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Journal of Educational Technology Development and Exchange
(JETDE)
#3091) ?779) 68-'0)
How Can Self-regulated Learning Be Supported in
E-learning 2.0 Environment: a Comparative Study
Hong Zhao
Li Chen
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1Volume 9, No. 2, December, 2016
Hong Zhao
Beijing Normal University
Li Chen
Beijing Normal University
Abstract: As a critical factor that aects the learning performance in distance education, self-
regulated learning (SRL) has elicited considerable interest. Self-regulated learners can manage
their learning activities eciently but researchers indicate that learners have diculties in SRL
behavior. Thus, providing support to facilitate self-regulatory processes is important. E-learning
has the potential to be a learning tool to examine students’ self-regulatory skills. New advances
in technology, especially the development of Web 2.0 technology, have provided eective support
for self-regulated learning. This study addresses the following research question: How can
SRL be supported properly in E-learning environment? Because learning processes cannot be
conceptualized without the sociocultural context this study investigate environmental variations
between two samples of Mainland China and Hong Kong distance learners (N=289). The
purpose is to chart the underlying relationships between learner self-regulation and distance
education environments using regression analysis and to nd dierences of environmental factors
and self-regulation in dierent cultural orientations. The study has found signicant dierences
between Mainland China and Hong Kong distance students on demography variables except age
characteristics. In the relation model however, no dierence has been found. Self-regulation is to
be equivalent in the two cultures and can be inuenced by the same environmental factors.
Keywords: self-regulated learning, distance environment
Zhao, H., & Chen, L. (2016). How Can Self-regulated Learning Be Supported in E-learning 2.0 Environment:
a Comparative Study. Journal of Educational Technology Development and Exchange, 9(2), 1-20.
How Can Self-regulated Learning Be Supported in
E-learning 2.0 Environment: a Comparative Study
1. Introduction
There is increasingly empirical evidence
showing that self-regulated ability is an
effective key factor in predicting students’
academic achievements in e-learning
environment (Zimmerman & Schunk,
2001; Liaw & Huang, 2013). In e-learning
environment, students are responsible for
their own studies and have to actively take
part in the management of learning process.
They have to set learning objectives, monitor
and introspect their own learning processes,
and evaluate learning outcomes. However,
2
Journal of Educational Technology Development and Exchange
Volume 9, No. 2, December, 2016
researchers have found that these self-
regulated learning processes are not taking
place naturally for most of learners. In
general, learners could not realize that they
should manage their own learning processes
and do not know how to effectively regulate
these processes. Therefore, supports are
needed to help learners acquire effective
self-regulating strategies(Kramarski
&Mevarech 2003; Azevedo et al., 2004).
For students, how to learn and what to learn
were largely determined by the learning
environment provided by distance education
institutions(AI-Harthi, 2010).In other words,
self-regulated learning process is organized
and guided by the learning environment
(Kitayama, 2002).Therefore, what kind of
online learning environment can effectively
promote self-regulated learning? What
components should be included in an eective
online self-regulated learning environment?
Based on the questions above, the composition
of a successful online self-regulated learning
environment was discussed in the present
research and a survey of online learning
environments was designed accordingly. That
is to say what factors have impact on students’
self-regulated learning. Furthermore, a
survey of online learning environments and a
comparative study have been conducted based
on the above questions.
1.1. E-learning 2.0Environment Plays a Role
in Promoting Self-regulated Learning
E-learning, as a new mode of modern
distance education, provides a dynamic,
interactive and nonlinear learning environment
for learners through a series of synchronous
or asynchronous network communication
technologies. It breaks the limit of time and
space and offers an opportunity for students’
self-regulated learning. In recent years,
Web2.0 technology represented by social
software such as blogs, Facebook, Twitter
and wiki had been widely used in e-learning,
which enabled learners to achieve more
participation and collaboration in dynamic
social interaction of creating, communicating,
and sharing knowledge.
Though there is no consistent definition
of Web2.0, the core characteristics of
“interaction, participation and sharing” give
the medium a great potential for effectively
supporting learners’ self-regulated learning.
This potential has been tested by many
researchers. Kitsantas and Dabbagh (2010)
thought that e-learning 2.0 environment
had the teaching function of helping and
promoting students’ self-regulated learning.
They took three kinds of social software to
analyze how teachers made use of social
software to promote students’ self-regulated
learning. Furthermore, they pointed out that
Web2.0 social software achieved innovation
in supporting students’ self-regulated learning.
Harrison (2011) surveyed the application
status of blogs used by undergraduates for
the study. He found that blogs contributed
to the management of one’s own studies, as
well as the increase of students’ participation
and promoted the development of informal
learning community. Hilton (2009)
emphasized that Web2.0 tools, especially
the social software, supported students for
managing their own studies. Chen (2009)
believed that Web2.0 technology provided
strongly self-regulated tools for students, and
thus, made self-regulated learning available
for students and helped improved students’
academic. Liaw and Huang (2013) proved
that learning motivation can be stimulated
by establishing an effective interactive
online learning environment, thus promoting
students’ self-regulated learning. From the
research, e-learning2.0 environment had
an advantage in supporting and promoting
students’ self-regulated learning. Therefore,
designing and developing effective online
3Volume 9, No. 2, December, 2016
How Can Self-regulated Learning Be Supported in E-learning 2.0 Environmsent: a Comparative Study
learning environment to promote learners’
self-regulated learning has been the primary
task for both of practitioners and researchers
of distance education.
1.2. Factors for the Success of e-Learning
Environment
The issue of what factors and
characteristics should be included in a
successful e-learning 2.0 environment has
been discussed by many researchers. Previous
studies suggested that users’ satisfaction was
one of the key factors in evaluating whether a
learning environment was successful or not.
For example, Liaw and Huang (2007) thought
that four factors (environmental usefulness,
learners’ satisfaction to environment, the
eectiveness of learning activity and learners’
characteristics) should be considered when
developing an e-learning environment.
Furthermore, Liaw and Huang (2013 thought
that users’ satisfaction referred to the
combination of users’ feeling and experience,
especially expressed as the acceptance degree
to learning environment. Additionally, learners’
satisfaction with the learning environment
determined whether or not learners wanted to
study in that environment. A lot of research
proved that users’ satisfaction was highly
correlated to self-regulated learning, a crucial
factor that had aected learners’ self-regulated
learning in an e-learning environment
(Kramarski & Gutman, 2006; Liaw & Huang,
2013; Roca & Gagne, 2008).Therefore, how
to improve users’ satisfaction in an e-learning
environment has become a hot issue discussed
by many researchers.
At the same time, a number of
correlational research and success models
of information system have emerged. The
D&M model was one of the most widely
referenced and tested models by these
researchers. In 1992, DeLone and McLean
rst proposed a success model of information
system consisting of six factors that they
had adjusted and upgraded in 2003. This
model proposed a theoretical framework for
evaluating information systems. According to
D&M model, six factors determined whether
or not a learning system was successful:
information quality, system quality, service
quality, adoption intention, users ’satisfaction,
and net benefit. Many researchers proposed
a new model based on D&M. For example,
Wang, Wang, and Shee (2007) proposed a
success model of e-commerce consisting of
five factors: system quality, service quality,
information quality, users’ satisfaction, and the
intention to use it again. Seddon (1997) who
proposed a new model based on the analysis
of D&M (1992) thought determining whether
or not an environment was successful could be
based on ve factors including system quality,
information quality, perceived usefulness,
individual income, and organization income.
Wang and Chiu (2011) proposed a new
success model of learning system aimed at
the e-learning 2.0 environment. This model
reformed the D&M model by adopting the
“quality-satisfaction-loyalty” theoretical
model, and communication quality had been
added to the new model, while net benet was
replaced with loyalty intention. In the W&C
model, users’ satisfaction was influenced
by four factors including system quality,
service quality, information quality, and
communication quality.
D&M model and W&C model provide a
theoretical model for learning environment
evaluation. According to these two theoretical
models, the success of online learning
environment is determined by five crucial
factors: system quality, information quality,
service quality, communication quality, and
users’ satisfaction. Among the five factors,
system quality is used to measure the
characteristics of learning environment itself,
4
Journal of Educational Technology Development and Exchange
Volume 9, No. 2, December, 2016
including usability, practical applicability,
reliability, flexibility, and adaptability.
Information quality refers to learning content
quality. The learning content provided by
e-learning 2.0 environment should have
the following characteristics: has the latest
tools, permits personalization, sufficient in
resources, be intelligible, and is closely related
to working or learning. Service quality refers
to learning support quality, or whether or not
can learners can eectively obtain instruction
and help from teachers in time. As proposed
by Chen and Hwang (2012), characteristics
of Web2.0 are embodied on service rather
than on technology. Thus, service quality
plays a crucial role in ensuring learners’
participation and durability. Communication
quality refers to what extent learners can
benet from the dynamic interaction provided
by learning environment including sharing,
feedback, and discussion. Users’ satisfaction,
which means learners’ attitudes to online
learning environment, covers the whole usage
experience including software, content, and
service. The current research adopted the
above ve factors as indicators for evaluating
the e-learning environment.
2. Methodology
2.1. Population
The study concentrated on distance
learners who studied in the Network Education
Institution of Beijing Language & Culture
University and Hong Kong Open University.
All of these participants were adults with
working experience and took online courses
provided by the two universities. Web2.0
technology and tools were adopted by both
of the above two universities to help and
support students’ studies including wiki,
blog, BBS, YouTube, RSS, and Facebook. A
random sampling technique was adopted to
obtain a representative sample. Of the total
administered 339 questionnaires, 289 valid
ones were retrieved with an eective response
rate of 85.2%, including 129 ones from
Table 1. Descriptive statistics of study participants
Beijing Language & Culture University Hong Kong Open University
N p N P
gender Male 56 56.6% 26 83.8%
Female 73 43.4% 134 16.3%
age <20 5 3.9% 43 26.9%
20~25 38 29.5% 28 17.5%
26~30 38 29.5% 37 23.1%
31~35 27 20.9% 18 11.3%
36~40 12 9.3% 20 12.5%
41~45 9 7% 12 7.5%
45~50 0 0 2 1.3%
Working years <1 0 0 37 23.1%
1~5 50 38.8% 59 36.9%
6~10 46 35.7% 19 11.9%
11~15 19 14.7% 21 13.1%
16~20 10 7.8% 19 11.9%
21~25 3 2.3% 4 2.5%
26~30 1 0.8% 1 0.6%
Years of attending distance
learning
<1 75 58.1% 52 32.5%
1~3 54 41.9% 60 37.5%
4~6 0 0 46 28.8%
7~10 0 0 2 1.3%
TOTAL 129 160
5Volume 9, No. 2, December, 2016
Beijing Language & Culture University and
160 ones from Hong Kong Open University.
Table 1 shows the detail.
2.2. Instrument
The current study used the questionnaire
survey method to collect information of
the five indicators presented in Table 1
and information of students’ self-regulated
learning. To ensure content validity, items
adopted by the questionnaires of the
current study were modified from the items
of previous related questionnaires. The
questionnaires of system quality, information
quality, service quality, communication
quality, and users’ satisfaction were adapted
according to the studies of Wang et al.
(2007), Sun et al. (2008), Wang et al. (2011)
and Liaw et al.(2013). The self-regulated
learning questionnaire referred as the Distance
Learners’ Self-regulated Learning Ability Self-
rating Scale was developed by the Research
Center of Distance Education of Beijing
Normal University.
The final questionnaire consisted
of 32 items using a 5-point Likert-type
How Can Self-regulated Learning Be Supported in E-learning 2.0 Environmsent: a Comparative Study
Table 2. Questionnaire structure
Construct Number of items Sources
System quality 6
Wang, Wang & Shee , 2007
Sun, Tsai, Finger, Chen, & Yeh , 2008
Wang & Chiu, 2011
Liaw & Huang, 2013
Information quality 6
Service quality 4
Communication quality 5
User satisfaction 4
Self-regulation 7Dilireba Zhao & An, 2010
2.2.1.1. Reliability. The internal consistency
reliability was conducted to examine the
reliability of the questionnaires in the current
research.Cronbach αcoecient was calculated
for all six dimensions of the questionnaire
between the two universities.Table 3 shows
that Cronbach’salphafor the six dimensions
scale that included “strongly agree,”
“agree,”“neutral,”“disagree,” and “strongly
disagree” in regards to the statement items.
The questionnaire included two parts:
(1) the first one collected demographic
information of subjects such as gender,
age, working years, and years of attending
distance learning, while (2) the second one
collected students’ evaluations on system
quality, information quality, service quality,
communication quality, users’ satisfaction, and
self-regulated learning.
2.2.1. Reliability and validity analysis of
questionnaires. Reliability and validity are
crucial indicators for measuring eectiveness
and reliability of a scale. The following
methods were conducted by the current
research to ensure the reliability and validity
of the questionnaires.
6
Journal of Educational Technology Development and Exchange
Volume 9, No. 2, December, 2016
the difference in cultures between Hong
Kong and the mainland. Therefore, to ensure
the consistency of measurement content in
the context of different cultures, four Hong
Kong natives who researched in the field of
distance education were invited to modify the
expression of the primarily formed Chinese
version questionnaire to finally form the
Cantonese version questionnaire.
Third, researchers in the eld of distance
for the two universities all exceeded 0.7,
which indicated a high reliability and internal
consistency.
2.2.1.2. Content validity. The current study
involved a three-phase approach to validate
the content of the questionnaires. First,
back-translation method was carried out in
the process of questionnaire development
to ensure construct equivalence in that
most of the items were modified from
foreign questionnaires. In the process of the
questionnaire’s development, three bilingual
experts who had a good command of English
were invited to translate and back-translate
items. All of them were experts in the field
of distance education with years of overseas
study experience. Through the process of
back-translation, some items which had a
dierent meaning from the original ones were
deleted.
Second, many technical terms were
expressed in a different way as a result of
Table 3. Cronbach alpha coecient
Cronbach’s α
Dimensions Items in scale Beijing (N=129) Hong Kong (N=160)
System quality 6 0.864 0.830
Information quality 6 0.883 0.810
Service quality 4 0.738 0.760
Communication quality 5 0.866 0.807
User satisfaction 4 0.901 0.837
Self-regulation 7 0.852 0.885
education and master students that majored
in distance education were invited to make
conformance judgments for the questionnaire
as well as its relevant content. Words that may
have caused ambiguity or misunderstanding
were modified, and ambiguous or poor
relevant items were deleted to ensure all of
the items were expressed in an accurate way.
The content validity of the questionnaires was
guaranteed through all this work.
2.2.1.3. Structure validity. AMOS 2.0 was
used to examine structure validity. Goodness
7Volume 9, No. 2, December, 2016
between the two universities (Table 5).
The results showed that significant
difference was found in all six dimensions
between distance learners from Beijing and
Hong Kong. The system quality, information
quality, and service quality of Network
Education Institution of Beijing Language
& Culture University were founded to be
better than Hong Kong Open University. The
communication quality, user satisfaction,
as well as self-regulated learning of Beijing
Language & Culture University, were also
found to be better than Hong Kong Open
University.
x2/df CFI NFI RFI RMSEA
1.01 0.998 1.000 0.990 0.008
Table 4. Construct validity
How Can Self-regulated Learning Be Supported in E-learning 2.0 Environmsent: a Comparative Study
indices including x2/df, CFI, NFI, RFI,
RMSEA were calculated as Table 4 shows
x2/df is 1.01 which was less than 3; NFI, RFI,
CFI is 0.998, 0.990, 1.000, all of which were
above 0.9; RMSEA was 0.008 which had been
lower than 0.05. The results revealed that
construct validity was good.
3. Results
3.1. The general comparison between Beijing
and Hong Kong
Independent-samples T test were used
to analyze the differences in six dimensions
Table 5. The general comparison between Beijing and Hong Kong
school N Mean T sig
System quality Beijing 129 25.84 13.445 .000***
Hong Kong 160 20.38
Information quality BJ 129 24.53 9.317 .000***
HK 160 20.59
Service quality BJ 129 16.46 11.715 .000***
HK 160 13.42
Communication
quality
BJ 129 20.12 11.010 .000***
HK 160 16.19
User satisfaction BJ 129 16.76 11.531 .000***
HK 160 13.30
Self-regulation BJ 129 28.79 13.478 .000***
HK 160 22.19
***p<.001, **p<.01, *p<.05
8
Journal of Educational Technology Development and Exchange
Volume 9, No. 2, December, 2016
The results showed that with the exception
of the communication dimension, there was
a significant difference between female and
male students in users’ satisfaction dimension
and in self-regulated learning dimension.
Male students were more satisfied with their
3.2. Demographic Characteristics Dierence
Analysis
In this section, difference analysis of
demographic characteristics was conducted
in the communication dimension, users’
satisfaction dimension, and self-regulated
learning dimension, all of which represent
leaners’ experience to online learning
environment.
3.2.1. Gender dierence analysis of distance
learners’ online learning experience.
First, the general gender differences of
online learning experience were analyzed by
using a T-test. Table 6 shows the results.
Table 6. The general gender dierences
Gender N Mean T sig.
Communication
quality
Female 207 17.68
-1.839 .068
Male 82 18.62
User satisfaction
F 207 14.58 -2.309 .022*
M 82 15.50
Self-regulation
F 207 24.49
-3.342 .001**
M 82 26.76
***p<.001, **p<.01, *p<.05
learning experience than female students. In
addition, male students reported higher self-
regulation abilities.
Then gender difference analysis was
carried out for the two universities separately
to see if the pattern was consistent across
different universities. Table 7 shows the
results.
For students from Beijing Language
& Culture University, there was only a
significant gender difference in the self-
regulated learning dimension and men
reported higher self-regulated learning ability
than women. Students from Hong Kong Open
University had signicant dierences in three
dimensions including communication quality
dimension, users’ satisfaction dimension, and
self-regulated learning dimension. However,
women reported higher communication
quality, higher satisfaction, and better self-
regulation learning ability than men.
9Volume 9, No. 2, December, 2016
3.2.2. Age difference analysis of distance
learners’ online learning experience.
ANOVA was conducted to analyze age
difference of the whole subjects’ online
learning experiences. Table 8 shows the
results.
An analysis of variance (ANOVA)
on user’s satisfaction revealed significant
variation among students from different age
groups. However, Post Hoc analysis indicated
that there was no significant difference
between any dierent age groups.
An ANOVA was carried out separately in
Beijing Language & Culture University and
in Hong Kong Open University to analyze
students’ online learning experiences of
dierent age groups in the current study.
How Can Self-regulated Learning Be Supported in E-learning 2.0 Environmsent: a Comparative Study
Table 7. The gender dierence analysis in Beijing& Hong Kong
School dimensions Gender N Mean t sig.
Beijing
Communication
quality
F 72 19.83 -1.098 .274
M 56 20.43
User satisfaction F 72 16.46 -1.475 .143
M 56 17.09
Self-regulation F 72 28.11 -2.152 .033*
M 56 29.59
Hong Kong
Communication
quality
F 134 16.47 2.764 .006**
M 26 14.73
User satisfaction F 134 13.54 2.645 .009**
M 26 12.08
Self-regulation F 134 22.49 1.999 .047*
M 26 20.65
***p<.001, **p<.01, *p<.05
SS df MS F Sig.
Communication
quality
Between
groups 135.176 6 22.529 1.769 .105
User satisfaction Between
groups 141.883 6 23.647 2.607 .018*
Self-regulation Between
groups 329.147 6 54.858 2.008 .065
Table 8. The general age dierences
***p<.001, **p<.01, *p<.05
10
Journal of Educational Technology Development and Exchange
Volume 9, No. 2, December, 2016
The results showed that students from
dierent age groups did not dier signicantly
in any of the three dimensions for students
from Beijing Language & Culture University
and from Hong Kong Open University.
3.2.3. Working years difference analysis of
distance learners’ online learning experience.
An ANOVA was conducted to analyze
dierence in the working years of all subjects’
online learning experiences (see Table 9).
An analysis of variance (ANOVA) on all
of the three dimensions yielded significant
variation among students who have different
working years. Post-hoc test was carried out
in the current research to further analyze the
dierence (see Table 10).
The results showed that learners whose
working years were less than 1 year differed
significantly from those whose working
years were 1-5 years or 11-15 years in
communication quality dimension. Learners
whose working years were 1-5 years or 11-15
years reported higher communication quality
than those whose working years were less than
1 year.
In addition, learners whose working years
were 6-10 years or 11-15 years reported better
users’ satisfaction and better self-regulated
learning abilities as compared to those whose
working years were less than 1 year.
Working years difference analysis were
carried out separately in Beijing Language
& Culture University and Hong Kong Open
University(see Table 11).The results showed
that working years had non-significant
difference in all of the three dimensions
for Hong Kong Open University. While for
Beijing Language & Culture University,
Sum of
Square df Mean
Square F Sig.
Communication
quality
Between
groups 223.652 6 37.275 3.000 .007**
User satisfaction Between
groups 186.565 6 31.094 3.489 .002**
Self-regulation Between
groups 520.142 6 86.690 3.254 .004**
Table 9. The general dierences in working years
***p<.001, **p<.01, *p<.05
working years had signicant dierence in the
communication dimension and self-regulated
dimension.
Pairwise comparison was used as a post-
hoc test to determine the differences. The
results showed that working years only had
significant difference in the self-regulated
learning dimension.
Post-hoc test results showed that for
students from Beijing Language & Culture
University, working years in self-regulated
learning dimension mainly existed between
learners whose working years were 6-10
(MD=-3.519, p<0.05) and learners whose
working years were 11-15years (MD=3.519,
p<0.05).Learners whose working years were
11Volume 9, No. 2, December, 2016
Table 10. The post-hoc test for dierences in working years
(I)working
years
(J) working
years
Mean
Dierence (I-J) Sig.
Communication
quality 1
2 -2.462 .039*
3 -2.528 .063
4 -2.947 .040*
5 -2.470 .244
6 -2.583 .788
7 -3.797 .899
User satisfaction 1
2 -1.915 .082
3 -2.447 .017*
4 -2.608 .025*
5 -2.281 .152
6 -2.680 .578
7 -3.608 .836
Self-regulation 1
2 -3.126 .124
3 -4.093 .024*
4 -4.408 .032*
5 -4.074 .124
6 -3.680 .809
7 -5.108 .932
***p<.001, **p<.01, *p<.05
How Can Self-regulated Learning Be Supported in E-learning 2.0 Environmsent: a Comparative Study
Table 11. Working years dierences in dierent school
School dimensions SS df MS F Sig.
Beijing
communication
quality
Between
groups 110.319 4 27.580 3.181 .016*
User satisfaction Between
groups 53.801 4 13.450 2.415 .052
Self-regulation Between
groups 209.135 4 52.284 3.712 .007**
***p<.001, **p<.01, *p<.05
12
Journal of Educational Technology Development and Exchange
Volume 9, No. 2, December, 2016
between 11-15 years were found to be better
than learners whose working years were
between 6-10 years.
3.2.4. Years of attending distance learning
difference analysis of distance learners’
online learning experience.
First, an ANOVA was conducted in the
current study to analyze years of attending
distance learning difference of the whole
subjects’ online learning experiences(see Table
12).
Table 12. The general dierences of years of attending distance learning
SS df MS F Sig.
communication quality Between
groups 114.240 3 38.080 3.004 .031*
User satisfaction Between
groups 62.691 3 20.897 2.258 .082
Self-regulation Between
groups 289.253 3 96.418 3.548 .015*
***p<.001, **p<.01, *p<.05
Significant difference was only found in the
self-regulated dimension (see Table 13).
The results of post-hoc test showed that
Learners who attended distance learning
for 4-6 years had significant difference with
those who attended distance learning for less
than 1year or between 1-3 years in the self-
regulated learning dimension. Learners who
attended distance learning for less than 1 year
and between 1-3 years were found to be better
than those who attended distance learning for
4-6 years in the self-regulated dimension.
Table 13. The post hoc test for general dierences of years of attending distance learning
(I)working
years
(J) working
years
Mean
Dierence (I-J) Sig.
Self-regulation 3
1 -2.739 .027*
2 -2.635 .041*
4 -.109 1.000
***p<.001, **p<.01, *p<.05
The results showed that years of attending
distance learning difference had significant
difference in communication dimension and
in self-regulated learning dimension. Further
analysis of post-hoc test was conducted.
An ANOVA was separately conducted to
analyze years of attending distance learning
difference between Beijing Language &
Culture University and Hong Kong Open
University.
13Volume 9, No. 2, December, 2016
How Can Self-regulated Learning Be Supported in E-learning 2.0 Environmsent: a Comparative Study
The results showed that years of attending
distance learning had a non-significant
difference in three dimensions for distance
learners from Beijing Language & Culture
University. For distance learners from Hong
Kong Open University, years of attending
distance learning had significant difference
in users’ satisfaction dimension (F=4.106,
p<0.01). A post-hoc test was conducted to
further analyze the dierence. Table 14 shows
the results.
The results of post-hoc test showed that
for distance learners from Hong Kong Open
University, years of attending distance learning
mainly had significant difference between
learners who attended distance learning for
less than 1 year and those who attended
distance learning for 1-3 years, and between
learners who attended distance learning for
less than 1 year and those who attended
distance learning for 4-6 years. Learners who
attended distance learning for 1-3 years or 4-6
years were found to be better than those who
attended distance learning for less than 1 year
in users’ satisfaction dimension.
Table 14. The post hoc test for years of attending distance learning in Hong Kong Open
University
(I) years of
learning
(J) years of
learning MD (I-J) Sig.
User satisfaction
1
2 -1.412 .040*
3 -1.625 .022*
4 -1.212 .933
2
1 1.412 .040*
3 -.213 .980
4 .200 1.000
3
1 1.625 .022*
2 .213 .980
4 .413 .997
4
1 1.212 .933
2 -.200 1.000
3 -.413 .997
***p<.001, **p<.01, *p<.05
14
Journal of Educational Technology Development and Exchange
Volume 9, No. 2, December, 2016
3.3. Differences Analysis for Relationship
between Self-regulation and Environment
The author had developed a model
to explain the relationship between self-
regulation and e-learning 2.0 environment
as Fig. 1 shows (Hong, 2014, unpublished
result). In this model, system quality,
information quality, service quality, and user
satisfaction were tested to be key factors
affecting self-regulation in the e-learning 2.0
environment. User satisfaction was proved
to be the most significant intermediary
variable. Communication quality impacts
self-regulation through user satisfaction. The
Fig 3shows the final model for Hong Kong
Open University.
For Beijing Language & Culture
University, self-regulation was predicted by
information quality, communication quality,
and user satisfaction. For Hong Kong Open
University, self-regulation was affected
by system quality, information quality,
communication quality, and user satisfaction.
System quality is the only statistical dierence
for both universities. In both universities, the
path analysis demonstrates that communication
quality and user satisfaction were the most
significant intermediary variable. Statistic
Figure 1. Hong’s research model (2014)
directions of arrows show the positive impact
of environmental variables on self-regulation.
In this article, the model was tested
again separate regression analysis in dierent
schools. The author tried to nd whether there
were differences in the relationship between
self-regulation and environment in different
areas and school. Fig 2 shows the nal model
for Beijing Language & Culture University;
result also presented evidence that user
satisfaction played an important role for self-
regulation in both schools. It corresponded
to the author’s earlier study results. The
connection from communication quality
to self-regulation is significant for both
universities, which was different from the
early results.
15Volume 9, No. 2, December, 2016
Figure 2. Model for Beijing Language & Culture University
How Can Self-regulated Learning Be Supported in E-learning 2.0 Environmsent: a Comparative Study
Figure 3. Model for Hong Kong Open University
16
Journal of Educational Technology Development and Exchange
Volume 9, No. 2, December, 2016
4. Discussion
In this study, the online learning
environment in Beijing Language & Culture
University were found to be better than
Hong Kong Open University. The learning
experience of distance learners from Beijing
Language & Culture University were
also found better than the learners from
Hong Kong Open University. The results
corresponded with the teaching situation of
the above two universities. At Hong Kong
Open University, the online learning and face-
to-face tutoring were offered blended. Face-
to-face interactions were arranged at a fixed
time every week while the online learning
environment was mainly designed for learners
to acquire course’s information, discuss issues
online, submit assignments, and so on. The
interactions between teachers and students
mainly occurred during the process of face-
to-face tutoring. However, Beijing Language
& Culture University offered students with
a fully online program whereby registration,
curricula-variable, course learning, questions
and answering, and assignment submissions
happened on the learning platform. Learning
support provided by teachers was also
achieved based on the network platform.
Therefore, students from Beijing Language
& Culture University reported better on the
online learning environment and enhanced
learning experiences than students from Hong
Kong Open University.
Furthermore, the analysis of demographic
characteristics suggests that for whole
subjects, users’ satisfaction and self-
regulation ability of men were found to be
better than that of women. However, the
results of difference analysis carried out
separately in the two universities suggest
that there was only a significant difference
between female students and male students
in the self-regulated learning dimension for
students from Beijing Language & Culture
University. Men were found to be better than
women. The results were consistent with
previous research studies Hongetal., 2014).
As proposed by Hong, mainland China was
a male-dominated society and this particular
culture had a unique understanding about the
dierent social divides of labor and duties for
men and women. Men had more opportunities
to control their lives compared to women.
Women were more dependent upon men. This
traditional Chinese perception of gender leads
to gendered stereotypes. Besides, it may also
contribute to the difference of self-regulated
learning between men and women. However,
the analysis of gender difference presented
completely opposite results for distance
learners from Hong Kong Open University.
The significant differences were found in
the three dimensions. Women were found to
be better than men. Subjects’ backgrounds
may play a role in the results. Subjects from
Hong Kong Open University mainly majored
in nursing where most of the subjects were
women, thus women may have an advantage
in the learning process.
Dierent results were also presented in the
current study through age dierence analysis
based upon examining different regions and
universities. Results suggested that gender
difference had an impact on online learning.
However, students from different regions,
different universities, different majors, and
dierent gender presented dierence in online
learning process.
No matter for the whole subjects or for
individual university, age difference had
no significant difference in any of the three
dimensions. The results were in line with
previous research that found age difference
to have had no impact on learners’ online
learning experience (Cui et al., 2014;
Richardson & Swan, 2003).
17Volume 9, No. 2, December, 2016
For the whole subjects, working years
had a significant difference in the three
dimensions. The difference mainly existed
between learners whose working years were
less than 1 year and learners whose working
years were between 1-5 years and for learners
whose working years were between 6-10 years
and learners whose working years were from
11-15 years. Learners whose working years
were 1-5 years or 11-15 years reported better
communication quality than those whose
working years were less than 1 year did.
Learners whose working years were between
6-10 years or 11-15 years reported better
satisfaction and better self-regulated learning
ability than those whose working years were
less than 1 year. For students from Hong Kong
Open University, differences in any of the
three dimensions among different working
year groups were not signicant. For students
from Beijing Language & Culture University,
the comparison between the 6-10 working
years group and 11-15 working year groups
showed a significant difference. Students
who had longer working years reported better
self-regulated learning abilities. Therefore,
students’ working experiences may have had
an impact on their online learning experience,
especially on the self-regulated learning
process. Students with longer working years
and more working experience had better online
learning outcomes. Liuand Ginther (1999)
pointed out that compared to younger students,
older students tended to have more motivation
to learn and set more clear learning objectives,
thus have had better online learning outcomes.
For whole subjects, signicant dierences
in self-regulated learning ability were found
among students who have different years of
attending distance learning. Learners who
attended distance learning for less than 1
year were reported higher self-regulated
learning ability as compared to students who
attended distance learning for 4-6 years.
Researchers found that time management
and self-regulated learning were two major
problems encountered by students except
tuition (Zhang, 2003).Examples and help were
provided for students as far as possible by
Beijing Language & Culture University and
Hong Kong Open University in the process
of designing and developing online learning
environments to help students overcome these
problems. Both of the universities provided a
lot of personalized support and tutorship for
first-year students in the aspect of learning
guidance that covered registration, curricula-
variable, learning method and learning
process, which did help first-year students’
self-regulated learning.
The results of difference analysis
separately carried out between the two
universities showed no significant difference
in any of the three dimensions among students
who have dierent years of attending distance
learning for Beijing Language & Culture
University. While students from Hong Kong
Open University had a significant difference
in user satisfaction. Learners who attended
distance learning for 1-3 years or 4-6 years
reported higher satisfaction than those who
attended distance learning for less than 1 year.
These results suggest that students’ online
learning experiences may have impacted their
online environment satisfaction. These results
were consistent with previous research studies.
Atkinson and Kydd (1997) found that online
learning experience was a good indicator
for evaluating students’ attitudes to online
learning. Dziuban and Moskal (2001) also
found that students with richer online learning
experiences tended to be more satisfied with
online learning. This also had been proved in
the experimental studies conducted by other
researchers. For example, Wu et.al. (2014)
found that there was a signicant dierence in
learning satisfaction between learners who had
online learning experience (such as MOOCs)
How Can Self-regulated Learning Be Supported in E-learning 2.0 Environmsent: a Comparative Study
18
Journal of Educational Technology Development and Exchange
Volume 9, No. 2, December, 2016
and those who never had online learning
experience.
In this study, the relationship between
e-learning 2.0 environment and self-regulation
were tested in different areas and between
the two universities. The results indicate
that factors of e-learning environment
influencing self-regulation were similar
in both universities. Self-regulation was
signicantly inuenced by information quality,
communication quality, and user satisfaction
in both universities. System quality and
service quality also inuenced self-regulation
by way of communication quality and user
satisfaction as the intermediated variable
in both universities. The results supported
previous research ideas that self-regulated
learning process was guided and organized
by successful learning environment
(Kitayama, 2002; Vighnarajak et al., 2009).
Communication quality signicantly predicted
both user satisfaction and self-regulation,
which was consistent with previous research
results that high quality of interaction and
communication can improve user satisfaction
and promote self-regulation (Chen, 2009;
Wang & Chiu, 2011).Liaw and Huang (2013)
also stated that a satisfactory environment
should be an eective interactive environment
which was conducive to self-regulation.
This study provided evidence that user
satisfaction was one of the most important
factors in assessing the success of e-learning
environment. The data yielded by this study
provided strong evidence that user satisfaction
was a key intermediated factor for self-
regulation in e-learning environment in both
universities. System quality, information
quality, and service quality all significantly
predicted user satisfaction and furthered
self-regulation. Current research appears to
validate such a result. For example, Liaw
and Huang (2013) had put forward the view
that enhancing user satisfaction can promote
learners’ self-regulation in an e-learning
environment.
5. Conclusion
The results of this research have
significant implications on the construction
of online learning environments to promote
online learning experiences for distance
learners, especially self-regulation. The
factors affecting self-regulation in e-learning
environments were identified and further
proved to be similar in different cultures
and areas. There was compelling evidence
confirming the opinion that user satisfaction
played a key role for e-learning success and
self-regulation. This research turned out to
validate this conclusion. User satisfaction
covers the entire usage experience including
technology, content and service, which
represent learner’s attitude toward e-learning
environment (DeLone & McLean, 2003).Many
variables have been proved to have strong
correlation with satisfaction in e-learning
environment. Results recommend that future
research explore other variables aecting user
satisfaction and examine their eects on self-
regulated learning.
19Volume 9, No. 2, December, 2016
How Can Self-regulated Learning Be Supported in E-learning 2.0 Environmsent: a Comparative Study
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Contact the Author
Hong Zhao
lecturer,
Beijing Normal University
Email: u zhaohong@bnu.edu.cn
Li Chen
Professor,
Beijing Normal University
Email: lchen@bnu.edu.cn
