was

obtained for Non-verbal IQ.

The independent t-tests statistics for the SAT are reported in Table 4.15. and


As can be seen from Table 4.15, Levene's F-ratio is significant only for "Word

Pairs" F(ratio) = 6,334, p(F) = 0„012, and "Verbal Reasoning" F(ratio) = 4,571,

p(F) = 0,033 indicating that the null hypothesis cannot be rejected for the rest of

the dependent variables; that is, the variances are homogeneous. The

homogeneity assumption has therefore been met for all the dependent variables

except for "Word Pairs" and "Verbal Reasoning".

TABLE 4.15


SCORES ON THE SAT

Levene's Test for

Equality

Variances

of t-test for Equality of Means

F-ratio p(F)

t-

value df p(t)

Mean

Difference Eta

Word Analogy Assumed 1,857 ,174 2,285 480 ,023 ,696 0,073

Number Series Assumed ,734 ,392 ,682 480 ,496 ,210 0,088

Verbal

Reasoning

Not

assumed

4,571 ,033 3,028 473,345 ,003* ,895 0,072

Pattern Assumed 2,523 ,113 1,919 479 ,056 ,622 0,015

Completion

Word Pairs Not

assumed

6,334 ,012 3,416 478,416 ,001* 1,025 0,145

Figure Analogy assumed ,665 ,415 1,381 479 ,168 ,432 0,01

Non Verbal IQ assumed ,363 ,547 1,121 480 ,263 1,434 0,055

Verbal IQ assumed ,371 ,543 3,243 480 ,001* 3,614 0,126

Total IQ assumed ,214 ,644 2,377 480 ,018* 2,790 0,033


dependent variables. The results indicate that for Word Analogy, Verbal

Reasoning, Word Pairs, Verbal IQ and Total IQ, the group means of online and

conventional students differ statistically significantly.

The observed t-value for Word Analogy was 2,285 (p= 0,023) Verbal Reasoning

3,028 (p= 0,003), Word Pairs 3,416 (p= 0,001), Verbal IQ 3,243 (p= 0,001) and

for Total IQ 2,377 (p=,018). Therefore the null hypothesis of no differences

between the groups with respect to these dependent variables is rejected. This

implies that the alternative hypothesis, H5, which states that there is a statistically

significant difference between the vectors of means of the two groups in respect

of the SAT (Word Analogy, Verbal Reasoning, Word Pairs, Verbal IQ and Total

IQ), is accepted. This also implies that the alternative hypothesis, H5, for the

other SAT dependent variables is not accepted.


last column labelled "eta"). The estimated effect sizes were calculated using eta

and yielded very small effect sizes for Figure Analogy (eta = 0,01) while for Word

Pairs (eta = 0,145) and Verbal IQ (eta = 0,126) a small effect size was obtained.


to academic performance at school (H 6)


difference between the vectors of means of the two groups in respect of

academic performance at school (H6), one-way multivariate analyses of variance

(MANOVAs using Hotelling's T 2) followed by Student's t-tests were conducted.

The results of these analyses are reported in Tables 4.16 — 4.18.

TABLE 4.16

MULTIVARIATE TESTS OF SIGNIFICANCE FOR ACADEMIC

PERFORMANCE AT SCHOOL

Hypothesis

Partial Eta

Value F df

Error df Sig, Squared

Hotelling's 0,041 0,972 6,000 141,000 0,447 ,040

Trace


F (6, 141) = 0,972; p = 0,447


test F (6, 141) = 0,972; p = 0,447. This test was compared to the F distribution for

interpretation. Since the observed significance levels were greater than p = 0,05

(p = 0,447) for the T 2 test, the null hypothesis was not rejected and the


vectors of means of the two groups with respect to the academic performance at

school did not differ statistically significantly from one another.

Based on Cohen (1988) argument that multivariate tests usually have lower




TABLE 4.17

DESCRIPTIVE STATISTICS ON ACADEMIC PERFORMANCE AT SCHOOL



Afrikaans 236 305 3,8517 3,6787 1,10667 1,22811 ,07204 ,07032

Biology 98 147 3,3724 2,9184 1,33415 1,28484 ,13477 ,10597

English 237 319 3,8861 3,6144 ,90414 ,95343 ,05873 ,05338

Mathematics 237 319 3,1245 2,8448 1,47949 1,43581 ,09610 ,08039

Science 179 233 3,1006 2,7961 1,40563 1,33272 ,10506 ,08731

Accountancy 204 277 3,8824 3,4982 1,28879 1,39129 ,09023 ,08359

The descriptive statistics for the academic performance at school are reported in

Table 4.17. As can be seen from Table 4.17, the two sample sizes differ, but it

seems that the mean scores do differ statistically significantly foi . the online and

conventional groups. Results indicate that the lowest mean score difference

(1,730), was obtained for Afrikaans (Online M=3,8517 SD =1,10667;

conventional M=3,6787 SD =1,22811), while the highest mean scores difference

obtained for Biology was 4,540 (Online M=3,3724 SD =1,33415; conventional

M=2,9184 SD =1,28484). A mean score difference of 3,842 (Online M=3,8824

SD =1,28879 conventional M=3,4982 SD =1,39129) was obtained for

Accountancy. A mean score difference of 3,045 (Online M=3,10061 SD

=1,40563 conventional M=2,79611 SD =1,33272) was obtained for Science.

The independent t-tests statistics for the academic performance at school are

reported in Table 4.18. and include Levene's test of homogeneity of variance and

eta.

As can be seen from Table 4.18, Levene's F-ratio is not significant for any

TABLE 4.18


SCORES FOR ACADEMIC PERFORMANCE AT SCHOOL

Levene's Test for West for Equality of Means

Equality of Variances

F-ratio p(F)

t-

value df p(t)

Mean

Difference Eta

Afrikaans Assumed 1,800 ,180 1,696 539 ,090 ,17301 0,021

Biology Assumed ,629 ,429 2,669 243 ,008 ,45408 0,166

English Assumed 1,240 ,266 3,396 554 ,001 ,27166 0,232

Mathematics Assumed ,454 ,501 2,242 554 ,025 ,27964 0,108

Science Assumed ,474 ,492 2,244 410 ,025 ,30442 0,132

Accountancy Assumed ,796 ,373 3,087 479 ,002 ,38416 0,104

dependent variable, indicating that the null hypothesis cannot be rejected for the

dependent variables; that is, the variances are homogeneous. The homogeneity



dependent variables. The results indicate that for Biology, English, Mathematics,

Science and Accountancy, the group means of online and conventional students

differ statistically significantly. The observed t-value for Biology was 2,669 (p= 0,

,008), English 3,396 (p= 0„001), Mathematics 2,242 (p= 0„025), Science 2,244

(p= 0„025) and Accountancy 3,087 (p=,002).

Therefore the null hypothesis of no differences between the groups with respect

to these dependent variables are rejected. This implies that the alternative

hypothesis, H6, which states that there is a statistically significant difference

between the vectors of means of the two groups in respect of the Academic

Performance at School (Biology, English, Mathematics, Science and

Accountancy), is accepted. This also implies that the alternative hypothesis, H6,

for the dependent variables Afrikaans is not accepted.



and yielded small effect sizes ranging from (eta = 0,104) for Accountancy to (eta

= 0,232) for English, because the values were smaller than 0.3 (Cohen 1988). A

small effect is indicated if eta is 0.1, a medium effect equals 0.3, whereas a large

effect is obtained if eta is 0.5 (Cohen 1988).


to General Average Matriculation Achievement (GAMA). (H7)


difference between the vectors of means of the two groups in respect of GAMA

(H7), Student's t-tests were conducted. The results of these analyses are

reported in Tables 4.19 — 4.20.

TABLE 4.19

DESCRIPTIVE STATISTICS ON GENERAL AVERAGE MATRICULATION

ACHIEVEMENT



M- 240 320

22,5125 20,4781 7,38098 6,20934 0,47644 0,34711 score

The descriptive statistics for GAMA are reported in Table 4.19. As can be seen

from Table 4.19 the two sample sizes as well as the mean scores do differ


that the mean score difference (2,034), was obtained for the M-score (online,

M=22,5125 SD =7,38098; conventional M=20,4781 SD = 6,20934),

The independent t-tests statistics for GAMA are reported in Table 4.20, and


As can be seen from Table 4.20 Levene's F-ratio is significant for the dependent

variable, indicating that the null hypothesis can be rejected for the dependent

variables; that is, the variances are not homogeneous. The homogeneity

assumption has therefore not been met for the dependent variables.


dependent variable "M-score". The results indicate that the group means of

online and conventional students differ statistically significantly.

TABLE 4.20


SCORES ON GENERAL AVERAGE MATRICULATION ACHIEVEMENT

Levene's Test for

t-test for Equality of Means

Equality of

Variances

t- Mean

F-ratio p(F) value df p(t) Difference Eta

Not 6,287 ,012 3,451 462,440 ,001 2,03438 0,092

MSCORE assumed

The observed t-value is 3,451 (p=,001). Therefore the null hypothesis of no

differences between the groups with respect to these dependent variables is

rejected. This implies that the alternative hypothesis, H7, which states that there

is a statistically significant difference between the vectors of mean of the two

groups in respect of GAMA (M-score), is accepted.

The results were further explored by determining the effect sizes (see Table 4.20,


and yielded very small effect sizes for (eta = 0,092).


to academic performance in the first semester at university (H8)



TABLE 4.21

MULTIVARIATE TESTS OF SIGNIFICANCE

FOR ACADEMIC PERFORMANCE IN THE FIRST SEMESTER

Partial

Hypothesis Eta


Hotelling's 0.048 8.993 3.000 558.000 0.000 .046

Trace


F (3, 588) = 8,993; p = 0,000

academic performance in the first semester at university (H 8), one-way

multivariate analyses of variance (MANOVAs using Hotelling's T 2) followed by

Student's t-tests were conducted. The results of these analyses are reported in

Tables 4.21 — 4.23. The multivariate tests of significance presented in Table

4.21 show Hotelling's T 2 test F (3, 588) = 8,993; p = 0,000. This test was

compared to the F distribution for interpretation. Since the observed significance

levels were smaller than p = 0,05 (p = 0,000) for the T2 test, the null hypothesis

was accepted and the alternative hypothesis HA was therefore rejected. This

means that the vectors of means of the two groups with respect to the first

semester academic performance differed statistically significantly from one

another. It was therefore decided to proceed with Student's t-tests to determine

whether there were statistically significant differences between the means of the

two groups.

The descriptive statistics for academic performance in the first semester are

reported in Table 4.22. As can be seen from Table 4.22, the two sample sizes

and the mean scores differ statistically significantly for the online and

conventional groups. Results indicate that the lowest mean score difference (3,

28), was obtained for Semester Mark (Online M=62,57 SD =9,255 conventional

M=59,29 SD =9,255), while the highest mean scores difference obtained for

Exam Mark was 4.87 (Online M=46,54 SD=13,978 conventional M=41,67=SD

=12,625)

TABLE 4.22

DESCRIPTIVE STATISTICS ON ACADEMIC PERFORMANCE IN THE FIRST

SEMESTER


Online Cony

Online Cony Online Cony Online Cony

Semester

mark 241 321 62,57 59,29 9,255 9,255 ,596 ,517

Exam

mark 242 323 46,54 41,67 13,978 12,625 ,899 ,702

Final

mark 242 323 54,55 50,47 10,902 10,272 ,701 ,572

A mean score difference of 4,08 (Online M=54,55 SD = 0,902 conventional

M50,47=SD =10,272) was obtained for Final Mark.

The independent t-tests statistics for academic performance in the first semester

are reported in Table 4.23. and include Levene's test of homogeneity of variance

and eta.

As can be seen from Table 4.23, Levene's F-ratio is significant for only the

dependent variable Exam Mark, indicating that the null hypothesis cannot be

rejected for the dependent variables; that is, the variances are homogeneous for

Semester and Final Mark. The homogeneity assumption has therefore been met

for the dependent variables Semester and Final Mark.

The last five columns of Table 4.18 contain the West results and eta for the


conventional students differ statistically significantly. The observed t-value for

Semester Mark was 4,166 (p= 0,000), Exam Mark 4,273 (p= 0,000) and Final

Mark was 4,553 (p= 0,000).

Therefore the null hypothesis that there is a difference between the groups with

respect to these dependent variables is accepted. This implies that the

alternative hypothesis, H8, which states that there is no statistically significant

TABLE 4.23


SCORES ON ACADEMIC PERFORMANCE IN THE FIRST SEMESTER

Levene's Test for t-test for Equality of Means

Equality of

Variances

Mean

F-ratio p(F) t-value df P(t) Difference Eta

Semester Assumed

mark ,109 ,741 4,166 560 ,000 3,286 0,158

Exam Not

mark assumed 4,255 ,040 4,273 488,923 ,000 4,873 0,215

Final mark Assumed 2,795 ,095 4,553 563 ,000 4,082 0,216


academic performance in the first semester at university, is rejected.



and yielded small effect sizes for Semester mark (eta = 0,158), Exam mark (eta =

0,215) and also for Final mark (eta = 0,216) because the values were smaller

than 0.3 (Cohen 1988).


to academic performance on the HRM course (H9)



academic performance on the HRM course (HO, Student's t-tests were

conducted. The results are reported in Tables 4.24-4.25.

TABLE 4.24

DESCRIPTIVE STATISTICS

ON ACADEMIC PERFORMANCE ON THE HRM COURSE

N

Mean Std, Deviation Std, Error Mean


Course Mark 241 322 21,77 18,86 9,352 7,984 ,602 ,445

The descriptive statistics for academic performance on the HRM course are

reported in Table 4.24. As can be seen from Table 4.24, the two sample sizes as

well as the mean scores differ statistically significantly for the online and

conventional groups. Results indicate that a mean score difference of 2,91 was

obtained for Course Mark (Online M=21,77 SD=9,352; conventional M=18,86 SD

= 7,984). This indicates that online students achieved higher mean scores for

Academic Performance on the HRM course.

The independent t-tests statistics for academic performance on the HRM course

are reported in Table 4.25, and include Levene's test of homogeneity of variance

and eta. As can be seen from Table 4.25, Levene's F-ratio is significant for the

dependent variable, indicating that the null hypothesis can be rejected for the

dependent variables; that is, the variances are not homogeneous. The

homogeneity assumption has therefore not been met for the dependant

variables.


dependent variable "Course Mark". The observed t-value for Course Mark was

3,888 (p= 0,000). The results indicate that there is a statistically significant


academic performance on the HRM course.

TABLE 4.25


SCORES ON ACADEMIC PERFORMANCE ON THE HRM COURSE

Levene's Test for

Equality of Variances t-test for Equality of Means

Course t- Mean

Mark F-ratio p(F) value df p(t) Difference Eta

Not

Assumed 11,107 ,001

3,888 468,918 ,000 2,912 0,252

Therefore the null hypothesis that there is a difference between the groups with

respect to these dependent variables is accepted. This implies that the

alternative hypothesis, Hg, which states that there is no statistically significant


academic performance on the HRM course, is rejected.


last column labeled "eta"). The estimated effect sizes were calculated using eta

and yielded small effect sizes (eta = 0,252).


differences between the two groups with respect to certain cognitive factors do

exist.

4.2.3 Differences in means between the two groups with respect

to objective 3" Biographical Differences

In order to test Hypothesis H 10 to H13 relating to Biographical differences, cross-

tabulations were calculated and the chi-square test was used to determine

whether relationships existed between the two groups in respect of (a) gender,

(b) age, (c) language, and (d) computer literacy. The null hypothesis for the chi-

square test applied to two-way designs states that the two variables are

independent, whereas the alternative hypothesis states that the two variables are

associated.

Cramer's V was also calculated as an index of the strength of the association

between the categorical variables (Field, 2000). Siegel and Castellan (1988)

argued that Cramer's V is a useful measure of association due to its wide

applicability. Cramer's V was calculated, because p is affected by a large

sample size and will therefore yield significant X2 value in almost all cases,

whereas Cramer's V is independent of large sample sizes. Rules of thumb for

correlation coefficients such as Cramer's V are (a) a value of 0 to +0,3 indicates

no association and are indicative of small effect sizes; (b) a value of +0,31 to

+0,6 equals a weak positive association; whereas (c) a value of +0,61 to +1,0

reflects a strong positive association.

In the following section the results of the statistical tests with respect to

biographical differences are given in Tables 4.26 to 4.33

4.2.3.1 Gender

Hypothesis H10 states that there is a statistically significant association between

gender and online vs conventional students. In order to test Hypothesis Hlo

cross-tabulations were calculated and the chi-square test was used to determine

whether relationships existed between the two groups. Cramer's V was also

calculated to confirm the strength of the association. The results are reported in

Tables 4.26 and 4.27.

TABLE 4.26

CROSS-TABULATION: DESCRIPTIVE STATISTICS ON GENDER

Gender Group

Total

Female

Male

Online 123 (22,7%) 110 (20,3%) 233 (43,0%)

Conventional 169 (31,2%) 140 (25,8%) 309 (57,0%)

Total 292 (53,9%) 250 (46,1%) 542

The cross-tabulation of gender against type of learner in Table 0.26 shows that

the majority of the participants were female (53.9%), compared to 46.1% male.

The online group also reflected that more females (22,7%) chose to participate in

online learning compared to 20,3% of males.

The null hypothesis for the chi-square test stated that there is no association

between gender and online vs conventional students.

TABLE 4.27

CHI-SQUARE TEST: GENDER VS TYPE OF LEARNER

(ONLINE VS CONVENTIONAL)

X2 df p Cramer's V

Pearson chi-square

0,194 1 0,660 0,019

N of valid cases

542

The alternative hypothesis stated that there is an association. No support was

found to reject the null hypothesis (see Table 4.27) and therefore gender was not

associated with the type of learning (online vs conventional students), x2 =

0,194, p = 0,660. This suggests that online learning was not dependent on

gender. A small effect size was obtained, because Cramer's V was 0,019.

4.2.3.2 Age


age and online vs conventional students. Cross-tabulations were calculated and

the chi-square test was used to determine whether relationships existed between

the two groups. Cramer's V was also calculated to confirm the strength of the

association. The results are reported in Tables 4.28 and 4.29.

TABLE 4.28 CROSSTABULATION: DESCRIPTIVE STATISTICS ON AGE

Age Group Total

518 ?.19

Online

Conventional

Total

188

258

446

(38,2%)

(52,4%)

(90,7%)

20

26

46

(4,1%)

(5,3%)

(9,4%)

208

284

492

(42,3%)

(57,7%)

The cross-tabulation of age group against type of learner (online vs conventional)

in Table 4.28 shows that 90,7% of the participants were from the age group 518.

38,2% in this age group participated in online learning as opposed to 4,1% from

the ?.19 age group.

The results in table 4.29 show that the Pearson chi-square was not statistically

significant De (1) = 0,03 with p < 0,0862]. The null hypothesis stating that there

TABLE 4.29

CHI-SQUARE TEST: AGE GROUP VS TYPE OF LEARNER


X2 df p Cramer's V

Pearson chi-square

0, 030 1 0,862 0,008

N of valid cases

492

is no association between age group and online vs conventional students is

therefore accepted.

This implies that the alternative hypothesis, H11, which states that there is a

statistically significant association, is thus rejected. The age group of students

who participated was not associated with the type of learning (online vs

conventional students). This suggests that differences between online and

conventional students were not dependent on age group. The very small effect

size (Cramer's V was 0,008) confirm this conclusion.

4.2.3.3 Computer literacy


computer literacy and online vs conventional students. In order to test

Hypothesis H12, cross-tabulations were calculated and the chi-square test was

used to determine whether associations existed between the two groups.

Cramer's V was also calculated to confirm the strength of the association. The

results are reported in Tables 4.30 and 4.31.

TABLE 4.30

CROSSTABULATION: DESCRIPTIVE STATISTICS

ON COMPUTER LITERACY

Computer literate Group Total

Not Literate Literate

Online 53 (9,4%) 188 (33,3%) 241 (42,7%)

Conventional 108 (19,2%) 215 (38,1%) 323 (57,3%)

Total 161 (28,6%) 403 (71,4%) 564

The cross-tabulation of computer literacy against online vs conventional students

in Table 0.30 indicates that 71,4% of participants described themselves as

computer literate while 28,6% of participants described themselves as not

computer literate. Most of the participants (38,1%) who described themselves as

computer literate did not join the online group, while 9,4% of the online group did

not describe themselves as computer literate.

TABLE 4.31

CHI-SQUARE TEST: COMPUTER LITERACY VS TYPE OF LEARNER


X2 Df p

Cramer'sV

Pearson chi-square 8,863 1 0,003 0,125

N of valid cases 564

The results in Table 4.31 indicate that the Pearson chi-square was statistically

significant [x2 (1) = 8,863 with p < 0,003]. The null hypothesis stating that there

is no association between computer literacy and online vs conventional students

is therefore rejected. This implies that the alternative Hypothesis, H12, which

states that there is a statistically significant association, is thus accepted.

The differences in frequencies between computer literacy and online vs

conventional students were thus greater than would be expected by chance. It is

therefore concluded that there is an association between computer literacy and

type of learning. Nevertheless, the estimated effect size was small, because

Cramer's V was only 0,125.

4.2.3.4 Preferred Language


preferred language and online vs conventional students. In order to test

Hypothesis H13, cross-tabulations were calculated and the chi-square test was

used to determine whether relationships existed between the two groups.

Cramer's V was also calculated to confirm the strength of the association. The

results are reported in Tables 4.32 and 4.33.

TABLE 4.32

CROSS-TABULATION: DESCRIPTIVE STATISTICS ON PREFERRED

LANGUAGE

Preferred Language Group Total

Other Language English

Online 75 (13,8%) 158 (29,2%) 233 (43,0%)

Conventional 75 (13,8%) 234 (43,2%) 309 (57,0%)

Total 150 (27,6%) 392 (72,4%) 542

The cross-tabulation of preferred language against online vs conventional

students in Table 4.32 indicates that the majority, 72,4% of the participants

preferred English while 27,6% of the participants preferred another language

(mostly Afrikaans). The majority of participants (43,2%) who preferred English

did not join the online group while 29,2% of the online group preferred English.

TABLE 4.33

CHI-SQUARE TEST: PREFERRED LANGUAGE VS TYPE OF LEARNER


X2 df P Cramer's V

Pearson chi-square 4.160 1 .041 0,088

N of valid cases 542

The results in table 4.33 indicate that the Pearson chi-square was statistically

significant [X2 (1) = 4,160 with p < 0,041]. The null hypothesis stating that there

is no association between preferred language and online vs conventional

students is therefore rejected. This implies that the alternative hypothesis, H13,

which states that there is a statistically significant association, is thus accepted.

It is therefore concluded that there is an association between preferred language

and type of learning. Cramer's V was 0,088 and therefore the estimated effect

size was very small.


differences between the two groups with respect to certain biographical factors

do exist.

4.3 Summary of Main Findings

The main findings, based on the research results of the statistical analysis will be

outlined in terms of the three objectives set for this research. The results of the

statistical analyses for the three objectives are:


Four Hypotheses relating to personality differences for the two groups in respect

of (a) the 16 Personality Factor Questionnaire (16PF); (b) Jung's Personality

Types; (c) the Locus of Control Inventory (LCI); and (d) the19 Field Interest

Inventory (19 FII) were formulated and tested, yielding the following results:

Hypothesis, H1, which states that there is a statistically significant difference

between the vectors of means of the two groups in respect of the 16PF

(Intelligence and Conscientiousness) is accepted.


between the vectors of means of the two groups in respect of the JPT is not

accepted.


between the vectors of means of the two groups in respect of the LCI, is not

accepted.

Hypothesis, H4, which states that there is no statistically significant difference

between the vectors of means of the two groups in respect of the 19FII (Nature)

is accepted.

It must be borne in mind that the above findings are based on the significance of

differences in group means, while further analyses carried out revealed that there

were very small to small effect sizes obtained. All hypotheses are rejected on

these grounds and imply that there are no statistically significant personality

differences between online and conventional students.


Five Hypotheses relating to cognitive differences for the two groups in respect of

(a) the Senior Aptitude Tests (SAT); (b) the academic performance at school; (c)

general average matriculation achievement (GAMA); (d) first-semester academic

performance at university; and (e) the academic performance on the HRM course

were formulated and tested, yielding the following results:


between the vectors of means of the two groups in respect of the SAT (Word

Analogy, Verbal Reasoning, Word Pairs, Verbal IQ and Total IQ), is accepted.


between the vectors of means of the two groups in respect of the academic

performance at school (Biology, English, Mathematics, Science and

Accountancy), is accepted.


between the vectors of means of the two groups in respect of GAMA (M-score),

is accepted.

Hypothesis, H8, which states that there is no statistically significant difference

between the vectors of means of the two groups in respect of academic

performance in the first semester, is rejected.

Hypothesis, Hg, which states that there is no statistically significant difference

between the vectors of means of the two groups in respect of academic

performance on the HRM course, is rejected.

However, again it must be borne in mind that the above findings are based on

the significance of differences in group means, while further analyses carried out

revealed that very small to small effect sizes were obtained. All hypotheses are

rejected on these grounds and imply that there are no statistically significant

cognitive differences between online and conventional students.


Four Hypotheses relating to biographical differences for the two groups in

respect of (a) gender, (b) age, (c) language, and (d) computer literacy were

formulated and tested, yielding the following results:

Hypothesis, H10, which states that there is a statistically significant association

between gender and online vs conventional students, is rejected.


between age and online vs conventional students, is rejected.


between computer literacy and online vs conventional students, is accepted.


between preferred language and online vs conventional students, is rejected.

Once again it must be borne in mind that the above findings are based on the

significance of associations, while further analyses carried out revealed that very

small to small effect sizes were obtained. All hypotheses are rejected on these

grounds and imply that there is no statistically significant biographical

difference between online and conventional students.

4.4 Conclusion

In this chapter the results of the various statistical procedures were analysed,

reported and various observations were made. The results of descriptive

statistics, one-way multivariate analyses of variance (MANOVA's using

Hotelling's T2), Student's t-tests, cross-tabulations and chi-square test were

revealed.

At first glance it would appear that there are significant differences between

online and conventional students. However, further analyses carried out

revealed that very small to small effect sizes were obtained. Therefore the main

findings, based on the research results of the statistical analysis, concluded that

no statistical significant differences existed between online and conventional

students.

In the next chapter, the results will be interpreted, discussed and integrated with

existing information, to form a synthesis of cutting-edge knowledge concerning

the personality and cognitive differences between online and conventional

students. The next chapter will also focus on reviewing the study and making

recommendations for future research.

Chapter 5

DISCUSSION OF RESULTS,

CONCLUSION AND RELATED

RECOMMENDATIONS

"Would you tell me please which way I ought to go from here?"

"That depends a good deal on where you want to go to," said the Cat.

"I don't much care where-" said Alice

Then it doesn't matter which way you go," said the Cat.

"- So long as I get somewhere," said Alice as an explanation.

Chapter 1 Introduction to the Study Chapter 4

Reporting of Empirical Results




CHAPTER 5:

DISCUSSION OF RESULTS, CONCLUSION AND RELATED

RECOMMENDATIONS

5.1 Introduction

Are there significant personality and cognitive differences between online and

conventional students? This final chapter not only integrates all the various

aspects of the study as envisaged in Chapter One but it also seeks to draw

specific conclusions based on the findings of the study. Figure 5.1 portrays the

relationship of this chapter to the context of this research.

FIG 5.1 : CHAPTER 5 IN CONTEXT

Before presenting the significance, main contributions and limitations of this

research, it is necessary to provide a brief overview of the study itself. This

summary will include the presentation of the research and the methodology and

procedures.

5.2 Presentation of this Research

The first chapter served as the introduction to this research and placed the total

investigation in context by providing a framework for the problem that was being

studied. The research problem, the purpose, objectives and hypotheses as well

as an overview of the methodology of the study were discussed. The value of

the research as well as the delimitations and limitations of the study were

indicated.

The second chapter reviewed an extensive literature study, which encapsulated

the current knowledge of online learning. The literature review mapped out the

main issues in the field being studied and various personality and cognitive

differences influencing online learning were described from both a theoretical and

research point of view. As such, an overview of previous research on the topic

and a summary of the status quo were also included

The third chapter outlined the research methodology and procedures. The

research methodology was described comprehensively. A detailed discussion on

the research design, the descriptions of the participants, the sampling plan, data

collection procedures and measuring instruments were portrayed. The research

was designed in such a way that it could adequately address the research

question in order to reach the objectives of the study.

The fourth chapter outlined the results and included the processing, analysis

and interpretation of the data in figures and tables. Thirteen hypotheses relating

to personality, cognitive and biographical differences for the two groups were

formulated and tested. The results of the various statistical procedures were

portrayed and the main findings, based on the statistical analyses, were given.

The results of descriptive statistics, one-way multivariate analyses of variance

(MANOVAs using Hotelling's T 2), Student's t-tests, cross-tabulations and chi-

square test were revealed. The main findings, based on the research results of

the statistical analysis, revealed that no statistically significant differences existed

between online and conventional students.

The current chapter will depict the findings of the study. The focus is on how the

theoretical and empirical objectives of the study were reached. Conclusions will

draw from both the literature research and the empirical research. The value, as

well as the limitations of the study will be pointed out. Recommendations will be

made, based on the findings of the study and suggestions for potential research

opportunities will be made.

In the following section a summary of the methodology employed is given.

5.3 A Summary of Methodology

A summary of the methodology of the study includes the basic characteristics of

the sample, the measuring instruments, the research procedure and the

statistical analyses.

5.3.1 The Research Participants

The sample, from which the primary and secondary data were obtained,

consisted of first-year students at a large University in South Africa. The study

population consisted of first-year students enrolled for a compulsory Business

Science Course, tested in 2001. Based on self-selection, 242 students voluntarily

made use of the online course, while 323 students used the conventional course

offered. The ages of the students varied from 18 to 21 years, 91% of them 18

years and younger. As far as gender is concerned 51,9% were female and

69,1% preferred English as the language of tuition.

5.3.2 The Measuring Instruments

In order to identify the personality and cognitive differences between online and

conventional students, the following measuring instruments were selected for use

in the current study:

5.3.2.1 Personality Differences

5.3.2.1.1 The 16 Personality Factors Questionnaire (16PF),

5.3.2.1.2 Jung's Personality Types,

5.3.2.1.3 The Locus of Control Inventory (LCI), and

5.3.2.1.4 The 19 Field Interest Inventory (19 FII).

5.3.2.2 Cognitive Differences

5.3.2.2.1 The Senior Aptitude Tests (SAT),

5.3.2.2.2 The Academic Performance at School,

5.3.2.2.3 General Average Matriculation Achievement,

5.3.2.2.4 The First-Semester Academic Performance at University, and

5.3.2.2.5 The Academic Performance on the HRM Course.

5.3.2.3 Biographical Differences

The biographical questionnaire included items requesting the respondents'

gender, age, computer literacy, and preferred language.

5.3.3 The Research Procedure

The prescribed psychometric battery of tests was administered to the full intake

of first-year university students by the Career Counselling Division during their

first month at the university. Testing was compulsory for all first-year students

and took place over four days under strict supervision. A course was designed

for conventional classes supplemented with an online version of the same course

and students were allowed to freely choose to enrol either in online or

conventional sections of the course. Performances of the students in the first

semester as well as during the course being presented were collected as primary

data. Due to incompleteness of some records, only 586 records could be used in

the sample.

5.3.4 Statistical Analysis


analyses of variance (MANOVAs using Hotelling's T2), followed by Student's t-


test hypotheses relating to biographical differences, cross-tabulations were

calculated and the chi-square test was used. Cramer's V was also calculated as

an index of the strength of the association between the biographical variables. All

calculations were done by means of the SPSS- Windows programme of SPSS -

International. The analysis was conducted with the assistance of a Statistical

Consultation Service.

5.4 Discussion of Findings

This section discusses the key findings and indicates how the theoretical and

empirical objectives of the study were reached.

5.4.1 Literature Research Objectives

Following is a summary of the key findings of the study in respect of the primary

and secondary objectives of the literature research.

5.4.1.1 Findings Regarding the Primary Objective of the Literature

Research

The primary objective of the literature research was to create a theoretical frame

of reference for the concept of online education.

Yet, despite the fact that there is an impressive amount of writing that concludes

that distance learning is viable and effective, the literature review revealed that

studies that examine the effect of individual differences in the online education

are grossly neglected. A review of the few studies being conducted indicates that

several learner characteristics have some effect on the success of the learner in

a distance education environment. Goldsmith (2001) claims that distance

learning studies since 1990 have only examined the use of technology and

learning, but studies focused on online education are lacking, as well as

"research into what makes online courses and online students successful'.

(p.108)

Hence, a need was clearly identified to assess personality and cognitive

differences in online education.

5.4.1.2 Findings regarding the Secondary Objectives of the

Literature Research

The secondary objectives of the literature review yielded the following:

5.4.1.2.1 To discuss the role of online education in Higher Education

This discussion provided a background for understanding online education and,

specifically, distance education and how it influences higher education. A brief


the current use of computer-mediated communication was outlined. Also briefly

reviewed were the theories underlying distance education, focusing on those

influencing online education. From the review it is evident that there are several

different viewpoints regarding distance education. Nevertheless Peters (2002, p.

13) concludes that there is clearly a structural relationship between distance

education and online-learning.

5.4.1.2.2 To Review the Research on distance- and conventional

education



education (Russell (2002); Charp (1999)). As indicated, there is a good deal of

research dealing with distance education. From the literature it seems that most

of the research being done focuses on the effectiveness of online education

compared to traditional face-to-face education and addresses a variety of issues.

Distance education research is concentrated primarily on three areas and

includes:

Course completion and dropout rate;

Student outcomes, such as grades and test scores;

Attitudes and perceptions about learning through distance education; and

Most of these studies conclude that, regardless of the technology used, distance

learning courses compare favourably with classroom-based instruction. Many

experimental studies indicate that students participating in distance learning

courses perform as well as their counterparts in a traditional classroom setting.

These studies suggest that distance-learning students have similar grades or test

scores, or have the same attitudes toward the course. The descriptive analysis

and case studies focus on student and faculty attitudes and perceptions of

distance learning. These studies typically conclude that students and faculty

have a positive view about distance learning. These examples of experimental

research are consistent with many other studies that indicate students

participating in distance learning courses perform as well as their counterparts in

a traditional classroom setting. In other words, distance is not a predictor of

learning.

5.4.2 Empirical Research Objectives

Following is a discussion on the findings in respect of the primary and secondary

objectives of the empirical research.

5.4.2.1 Findings regarding the Primary Objective of the Empirical

Research

The primary objective was formulated as to whether there is personality,

cognitive and biographical differences between online and conventional students.

The empirical findings did not support the expectations of the study. It was

expected that the study would identify significant personality and cognitive

differences between online and conventional students. The results of the

empirical research suggested that, in some instances, there are significant

personality and cognitive differences between online and conventional students.

However, further analyses carried out revealed that very small to small effect

sizes were obtained.

5.4.2.2 Findings regarding the Secondary Objectives of the

Empirical Research

The findings, based on the statistical analysis in respect of the secondary

objectives of the research, are discussed in more detail next.

5.4.2.2.1 Personality Differences

Four hypotheses relating to personality differences for the two groups in respect

of (a) the 16 Personality Factor Questionnaire (16PF); (b) Jung's Personality

Types; (c) the Locus of Control Inventory (LCI) ; and (d) the19 Field Interest

Inventory (19 FII) were formulated and tested.

The knowledge available in the literature was evidently not sufficient to predict or

confirm the results. The limited use of these personality measures in

international research (and as far as can be determined, non-existent for any

research in the South African environment) with regard to online students, makes

this research stand out as providing cutting-edge knowledge in this area.

Nevertheless, some discussion of the results in the broader context of the

literature is possible.

Hypothesis, H1, postulated a statistically significant difference between the

vectors of means of the two groups in respect of the 16PF.

The results of the study indicated that, except for Intelligence and

Conscientiousness, the group means of online and conventional students did not

differ statistically significantly. The findings do not support the above hypotheses

in full. The results are contradictory to the findings of Biner et al (1995) and

Macgregor and Donaldson (2000). Macgregor and Donaldson (2000) found that

the two groups were very different and concluded that "personality does matter"

(p. 114). Successful online students seem to be "more worrisome, serious, shy

and non-experimental than students in traditional classrooms.." (p. 114). Online

students tend to be "more introverted, accommodating and self-controlling" (p.

114) compared to students in the traditional classroom. Online students tend to

be more "cooperative, trusting and tough minded' than students in the traditional

setting. The current research therefore only seems to indicate that online

students tend to be more intelligent and more conscientious. The former is also

confirmed by the results of cognitive differences (par 5.5.4).

This clearly is an area in which further research is needed.


vectors of means of the two groups in respect of the JPT.

The results indicate that the vectors of means of online and conventional

students did not differ statistically significantly. The findings do not support the

above hypotheses. The results are contradictory to the findings of Biner et al

(1995) and Macgregor and Donaldson (2000) who found that online students

tend to be more introverted than conventional students. The research of Todd

and Raubenheimer (1991) on traditional students should also be considered in

this context.


vectors of means of the two groups in respect of the LCI.


students did not differ statistically significantly. The findings do not support the

above hypotheses. The results are contradictory to the findings of Wang and

Newlin (2000) who indicated that online students exhibited a greater external

locus of control than their counterparts in conventional courses. The results are

also contradictory to the findings of Dille and Mezack (1991a, 1991b) that

learners with an internal locus of control are more likely to persist in distance

education than those with an external locus of control.

Hypothesis, H 4 , postulated no statistically significant difference between the



students did not differ statistically significantly. The findings do support the above

hypotheses. With respect to the differences between traditional and distance

learning regarding interest, there seems to be a definite lack of literature in this

regard. According to Todd and Raubenheimer (1991) it seems only logical that

interest plays a major role; the more interested one is the more motivated and

enthusiastic one becomes.

Based on above findings the four Hypotheses relating to personality differences

for the two groups in respect of (a) the 16 Personality Factor Questionnaire

(16PF); (b) Jung's Personality Types; (c) the Locus of Control Inventory (LCI);

and (d) the19 Field Interest Inventory (19 FII) are rejected: there are no

statistically significant personality differences between online and

conventional students.

5.4.2.2.2 Cognitive Differences

Five Hypotheses relating to cognitive differences for the two groups in respect of

(a) the Senior Aptitude Tests (SAT), (b) Academic Performance at School, (c)

General Average Matriculation Achievement, (d) First-Semester Academic

Performance at University and (e) Academic Performance on the HRM Course

were formulated and tested. The results regarding these hypotheses will be

discussed in the following paragraphs.

A substantial portion of research on distance learning however seems to focus

on student outcomes, such as grades and test scores. A myriad such studies

conclude that, regardless of the technology used, there is no significant

difference in the learning outcomes of online students and face-to-face students

(Russell 1999; Navarro, & Shoemaker, (1999) Hammond (1997) Cheng,

Lehman, & Armstrong, (1991) Martin, & Rainey, (1993) Johnson (2002,

Shachar (2002) Brown, & Liedholm, (2002) Thomas (2001) Efendioglo, &

Murray (2000) Redding (2000) Stinson and Claus (2000) Navarro & Shoemaker

(1999) LaRose, Gregg, & Eastin, (2001) Gagne and Shepherd (2001) Johnson,

Aragon, Shaik, & Palma-Rivas (2000) Souder, (1993).

Hypothesis, H 5 , postulated a statistically significant difference between the

vectors of means of the two groups in respect of the SAT.


students did not differ statistically significantly. (Based on further analyses

carried out which revealed that very small to small effect sizes obtained).

Therefore the findings do not support the above hypotheses. Cognitive ability

(Intelligence measures) is well documented as one of the best predictors of

academic achievement, though most of the research being done focuses on on-

campus students. There seems to be no basis for predicting that online students

differ in the same ways as traditional students. Only in one study, Hiltz (1993),

used the Scholastic Aptitude Test (SAT) and found moderate to strong

relationships between academic ability and outcomes in the virtual classroom

compared to the traditional classroom.


vectors of means of the two groups in respect of the academic performance at

school.



carried out which revealed very small to small effect sizes obtained). Therefore

the findings do not support the above hypotheses.


vectors of mean of the two groups in respect of GAMA (M-score).




Therefore the findings do not support the above hypotheses.

Hypothesis, H8, postulated no statistically significant difference between the

vectors of means of the two groups in respect of academic performance in the

first semester.




Therefore the findings support the above hypothesis. The results are also

contradictory to the findings of Russell (1999); Navarro, & Shoemaker, (1999);

Hammond (1997); Cheng, Lehman, & Armstrong, (1991); Martin, & Rainey

(1993); Johnson (2002); Shachar (2002); Thomas (2001); Redding (2000);

Stinson and Claus (2000); LaRose, Gregg, & Eastin (2001); Gagne & Shepherd

(2001) and Souder (1993). The results confirm the findings of Brown, & Liedholm


(2000) and Navarro & Shoemaker (1999).

Hypothesis, H9, postulated no statistically significant difference between the

vectors of means of the two groups in respect of Academic Performance on the

HRM course.



carried out which revealed that small effect sizes obtained). Therefore the

findings support the above hypothesis. The results are also contradictory to the

findings of Russell (1999); Navarro, & Shoemaker, (1999); Hammond (1997);



LaRose, Gregg, & Eastin (2001); Gagne & Shepherd (2001) and Souder (1993).

The results confirm the findings of Brown, & Liedholm (2002); Efendioglo, &

Murray (2000); Johnson, Aragon, Shaik, & Palma-Rivas (2000) and Navarro &

Shoemaker (1999).

Based on above findings all five Hypotheses relating to cognitive differences for

the two groups in respect of (a) the Senior Aptitude Tests (SAT); (b) the

academic performance at school; (c) general average matriculation achievement

(GAMA); (d) first-semester academic performance at university; and (e) the

academic performance on the HRM course are rejected: there are no

statistically significant cognitive differences between online and

conventional students.

5.4.2.2.3 Biographical Differences

Four Hypotheses relating to biographical differences for the two groups in

respect of (a) gender, (b) age, (c) language, and (d) computer literacy were

formulated and tested.

Hypothesis H10, postulated a statistically significant association between gender




carried out which revealed that a very small effect size obtained). Therefore the

findings do not support the above hypothesis. This suggests that online learning

was not dependent on gender. The results are also contradictory to the findings

of Powell, Conway and Ross, (1990).

Hypothesis H11, postulated a statistically significant association between age






was not dependent on gender. The results are contradictory to the findings of

Navarro, & Shoemaker, (1999); Hammond (1997); Cheng, Lehman, &

Armstrong, (1991); Martin, & Rainey (1993); Johnson (2002); Shachar (2002);

Thomas (2001); Redding (2000); Stinson and Claus (2000); LaRose, Gregg, &

Eastin (2001); Gagne & Shepherd (2001); Souder (1993) and Powell, Conway

and Ross, (1990).

Hypothesis H12, postulated a statistically significant association between

computer literacy and online vs conventional students.





was not dependent on computer literacy.

Hypothesis H13, postulated a statistically significant association between

preferred language and online vs conventional students.





was not dependent on preferred language. The results are also contradictory to

the findings of Navarro, & Shoemaker, (1999); Hammond (1997); Cheng,

Lehman, & Armstrong.

Based on above findings all four hypotheses relating to biographical differences

for the two groups in respect of (a) gender, (b) age, (c) language, and (d)

computer literacy are rejected: there is no statistically significant

biographical difference between online and conventional students.

It is reasonable to conclude that there is insufficient evidence to support the

expectation that there is significant personality, cognitive and biographical

differences between online and conventional students. This is supported by

studies done by Schlosser & Anderson (1997) and Moore and Kearsley (1996).

What makes any course good or poor is a consequence of how well it is

designed, delivered, and conducted, not whether the students are face-to-face or

at a distance (Moore and Kearsley, 1996).

This concludes the discussion of key findings. In the following sections the

significance, contribution and limitations as well as suggestions for potential

research opportunities will be reflected upon. The intention is not to focus on

"what went well" or "what went badly", but, rather, on the questions: What was

learned?, and Why was there progress?

5.5 Significance of the study

In addressing the problem "Who undertakes and succeeds in online courses?"

the significance of this study was three-fold, namely theoretical, practical and

methodological. The current deficit in empirical data is unfortunate because

online learning and its inherent multimedia environment are increasingly

prevalent in the higher education environment.

5.5.1 Theoretical Significance

The research has more comprehensively shed light on expected differences

between online and conventional students with respect to personality and

cognitive differences than any other research carried out. The research provides

justification for future research into the question of who undertakes online

courses? Insights into the various aspects of online learning will contribute to

theory building, while the results of this research can be considered to be a

catalyst for additional research.

5.5.2 Methodological Significance

This study comprehensively contributes to the value of quantitative methods in

assessing differences between online and conventional students with respect to

personality, cognitive and biographical differences based on nationally accepted

and validated measuring instruments. It serves as a benchmark for future

research designs on differences between online and conventional courses. A

concerted effort has been made to ensure that the online course is as nearly

identical to the conventional course as possible, to control for extraneous

variables, which in turn limit the ability to demonstrate cause and effect.

5.5.3 Practical Significance

This study focussed on one of the burning 'people' issues in South Africa and

contributes to a better understanding of who undertakes online learning by

assessing personality and cognitive differences between online and conventional

students. Insight into personality and cognitive differences enables the effective

management thereof, which in turn contributes to the success of educational

institutions. It also contributes to providing a framework for institutions of higher

education to understand, manage and facilitate online and conventional students.

Educators and course designers will be able to match the needs and

expectations of their online students more effectively. This will ensure that, from

a pedagogical perspective, the design of a flexible learning environment within a

technology -rich medium is not hampered by a lack of understanding of the

needs of learners. This information will allow institutions of higher education to

increase the overall satisfaction of the learner in the online environment. Lastly,

it will make a contribution to ensure that course design does not become

technology driven but allows technology to serve as a resource in support of

student needs.

5.6 The main contribution of the study

According to Dubin (1976), theories represent a conceptual simplification of

complex, real world situations that enhance our understanding about a

phenomenon. Researchers (Christensen and Raynor, 2003; Dubin, 1976;

Mouton, 2001; Whetton, 1989) are adamant that in general, outstanding theories

have the following characteristics:

It is providing a basis for predictions ;

Providing a framework for the current reality to understand the what

and the why;

Creating a firm base of reference to a domain of study; and

Simplifies our understanding the world.

Specific theoretical, methodological and practical contributions of this study are

as follows:

5.6.1 Theoretical Value

The methodology was based on a well-constructed research design with

regard to such factors as the populations being compared; the treatments

being given, the validity and reliability of the measuring instruments; the

statistical techniques being applied, and the validity, reliability, and

generalisability of data on which the conclusions are based.

The research did not support the proposition that online and conventional

students are different in terms of personality, cognitive and biographical

variables;

The research created a theoretical frame of reference for the concept of

online education;

The research provided a background for understanding online education

and specifically distance education and how it impacts on higher

education;

The research used several means to try to take individual student

differences into account;

The research served as a catalyst for future research in online learning.

As can be seen from the above points, these theoretical contributions are

a first in the South African research context!

5.6.2 Methodological Value

The well-constructed research design contributes comprehensively to the

limited body of original research studies.

This study made lengthy use of inferential statistical procedures going

beyond bi-variate analysis, using one-way multivariate analyses of

variance and Student's t-tests while further analyses included estimated

effect sizes based on coefficient eta.

Very little empirical research has been conducted, certainly in the South

African context, but also internationally, in assessing differences between

online and conventional students;

The research provides guidelines for a quantitative assessment of

differences between online and conventional students with respect to

personality, cognitive and biographical differences;

5.6.3 Practical Value

The research contributes to a very important "burning people issue" of

human capital in South Africa, namely education, training and

development;

The research provides a framework for assessing differences between

online and conventional students with respect to personality, cognitive

and biographical differences;

The research enables institutions of higher education to better understand

and manage their online and conventional students and to see these

findings as support for offering asynchronous courses to diverse students;

The research provides evidence that should encourage educational

institutions to offer online education to all types of students.

The next section outlines the limits of the research.

5.7 Limitations of Study

Although this study has provided insights into personality and cognitive

differences between online and conventional students, especially within the

South African context, it is important to recognize limitations associated with this

study:

5.7.1 Delimitations

The following delimitations of the study were identified:

Firstly, only students from one large South African University, from one faculty

and registered for a compulsory first-year course were used. The sample was

chosen because the researcher was familiar with the online environment and

was assisted by the lecturers presenting this specific course. Since a limited

sample was used, the results should only be generalised to the population and to

other institutions with caution and not without additional empirical tests.

The research focuses on an online course in a specific South African Higher

Education context. Other institutions that might provide online education were not

included or represented. The lecturer contracted for the presentation of the

course had taught and designed various other online courses.

5.7.2 Limitations

The limitations of the study lay in the design, subjects and the nature of the

online course being presented. Each of the limitations will be elaborated on in

the following paragraphs.

The limitations inherent in the design of this research include the quasi-

experimental nature of the study. A completed random assignment of students

was not possible, but even if it had been, it would not have been possible to

identify certain types of students who preferred online courses versus

conventional courses.

The second limitation relates to the subjects being used for this research.

Although a relatively large sample of subjects was used it was, however, a

compulsory course. The two student populations were not distinctly separate

from each other. The online students were a subset of the populations of

students in the conventional face-to-face environment. The sizes of the two

student populations differ, which has an influence on both the statistical analyses

as well as on the findings based on the analyses, despite statistical measures to

counteract these effects. The relatively large sample did not allow control in

terms of limited interaction between the two groups. An additional concern was

the rate of participation of online students in the research. These could have

biased the results achieved in different unknown ways.

The third limitation relates to the nature of the online course. This was the first

time students were exposed to online education. The effectiveness of the

computer and software technology on students' decision to opt for the online

course was also not included in this study.

Lastly it must be borne in mind that the study suffers from the usual limitations of

survey research.

5.8 Recommendations

It is clear from the above that a number of recommendations can be made, but

with caution, taking into account what Christensen and Raynor (2003, p. 72)

proclaimed: "in business ..., no single prescription cures all ills".

Following are recommendations made regarding the theoretical, methodological

and the practical perspective:

5.8.1 Recommendations from a Theoretical Perspective


highlight a need for research to be conducted into the various areas of online

education (Russell, 2002; Charp, 1999). Merisotis and Olsen (2000), confirm this

view by concluding that "while a plethora of literature on the distance education

phenomenon is available, original research on distance education is limited".

From a theoretical perspective it is recommended that:

A meta-analysis of existing research may help to explain differences

between online and conventional students.

From the literature review it is evident that there are several different

viewpoints regarding distance education. Efforts should be made to

integrate these theoretical perspectives.

A more integrated, coherent, and sophisticated programme of research on

online learning based on theory needs to be developed.

5.8.2 Recommendations from a Methodological Perspective

The following suggestions may improve the methodology used:

Multivariate analyses should be used in future.

The benchmark that this research provides should be employed in

different institutions of higher education.

Virtually all of the research focuses upon individual courses and not on

a full programme, which would be recommended for a future project.

A concerted effort should be made to ensure that the online course is

as nearly identical to the conventional course as possible to control for

extraneous variables which, in turn, will ensure the ability to

demonstrate cause and effect.

A qualitative research paradigm should be employed to supplement

the quantitative surveys.

5.8.3 Recommendations from a Practical Perspective

To add value from a practical perspective the following recommendations are

suggested:

The framework provided for assessing differences between online and

conventional students should be employed in different institutions of higher

education;

Educational institutions should offer online education to all types of students.

The results should be used by institutions of higher education to understand,

manage and facilitate online and conventional students.

The effectiveness of the computer and software technology on a student's

decision to opt for the online course should be included.

5.9 Suggestions for Potential Research Opportunities

Edvinsson (2002, p. 202) refers to potential research opportunities by stating

"there is no end, just another question, another curious leap into the dark'. This

is supported by Christensen and Raynor (2003, p. 71) indicating, "The work of

building ever-better theory is never finished". Within the framework of this study

the following suggestions for potential research opportunities are made:

A comparison between students from different South African Universities, from

different faculties and registered for different courses to generalise the findings

should be made.

Further research should be undertaken to include a comparison between

students from the South African Higher Education context and other institutions

that might provide online education.

Given the findings of this study, there is still a large amount of effect size to be

explained. Individual characteristics such as learning styles and commitment

could be included.

5.10 Conclusion

In this chapter a summary of the methodology, the key findings in respect of the

objectives of the study, both the literature objectives and the empirical objectives;

the significance and main contributions as well as limitations of this research

were described. Suggestions were made as to where future research efforts

could contribute to the body of knowledge related to differences between online

and conventional students

The study of online education is a relatively new field of study and many gaps still

exist in the body of knowledge. The advances in information technologies have

created an array of possibilities for today's learners in institutions of higher

education. The findings of this study not only provide valuable insights into the

theory of online education, thereby contributing to the body of knowledge, but

also serve as a guide to educators and course designers to match the needs and

expectations of their online students more effectively.

At the end of this study the words of Jane M. Carey, Professor of Information

Systems, Arizona State University West School of Management, seems an

appropriate reminder of the area in which this research can make a small

contribution:

"However, we will never be able to halt the increasing rate at which we are

delivering online courses. These courses will be offered more and more,

regardless of outcomes. It is imperative that we begin to understand how to

measure and improve learning outcomes for online courses. If we don't begin, we

may end up with a generation of learners who have failed to grasp and

understand the skills and knowledge they need to succeed in their work and,

indeed, in their lives".

UNIVERSITY OF JOHANNESBURG UNIVERSITEIT VAN - .4.

JOHANNESBURG KINGSWAY CAMPUS / 'CAMPUS

—POSBUS 524 BOX 524 AUCKLAND PARK

2006 Tel: (011) 489-2165

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