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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.
iii
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
v
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
vi
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
vii
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
ix
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
x
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
xii
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),
6
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.
8
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