COLLABORATIVE CONTENT ORGANISER (CCO) LEARNING SYSTEM: STUDY OF

TECHNOLOGY ACCEPTANCE AND EFFECTIVENESS

KHOR EAN TENG

UNIVERSITI SAINS MALAYSIA

2015

COLLABORATIVE CONTENT ORGANISER (CCO) LEARNING SYSTEM: STUDY OF

TECHNOLOGY ACCEPTANCE AND EFFECTIVENESS

by

KHOR EAN TENG

Thesis submitted in fulfillment of the requirements for the degree of

Doctor of Philosophy

February 2015

ii

ACKNOWLEDGEMENT

First and foremost, I would like to extend my heartiest thanks and

appreciation to my supervisor, Professor Dr. Hanafi Atan, for his never-ending

support and valuable guidance. Particularly, his willingness to supervise and

generous flexibility with my schedule deserves many thanks. His support and

attention, has played a great role in ensuring that I continue to thrive to produce a

complete research work.

My sincere appreciation also extends to Associate Professor Dr. Shukri

Yahaya whom I contacted for his professional statistical advice. A token of

appreciation also goes to the other experts and researchers for their useful feedback. I

would also like to express my appreciation to survey respondents for the time they

took in providing data for this study.

Last but not least, I would like to express my gratitude to my husband, for his

ever-present support, love and constant encouragement in helping the research to be

a success. An honourable mention goes to my beloved family members for their

moral support and tolerance with me in pursuing my Ph.D programme. Without their

constant support, critique and encouragement, the road to completing this project

would have been desolate, long and hard.

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TABLE OF CONTENTS

Page

Acknowledgement ii

Table of Contents iii

List of Tables vii

List of Figures ix

List of Abbreviations xi

Abstrak xii

Abstract xiii

CHAPTER 1 - INTRODUCTION

1.1 Introduction 1

1.2 Research Background 2

1.3 Problem Statements 8

1.4 Significance of the Research 11

1.5 Research Objectives 12

1.6 Research Questions 13

1.7 Research Hypotheses 13

1.8 Research Limitations 15

1.9 Operational Definition 16

1.10 Theoretical Framework 20

1.11 Research Framework 21

1.12 Summary 25

CHAPTER 2 – LITERATURE REVIEW

2.1 Introduction 26

2.2 Collaborative Learning 26

2.3 Generative Learning 29

2.3.1 Concept Mapping 31

2.4 Learning Objects 33

iv

2.4.1 Metadata of Learning Object 34

2.4.2 Learning Object Design 35

2.4.3 Learning Object Design and Generative Learning 37

2.5 Behavioural Intention Theory 38

2.5.1 Theory of Reasoned Action (TRA) 39

2.5.2 Theory of Planned Behaviour (TPB) 40

2.5.3 Technology Acceptance Model (TAM) 41

2.6 Hypothesised Model 43

2.6.1 Computer Self-Efficacy (CSE) 44

2.6.2 Training (TR) 45

2.6.3 Cognitive Absorption (CA) 46

2.6.4 Intrinsic Motivation (IM) 47

2.6.5 Perceived Ease of Use (PEOU) 48

2.6.6 Perceived Usefulness (PU) 49

2.6.7 Behavioural Intention (BI) 50

2.6.8 Actual Use (AU) 50

2.7 Summary 52

CHAPTER 3 – PROTOTYPE SYSTEM

3.1 Introduction 53

3.2 Overview of Prototype System 53

3.2.1 Homepage 56

3.2.2 Add/Create 58

3.2.3 Design/View 65

3.2.4 E-Collaboration 73

3.3 Summary 74

CHAPTER 4 – RESEARCH METHODOLOGY

4.1 Introduction 76

4.2 Research Design 76

4.3 Population and Sample 78

4.3.1 Sampling Procedure 79

4.4 Survey Instruments Design and Construction 79

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4.5 Instrument Validation 81

4.6 Procedures for Data Collection and Analysis 83

4.7 Pilot Study 86

4.7.1 Reliability Analysis 86

4.8 Data Analyses and Statistical Data Analysis Techniques 88

4.8.1 Descriptive Statistics 88

4.8.2 Inferential Statistics 88

4.8.2 (a) Correlation Analysis 89

4.8.2 (b) Regression Analysis 90

4.8.2 (c) T-Test Analysis 91

4.9 Summary 92

CHAPTER 5 – DATA ANALYSIS AND RESULTS

5.1 Introduction 94

5.2 Demographics Data 94

5.2.1 Age 95

5.2.2 Gender 96

5.2.3 Online Learning Experience 96

5.2.4 Computer with Internet Experiences 97

5.2.5 Time Spent on Computer and Internet 98

5.3 Statistical Analysis 100

5.3.1 Research Question 1 101

5.3.1 (a) Descriptive Statistics 101

5.3.1 (b) Discriminant Validity 103

5.3.1 (c) Structural Model 104

5.3.2 Research Question 2 106

5.3.3 Research Question 3 110

5.4 Summary 120

CHAPTER 6 – DISCUSSION AND CONCLUSION

6.1 Introduction 121

6.2 Discussion 121

6.2.1 Discussion on Research Question 1 122

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6.2.2 Discussion on Research Question 2 127

6.2.3 Discussion on Research Question 3 129

6.3 Implications of Research 132

6.4 Recommendations for Future Research 133

6.5 Summary and Conclusion 134

REFERENCES

139

APPENDICES

APPENDIX A 161

APPENDIX B 162

APPENDIX C 167

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LIST OF TABLES

Page

Table 1.1 Learning Theories (Keesee, 2011) 4

Table 1.2 Operational Definition 16

Table 2.1 Learning Design Transition 36

Table 2.2 Predictive Model Summaries 39

Table 2.3 Literature Review Summaries 51

Table 4.1 Root Constructs and Source 80

Table 4.2 Cronbach’s Alpha Reliability of Measurement Scales of TAMs 87

Table 5.1 Age of Respondents 95

Table 5.2 Gender of Respondents 96

Table 5.3 Web Learning Experience of Respondents 97

Table 5.4 Computer Internet Experience of Respondents 98

Table 5.5 Respondents’ Time Spent on Computer and Internet 99

Table 5.6 Internal Consistency Reliability Testing (Actual Experimental Study) 100

Table 5.7 Descriptive Statistics of the Measurement Instruments 102

Table 5.8 Inter Constructs Correlation 103

Table 5.9 Multiple Regression Tests of the Research Model 104

Table 5.10 Result Summary of Research Question 1 106

Table 5.11 T-Test Analysis of Pre-Test and Post-Test Mean Score for EG 107

Table 5.12 T-Test Analysis of Pre-Test Mean Score between CG and EG 108

Table 5.13 T-Test Analysis of Post-Test Mean Score between CG and EG 108

Table 5.14 Mean Score of Pre-Test and Post-Test for CG and EG 109

Table 5.15 Results Summary of Research Question 2 110

Table 5.16 Number of Students versus Number of Postings 111

Table 5.17 Grading System 112

Table 5.18 Rubric Scoring Tool 113

Table 5.19 Mean and Standard Deviation of ‘Increment’ 113

Table 5.20 Postings versus ‘Increment’ Mean Score 114

Table 5.21 Linear Regression Result for ‘Postings’ on ‘Increment’ Mean Score 114

Table 5.22 Mean and Standard Deviation of ‘Post-Test’ 115

Table 5.23 Postings versus ‘Post-Test’ Mean Score 115

Table 5.24 Linear Regression Result for ‘Postings’ on ‘Post-Test’ Mean Score 116

viii

Table 5.25 Mean and Standard Deviation of ‘Knowledge Constructed’ 117

Table 5.26 Postings versus ‘Knowledge Constructed’ Mean Score 117

Table 5.27 Linear Regression Result for ‘Postings’ on ‘Knowledge Constructed’ 118

Table 5.28 Result Summary of Research Question 3 119

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LIST OF FIGURES

Page

Figure 1.1 Theoretical Framework 23

Figure 1.2 Research Framework 24

Figure 2.1 The Theory of Reasoned Action (Fishbein & Ajzen, 1975) 40

Figure 2.2 The Theory of Planned Behaviour (Ajzen, 1991) 41

Figure 2.3 The Technology Acceptance Model (Davis et al., 1989) 42

Figure 2.4 Collaborative Content Organiser Acceptance Model (CCOAM) 44

Figure 3.1 Software Diagram for Prototype Structure 55

Figure 3.2 Login Page 56

Figure 3.3 Homepage for Instructor and Student 57

Figure 3.4 Course List and Topic List 58

Figure 3.5 Add, Edit and Delete a Course 59

Figure 3.6 Add, Edit and Delete a Topic 60

Figure 3.7 Learning Object (LO) 61

Figure 3.8 Add, Edit and Preview a LO 62

Figure 3.9 Self-Test and Structured Question Upload 63

Figure 3.10 MCQs Upload 64

Figure 3.11 Learning Outcomes and Learning Objectives 65

Figure 3.12 LO Search and LO Repository 66

Figure 3.13 LO Organiser Tool 67

Figure 3.14 Student’s View of Assessment 68

Figure 3.15 Check List 69

Figure 3.16 Lesson Map 70

Figure 3.17 Email and Messaging 71

Figure 3.18 Notification Corner 72

Figure 3.19 Forum Discussion 73

Figure 3.20 Chat Room 73

Figure 4.1 Experimental Group and Control Group Pretest-Posttest Designs 83

Figure 4.2 Process Flow of Experimental Group 84

Figure 5.1 Bar Graph of Respondents’ Age 95

Figure 5.2 Bar Graph of Respondents’ Gender 96

Figure 5.3 Bar Graph of Respondents’ Web Learning Experience 97

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Figure 5.4 Bar Graph of Respondents’ Computer Internet Experience 98

Figure 5.5 Bar Graph of Respondents’ Time Spent on Computer and Internet 99

Figure 5.6 Mean of Variables 101

Figure 5.7 The Hypothesized Model Results 105

Figure 5.8 Line Graph of CG and EG for Pre-Test and Post-Test Mean Score 109

Figure 5.9 Line Graph (Postings versus ‘Increment’) 113

Figure 5.10 Line Graph (Postings versus ‘Post-Test’) 116

Figure 5.11 Line Graph (Postings versus ‘Knowledge Constructed’) 118

xi

LIST OF ABBREVIATIONS

CCO Collaborative Content Organiser

ICT Information and Communication Technology

IT Information Technology

IS Information System

WWW World Wide Web

LO Learning Object

LOR Learning Object Repository

TRA Theory of Reasoned Action

TPB Theory of Planned Behaviour

TAM Technology Acceptance Model

CSE Computer Self-Efficacy

TR Training

CA Cognitive Absorption

IM Intrinsic Motivation

PEOU Perceived Ease of Use

PU Perceived Usefulness

BI Behavioural Intention

AU Actual Use

CCOAM Collaborative Content Organiser Acceptance Model

XML Extensible Markup Language

IMS Instructional Management System

MIS Management Information Systems

MCQ Multiple Choice Questions

SQ Structured Questions

AVE Average Variance Extracted

CG Control Group

EG Experimental Group

SPSS Statistical Package for the Social Sciences

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SISTEM PEMBELAJARAN PENYUSUN KONTEN BERKOLABORATIF:

KAJIAN PENERIMAAN TEKNOLOGI DAN KEBERKESANAN

ABSTRAK

Kajian ini bertujuan untuk menyelidik persepsi pelajar Pendidikan Jarak Jauh

(PJJ) terhadap sistem pembelajaran Penyusun Konten Berkolaboratif (PKB) dan

menilai keberkesanannya. Data sampel telah dikumpulkan melalui instrumen kaji

selidik dalam talian. Peralatan tinjauan mencungkil jawapan pada beberapa soalan

jenis Likert dengan majoriti jawapan yang positif. Kajian ini meninjau hubungan

antara pembolehubah yang akan memberi kesan kepada tingkahlaku pelajar dalam

menggunakan system pembelajaran PKB. Keberkesanan sistem pembelajaran PKB

dinilai berdasarkan peningkatan prestasi pelajar. Hubungan antara penglibatan dalam

aktitiviti kolaborasi dan pencapaian pelajar juga diterokai. Keputusan statistik

mendedahkan bahawa sistem pembelajaran PKB diterima baik oleh pelajar dan

sistem pembelajaran itu dapat meningkatkan prestasi pembelajaran pelajar.

Keputusan yang diperolehi juga menunjukkan hubungan yang positif di antara

penyertaan dalam aktiviti kolaborasi dan pencapaian akademik pelajar. Kajian ini

menyumbang kepada amalan penyelidikan pendidikan dengan menunjukkan persepsi

dan prestasi pembelajaran generatif dan kolaboratif di kalangan pelajar PJJ.

Pendekatan ini boleh merangsang penyelidikan masa depan dalam reka bentuk dan

pembangunan sistem pembelajaran yang lebih kompleks.

xiii

COLLABORATIVE CONTENT ORGANISER (CCO) LEARNING SYSTEM: STUDY OF TECHNOLOGY ACCEPTANCE AND EFFECTIVENESS

ABSTRACT

This research aims to investigate the perception of distance education

students towards the Collaborative Content Organiser (CCO) learning system and

evaluate its effectiveness. The sample data were collected via online survey

instruments. The survey instrument elicited responses on a series of Likert-type

questions with the majority of responses being positive. It involves examining the

relationships between variables that affect students’ behavioural intentions to use the

CCO learning system. The effectiveness of the CCO learning system was evaluated

based on the improvement of students’ academic performance. The relationship

between the participation of students’ in e-collaboration and their achievement was

examined too. The statistical results revealed that the CCO learning system is well

received by the students and the learning system improves students’ learning

performance significantly. The results also indicated that there is a positive

correlation between participation of students in e-collaboration and their academic

achievement. Finally, the study contributes to educational research practice by

demonstrating the perception and performance of generative and collaborative

learning among distance education students. This approach may stimulate future

research in the design and the development of more complex, technology-enriched

learning environments.

1

CHAPTER 1 INTRODUCTION

1.1 Introduction

At present, the use of Information and Communication Technology (ICT)

provides a new form of communication that leads to changes and transforms the way

teaching and learning is conducted (McCormick & Scrimshaw, 2001). There has

been a colossal shift from traditional lecture to student-centred learning. Today,

several instructors advocate the student-centred approach in education, which is

currently focused on knowledge construction instead of knowledge transmission

(Amornsinlaphachai & Deejring, 2012). In student-centred learning, students

construct their own meaning by talking, presenting, discussing and sharing their

knowledge. Collaborative learning and generative learning strategies have been

incorporated into web-based online learning system in order to promote student-

centred learning.

Collaborative learning is a situation in which two or more people learn or

attempt to learn something together (Dillenbourg, 1999). Collaborative learning

breaks the teacher-centred nature of teaching practices and it serves as an essential

element for student-centred learning instruction. Generative learning is a process of

constructing links between new and old knowledge, or how a new idea fits into an

individual’s web of known concept (Wittrock, 1991). Generative learning solves a

problem of passive transmission on knowledge. The generative learning activity

engages students to be active participants in the process of learning and create their

own learning by constructing meaning of their own environment.

2

The prototype system used in this study, namely Collaborative Content

Organiser (CCO) learning system provides a pedagogically-enriched learning

environment to engage students in collaborative learning through online discussion

and interaction and generative learning via concept mapping. The existing learning

system is mainly for instructor to upload and student to download the course

materials. The students do not perform any generative learning activity to construct

new knowledge from prior knowledge and link all the concepts. In computer

education, the prior knowledge of students is the foundation of the further knowledge

construction (Mirmotahari et al., 2003; White, 2001; Holmboe, 1999; Scragg, 1991).

Hence, there is a need to have a learning system that is designed based on

pedagogical specifically generative learning for learning the computing courses.

1.2 Research Background

With the rapid development of ICT, instructors and administrators worldwide

have integrated and utilised Information Technology (IT) into education to improve

students’ learning with the appropriate instructional theories that are coupled with

technologies. Many courses and programs have been slowly transferred into fully

interactive online environments. As pointed out by Saettler (1990), the way of

teaching and learning has changed dramatically over the past decade as a result of

major developments in educational technology.

In recent years, there has been a phenomenal growth in the use of e-learning

for teaching and learning and increased interests in the investigation of the

effectiveness and efficiency towards various instructional design strategies

incorporating e-learning. E-learning serves as the main support and play a vital role

especially in distance learning environment. Distance learning is referred to any form

3

of learning where individuals are not physically present in a traditional setting that

provide individuals with the ability to learn at their own pace and in their own space

(Thoms & Eryilmaz, 2014).

There are evidences which indicate that the effectiveness of distance learning

programme can be enhanced by using student-centred learning strategies and

constructivist learning paradigm (Hannum & McCombs, 2008; Tam, 2000; Wagner

& McCombs, 1995). Moeller and Reitzes (2011) also confirm that technology is able

to support key practices of student-centred learning. Hence, in this study,

constructivism with the principle of student-centred learning was chosen to be

incorporated in the learning system to engage learners to be active participants in the

learning process. Students construct their own understanding from previous

knowledge in either collaborative or self-directed manner instead of predetermined

structure.

There are three main types of instructional design theories, namely

behaviourism, cognitivism and constructivism. Table 1.1 shows the differences

between the three learning theories. For behavioural approach, Gagné (1968)

emphasises the importance of creating particular conditions needed for a particular

type of learning. For cognitive approach, the emphasis was on design based on

characteristics of individual learners while constructivist approach emphasises the

learner’s own activities as the mechanism for learning (Elen & Clarebout, 2001).

There is a trend to shift from behavioural to cognitive and recently, it is

shifting to the constructivist approach. Students no longer passively receive

information by just memorising; they instead process the information meaningfully.

Learning is not just a cognitive issue but also a matter of participating in cultural

4

practices (Sfard, 1998) and creating new knowledge and ideas of value to a

community (Paavola & Hakkarainen, 2005; Scardamalia & Bereiter, 2003).

Table 1.1 Learning Theories (Keesee, 2011)

Behaviourism Cognitivism Constructivism

Key Concepts Focuses on

objectively

observable

behaviours and

discounts mental

activities.

Focuses on the

“brain”, how

humans process

and store

information.

Focuses on how

learners construct

their own

meaning.

Learner’s Role Learners are passive,

just responding to

stimuli.

Learners process,

store, and retrieve

information for

later use.

Learning is an

active process in

which learners

construct new

ideas or concepts

based upon their

current/past

knowledge and

social interactions.

Instructor’s Role Instructor designs

the learning

environment.

Instructor manages

problem solving

and structured

search activities,

especially with

group learning

strategies.

Instructor focuses

on making

connections

between facts and

fostering new

understanding in

students.

Collaborative learning (Levykh, 2008; Vygotsky, 1978) and generative

learning (Bannan-Ritland, Dabbagh & Murphy, 2000) are important elements in

constructivist learning environment. Existing researches suggested that incorporating

various strategies, such as collaborative learning (Marks, Sibley & Arbaugh, 2005)

5

and the use of concept mapping techniques (Chmielewski & Dansereau, 1998) for

generative learning could positively influence the effectiveness of e-learning.

Collaborative learning is drawing much attention among researchers because

of the belief that it is an effective model for the modern education system

(Lautenbacher, Campbell, Sorrows & Mahling, 1997). Active interaction,

collaboration and participation of learners and instructors are becoming more and

more important (Du, Fu, Zhao, Liu & Liu, 2013). Collaborative learning mainly

involves working, learning, building and changing together in a learning

environment. Educational research has shown that learning takes place more

effectively if learners are actively involved, rather than being passive listeners

(Nurmela, Palonen, Lehtinen, & Hakkarainen, 2003). According to Lave and Wenger

(1998), students actively construct knowledge by communicating and interacting

with their peers and achieve the required skills through ongoing interaction among

themselves. Van Merrienboer and Paas (2003) observed that working together while

accomplishing a task is seen as a characteristic of a powerful learning environment,

which facilitates active construction of knowledge. Eichler (2003) found in his study

that students in collaborative learning conditions had more constructive learning

processes.

Morgan and Berthon (2008) define generative learning as the process of

generation, distribution and interpretation of new ideas. Bonn and Grabowski (2001)

found in their studies that generative learning provides the necessary theoretical

framework for research in a constructivist perspective. In generative learning

environment, students are active participants who construct their own learning by

generating relationships between the concepts. According to Nickerson (1995),

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learning that emphasises on the connection between the new and old concepts, and

among the concepts is vital to enhance understanding.

Concept mapping provides an important tool on generative learning

environment (Osborne & Wittrock, 1983; Bannan-Ritland et al., 2000). Concept map

is a visual representation of knowledge organisation consisting of nodes for concepts

and links for their relationships (Novak & Gowin, 1984). According to Tseng,

Chang, Lou, Tan and Chiu (2012), Concept mapping allows learners to represent

relationships among concepts graphically by applying nodes and links. Jonassen

(2000) found in his study that concept map engages learners in the reorganisation of

knowledge, explicit description of concept and their interrelationships, deep

processing of knowledge that promotes better remembering; and relating new

concepts to existing ones that improve understanding. Alpert and Grueneberg (2000)

showed that the process of mapping the concepts in multiple forms is easier to

understand and learn. Moreover, the research findings from Jonassen, Mayes and

McAleese (1993) indicated that individuals learn the most from the design of

instructional materials.

Learning object design can be configured within generative learning

environments from the theoretical perspective of the generative learning (Bannan-

Ritland et al., 2000). Furthermore, the nature and unique attributes of learning object

design can be grounded in constructivist principle to provide learner-centred

learning. The emergence of World Wide Web (WWW) has provoked the web-based

learning. According to Lim (2000), web-based learning environment is able to

support learner-centred learning. The flexibility of learning object also allows

reusable content through the Web (Mohan & Brooks, 2003). Additionally, WWW

7

technology tools are able to support learning with the use of hypermedia to link the

learning objects.

With the potential benefits of constructivist collaborative learning, generative

learning and learning object design, there are questions raised on whether students

will accept and eventually adopt the learning system that incorporates generative

learning strategy, collaborative learning strategy and learning object instructional

design for their learning. It is crucial to understand the factors of information

technology system usage in order to evaluate the effectiveness of system and develop

solutions for user acceptance (Knight & Pearson, 2005; Davis, Bagozzi & Warshaw,

1989). In this regard, this research aims to examine the relationships of variables

that affect students’ behavioural intentions to use the CCO learning system.

The prevailing literature on the technology acceptance model (TAM) and its

variation have explored the critical determinants of technology adoption and user

behaviour (Venkatesh, Morris, Davis & Davis, 2003). TAM is one of the most

widely used theoretical models for numerous empirical studies of user acceptance in

terms of technology (Turner, Kitchenham, Brereton, Charters, & Budgen, 2010) and

have been used by many researchers to explain the behaviours in IT usage (Igbaria,

Guimaraes & Davis, 1995).

Although TAM has been applied in numerous studies testing user acceptance

of information technology, there is a limited study that uses TAM to analyse the

learning system which is incorporated with collaborative learning and generative

learning strategies as well as learning object design. With the extended version of

Davis’s (1989) TAM, this research attempts to determine students’ behavioural

intention to use the CCO learning system.

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1.3 Problem Statements

Gunawardena and McIsaac (2004) criticised the current distance education as

a replicate of conventional lecture-based classroom education where students learn

individually with little interaction among peers. According to Boling, Hough,

Krinsky, Saleem and Stevens (2012), learners do not form online learning

communities, but isolate and disconnect from each other most of the time. In

addition, several research studies revealed that the lack of personal interaction is the

dominant factor to the withdrawal or dropping out of distance education students

(Blaney & Mulkeen, 2008; Yorke & Longden; 2008). Hence, collaborative learning

activities need to be integrated into the existing learning system to enhance student-

instructor interactions or peer-to-peer interactions. In the collaborative learning

environment, the instructors facilitate and initiate the discussion of a particular

content and moderate the online discussion from time to time during the semester.

Students are given the chance to share their knowledge in different ways and

participate in a coordinated effort to solve problems together. With this, distance

education students will not feel alone throughout their studies.

Numerous studies show that Computer Science students lack understanding

of the relationships between the facts they have learned (Mirmotahari, Holmboe &

Kaasboll, 2003; Scragg, Baldwin & Koomen, 1994). New information must be

linked and correlated to the information already understood (Hamza, Alhalabi &

Marcovitz, 2000; Rosenberg, 1976). Rosenberg (1976) also noted that the connection

among the concepts is equally important in the landscape of learning in Computer

Science. The generative learning performs well with respect to active processing in

the linkage of concepts that encourage students to think and construct knowledge

from their own understanding. The generative learning strategy also needs to be

9

integrated into the existing learning system to empower the learning capabilities of

Computer Science students.

There have been more and more institutes of higher education, organisations

and corporations which integrated learning objects into their e-learning systems in

order to make instructional resources more efficient and meet the diverse needs of

learners (Urden & Weggen, 2000). Learning objects have become popular

instructional resources for teaching and learning (Bannan-Ritland et al., 2000). It is a

new type of computer-based instruction grounded in the object-oriented paradigm of

Computer Science to decompose course structure into smaller reusable chunks of

instructions that can be assembled with other objects or can be used as stand-alone

units. Ip and Morrison (2001) have also highlighted that it is crucial to integrate

pedagogies into the learning object design for reusable and flexible learning

environment.

Additionally, Chao (1999) mentioned that distance education, as a pioneer in

the use of technology, not only needs to adjust to the possibilities of advanced

technology, but also needs to modify existing structures to enhance lifelong learning.

Willis (1995) highlighted that although technology plays an important role in the

delivery of distance education, the educators must remain focused on instructional

outcomes and not solely relies on the delivery technology. It has also been

highlighted that Information Technology (IT) must be accepted and appropriately

used by its intended users to achieve the desired goal and for its benefits to be

realised (Agarwal & Prasad, 1998; Igbaria, Zinatelli, Cragg & Cavaye, 1997;

Goodhue & Thompson, 1995). Moreover, research has found that, the values of an IT

innovation lies in its effective and efficient usage but not so much in the technology

itself (Kremers & Van Dissel, 2000).

10

Hoffman (2005) highlighted that the present models employed for evaluation

of complex interaction between the three pedagogical dimensions namely

technologies, learning resources and management factors influencing e-learning

effectiveness have not been entirely tested and developed. Furthermore, Russell

(1999) indicated that technology is unable to replace the important role contributed

by human factor in higher education. Students’ satisfaction, perception and attitude,

motivation and demographics will influence how the courses are designed and the

type of technology to be used (Rourke & Szabo, 2002).

There is no guarantee for the adoption of an instructionally sound and

technically superior instructional resource itself as it is a complex process that is

influenced by many factors, such as individual attributes, system characteristics,

organisational and social interactions (Surry & Ely, 2002). Hence, it is important for

instructors and instructional designers to understand the reason learners use the

educational technology to ensure they are using the technology in the expected ways.

The success of any information systems (IS) largely depends on users’

satisfaction and acceptance (Bharati, 2003; Seddon, 1997; DeLone & McLean,

1992). Tallman (1994) pointed out that a high level of user acceptance and

satisfaction reflects that the users are more willing to continue using the technology.

Thus, the understanding of the factors that promote the effective use of systems (Yi

& Hwang, 2003) and the measurement of the users’ perception (McMahon, Gardner,

Gray & Mulhern, 1999) has become an important issue.

User acceptance model was studied tremendously in many fields since the

last few decades. Although user acceptance has received extensive attention in many

fields, there has been little research in evaluating students’ acceptance and their

perceived impact on the effectiveness of web-learning system, particularly in the

11

learning system that incorporates collaborative, generative learning strategies and

learning objects design.

With the new delivery methods of e-learning systems, TAM can be used in

predicting students’ acceptance of IT or IS system. It has been found to be a

generous and vigorous model in many e-learning studies (Cheung & Huang, 2005;

Drennan, Kennedy & Pisarki, 2005; Liaw & Huang, 2003). Hence, TAM was chosen

in this research study to evaluate students’ acceptance of the CCO learning system.

The four external variables (computer self-efficiency, training, cognitive absorption

and intrinsic motivation) were examined to understand how students’ belief and

perceptions will influence the usage of the learning system.

1.4 Significance of the Research

This research study incorporated methods and theories that may be useful in

measuring users’ technology adoption or aversion of other information systems and

applications. Students’ acceptance and perceptions behaviour should be assessed as

this reflects if the students will eventually adopt and use the learning system in their

learning.

The successful implementation of a system is dependent on the extent to

which such a system is used and eventually adopted by potential users. One of the

key attributes is to identify the learners’ needs, and economically customise the

individual learning in order to promote the successful learning (Wiley, 2000). Hence,

the students’ perception is an important issue to consider before the investment of the

learning system as it might become underutilised or even unused.

The research attempts to fill the need of assessing students’ perception by

constructing a set of questionnaires to measure perceived ease of use and usefulness

12

of learning system that incorporates collaborative and generative learning activities.

The findings of this research study can be used as a guideline for the relevant

stakeholders to make necessary improvement to enhance the functionality and

features of their learning system. The results gained from this study provide

guidelines to further study in implementing other potential instructional technologies

in collaborative and generative learning platform.

The findings of this research study will provide better understanding on how

well the students perceive the benefits of the collaborative and generative learning

strategies in promoting the innovative use of modern educational technology. The

result of this study will allow e-learning vendors or administrators to improve

students’ learning and enhance technology integration by incorporating these

learning theories to their existing learning systems.

1.5 Research Objectives

This research aims to:

1) Examine the relationship of variables that affect students’ behavioural intention

to use the CCO learning system.

2) Evaluate the effectiveness of the CCO learning system (improvement of students’

learning performance).

3) Investigate the relationship between the participation of student in e-collaboration

and their academic achievement.

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1.6 Research Questions

Based on the research objectives discussed earlier, the research is carried out to fulfil

the following research questions:

1) What are the relationships between variables that affect students’ behavioural

intention to use the CCO learning system?

2) Is the CCO learning system effective in enhancing the performance of students?

The research question is broken down into sub research questions as follows:

(a) Is there any statistically significant difference between the pre-test and

post-test mean score of experimental group?

(b) Is there any statistically significant difference of pre-test mean score

between control group and experimental group?

(c) Is there any statistically significant difference of post-test mean score

between control group and experimental group?

3) Is there any relationship between the participation of students in e-collaboration

and their academic achievement?

1.7 Research Hypotheses

The following hypotheses are associated with quantitative analyses of the research

procedure.

Research Question 1:

H1: There is positive direct relationship between computer self-efficacy (CSE)

and perceived usefulness (PU).

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H2: There is positive direct relationship between computer self-efficacy (CSE)

and perceived ease of use (PEOU).

H3: There is positive direct relationship between training (TR) and perceived

usefulness (PU).

H4: There is positive direct relationship between training (TR) and perceived ease

of use (PEOU).

H5: There is positive direct relationship between cognitive absorption (CA) and

perceived usefulness (PU).

H6: There is positive direct relationship between cognitive absorption (CA) and

perceived ease of use (PEOU).

H7: There is positive direct relationship between intrinsic motivation (IM) and

perceived usefulness (PU).

H8: There is positive direct relationship between intrinsic motivation (IM) and

perceived ease of use (PEOU).

H9: There is positive direct relationship between perceived ease of use (PEOU)

and perceived usefulness (PU).

H10: There is positive direct relationship between perceived ease of use (PEOU)

and behavioural intention (BI).

H11: There is positive direct relationship between perceived usefulness (PU) and

behavioural intention (BI).

H12: There is positive direct relationship between behavioural intention (BI) and

actual system usage (AU).

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Research Question 2:

H13: There is significant difference between the pre-test and post-test mean score

of experimental group.

H14: There is significant difference of pre-test mean score between control group

and experimental group.

H15: There is significant difference of post-test mean score between control group

and experimental group.

Research Question 3:

H16: There is positive relationship between participation of e-collaboration and

mean score increment (post-test minus pre-test).

H17: There is positive relationship between participation of students in e-

collaboration with their post-test mean score.

H18: There is positive relationship between participation of the students in e-

collaboration with the quality of knowledge constructed measured from the

rubric.

1.8 Research Limitations

The followings are limitations which could affect this study:

1. The participants were voluntary. Results from this study cannot be

generalised to the population of distance education students.

2. This study confined the scope to selected variables, which were drawn

from the previous related literature.

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3. Likert-5 scale was used to measure the constructs of the research model.

There might be other scale-configurations which fit better to measure the

dimensions of the constructs.

4. The results of the study are limited to the population of distance education

undergraduates in an institution. Hence, generalisations beyond this

population and context need to be taken carefully.

5. The sample of this study is recruited from the population of adult learners.

This could mean that different populations might respond differently and

the results might be different for young learners.

6. The study is performed on a middle level computer science course.

Different course may yield different results.

1.9 Operational Definition

Table 1.2 lists the definitions of important terms used in this research study.

There are thirteen terms with their definitions and relevant sources.

Table 1.2 Operational Definition

Term Definition Source

Collaborative Learning “a situation in which two or more

people learn or attempt to learn

something together”

In this study, students collaborate

and interact with instructor and their

peers via discussion forum and chat

room for knowledge construction

and to improve their understanding

of a topic.

Dillenbourg

(1999)

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Generative Learning “cognitive activities, such as

selecting, organising and relating,

directed by learners who actually

create relationships and meaning

between or among information to be

learned and learners’ existing

knowledge”

In this study, students are active in

generating their own knowledge by

constructing lesson maps and

creating relationship among the

concepts.

Wittrock (1991)

Learning Object “an object or set of resources that

can be used to facilitate the

learning”

In this study, learning object is

referring to learning chunk that is

uploaded to the knowledge base that

can be used by the students for

learning and knowledge

construction.

Millis (2002)

Concept Map “a type of knowledge representation

used in education which is a

graphical node representation

illustrating the relationship between

concepts”

In this research, concept mapping

activities is part of generative

learning process.

Novak & Gowin

(1984)

Lesson Map “the hierarchical outline of concept

map”

In this research, lesson map is a tool

used by the students to construct

Tan (2006)

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their own knowledge by generating

and linking the relationship between

learning objects.

Effectiveness “the degree to which something is

successful in producing a desired

result”

In this study, the effectiveness of the

learning system is determined by

students’ post-test mean score.

Experimental groups who used the

system are expected to have greater

improvement from pre-test to post-

test than control groups who did not

use.

Effectiveness

(n.d.)

Computer Self-Efficacy “individuals’ judgment of their

ability to use a computer or

computer system in the context of

IT usage”

In this study, this construct

measures if students are able to use

the CCO learning system without

any problems.

Compeau &

Higgins (1995);

Compeau,

Higgins & Huff

(1999)

Training “the acquisition of knowledge and

skill to present tasks”

In this study, this construct

measures if the provided training

guide help familiarise students with

all the functionalities and features of

the CCO learning system.

Fitzgerald

(1992)

Cognitive Absorption “a state of deep involvement or

holistic experience with the

underlying technology”

In this study, this construct

Agarwal &

Karahanna

(2000)

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measures if students are absorbed in

what they are doing while using the

CCO learning system.

Intrinsic Motivation “the performance of an activity for

its inherent interests and enjoyment

other than a separable outcome”

In this study, this construct

measures if students find the CCO

learning system enjoyable and

motivate them to use the system.

Deci (1972)

Perceived Usefulness “the degree to which a person

believes that using a particular

system would enhance his or her job

performance”

In this study, this construct

measures if students find the CCO

learning system useful to achieve

the learning objectives.

(Davis, 1989)

Perceived Ease of Use “the degree to which a person

believes that using a particular

system would be free from effort”

In this study, this construct

measures if students find the CCO

learning system easy to use,

navigate with tools and features are

user friendly.

(Davis, 1989)

Behavioural Intention “a measure of the strength of one’s

intention to perform a specified

behaviour”

In this study, this construct

measures if students have intention

to use the CCO learning system

again.

(Fishbein &

Ajzen, 1975)

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1.10 Theoretical Framework

Figure 1.1 shows the theoretical framework of this research study. It is based

on constructivist approach where students construct their own knowledge through the

interaction with peers and facilitated by their instructor. The pedagogical principle of

the CCO learning system was based on generative learning from constructivism

learning of Bonn & Grabowski (2001), Grabowski (1996) and Dunlap & Grabinger

(1996). The learning objects and generative learning are based from Bannan-Ritland

et al. (2000) and Tan (2006). Collaborative approach is based on Vygotsky’s theory

which emphasises that knowledge cannot be achieved by individual efficiently but

rather co-constructed socially in a learning community (Vygotsky, 1978).

In 1978, Vygotsky’s theory promotes learning contexts in which students

play an active role in learning. Roles of instructor and learner are shifted from

traditional way of teaching where an instructor transmits information to students and

students passively receive the information. Instead, the instructor collaborates with

his or her students and facilitates the construction of knowledge among students.

Constructivism is an educational philosophy that includes two main

principles: (1) learning is an active process of constructing knowledge; (2) the

instruction is a process of supporting the construction of knowledge (Duffy &

Cunningham, 1996; Jonassen & Reeves, 1996). Constructivism learning environment

requires students to actively generate knowledge rather than passively accept

knowledge. Students are active in the learning process and engage in knowledge

construction for the learning based on a constructivist approach (Jonassen, Mayes &

McAleese, 1993).

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According to DeLone and McLean (2003), individual’s use of systems is an

important determinant to measure IS success. Technology Acceptance Model (TAM)

has been widely applied to studies of technology acceptance and usage behavior

(Bruner & Kumar, 2005). In TAM, technology acceptance and actual usage is

determined by behavioural intention (BI). BI in turn, is affected by perceived

usefulness (PU) and perceived ease of use (PEOU), as well as the direct and indirect

effects of external variables (computer self-efficacy, training, cognitive absorption

and intrinsic motivation).

The effectiveness of the learning system that integrates collaborative learning

and generative learning strategies as well as learning object design is evaluated in

terms of students’ perception and performance. The performance of students is

assessed based on their pre-test, post-test and knowledge constructed mean score.

1.11 Research Framework

The research framework of this research study is illustrated in Figure 1.2. The

research activity consists of three phases, namely ‘(i) input’, ‘(ii) process’ and ‘(iii)

output’. In ‘(i) input’ phase, there are course activity, learning object and content

organiser. Course activity consists of learning outcomes, learning objectives,

learning content of each topic and assessments. Learning objects can be in the form

of html web page, pdf file, word document, graphic file, animation file, audio file or

video file. Content organiser was used by students to construct their own knowledge

and meaning in the form of lesson maps.

In ‘(ii) process’ phase, students use the CCO learning system for

collaborative learning activity (collaboration and interaction process via

asynchronous forum or synchronous chat room). They also use the learning system to

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perform the generative learning activity (knowledge construction process by

generating link between new and old knowledge) by generating the relationship

between the learning objects.

In ‘(iii) output’ phase, the effectiveness of the CCO learning system is

evaluated. To answer Research Question 1, survey questionnaires with five points

Likert-type scale were administered online. Correlation and multiple regression

analyses were conducted to hypothesise relationships of study variables that affect

students’ behavioural intention to use the learning system. For Research Question 2,

pretest-posttest method was applied to assess students’ learning performance. T-test

analysis was carried out to compare the post-test mean score between control group

and experimental group. Linear regression analysis was conducted to answer

Research question 3 to examine the relationship between participation of students in

collaboration and their achievement (post-test and knowledge constructed mean

score). The grading system of knowledge constructed utilised a rubric with specific

criteria and level of quality.

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Constructivism

TAM (Davis, 1989)

Figure 1.1 Theoretical Framework

Learning Objects

(Wiley, 2000)

Collaborative Learning (Vygotsky, 1978)

Generative Learning (Grabowski, 1996) + +

Computer Self-Efficacy

Training

Cognitive Absorption

Intrinsic Motivation

Perceived Usefulness

Perceived Ease of Use

Behavioural Intention

Actual Use

Student

Performance

Perception

Pre-Test Mean Score Post-Test Mean Score Knowledge Constructed

Mean Score

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INPUT

PROCESS OUTPUT

Figure 1.2 Research Framework

CCO Learning System

Course Activity (Assessments,

Quizzes)

Learning Object (html web page,

pdf file, word doc, graphic file, animation file,

audio file, video file)

Content Organiser

(Lesson Maps)

Performance

Perception

Knowledge Construction

(Concept Mapping)

Collaboration (Forum, Chats)

Mean Score (Pretest- Posttest)

Knowledge Constructed Score

(Rubric)

Training

Computer Self-Efficacy

Cognitive Absorption

Intrinsic Motivation

Perceived Usefulness

Perceived Ease of Use

Behavioural Intention

Actual Use

Questionnaire

RQ1: Multiple Regression + Correlation

RQ2: T-Test

RQ3: Linear Regression