108_[International_Conference_on_Web-based_Le,_Reggie_232

182

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2004 from http://www.astd.org/ASTD/Resources/dvor/articalrechives.htm.
Education and Manpower Bureau (2004). Empowering Learning and Teaching
with Information Technology.
Galliers, R. (1992). Information Systems Research: Issues, Methods and
Practical Guidelines. Blackwell Scientific Publications, Oxford.
infoDev (2001). E-Readiness as a Tool for ICT Development. Retrieved on Feb.
24,2005 from http://www.infodev.org/library.
Kaur, K. and Abas, Z. (2004). An Assessment of e-Learning Readiness at the
Open University Malaysia. International Conference on Computers in Education
(ICCE2004), Melbourne,Australia.
McConnell International LLC. (2000). Risk E-Business: Seizing the Opportunity
of Global E-Readiness Report. Retrieved on Feb. 24, 2005 from
http://www.nicconnellinternational.com/ereadiness/EReadinessReport.htm.
Ministry of Education. Are you ready for e-learning? Retrieved on Dec. 19,
2004 from http://www.moe..gov.s.g/edumalI/rd/researscuhmmaries.htm.

Russell, G. and Bradley, G. (1997). Teachers’ computer anxiety: Implications for

professional development. Education and Information Technologies, 2, 17-30.

The Economist Intelligence Unit Limited and IBM (2003). The 2003 e-Learning
Readiness Rankings: A White Paper from the Economist Intelligence Unit 2003.
Retrieved on Dec. 19,2004 from http://www.eiu.com.

Young, B. (2000). Gender Differences in Student Attitudes Toward Computers. Journal
of Research on Computing in Education, 33(2), 204-217.

Yuen, A. and Ma, W. (2002). Gender Differences in Teacher Computer
Acceptance. Journal of Technology and Teacher Education (2002) 10(3),
365-382.

IMPLEMENTATION ISSUES ON THE SPECIFICATION FOR
SERVICE QUALITY MANAGEMENT OF E-LEARNING*

YI ZHANG~+
'Graduate School of Education, Huazhong Universityof Science and Technology

[email protected], [email protected]

ZHITING ZHU', ZONGKAI YANG3, CHENGLING ZHA03, SANLAN LU4
'Department of EducationalInformation Technology,East China Normal University,

3EngineeringCenter of Educational Information Technology,
Central China Normal University,

'Department of Electronics & InformationEngineering
Huazhong Universityof Science and Technology

The emergence of e-learning as a kind of education has led a lot of organizations
including schools, colleges and corporations to provide web-based learning and distance
learning for lifelong learners. The Chinese e-Learning Technology Standardization
Committee is developing a specification for evaluating service quality and management
system of e-learning .This paper introduces the background on the specification, CELTS-
24, and its application information. Further, some critical implementation issues are
suggested by a case study of questionnaire and evaluation scale that are used by online
and post survey.

1. Introduction

CELTSC(Chinese e-Learning Technology Standardization Committee) is
chartered by the Chinese Information Technology Standards Committee and
sponsored by the Chinese government. To improve the service quality of e-
learning in China, the aim of CELTSC is mainly to make and develop standards
for enabling interoperability of e-learning system and reusability of e-learning
resources. It's also responsible for developing related consistency test software
and expanding the influence and application of e-learning standards.

As one of the important standard in development within the framework of
CELTS, the CELTS-24 specification defines a reference model and scale for
service quality assessment in the context of e-learning in college education,
compulsory education and profession training to measure and evaluate their
service elements as service attitude and related condition in implementing
instruction, training and management. This specification develops process
elements for e-learning service quality management system according to the
scale and a process model. The e-learning institution can improve its service

'This work is supported by CELTSC project grant.
Work partially supported by grant 20040075 of Hubei Province Instructional Research and
A2004 146 of Hubei Province Educational Science Research.

183

184

quality by the results of assessment and the specification to ensure learner’s
proper rights to enjoy high service quality finally [l].

2. CELTS-24 Specification

2.1. ConceptualModelfor Service Quality of e-Learning
The definition for the service quality of e-learning is the overall collection of
implicit and explicit characteristics that the service can satisfy the customer. At
present, conceptual model for service quality from Parasuraman [2] has had a
good effect on commerce, which is practicable in business application.
Therefore, we make reference to it and construct a conceptual model for service
quality of e-learning, which is as follows (see Fig. 1.).

e-Learners e-Learner’s Assurance
Perceived Service 4 +Validity

t ---, Empathy

Specification

Management Perceptions of

, Learners expectations 1

Fig.l.Conceptual Model for Service Quality of e-Learning

2.2. Conceptual Frameworkfor Service Quality of e-Learning

According to suggestion from experts and e-learners on service quality of e-
learning, we design questionnaire for service quality of e-learning and develop
an online survey program which is hanged on following websites: CELTSC and
Shanghai Distance Education [3]. After analyzing the results of investigation on
41 items by principal component analysis of factor analysis in SPSSl1.0, we
find if there are five factors, and cumulative of variance has reached 79.248%,

namely, approximate 80% information can e explained and satisfy the
condition for factor analysis Fig.2

30

10

10 I

-D

I

mo

Based on the results from variance analysis of items quantitative analysis
of rotation factor loaded matrix and qualitative analysis we extract 5 factors and

25 items from 41 items with incorporation and complification and reaction the
questionnaire for service quality of e-leaning which is as follows (see Table 1)

~ ~~ Validation of learning resources: e-learning
providers can offer credible, effective and rich
Reliability:e-learning providers has the ability learning resources.
to perform promised service dependably and 4.1 Scientificity
accurately 4.2 Accessibility
1.I Reliability of education institution 4.3 Integrity
1.2 Reliability of network system 4.4 Real-time
1.3 Reliability of question answer 4.5 Selection of media
I .4 Reliability of evaluation
Empathy: e-learning providers understand
Responsiveness: e-learningproviders would like needs of users and can offer individualized
to help learners and provide prompt service service
2.1 Responsiveness of service request 5.1 Convenient learning schedule and facility
2.2 Responsiveness of teacher 5.2 Assistant service
2.3 Publishing information in time 5.3 Easy to use
Assurance: Faculty and sfaff engaging in e- 5.4 Customized service
learning are professional and knowledgeable to 5.5 Care
let the learner trust them andfeel them reliant 5.6 Comfort environment
3.1 Integrity o f instruction plan 5.7 Interactivity
3.2 Providing related information on courses
3.3 Security of private information
3.4 Technology guidance
3.5 Professional knowledge of the teacher
3.6 Complaint mechanism

186

The reliability coefficient of whole scale and each factor in e-learning
service quality are calculated by Cronbach alpha and split-half (see Table 2)

Table 2 Reliability Analysis

Name of the Factors Coefficient of Reliability
ICronbach Alpha
Reliability Split-Half
Responsiveness
0.8735 0.8432
Assurance
Validation of Learning Resources 0.8022 0.8281

Empathy 0.8509 0.8408
Scale
0.8912 0.8589

0.8872 0.8706

0.9264 0.9064

The results show that their reliability are very significant, all reliability
coefficient are over 0.8 and total reliability is about 0.9, which indicates the new
questionnaire is reliable and repeatable. Therefore, the questionnaire is named
the scale for service quality of e-learning.

To evaluate validation of questionnaire is involved in three respects[4].
Firstly, content validation, which has been assessed by experts and enterprise
representative related to e-learning. Secondly, before performing the
investigation, the questionnaire has been passed primary test. Thirdly, factor
analysis proves that most of the items attribute to 5 factors. Thus, the
questionnaire possess reliable content validation, practicability and efficient
constructive validation.

2.3. Process Elements in Service Quality Management System

e-Learning is a process-based activity, in order to ensure its service quality, sub-
system in e-learning service quality management system has to be identified,
confirmed and analyzed. The process elements in e-learning service quality
management system keep to the process approach and process model in I S 0
9001:2000.

Four process elements including management responsibility, resource
management, service realization and evaluation are determined, and then
corresponding sub-process elements are also made (See Fig.3).

187

r 1Continual improvement of e-learning service quality

Fig.3. The process model for e-learning

3. Casestudy
This case study examines the implementation of CELTS-24. The study takes
post and online survey to investigate. The questionnaire is on the following
websites: CELTSC, Shanghai Distance Education, Online Education College
Renmin University of China, Distance Education College of East Central
Normal University. During half a year more than 900 e-learners have submitted
their answers. As a result, 589 available answer sheets are selected.

3.1. Evaluation the Service Quality of e-Learning
According to the questionnaire, the results of expected service (ES) and
perceived service (PS) can be gained. If ES<PS, we would say e-learning
service quality is over expectation and wonderful. ES=PS indicates that it meets
expectation and learners feel satisfactory .If E S P S we would say it’s under
expectation and learners feel unsatisfactory.

188

Table 3 Compare Means between Expectationand Perception of Service Quality

-~~

Name of the Factors Expected Service: ES Perceived Service: PS

Reliability 4.4741 3.7568

Responsiveness 4.4536 3.6344

Assurance 4.4269 3.7180

Validation of learning resources 4.4215 3.7376

Empathy 4.3 I40 3.5529

Scale 4.4633 3.6776

The descriptive data of table 3 shows that ES>PS and there are significant
gaps between expected service and perceived service quality of e-learning.
Verified by T test, it indicates that these gaps exist indeed, so most e-learners
don't satisfy the present service quality of e-learning.

3.2. Effect of Some Variableson Service Quality

In order to reveal the reason why e-learning doesn't reach anticipative effect, we
analyze 5 demographic indexes including gender, age, region, profession and
education, and 3 variables related to learning manner including major, learning
place and learning frequency to aim at investigating whether each of them has
an effect on 5 factors of service quality on e-learning or not. When age is as
independent variable, we divide age into six groups, such as age<20,
20<age<24, 25<age<30, 39<age<40, 40<age<50 and age>50, then the
reliability of expected service quality is regarded as a dependent variable. Firstly
by test of homogeneity of variances, P=0.223 indicates that P>0.05 and the
variances among the six groups are equal, so the condition of one-way ANOVA
(analysis of variance) is satisfied. As a result, analysis of variance shows that
F=2.365 and P>O.01 which indicates that age variable has no significant effect
on the reliability of expected service quality, which is as followingFig 4.

1

I- =----

41 .

1f :::

b 28 -2. SI-ll ,140 .,a &m

I .IS
1 IB
*ge

Fig.4. Effect of Age on Reliabilityof Expected Service Quality

189

The result shows that each of 5 demographic indexes including gender, age,
region, profession and education has no significant effect on the reliability of
expected service quality. The statistical methods and conclusions are the same
with responsiveness, assurance, validation and empathy of expected service
quality. That is to say, the evaluation of e-learning on expected service quality is
independent of some demographic variables.

At the same time, we divided learning frequencies into four groups, such as
daily, weekly, monthly and aperiodic. As a result, analysis of variance shows
that the variable of learning frequencies has significant effect on expectation of
service quality, which is as following table 4. Furthermore, learning frequencies
is the only influential factor, while other factors related to learning manners
have no significant effects on service quality of expectation

Table 4 Effect of Learning Frequencies on Reliability of Expected Service Quality

ANOVA

Groups (Combined) Unweighted Sum of
Linear Term Weighted
Within Groups Deviation 7.775
Total 2 I.466 I
2.430 514
5.34s
239.331
247.106

The same measure is applied and the same conclusion is gained to the
perceived service quality. All in all, evaluating indexs for service quality of e-
learning are independent of demographic indexes given above,which have no
significant difference among various cultures and different regions, while some
variable such as e-learning frequencies related to learning manner has a
significant effect on evaluating indexes.

3.3. e-Learning Service QualityManagement System

According the clauses of CELTS-24 about process elements in e-learning
service quality management system, the sponsor and supervisor of e-learning
can find some problems in process of e-learning service and management,
moreover, they will analyze the reasons and resolve these problems. In addition,
based on the system, they will take some measures to improve the quality of
service and management. However, this study does not aim at any organization
or unit, so we can’t provide the details about implementation on management
processes of e-learning organizations.

190

4. Summary and Future Work

The CELTS-24 specification describes the general service quality characteristics
of e-learning and corresponding evaluation scale. This specification also defines
process model and process elements in e-learning service quality management
system which can be used as a general guideline for e-learning service quality
management. Many critical implementation issues can be identified from this
case study, the importance of these issues and other issues about service quality
management should be recognized while the CELTS-24 specification is to be
implementedby an organization. The following issues can be summarized when
evaluating potential application of the CELTS-24 specification:

9 e-Learners can select e-learning providers suitable to their own learning
demands so that desirable level of service could be expected and perceived.

9 Authentication agency can evaluate levels of service quality provided by e-
learning delivers according to the standardized rubric, and training is
necessary to enable e-learning providers to evaluate and analyze e-learning
service quality by using this specification.

9 e-Learning providers can carry out self-evaluation of their service quality by
applying a standardized scale of service quality; and carry out quality
management of e-learning service through corresponding service
management processes.
The problems remain to be further studied :

9 To clarify the following factors whether affect e-learning service quality.
Factors relative to e-learners: personal needs, past e-learning experiences,
self-perceived service role, learning method, personal information
technology level and so on. Factors relative to e-learning providers: word-
of-mouth communication, explicit service promises, implicit promises,
attitude of managers, management specification and so on.

9 The specification will be localized and customized by implementersto meet
the needs of e-learning providers. CELTS-24 will be performed on the given
organization in the next case study, the management quality of organization
will be evaluated and improved during the processes of e-learning service
management.

References

1. Chinese e-Learning Technology Standardization Committee Specification
for Service Quality Management System of e-Learning, CELTS-24.1
WD l.O.,2003,http:/I www.celtsc.edu.cn/index.isv

191

2. A. Parasuraman, Valarie A.Zeitham1 & Leonard L. Berry: Refinement and
Reassessment to the SERVQUAL Scale. Journal of Retailing, Vol. 67
Winter(1991)

3. Zhang Yi, et al.: Basic Research on Service Quality for e-Learning, CET
China Educational Technology, 193(2):68-72( 2003).

4. The Institute for Higher Education Policy, Quality on the Line: Benchmarks
for Success in Internet-Based Distance Education,
http://www.ihep.com/Pubs/PDF/Ouali.tp~df(2000.4)

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A COMPARATIVE EVALUATION AND CORRELATION
BETWEEN LEARNING STYLES AND ACADEMIC
ACHIEVEMENT ON E-LEARNING

DANIEL SU KUEN SEONG
[email protected]
Division of ComputerScience and Information Technology
The University of Nottingham (Malaysia Campus)

ABSTRACT

The use of technology in higher education has increased significantlyover the years.
There is a paucity of controlled research which explores the evaluation and
correlation between learning styles and academic achievement. Hence, the aim of
this paper is to evaluate the significant difference and correlation between learning
styles and academic achievement. Research outcomes have typified there was
significant difference between learning styles and academic achievement. Initial
experimentation indicates different learning styles potentially signify different
academic achievement.Furthermore, the result exemplifies academic performance is
improved after 13 weeks of e-Learning sessions. Indeed, Learners Reflection
Questionnaire (LRQ) has shown significant correlation with learning styles.
Nonetheless, there was no significant correlation between LRQ and academic
achievement. It is possibly and yet promising to have quality learning experiences
via e-Learning delivery. Learners found e-Learning challenging, learning experience
beneficial and satisfactory.

I INTRODUCTION
The practice of using technology to enhance teaching-learning in higher
education has seen a veritable explosion. The use of technology has not only
created new opportunities within the conventional classroom but also served to
expand learning experiences beyond the popular notion of classroom (Scott,
Ken, and Edwin, November 1999). This innovation is expanding almost
exponentially, and its potential to facilitate quality learning experiences has
caused a flurry of activities in academic institutions(Schutte, 1997). Emergence
of technology influence people’s personality preferences the way they may or
may not want to become more actively involved in their learning. Thus, one
mechanism for influencing the design of the electronic education course
examines how learners may react, to the instruction, given their preferred
learning styles.

193

194

I1 PROBLEMS
Evaluations of new educational technologies have tended to compare learning
outcomes of instructional delivery methods with the hope that e-Learning, “will
be the one to revolutionise learning” (Parson, 1998). Furthermore, taxonomy of
learning styles developed by Curry (1990) uses the concepts of learning styles
and students’ academic achievement to explain the process of learning. Curry’s
taxonomy (1990) seems to suggest that learning styles, and student learning
outcomes, particularly students’ academic achievement is associated, and
interrelated. Parson (1998) recommends that it is important to understand how
the new technology can affect learning when it is used by different types of
learners with different learning styles. Thus, the aims of this paper are
evaluating students’ academic achievement differed by their learning styles, and
examining the significant correlation between students’ learning styles and
academic achievement.

111 LITERATUREREVIEW
Since research into learning styles suggests that individuals learn differently, it
is logical that some learners would prefer to learn individually, while others
would prefer to learn from interaction in groups. Liu (2001) has indicated that
people exhibit individual differences in the learning styles that they adopt in
problem solving and other similar decision-making activities. This implies that
it is important to incorporate learning styles into consideration when e-Learning
design is concerned. Studies by Keirsey (1998 and 2001) developed four
temperaments (SJs, SPs, NTs, and NFs) based on the Myers-Briggs Type
Indicator (MBTI) by Myers, McCaulley, Quenk and Hammer (1998) with the
highlighted differences in learning styles. See Table 1.1.

Learning Styles DescriDtion
SPs Artisan (Sensing, Perceiving)
NFs Idealist (Intuitive, Feeling) SPs prefer applying, testing with reality, hands-on
experience, exploring, experimenting. They are
1 SJs Guardian (Sensing, Judging) action-oriented in their learning approaches.
NFs prefer brainstorming, listening, speaking,
NTs Rational (Intuitive, Thinking) interacting with others. They are people-oriented in
their learning approaches.
SJs prefer manipulating materials, following
directions, building on given tasks, making things
work. They are details-and facts- oriented in their
learning approaches.
NTs prefer logic, analysing, classifying, and drawing
conclusions. They are concepts-oriented in their
learning approaches.

195

Much empirical research signals that learning styles can hinder or enhance
academic achievements in several respects (Riding and Grimley, 1999; Ross,
1999). Other researches on learning styles and achievement have shown that
teaching students how to learn and how to monitor and manage their own
learning styles is crucial to academic success (Matthews, 1991; Atkinson, 1998;
Biggs and Moore, 1993). Nevertheless, in assuming stability as well as lack of
individual control, learning style literature suggests that it may be difficult for
students to change their learning styles (Pintrich and Johnson, 1990).

IV RESEARCH DESIGN AND METHODOLOGY
The entire research design was categorized into Part A and Part B. Part A
assessed empirically the significant difference between the control and
experimental groups. Part B involved the use of Learners Reflection
Questionnaire (LRQ) in experimental group. LRQ was modified and directly
tap into each of the facets of the constructs based on Krzycki (2002). It aims to
assess students’ readiness on e-Learning delivery in experimental group. The
data collected from this study were derived from students who enrolled in
Diploma in Electrical and Electronic Engineering (DEE) programme - semester
6, at INTI College Malaysia April 2002 session, which covered 14 weeks of
lessons. Pretest and learning styles questionnaire were administered to control
and experimental groups in Week 1. Keirsey’s Temperament Sorter’s (KTS)
questionnaire contained seventy questions, through which the subjects were
categorised into SJs, SPs, NTs, and NFs (Keirsey and Bates, 1984). Control
group followed conventional class, whereby e-Learning is used as to supplement
the conventional lessons in experimental group. The treatment (e-Learning
delivery) was applied in week 2 in experimental group. Posttest was
administered to both groups in Week 14. The course that has been chosen from
DEE programme was “Object-oriented Programming in Java”. Two classes
have been selected which were CSC243A1 and CSC243A2. It was a three-
credit lecture and one-credit tutorial, which met for sixty-minute per lecture
session (full class) and sixty-minute per tutorial session on every week. Both
classes consisted of 52 and 46 students respectively. The sampling procedure
followed stratified random sampling and filtering process was applied to rectify
and ensure there was no repeating student in both classes.

196

V RESULTS AND DISCUSSIONS
The following hypotheses (Figure 1.1 and Table 1.2) were formulated to test and
evaluate at 95% (0.05) significant level as to fulfill the research objectives.

Learner , I . .
Profile
Comfori Level in Computer PerceivedLevel Learning
using Computer Competency in “ndenmding Preferences

Hs,H6

Figure 1.1 Overview of research hypotheses

Majority of learners from control group are SJ (32.70%) as compared to other
learning styles. Keirsey’s findings (2001), showing SJs as the dominant
learning styles. Indeed, Schroder’s observation (2001) comments that S
(Sensing) is the dominant learning characteristics in college population.
Whereas, SJ conquered the other learning styles with 30.40% in experimental
group. See Figure 1.2.

Part A: Learning styles versus academic achievement
Initial experimentation manifested a rejection of null hypothesis in Hypotheses 2,
3, 4, and 6 as tabulated in Table 1.3. Temperaments SJs, SPs, and NTs
experienced the rejection of null hypothesis. The conclusion drawn principally
those learners with different learning styles did affect learners’ academic
achievements, except NFs learning styles. The mean scores between NFs and
NTs decreased as 18.44% and 18.37% respectively as opposed to other learning
styles (Table 1,4). The characteristicsofNFs, “people-oriented”inbuilt tends to
enforce this group of learners to achieve their highest learning satisfaction by
mixing, interacting and communicating among one another. Mean scores
between SJs and SPs learners significantly increased 89.47% and 57.04%
respectively. Furthermore, SJs scored the highest in posttest assessment as
compared to other learners. Mean score analysis has explicated a regression

197

Narrative Description
There is a significant difference between pretest mean of learners in control group and
experimental group
There is a significant difference between posttest mean of learners in control group and
experimental group
There is a significantdifference between posttest mean of Guardian (SJ) learners in control group
and experimental group

There is a significant difference between posttest mean of Artisan (SP)learners in control group

and experimental group
There is a significant differencebetween posttest mean of Idealist (NF) learners in control group
and experimental group
There is a significant difference between posttest mean of Rational (NT) learners in control
group and experimentalgroup
There is a significant correlation between learners’ profile and learning styles among learners in
experimentalgroup
There is a significant correlation between comfort level in using computer and learning styles
among learners in experimental group
There is a significant correlation between level of computer competency and learning styles
among learners in experimental group
There is a significant correlation between perceived level of understanding and learning styles
among learners in experimental group
There is a significantcorrelation between learningpreferences and learning styles among learners
in experimental group
There is a significant correlation between learners’ profile and posttest mean among learners in
experimental group
There is a significant correlation between comfort level in using computer and posttest mean
among learners in experimental group
There is a significant correlation between level of computer competency and posttest mean
among learners in experimental group
There is a significant correlation between perceived level of understanding and posttest mean
among learners in experimental group
There is a significantcorrelation between learning preferences and posttest mean among learners
in experimental group

Table 1.2 Research hypotheses

198

Ststistlcs :Percentagewithin Controland Exptimentsi Gmup CromabuIaUon

32.x

Figure 1.2 Statistics:percentage within control and experimental groups’ crosstabulation

subsequently among learners in SJs, SPs, and NFs in control group, whereas
NFs and NTs in experimental group in pretest and posttest assessments. NFs in
control group voted highest mean regression with mean -7.40 (-24.02%).
Meanwhile, NFs dominated the mean regression with -5.70 (-18.44%) in
experimental group than other learners. It suggests that SPs and SJs learners
could comprehend well without much guidance and particularly appropriate to
learn via e-Learning. In contrast, NFs and NTs learners require careful planning
and attention in practical subject like programming when e-Learning is
incorporated into the learning process.

Part A: Summary of Hypotheses Results

P

Significant

Hypothesis Level Result/Outcome

* (p = 0.05)

HI 0.361 Accepted HO

H2 0.029 Rejected HO

H3 0.004 Rejected Ho

H4 0.001 Rejected Ho

-Hs 0.759 Accepted HO

H6 0.013 Rejected Ho

* Significant level was measured based on Independent-SamplesT Test

Table 1.3 Part A: summary of hypothesesresults

199

Part B: Learners Reflection Questionnaire’s empirical results
Hypotheses 7 to 16 were evaluated in this section. Table 1.5 summarises the
results evaluated on Part B. Results demonstrate that there was a significant
correlation between level of computer competency (Hg), perceived level of

Learning Styles versus Academic Achievement

Category Learning Pretest Posttest Mean Gain -Percentage (YO)
(Group) Styles Mean Mean
Control *-9.91%
SJ 24.20 21.80 *-2.40 *-12.50%
Experimental *-24.02%
SP 28.00 24.50 *-3.50 97.70%

NF 30.80 23.40 *-7.40 89.47%
57.04%
NT 17.40 34.40 17.00 *-18.44%
*-18.37%
SJ 22.80 43.20 20.40

SP 28.40 44.60 16.20

NF 30.90 25.20 *-5.70

NT 24.50 20.00 *-4.50

* Indicated mean score and percentage (%) decreased

Table 1.4 Learning styles versus academic achievement

understanding (Hlo), and learning preferences (HI,) with learning styles,
whereby p = 0.005, 0.002 and 0.021 correspondingly. These conclude that
learners who possessed certain degree of computer competency enabling them to
learn via e-Learning efficiently and effectively as opposed to other who
possessed lower degree of computer competency. However, there was no
significant correlationbetween learning styles and learner profile (H,) where p =
0.4730. In addition, outcomes signified that comfort level in using computer
(H8), where p = 0.086, and learning styles was not significantly correlated. The
possible discussion was learning styles did not relate with how comfort a learner
used a computer to achieve learning objectives. As a result, technological aspect
much relies on an individual ability to explore in the cyberspace. Therefore,
technological awareness, operational and application skills seem to be essential
to successfully promote e-Learning technology and directly affected learners’
academic performance. It was advantage for a learner to posses as competent as
possible to adapt in e-Learning environment. The hypotheses which assessed
LRQ and posttest mean accepted all null hypotheses. Research outcomes
suggest that learners’ LRQ was strongly linear related with learning styles.
Nonetheless, LRQ seems to have no linear correlation with academic
achievement. Table 1.6 summarises the overall hypotheses results.

200

Correlationbetween Learning Styles and Academic Achievement using LRQ

Lyz2Hypothesis Variable Academic Significant Result/
Level Outcome

* (p = 0.05)

H7 1 d 0.473 Accepted Ho

Ha 2 .I 0.086 Accepted Ho

H9 3 d 0.005 Rejected Ho

Hio 4 d 0.002 Rejected Ho

HII 5 d 0.021 Rejected Ho

Hi2 1 d 0.720 Accepted Ho

HII 2 d 0.540 Accepted &

HI4 3 d 0.942 Accepted Ho

HIS 4 d 0.860 Accepted &

HI6 5 d 0.419 Accepted Ha

* Significantlevel was statisticallytabulatedbased on Pearson Chi-square Tests procedures

NOTE The variables are: 1. Learner Profile, 2. Comfort Level in Using Computer, 3. Level of
Computer Competency, 4. Perceived Level of Understanding,and 5. Learning Preferences

Table 1.5 Part B: summaryof hypotheses results

Overall Hypotheses Results

Learning Styles Academic Achievement I Significantdifference
LRQ
Learning Styles I Significantcorrelatedon (Hs), (HI&
(Hid
Academic
Achievement LRQ Not significant correlated

Table 1.6 Overall hypothesesresults

VI CONCLUSIONS
Initial research outcomes exemplify that it is possible to have quality and
beneficial learning experiences through e-Learning delivery. Explicitly, the
messages from this research are strongly encouraging. Learners involved in e-
Learning have significantly addressed higher level of academic achievement as
opposed to conventional learning. Primarily, learners with different learning
styles potentially affect learners’ academic achievements. In short, e-Learning
appears to have no negative effects on academic achievement in relation to
learning styles. Use of technology in any capacity does not guarantee academic
success, however indications from this study suggest that it does not necessarily
have any significant negative effects. The most important consideration is to

201

understand and evaluate students’ learning styles before employing e-Learning
in any learning environments.

RECOMMENDATIONS
Further research is required to understand other attributes such as learners’
motivation, cognitive process and review students learning factors that influence
leamers in accessing the e-Leaming application. Future work involves sample
design which across distinct geographical areas with different nature of subjects
(more theoretical subjects) to other majors of students.

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A WEB-BASED ENVIRONMENT FOR BETTER
ADMINISTRATION OF DISTANCE LEARNING COURSES

S. C. NG, S. 0.CHOY, R. KWAN AND Y. C. TSANG

School of Science and Technologv
The Open University of Hong Kong
30 Good Shepherd St., Homantin, Hong Kong

E-mail: [email protected]

The introduction of web technology in course delivery in the Open University of Hong Kong has led
to an effective and cohesive teaching and learning environment for course coordinators (CC), tutors
and students. More and more distance learning courses have made use of the online platform to
deliver course materials, assignments, and teaching schedules. This has led to a change in the
working pattern of course coordinators in the University. Teaching staff rely on a convenient and
resourceful environment for better administration of their courses. Students are provided with a
learning environment with sufficient information given and a good communication channel with
their teachers. In this paper, we introduce a web-based course learning system (Course Learning
Web - CLW) to ensure smooth course operations and to enhance student comfort levels. The
system provides different services that enable students, tutors and course coordinators to interact and
communicate effectively during the course of study.

1. Introduction

With the advent of Internet technology, the number of online tools for teaching
and learning has increased dramatically [11. Course management systems such
as WebCT [2] and Blackboard [3] facilitate the creation of web-based learning
environments and tracking of students within those environments.

The Open University of Hong Kong (OUHK) has adopted an online learning
environment (OLE) for more than 5 years. The OLE provides a platform for
easy access to course materials and for rapid information delivery to distance
learners. As the OLE of the University becomes a hub of course delivery and
communication, it provides a good opportunity for the teaching team to know
more about the students’learning progress and their behavior in using the OLE.

The key players in the OUHK education process are students, tutors, and
academic staff. Every course at the OUHK has an academic staff member as the
course coordinator (CC). The coordinator’s major responsibility is to supervise
the teaching and learning process for that course. This includes setting
assignments and examination papers, delivering the course materials and
supplementary teaching notes, and monitoring students’ learning progress,
tutors’ teaching performance and the assignment marking process. Students
normally spend much of their time studying alone. To complement the distance

203

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learning process, face-to-face sessions are provided. Part-time tutors conduct the
face-to-face sessions, such as tutorials and surgeries.

During the whole year of studying a course, students are required to submit
several tutor-marked assignments (TMAs) to their tutors. Tutors then mark the
assignments and provide feedback to students. For assignment handling,
students can submit their assignments electronically,apply for an extension of a
submission deadline, enquire about the status of their submitted assignments,
and receive feedback on assignments from the teaching staff. The online system
can keep track of both the students’work and the marked scripts. In other words,
the online system provides a repository for the students and the teaching staff to
keep and store their work, and to manage and access the required information.

In this paper, we propose a web-based learning environment called “Course
Learning Web (CLW)” for better administration of distance learning courses.
The CLW includes three main functions - course administration, assignment
submission and marking, and course reminder (e-alert) service. The course
administration function allows CCs to manage course and student information
more effectively. The assignment submission and marking function allows easy
management of student submission and provides a rapid way for students to
retrieve the submitted work and track the marking status. Tutors can enter marks
into the electronic marking sheet. The system also provides a feature for tutors
to detect suspected plagiarized programs using the algorithm shown in [4-51 and
to run and test the submitted computer programs using different test cases. The
e-alert service reminds students about important course events proactively. The
system will send email messages on behalf of the CC to tutors and/or students
for timely alerts and advice.

With the use of this course learning website (CLW), teaching and learning of
distance learning courses can be done more efficiently and the communication
between course coordinator,tutors and students can be effectivelyenhanced.

2. System Overview

The overview of our system is shown in Figure 1. The main page of the CLW is
shown in Figure 2.

205

-Submission,-information Repositoty

Web tc

Admin. - Assignment Assignment
Submission Cut-offdate

Records Records

Figure 1. System Overview

The CC can access the CourseAdministrationInterface as follows; the:

1. CC inputs course information, such as number of surgeries, tutorials and
assignments.

2. CC sets up accounts for students and tutors.
3. CC enters information such as date, time and venue of surgeries and

tutorials.
4. CC has the right to amend any course informationwhenever necessary.

CCs, tutors and students can access the TMA submission Interface as follows:

1. Students submit assignment files through the World Wide Web, and he or
she will then receive an acknowledgement email from the system with a
security code.

2. The system records the filenames submitted by students and the current
submission status.

3. The CC and tutors can view and download the submitted files for marking.
4. Students can check the marking status and the assignmentresults.

The CC, tutors and students can access the Course Web Interface as follows:

1. The CC and tutors are allowed to compose course news.
2. The CC can upload course materials.
3. Students can view course news.
4. Students can download course materials.
5. Students can obtain tutorial, surgery and course presentation schedules.

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Figure 2. Student Course Web Interface Screen

3. System Functions
The objective of our system is to provide hnctions such as Course
Administration, Assignment Submission and Marking, and e-Alert service,
which helps CCs manage courses efficiently and improve communication with
students. We will briefly describe the three sub-systemsin this section.
3.1 CourseAdministration Subsystem
This system provides an administration interface for CCs to manage courses
easily on the web. A sample screen of the course administration subsystem is
shown in Figure 3, which provides a simple and clear look and feel of data entry
and data amendment. CCs can set up the distance learning environment for their
students with the given student data, tutor information and course schedules.

Figure 3. Course Administration Menu

207
Electronic forms are used to provide data entry, amendment and retrieval (see
Figures 4 and 5). To assist rapid data entry, the system provides a facility to
allow a CC to upload a data file of student data (see Figure 6 ) . The system is
able to parse the uploaded data file automatically by referring to the
corresponding fields in database tables. Retrieved data will be reconstructed to
form a number of xml records first. These xml records are then processed and
stored in corresponding database tables. To ensure data integrity and accuracy,

the system will determine the action to be taken for each set of records, such as
addition, deletion or modification.

Figure 4. Data Entry Form Screen

Figure 5. Surgery Data Amendment

Figure 6. Uploading of Student Data File

3.2 TMA Submission Subsystem
The system provides a web interface for students to submit assignment files
online. A sample screen is shown in Figure 7. To help students submit files
required by the CC, the system will display an expected file list. This will be
especially useful when students are required to submit program files with correct

208

names. The system also provides information such as original assignment due
dates, extended due dates (if approval is granted), and the last submission status.

Students can submit separate files or a zip file. There is no limit to the number of
submission trials before the cut-off date. Students are free to amend and upload
their files on the server. Once the file is submitted, the system will provide
automatic feedback about the submitted programs and current submissionrate. It
will also automatically send an email to the student with a safety code as
evidence of successful submission.

Figure 7. Assignment Submission Screen

7 b IC P ryrbm dPlrnie de not repb
BoBo Wong subnntkdh e fonowq 6lcr for
Coura UlOl
Asrlgnmcnt Tm.1
Fdc 1 MerrageBoardjava
Fdc 2 UsZp2Java
Secuwy c& ISWDT~UrKSINIYxQUIDWM3drEiMuDMLvNh3JJIYZxr

Figure 8. Acknowledgment Email ofAssignment Submission

The system will help the CC retrieve student assignment submission status and
marking records automatically (see Figure 9). This helps the CC in handling late
submission cases. After receiving the current assignment submission status, the
CC and tutors can take proper actions to deal with late submission cases, such as
sending emails to remind students to submit their work within a short period.
Secondly,the marking status can help the CC in monitoring tutors’ performance.
Additionally, the system is able to detect suspected cases of plagiarism
automatically on behalf of the markers. Our plagiarism detection algorithm uses
parse trees to check for program similarities [4-51. If the score is higher than a

209

preset threshold, the system considers those cases as suspicious plagiarized
copies.

Figure 9. Assignment Management Screen

3.3 e-AlertService
This function is to remind the students of important events such as an urgent
announcement, assignment cutoff dates, and the schedules of face-to-face
sessions. Students, tutors and the CC are free to subscribe to this course
reminder service. Figure 10 shows the reminder service subscription screen.
Users can choose the frequency of those reminder emails, e.g. 1-day before the
cutoff date of an assignment. Except for course news, we allow the users to
select their preferred number of days before an event happens to receive email.
Users can specify any valid email account. The system will deliver email to this
address whenever a course event matches the specified criteria. A sample of a
reminder email is shown in Figure 11. As a result, this service enables the course
coordinator to remind students about some important course events and even
prevent tutors from forgettingto conduct face-to-face sessions.

Figure 10. e-Alert Service Application Screen

210

Figure 11. Reminder Email Screen

4. Discussion
We have used the CLW in four OUHK distance learning courses, of which one
commenced in April 2004 and three in October 2004. Choy and Ng are the
course coordinators of the four courses. Preliminary evaluation through
qualitative study indicates that the system was well-accepted by the students and
tutors. Students commended that their teachers were enthusiastic about their
studies and aware of their learning progress and performance in the course. They
appreciated the close communication between coordinators, tutors and students.
This communication, in turn, helped create a good learning atmosphere for
distance learning courses.

5. Conclusions
This paper reported on the development of an integrated web-based system for
supporting and managing course activities in a distance learning environment.
The system, called as Course Learning Web (CLW), is composed of course
administration, e-Alert service and assignment submission systems. The
administration system streamlines the running of distance learning courses by
providing different administration and monitoring tools to relieve the burden of
course coordinators. The e-Alert system sends timely alerts and advice to
students and tutors as reminders of course events and activities. With the use of
the assignment submission system, students can enjoy convenient online
submission and checking of marking progress. The CLW can enhance the
communicationsbetween teaching staff and students, and increase the efficiency
of CCs in managing distance learning courses.

211

6. Acknowledgments

The authors would like to thank the OUHK and Education and Manpower
Bureau (EMB) for the Earmarked Research Grant (Ref: 9004/03H) that allowed
the system to be developed.

References

1 . Lever-Duffy, J., McDonald, J. and Mizell, A. (2003). The 21st Century
Classroom: Teaching and Learning with Technology, Boston: Allyn and
Bacon.

2. WebCT: httu://www.webct.com
3. Blackboard: httu://www.blackboard.com
4. Ng, S. C., Li, T. S., Kwan, R., and Ngai, H. S. (2004). An Integrated

Assignment Marking System for Online-Submitted Assignments, Proc. of
International Con$ on Computers in Education (ICCE 2004), Melbourne,
Australia, Nov 30 - Dec 3, 2004.
5 . Ng, S. C., Li, T. S., and Ngai, H. S . (2004). “Plagiarism Detection in
Programming Assignments”, In D Murphy, R. Cam, J. Taylor and T. M.
Wong, Distance Learning and Technology:Issues and Practice, The Open
University of Hong Kong Press, pp. 366-77.

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Author Index

Al-Nais, M.O. 57 Ng,S.C. 203
Peco, PedroP. 67, 117
Bedri, R. 57 Poveda, Maria
Rong,Gu 3
Chan, Alan Y. K. 87 29,37
So, Teddy K.K.
Cheng, Wenqing 125 Su,Daniel 175
193
Cheung, Kent K. T. 87 Such, Manuel M. 67, 117
Tsang, Kerry 135
Cheung, Ophelia 23 Tsang, Eva 95
Tsang, Philip 155
Cheung, Ronnie 145 Tsang, Y. C. 203
Uchida, Satoshi
Cheung, Yannie 165 Wong, S. M. 13
Wu, Di 47
Chow, Paul K.0. 87 Yabo, Dong 125
Yang, Zongkai 29,37
Choy, S. 0. 203 Yeung, Yin Fei 125, 183
Yi, Zhang 105
Fong, Joseph 105 Yuan, Shyan-Ming 183
Zhao, Chengling 75
Hsien, Tang Lin 75 Zhi, Feng Liu 183
Zhu, Zhiting 75
Kalogerou, V. 3 183

Kwan, Reggie 47, 155,165,203

Leung, Howard 135

Li, T. S. 47

Lo, Henry 155

Lu, Sanlan 183

Lui, Andrew K. 155,162

Martin, Daniel M. 67, 117

Miaoliang, Zhu 29,37

Navarr, Leone1 I. 67, 117

213