A Pedagogical-based Learning Object System to Support Self-regulated
Ali Alharbi, Frans Henskens and Michael Hannaford
School of Electrical Engineering and Computer Science, The University of Newcastle, Newcastle, Australia
Self-regulated Learning, Computer Science Education, Learning Styles, Learning Objects.
Self-regulated learning has become an important construct in education research in the last few years. Self-
regulated learning in its simple form is the learner’s ability to monitor and control the learning process. There
is increasing research in the literature on how to support students to become more self-regulated learners.
However, advancements in information technology have led to paradigm changes in the design and develop-
ment of educational content. The concept of learning object instructional technology has emerged as a result of
this shift in educational technology paradigms. This paper presents the results of a study that investigated the
potential educational effectiveness of a pedagogical-based learning object system to assist computer science
students. A prototype learning object system was developed based on the contemporary research on self-
regulated learning. The system was educationally evaluated in a quasi-experimental study over two semesters
in a core course on programming languages concepts. The evaluation revealed that a learning object system
that takes into consideration contemporary research on self-regulated learning can be an effective learning
environment to support computer science education.
The aim of any instructional approach is to provide
students with high-quality learning material and ed-
ucational tools. In the last few years, the concept
of self-regulated learning has received increasing at-
tention in educational research, especially higher ed-
ucation research, because of its importance for aca-
demic success and lifelong learning (Dettori and Per-
sico, 2008). Self-regulated learning (SRL) focuses on
the learner as central in the learning process and on
the explicit use of a variety of learning strategies. It
is therefore reasonable to gain a greater understand-
ing of the learner, as a precursor to best integrating
aspects of self-regulated learning in the teaching and
learning process. We argue that the traditional vision
of designing and delivering learning material must be
altered to place greater emphasis on students’ pref-
erences and needs and increase students’ control and
monitoring of their self-regulated learning.
In accordance with the advancement of educa-
tional technology, the current trend in the instruc-
tional design of learning material is the use of dig-
ital educational resources that have pedagogical ob-
jectives as learning objects (Sosteric and Hesemeier,
2002). Learning objects are distributed via online dig-
ital libraries known as learning object repositories.
There is an increasing effort to develop standards and
specifications for these learning objects, but most of
this effort focuses on technical development and ig-
nore pedagogy or educational theories, particularly
learning styles and self-regulated learning.
Learning objects can improve the teaching and
learning of many disciplines. In particular, computer
science education has been criticised for a lack of
reference to pedagogical theories. The teaching and
learning of computer science concepts are challeng-
ing tasks for both teachers and students (Ben-Ari,
1998). This has been reflected in the low level of
retention and success among computer science stu-
dents (Biggers et al., 2008). Today, computer sci-
ence students have diverse backgrounds, experiences
and preferences. Computer science involves study-
ing dynamic and abstract concepts that are difficult
for students to understand using traditional teach-
ing and learning methods. Computer science is a
rapidly changing area that is driven by new technolo-
gies rather than pedagogy (Holmboe et al., 2001).
Self-regulated learning behaviour is typical of com-
puter science students because they must learn differ-
ent concepts in a very short time to keep abreast of
the dynamic changes in the field (Rodriguez-Cerezo
Alharbi A., Henskens F. and Hannaford M..
A Pedagogical-based Learning Object System to Support Self-regulated Learning.
DOI: 10.5220/0004383801060115
In Proceedings of the 5th International Conference on Computer Supported Education (CSEDU-2013), pages 106-115
ISBN: 978-989-8565-53-2
2013 SCITEPRESS (Science and Technology Publications, Lda.)
et al., 2011).
This paper addresses the challenges associated
with the design and use of learning objects to improve
the teaching and learning of computer science. A ped-
agogical framework is proposed to improve the design
and use of learning objects based on the concept of
self-regulated learning and students’ learning styles.
The framework is then used to develop and evaluate
an online learning object system for a core course on
programming languages.
Self-regulated learning educational paradigms focus
on the role of the learner in the learning process, and
view the teacher as facilitative rather than dominant
over the learning process. Self-regulated learners are
active participants in the learning process who utilise
metacognitive, motivational and behavioural strate-
gies (Zimmerman, 1990). Metacognition refers to
the awareness and control of the cognition process
and includes processes, such as goal setting, planning
and self-evaluation to control and monitor the learn-
ing process (Pintrich, 2004).
Although various models have been developed to
illustrate the process of self-regulated learning, they
are all based on Zimmerman’s Cyclical Model of
Self-Regulated Learning (Zimmerman, 2000). Zim-
merman’s model views self-regulated learning as an
integration process between personal, behavioural
and environmental processes. According to this
model, self-regulated learning occurs via three cycli-
cal phases: forethought, performance control, and
self-reflection (Zimmerman, 2000). These phases are
cyclical; feedback from the previous phases is used to
adjust the next phase.
The forethought phase involves processes that oc-
cur prior to learning, including goal setting and strate-
gic planning. Goal setting is the process of determin-
ing the outcomes of the learning task. Strategic plan-
ning involves the selection of strategies that are suit-
able for performing the task. The activation of pre-
vious knowledge that is required to accomplish the
learning task is essential in this phase.
The performance phase involves processes that
occur during learning, such as self-control and self-
observation. Self-control involves actual learning
strategies that students use to manage the learning
material (e.g., reading, note-taking, critical think-
ing, help-seeking, etc.). Self-observation involves
metacognitive monitoring strategies that students may
use to track and evaluate their progress, such as self-
recording and self-questioning. Students employ the
technique of self-recording to record each learning ac-
tivity and its results. They utilise the strategy of self-
questioning or testing to assess their understanding of
the learning material by performing a test to evaluate
performance against a predefined goal or standard.
Self-reflection involves processes that follow
learning, such as self-judgment and self-reaction.
These processes are closely associated with self-
observation. Self-judgment involves two sub-
processes, self-evaluation and causal attributions.
Self-evaluation is the comparison of individual per-
formance against predefined goals. It also involves
comparisons with the performance of other students
in the same class. The result of self-evaluation is
linked to the causal attribution to determine the cause
of this result. For example, a student’s poor perfor-
mance can be attributed to bad strategy selection, in-
sufficient effort, or limited abilities. Self-judgment
is linked to self-reaction. Self-reaction involves two
sub-processes, self-satisfaction and adaptive infer-
ences. Self-satisfaction is the learner’s perception
about his/her performance, i.e., whether the learner
is satisfied or disappointed. Based on this perception,
the learner employs adaptive inferences to determine
how to change the self-regulated learning process to
achieve a better result. Adaptive inferences involve
changing the goals defined in the forethought phase,
or choosing other strategies to perform the task.
Learning is the process whereby individuals acquire
new knowledge. Research indicates that students tend
to gather and process information in different ways.
These differences are known as learning styles. Many
definitions of the term ’learning style’ can be found
in the literature. Keefe (Keefe, 1988) defines learn-
ing style as the characteristic cognitive, affective and
psychological behaviors that serve as relativity sta-
ble indicators of how learners perceive, interact with
and respond to the learning environment. This is one
of the most comprehensive definitions of the learning
style, and is adopted by the National Association of
Secondary School Principals.
3.1 Summary of Research on Learning
Education researchers agree there are different learn-
ing styles that must be accommodated to improve the
teaching and learning process. In addition, empiri-
cal studies on the implications of different learning
styles for students’ performance have found signifi-
cant differences in the levels of academic achievement
of students with different learning style (Akdemir and
Koszalka, 2008). One explanation for this result is
that the learning materials favour specific learning
styles and ignore other styles.
There is current debate on how to integrate learn-
ing styles into curriculum design and teaching and
learning activities. Lack of empirical studies that
evaluate the effectiveness of learning styles-based in-
terventions has made it difficult to generate recom-
mendations for teachers and curriculum designers.
The research on learning styles focuses primarily on
identification of students’ learning styles, and how
this might affect their academic achievements. In ad-
dition, research on learning styles follows a track that
differs from that of other educational theories. The
role of learning styles in self-regulated learning has
not been investigated and appears to offer a potential
direction for future research.
The main hypothesis that dominates research on
learning styles is called the matching hypothesis
(Coffield et al., 2004). This hypothesis argues that
if a learner is presented with learning material that
is compatible with his/her own learning style, his/her
learning process improves. Further, teaching meth-
ods that are mismatched with the learner’s style might
lead to difficulties in learning. However, research on
how this hypothesis could be applied in context to im-
prove the teaching and learning process in many disci-
plines, including computer science, is scarce. Learn-
ing style awareness was proposed in response to criti-
cal reviews of learning style theories as an alternative
and promising hypothesis for future research on learn-
ing styles (Coffield, 2004). This hypothesis claims
that knowledge of learning styles should be used to
increase self-awareness, which leads to improvements
in the learning and teaching process. Learners who
become aware of their learning styles are more likely
to be aware of their strengths and weaknesses and,
therefore, have greater control of their learning pro-
cesses. In addition, teachers who are aware of the
diversity of learning styles amongst their students are
most likely to adopt teaching approaches that appeal
to different types of students. In this case, knowl-
edge about learning styles is used to enhance meta-
cognition, which is an important component in any
self-regulated learning model.
3.2 Felder-Silverman Learning Style
Learning styles can be identified using different learn-
ing style models. There are many learning style mod-
els proposed in the literature. The FelderSilverman
Learning Style Model (Felder and Silverman, 1988)
is a well-known learning style model that is heavily
used to identify students’ learning styles in many dis-
ciplines, especially in science and engineering educa-
tion. The Felder Silverman Learning Style Model has
been adopted in this study as the basis for identifying
students’ learning styles due to many reasons, some
of them are the following:
It covers more than one level of learning style,
thus provides categorisation of students’ learning
styles based on multiple dimensions.
It has been used to investigate the learning styles
of engineering students, groups of learners similar
to the target population of this study (e.g., (Felder
and Brent, 2005)) .
The instrument used by this model is reported to
have satisfactory level of reliability (Felder and
Spurlin, 2005).
The Index of Learning Styles (ILS) is the instru-
ment that is used to identify learning styles based on
this model. This model consists of four dimensions
(Felder and Silverman, 1988):
Perception (Sensing/Intuitive): this dimension de-
scribes the type of information an individual preferen-
tially perceives. Sensing learners prefer concrete con-
tents and facts, and are detail-oriented, whereas intu-
itive learners prefer abstract concepts, theories, and
mathematical formulas, and dislike details. Sensing
learners tend to solve problems using well-established
methods, and dislike complications. Intuitive learners
like innovations, new ideas of solving problems, and
dislike repetition.
Input (Visual/Verbal): this dimension describes the
type of presentation an individual prefers. Visual
learners prefer learning through visual media, such as
pictures, charts, and diagrams, whereas verbal learn-
ers prefer spoken or written materials and explana-
tions. Both types of learners learn better when the
material is delivered using visual, verbal, and written
Processing (Active/Reflective): this dimension de-
scribes how the learner processes information. Active
learners prefer learning in groups, and they tend to try
things out, whereas reflective learners prefer working
alone, and tend to think about how things work before
attempting them.
Understanding (Sequential/Global): this dimen-
sion describes how the learner progresses towards
understanding information. Sequential learners pre-
fer following a logical, step by step linear approach,
whereas global learners prefer absorbing the learning
materials randomly, in large jumps, without follow-
ing a step by step approach, until they grasp the full
picture. Global learners need to grasp the full pic-
ture before going into the details. Courses are nor-
mally taught according to a sequential presentation
format. Sequential learners can learn effectively un-
der this method of instruction.
Advances in information technology have led to a
paradigm shift in the way that people communicate
and learn. Consequently, the development and de-
livery of learning materials are changing. To reflect
this paradigm shift, a new instructional technology
called learning objects emerged as a next generation
technique for instructional design, due to its capacity
for reusability, adaptability and scalability (Hodgins,
Increased interest in the concept of learning ob-
jects has led to a number of definitions and terms to
describe the idea behind learning objects. Sosteric
and Hesemeier(Sosteric and Hesemeier, 2002) syn-
thesised several definitions and defined a learning ob-
ject as ”a digital file (image, movie, etc.) intended to
be used for pedagogical purposes”. A learning object
can be published through a variety of methods. The
most formal method of publishing learning objects
is through learning object systems. A learning ob-
ject system is any online platform or environment that
is used to facilitate authoring, indexing, distributing,
and delivering of learning objects (Ritzhaupt, 2010).
A learning object system uses a database in which
learning objects are stored along with their metadata
to be shared. These databases are usually knows as
learning object repositories.
Learning objects can support students in their self-
regulated learning of, for example, computer science
if pedagogical foundations are considered during the
design and delivery of these learning objects. There is
a paucity of underlying theory that guides the design
and use of learning objects (Wiley, 2000). Moreover,
the delivery of learning objects in online learning ob-
ject systems does not follow a predefined pedagogical
model based on the latest research in self-regulated
learning (Alharbi et al., 2011a).
Education research on learning styles, and that on
self-regulated learning, appears to be isolated from
one another. Self-regulated learning models that con-
sider the diversity of students’ learning styles have
the potential to provide a comprehensive understand-
ing of the learning process (Cassidy, 2011). This
leads us to return to the metacognitive component
of self-regulated learning, which concerns the impor-
tance of the learner’s awareness and ability to con-
trol his/her cognition process. According to this com-
ponent, learning styles can be used to improve the
metacognitive process, which in turn enhances stu-
dents’ motivation and learning. In this way, the future
research on self-regulated learning and learning styles
can interact to provide a basis for empirical studies
that can produce pedagogical recommendations for
teachers and instructional designers.
This study presented in this paper synthesises con-
temporary educational research to provide a greater
understanding of the theory of learning styles by plac-
ing it in the context of self-regulated learning mod-
els. The result of this synthesis is a pedagogical self-
regulated learning framework with learning style as
one of its main components (Alharbi et al., 2011b).
This framework can be used as the basis for improv-
ing learning and teaching in many disciplines. How-
ever, in the current research, the framework is applied
to improve the design of learning object instructional
technology in computer science education.
5.1 System Components
Based on the proposed pedagogical framework, the
current study develops and evaluates a learning object
system with self-regulated learning support. Figure 1
shows the main components of the proposed learning
object system.
5.1.1 Learning Object Repository
The learning object repository is responsible for stor-
ing different learning objects that are designed to
support students in learning about programming lan-
guages concepts. This repository stores all learning
objects along with their optional XML files that de-
scribe the structure of learning objects, and define the
animation inside the learning object. All the learning
objects are stored in the repository and tagged with
relevant metadata to make it easy for students to find
5.1.2 Learning Style Awareness Module
The objective of this module is to increase students’
awareness of their learning styles and their use of
self-regulated learning strategies. This module con-
sists of an initial assessment of students’ use of self-
Figure 1: Learning Object System Components
regulated learning strategies and the identification
of students’ learning styles. A research instrument
is used to measure students’ self-regulated learning
strategies. The learning strategies are categorised
based on the research on self-regulated learning. The
Index of Learning Styles (ILS) is used in this module
to identify students’ preferred learning styles based on
Felder-Silverman learning style model (Section 3.2)
This model describes the learner’s preferences based
on four dimensions, Sensing-Intuitive, Visual-Verbal,
Active-Reflective and Sequential-Global. Upon first
login to the system, the student is redirected to com-
plete the learning style assessment. After completing
the assessment, the module evaluates the responses
and determines the student’s preferred learning style.
The module increases students’ awareness by provid-
ing the result, together with a description and recom-
mended learning strategies. Although students are
permitted to access all learning objects in the sys-
tem, the recommended learning strategies consider
the strengths and weaknesses of students’ preferred
learning styles.
5.1.3 Self-assessment and Misconceptions
Detection Module
This module is responsible for generating self-
assessment questions that help students detect their
misconceptions related to the programming languages
concepts. These self-assessments are associated with
the learning objects that are designed to help stu-
dents overcome these misconceptions. This mod-
ule is also responsible for recording each student’s
self-assessment results in the self-regulated learning
record. Each assessment exercise is linked to a spe-
cific misconception about programming languages
concepts. Learners are given instant feedback after
completing each assessment.
5.1.4 Self-reflection Module
Meta-cognition is the most important self-regulated
learning process that requires greater attention in on-
line educational environments. This module extracts
information from the analysis of students’ behaviours,
which is stored in the self-regulated learning record,
and uses this information to help students develop
self-reflection skills. To detect a specific misconcep-
tion, a number of questions are developed and inte-
grated into the self-assessment. The self-reflection
support module extracts information from the re-
sults of self-assessments that are stored in the self-
regulated learning record, and uses them to calcu-
late the degree of misconception related to a specific
concept. In addition, when a misconception is listed
in the student’s self-regulated learning record inter-
face, the module shows information on the propor-
tion of students with this misconception. This infor-
mation is shown to the leaner to encourage him/her
to overcome these misconceptions. The learner can
view additional information on the possible reasons
behind these misconceptions, and how to overcome
them by considering his/her learning styles. The mod-
ule also allows a student to view detailed information
on his/her behaviour inside the system, including the
time spent on each learning object compared to the
time spent by other students, and the results of the
self-assessment exercises.
5.1.5 Self-regulated Learning Record
The Self-Regulated Learning Record (SRLR) is a pro-
posed component that records the user’s interactions
with learning objects and other educational tools. The
self-regulated learning record provides an alternative
approach for the communication between LMS and
different types of learning objects. The content of the
self-regulated learning record can be accessed by any
LMS or educational tool, which in turn supports self-
regulated learning. In the proposed online learning
object system, the SRLR stores information related to
the learner and his/her use of learning objects. This
information includes the following:
Time student spent on each learning object per
The results of students’ learning styles and learn-
ing strategies assessments.
The results of students’ self-assessments.
Students’ navigation behaviour in each session.
This section presents details of the research method-
ology that was adopted to evaluate the educational ef-
fectiveness of the proposed learning object system.
These details include a description of the research
participants, design, and procedure. In addition, the
instruments that were used to collect the data are de-
scribed. This section concludes by describing the data
analysis techniques used to analyse the data and how
the results were interpreted to test the hypotheses and
answer the research questions.
6.1 Research Design:
Quasi-experimental Study
The qualitative portion of the study follows a quasi-
experimental control group design with pre-tests and
post-tests (Creswell, 2012). Quasi-experimental de-
sign is the same as the control experimental design
except that the participants are not randomly assigned
to the experimental conditions. Rather, intact con-
venience groups are used. This design is commonly
used in educational research due to difficulties associ-
ated with randomly dividing participants into groups.
The purpose of the experiment is to determine the ef-
fect of the proposed educational intervention on stu-
dents’ academic achievement in a core computer sci-
ence course.
6.2 Description of the Course and
The participants in this study are students enrolled
in the programming languages and paradigms course
at the University of Newcastle, Australia, in the first
semesters of 2011 and 2012 respectively. The over-
all sample size was 62 students: 34 in 2011 (control
group) and 28 in 2012 (experimental group).
The online learning object system is used and
evaluated in the course Programming Languages and
Paradigms. A course that covers programming lan-
guage concepts is important for computer science and
software engineering students and such a course is
an integral part of any computer science and soft-
ware engineering program (IEEE/ACM, 2005). Pro-
gramming language concepts are presented by com-
paring the features of programming languages, such
as Java and C++. In addition, several program-
ming paradigms are discussed and compared. The
Programming Languages and Paradigms course at
the University of Newcastle is a compulsory second
year course for undergraduate students enrolled in the
computer science and software engineering programs.
The course follows a traditional teaching method that
consists of weekly lectures and workshops.
6.3 Data Collection Instruments
A number of data collection instruments were utilised
to address the research the following research ques-
What is the effect of the proposed learning object
system on students’ academic achievement?
To what extent are students satisfied with the edu-
cational effectiveness of the system?
The following instruments were used to collect
both quantitative and qualitative data.
6.3.1 Students’ pre- and post-tests
Students tool the pre-test in both the experimental and
control groups at the beginning of the semester, and
before the experimental group was introduced to the
online learning object system. The pre-test consists
of questions to help students refresh their knowledge
about several object-oriented and data structures con-
cepts, and how to apply them to solve a real-world
6.3.2 Students’ Satisfaction Questionnaire
This instrument is an online questionnaire completed
by students to evaluate the educational effectiveness
of the entire learning object system at the end of the
semester. This instrument includes questions about
students’ perceptions of the educational effectiveness
of the online learning object system. The question-
naire utilises a 7-point Likert scale, with 1 repre-
senting strongly disagree and 7 representing strongly
agree. The questionnaire consists of dimensions that
are related to a specific feature of the online learning
object system. Each dimension has a number of ques-
6.3.3 Self-regulated Learning Record
Self-Regulated Learning Record (SRLR) is a compo-
nent of the online learning object system that auto-
matically logs all activities performed by students in
each session. This includes the frequency and time
spent on learning objects and the results of students’
self-assessments. Also, the students’ navigation be-
haviour in each session can be discovered by extract-
ing the information stored in the SRLR. The data col-
lected using the SRLR was used to study students’
behaviour inside the system.
6.4 Method and Procedure
The study was conducted in two consecutive phases.
In the first phase (first semester 2011), the control
group did not receive intervention and were taught
using the traditional instructional approach. In the
first week, students were given an information state-
ment that described the research objectives and in-
vited them to participate. Those who agreed to par-
ticipate signed a consent form that indicated that they
were willing to participate in the research study as de-
scribed in the information statement. Then, the Index
of Learning Style (ILS) was administered to students
who signed the consent form to identify their pre-
ferred learning styles. In addition, the Self-Regulated
Learning Strategies Questionnaire was administered
to students to measure the level of use of different
self-regulated learning strategies. In the second phase
(first semester 2012), the experimental group received
the online learning object system as an educational
intervention to aid in developing self-regulated learn-
ing strategies while studying the course material. The
Index of Learning Style (ILS) was given to the stu-
dents at the beginning of this phase using the same
questionnaires that were used in first phase. How-
ever, in the second phase, additional research instru-
ments were used to measure the educational effective-
ness of different aspects of the research intervention.
These include the students’ satisfaction questionnaire
and the self-regulated learning record.
7.1 The Result of the
Quasi-experimental Study
The first step to evaluate the educational effectiveness
of the intervention is to report descriptive statistics
that describe the academic performance of the stu-
dents in the control and the experimental groups. The
second step is to perform hypothesis testing. This is
the formal procedure used by statisticians to accept or
reject hypotheses. The statistical level of significance
(α) is set to 0.05 for hypothesis testing. The anal-
ysis also evaluates the influence of students’ learn-
ing styles and level of self-regulated learning on their
academic performance in both groups. An analy-
sis of students’ behaviour in the online learning ob-
ject system was also conducted. An analysis of co-
variance (ANCOVA) was used to measure the differ-
ence between the control and the experimental groups
while taking into consideration the possibility of pre-
existing differences between the two groups.
In the control group, the mean final exam score
was 55.2, while it was 65.3 in the experimental group.
To test whether this difference is statistically signifi-
cant, a one-way analysis of variance (ANCOVA) was
used. The following hypotheses were formulated:
H0: there is no significant difference in the final
exam scores between the control and the experimental
HA: there is a significant difference in the final
exam scores between the control and the experimental
The independent variable is the medium of in-
struction, which consists of two levels, traditional
or intervention. The dependent variable is students’
achievement scores on the final exam. Students’
scores on the pre-test were considered as the covari-
ate in the ANCOVA to control for the pre-existing
differences between the control and the experimental
groups (Table 1).
Table 1: The result of the statistical test for the final exam
scores comparison.
Group N M SD F P
Control 34 55.2 15.9
9.83 0.003*
Experimental 28 65.3 19.5
The result of the ANCOVA was significant (F
=9.83, p=0.003 <0.05). Based on this result, the
null hypothesis (H0) was rejected and we accepted
the alternative hypothesis (HA) that the difference in
the mean final exam scores between the experimen-
tal and control groups is statistically significant. Stu-
dents in the experimental group (M=65.3, SD=19.5)
significantly outperformed those in the control group
(M=55.2, SD=15.9) on the final exam after consider-
ing the pre-test scores as a baseline for both groups.
Thus, regardless of the pre-existing difference in stu-
dents’ achievement on the pre-test between the study
groups, the online learning object system had a sta-
tistically significant positive effect on the final exam
scores of the experimental group.
7.2 Students’ Satisfaction
At the end of the semester, the final feedback ques-
tionnaire was made available online inside the learn-
ing object system. This questionnaire measured stu-
dents’ degree of satisfaction with the learning object
system. The satisfaction questionnaire consists of a
number of dimensions; each measures students’ sat-
isfaction in terms of their perceptions about a specific
feature of the online learning object system Nineteen
(experimental group) students completed the ques-
tionnaire at the end of the course. This section sum-
marises the analysis of students’ responses to the
questions related to each dimension in the question-
naire. Each dimension consists of a number of ques-
tions. Students responded using a 7-point Likert
scale ranging from 1 (strongly disagree) to 7 (strongly
agree). The average percentage of students who re-
sponded to each level of the Likert scale was calcu-
lated for each dimension.
The first dimension of the questionnaire measured
students’ satisfaction with the online learning object
system in terms of their perceptions about the abil-
ity of the system to correctly identify their preferred
learning style, and the system’s recommendations and
guidelines. The result is presented in Figure 2. The
majority of students (93%) had a positive percep-
tion of the learning style identification and awareness
module. They considered it to be useful in providing
recommendations and guidelines that reflected their
preferred learning styles. The majority of students
agreed that the system helped them to identify their
preferred learning styles and that the learning strate-
gies were easy to follow and useful. The result indi-
cates that the system helped students gain awareness
of their learning styles. Thus, many of them will be
aware of their learning styles in their future studies,
and should be able to use this knowledge to aid their
self-regulated learning, utilising the strengths of their
learning styles and overcoming their weaknesses.
The second dimension of the questionnaire mea-
sured students’ satisfaction in terms of perceptions
about the self-reflection support module which was
used in the system to help students monitor and
Figure 2: Students’ satisfaction with the learning style
awareness module.
Figure 3: Students’ satisfaction with the self-reflection
control the self-regulated learning process. Figure
3 shows that 72% of students agreed that the self-
reflection support module, which was used to record
students’ interactions with learning objects, informa-
tion related to their misconceptions, and indicators of
their progress, was educationally effective. Of the stu-
dents, 16% had a neutral opinion and 12% disagreed
with the educational benefit of the self-reflection sup-
port module.
The last dimension measures students’ overall
perceptions of the idea of using online learning ob-
ject systems to support self-regulated learning. The
result is presented in Figure 4. The final result of
the questionnaire indicates that 94% of the students
agreed with the statements comprising this dimen-
sion. Nearly all students strongly supported the idea
of applying online learning object systems to other
computer science courses.
Figure 4: Students’ satisfaction with the idea of learning
object systems.
7.3 Analysis of Navigation Behaviour
Trace analysis of students’ navigation behaviour was
conducted to study students’ behaviour inside the
system, using the information recorded in the self-
regulated learning record. Based on the self-regulated
learning model used in this study, we proposed a navi-
gation behaviour analysis method to classify learners’
self-regulated learning behaviour. A number of nav-
igation behaviour patterns were observed. We con-
ducted further analysis only on the patterns that were
followed frequently. These patterns were categorised
as follows:
Browsing: this behaviour implies that students jump
between different pages inside the system in the same
session without spending more time on the learning
objects or their self-regulated learning record.
Unplanned View of Learning Objects: this be-
haviour implies that students view learning objects
that are most likely not related in the same session.
Students who adopted this behaviour typically did not
complete a self-assessment after viewing the learning
Inefficient Use of Self-assessments: this behaviour
implies that the student tends to take self-assessments
for different topics in the same session without or
with a limited view of learning objects and their self-
regulated learning record inside the system.
High Level of Meta-cognition: this behaviour im-
plies that students tend to follow a navigation path that
is consistent with the self-regulated learning model.
They tend to view their self-regulated learning record
at the beginning of each session, then view learn-
ing objects related to one topic only, complete self-
assessments and spend time reading the feedback af-
ter submitting the self-assessment. They also tend
to make decisions based on their results in the self-
assessments, such as viewing learning objects again
and then completing the self-assessment again.
Table 2: Navigation behavior patterns.
Navigation pattern Proportion
of stu-
Browsing 14%
Unplanned view of learning objects 14%
Inefficient use of self-assessments 18%
High level of meta-cognition 54%
The proportion of students who frequently
adopted each navigation pattern is presented in Ta-
ble 2. More than half of the students (54%) adopted
a behaviour pattern that reflects a high level of meta-
cognition inside the online learning object system. Of
the students, 18% showed a tendency to adopt naviga-
tion behaviour that reflected inefficient use of the self-
assessment exercises and 14% frequently adopted a
behaviour that reflected browsing behaviour or un-
planned view of learning objects.
Further analysis of the data was conducted to in-
vestigate the influence of the most frequently adopted
navigation behaviour pattern on students’ academic
achievement as measured by the post-test. To fa-
cilitate the analysis, the three groups who did not
adopt the meta-cognition pattern frequently were
combined together to form one single group (non
meta-cognition). After that, an ANCOVA test was
conducted to test if there is any significant difference
in the post-test scores between students in the meta-
cognition pattern and the non-meta cognition pattern
groups, after controlling for their pre-test scores. Ta-
ble 3 presents the result of the ANCOVA statistical
Table 3: Navigation patterns.
Navigation pattern Mean SD F P
Meta-cognition 74.9 12.2
7.68 0.010*
Non meta-cognition 54.3 20.9
Table 3 shows that the meta-cognition be-
haviour pattern group had higher post-test scores
(M=74.9, SD=12.2) than the non meta-cognition
group (M=54.3, SD=20.9). The result of the AN-
COVA test confirms that this difference is statistically
significant after adjusting for the pre-test scores, (F
=7.68, p=0.010 <0.05). To sum up, students who
adopted the meta-cognition behaviour pattern more
frequently had higher academic achievements as mea-
sured by the post-test.
This paper presented the result of an empirical study
that evaluated the educational effectiveness of an on-
line learning object system to support self-regulated
learning of programming languages concepts. The
system design was based on a pedagogical frame-
work that was adopted to improve the role and im-
pact of learning object repositories. The result of the
study revealed that the learning object system is an
effective intervention in supporting students as self-
regulated learners. This was also reflected in the re-
sults of the students’ satisfaction questionnaire, which
showed that the students had positive perceptions of
the features of the system.
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