Kuo, Y.-C., Chu, H.-C., & Huang, C.-H. (2015). A Learning Style-based Grouping Collaborative Learning Approach to Improve
EFL Students’ Performance in English Courses. Educational Technology & Society, 18 (2), 284–298.
A Learning Style-based Grouping Collaborative Learning Approach to Improve
EFL Students’ Performance in English Courses
Yu-Chen Kuo*, Hui-Chun Chu and Chi-Hao Huang
Department of Computer Science and Information Management, Soochow University, 56, Sec. 1, Kui-Yang St.,
Taipei 100, Taiwan // yckuo@csim.scu.edu.tw // carolhcchu@gmail.com // erica.huang0716@gmail.com
*
Corresponding author
(Submitted March 14, 2014; Revised June 30, 2014; Accepted July 25, 2014)
ABSTRACT
Learning English is an important and challenging task for English as Foreign Language (EFL) students.
Educators had indicated that, without proper learning support, most EFL students might feel frustrated while
learning English, which could significantly affect their learning performance. In the past research, learning
usually utilized grouping, but few studies have considered the difference in group members’ learning styles. In
this study, a learning style-based collaborative learning approach is proposed to cope with this problem. To
evaluate the effectiveness of the proposed approach, an learning style-based online collaborative learning
platform has been developed. Moreover, an experiment has been conducted on a university English course to
compare the learning performance and learning interest of the students who learn with the proposed approach
and those who learn with the conventional collaborative learning approach. It was found that the homogeneous
learning style groups outperformed the heterogeneous groups. Besides, those students who used the online
English collaborative learning approach outperformed those who used the traditional paper-based English
collaborative learning approach. Sequence analysis was also carried out to analyze the students’ online
discussion.
Keywords
Architectures for educational technology systems, Improving classroom teaching, Cooperative/collaborative
learning, Applications in subject areas, Evaluation of CAL systems
Introduction
Research background and motivation
In the modern environment in which the global village is emphasized, English has become an essential second
language as well as a language for international communication. Many researchers have emphasized the importance
of English learning (Hsu, Hwang, & Chang, 2013). In most universities in Taiwan, the students must satisfy the
English requirements before they can graduate. The TOEIC test (published by Educational Testing Service) is the
standardized test for English proficiency and is often required for young people entering a business company as a
working ability in Taiwan (Yang, Chuang, Li, & Tseng, 2013). Therefore, getting higher grades of TOEIC test is a
common target of undergraduate students nowadays.
With the rapid progress of information technology, many researchers have attempted to apply various online
instruction strategies to provide students more efficient way to learn second language via Internet (Chen, Wang, &
Chen, 2014; Hsu et al., 2013; Liu et al., 2011; Wen, Looi, & Chen, 2012). Scholars have also indicated that the
importance of managing students’ learning conditions, addressing their weaknesses, and leading collaborative
learning activities (Chu, Hwang, & Tsai, 2010; Hwang, Chu, Shih, Huang, & Tsai, 2010; Zhan, Xu, & Ye, 2011).
Researchers have therefore carried out relevant research on students interacting and learning online with their peers.
It can be seen that learning by sharing information and exchanging knowledge among peers could not only enhance
the competitiveness of students, but could also promote their learning performance (Cooper & Cowie, 2010; Hwang,
Chu, Lin, & Tsai, 2011; Wang, 2010; Wen et al., 2012).
As a way of ensuring students’ online learning interaction and discussion, the grouping method has been discussed
widely based on students’ characteristics (AbuSeileek, 2012; Chiu & Hsiao, 2010; Moreno, Ovalle, & Vicari, 2012)
or their learning achievements (Lin, Huang, & Cheng, 2010; Wang, 2010). Grouping students for collaborative
learning allows them to share their ideas and learning experiences, and further promotes the learning performance of
the group as well as of the individuals (Huang, Wu, & Chen, 2012; Wang, & Hwang, 2012). Lin, Huang, and Cheng
ISSN 1436-4522 (online) and 1176-3647 (print). This article of the Journal of Educational Technology & Society is available under Creative Commons CC-BY-ND-NC
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considered “student level” and “interests” as two important criteria for grouping students. Hsieh et al. (2011)
indicated that students could be benefited more if they received the teaching strategies matched their learning style
were taken into account in learning design.
Kolb (1984) classified learning styles into Diverger, Assimilator, Converger, and Accommodator. With different
learning methods, distinct learning styles could provide adaptive learning for enhancing learning performance
(Mampadi, Chen, Ghinea, & Chen, 2011; Tseng, Chu, Hwang, & Tsai, 2008). Adán-Coello et al. (2011) further
indicated that group members’ learning styles might affect peer interactions in collaborative learning activities.
Therefore, this study investigates the effects of different learning style-based grouping strategies on collaborative
learning environments in order to compare the interaction among students who were grouped with the same and
different learning styles, and to investigate the effects of the two different grouping strategies on the online
collaborative learning environment for each of Kolb’s learning styles. For instance, Diverger students presenting the
same information processing skills might suit collaborative learning in a group with students of the same learning
style.
Moreover, in this study, to further understand the students’ interaction patterns in the asynchronous discussion of the
proposed English collaborative learning platform, the students’ discussion content has also been analyzed. The
interaction patterns of students in groups with different learning styles are further explored. The effects of English
collaborative learning with a focus on TOEIC are investigated. Meanwhile, the effects of learning style-based
grouping on English collaborative learning are discussed.
Research purposes
The research method involves collaborative learning and introduces learning styles to group students with the same
learning styles into homogeneous groups, and others with different learning styles into heterogeneous groups. The
learning performance and behavior of the students who learned with different grouping strategies (homogeneous,
heterogeneous, or random) in the English course were analyzed to accomplish the following research purposes:
 To investigate whether the online collaborative learning platform could enhance students’ English learning
performance.
 To compare the learning performance of the students who learned with different grouping strategies
(homogeneous group, heterogeneous group, and random) in the English course.
 To find a suitable way of using Kolb’s Learning Styles for grouping students.
 To compare the interaction patterns of the students assigned to the collaborative learning groups based on the
learning style-based homogeneous and heterogeneous grouping strategies.
Related work
Online collaborative learning and English learning
The convenience of the Internet has changed the traditional face-to-face collaborative learning; that is, students can
cooperatively complete their learning tasks online. Many researchers have therefore invested strategies of online
collaborative learning. For instance, Wang (2010) proposed the application of an online sharing platform for
collaborative learning. With the aim of increasing the effectiveness of team teaching, Cooper and Cowie (2010)
mentioned that collaborative learning allows students to mutually create new knowledge. Recently, online
collaborative learning has become a popular research issue.
English is a language for communication. During independent learning, students cannot effectively promote their
learning performance because of the lack of interaction and discussion practices. For this reason, many schools in
Taiwan have opened TOEIC courses for students to learn together in class. By in-class learning, students could
interact with the teachers, mutually learn among peers, and solve problems on the spot. Nevertheless, the time and
location for in-class learning are restricted. Due to the popularity of the Internet, researchers have proposed the
benefits of collaborative learning for students’ English reading capability in TOEIC (Yang, Chuang, Li, & Tseng,
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2013). However, few researchers have discussed the effects of learning styles on students’ English collaborative
learning. This study therefore explores this aspect in depth.
Kolb learning style
Kolb (1984) classified learning styles into Diverger, Assimilator, Converger, and Accommodator as shown in Figure
1. Divergers (Feeling and Watching, CE/RO) tends to watch from distinct aspects, proposes specific opinions and
ideas, prefers watching to doing, enjoys working with others, and listens to others’ opinions with an open mind.
Accommodators (Doing and Feeling, CE/AE) prefers to prove things with actions, is likely to be attracted by new
ideas and challenges, does things without logical thinking, shows courage and insistence, and enjoys completing
work with group cooperation. Convergers (Doing and Thinking, AC/AE) shows excellent ability to look for suitable
solutions, prefers challenging technical tasks, but lacks interest in people or interpersonal interaction. They are good
at doing and are brave in the face of problems and reveal better ability to solve technical problems than to exchange
ideas or to have interpersonal interaction. Assimilators (Watching and Thinking, AC/RO) prefers simple and logical
thinking, focuses more on ideas and concepts than on others’ opinions, requires clear and powerful explanations,
presenting them with clear logic, excels at logic, theories, and reasoning, and favors reading, exploring, and thinking.
Figure 1. Kolb’s (1984) Learning Styles
Many researchers have discovered the effects of learning styles on learning performance (Hwang, Sung, Hung, &
Huang, 2013; Mampadi et al., 2011; Tseng et al, 2008). For example, both Divergers and Accommodators present
feeling, suggesting that they might favour group collaborative learning. In this case, students with such learning
styles being grouped together to form a homogeneous group might exhibit better online collaborative learning
performance than students with other learning styles. Huang et al. (2011) proposed heterogeneous grouping for
students to collaboratively learn the engineering curriculum and showed good learning performance of the
heterogeneous group. Adán-Coello et al. (2011) proposed homogeneous and non-homogeneous grouping for students
to collaboratively learn a programming language and showed that the homogeneous group outperformed the other
groups, and argued that the homogeneous group, because it is made up of students with the same learning style, can
avoid undesirable conflict and can easily achieve a consensus solution. Consequently, how to group students become
an important issue for leading a collaborative learning activity.
Previous studies mainly focused on investigating the effects of technology-enhanced learning approaches on learning
performances of the students with different learning styles (Chu, 2014; Huang et al., 2011; Hwang, Sung, Hung,
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Huang, & Tsai, 2012; Zhan et al., 2011). For example, Hwang, Sung, Hung, Huang and Tsai (2012) developed an
adaptive educational computer game by taking students’ learning styles into consideration and found that playing
style-fit games significantly improved the students’ learning achievements. Only few studies have attempted to
investigate the issues related to learning style-based grouping in collaborative learning; for example, Kyprianidou et
al. (2012) compared the communication frequencies of the students who learned with style-based heterogeneous
grouping approach and the random grouping approach. To our knowledge, none of these studies have compared the
effectiveness of learning style-based heterogeneous and homogeneous grouping strategies from various dimensions
of learning performances and learning behaviors. Therefore, the purpose of this study is to develop a learning stylebased collaborative learning platform and explore the collaborative learning behaviors and performances of the
students who learn in homogeneous and heterogeneous groups formed based on the four dimensions of Kolb’s
Learning Styles (Feeling, Doing, Thinking, & Watching).
Research method
The design of collaborative learning strategy and learning environments
An online collaborative learning platform with TOEIC English tests as the learning content was established. Students
collaboratively interacted with their peers in groups of homogeneous or heterogeneous learning styles. With gradual
guidance, learners who spend sufficient time on a learning process will transfer the learning results into knowledge.
Furthermore, learners could deliver their knowledge to their peers and create new knowledge by collaborative
interaction and discussion. The collaborative learning strategy in our online learning system is shown in Figure 2.
Students log into the system (Step 1) and then start to answer the questions (Step 2). When a student completes a
question, he/she has to wait for other group members to complete the question (Step 3). After all members have
completed the question, the system detects the consistency of their answers (Step 4). When there are different
answers, the group members have to discuss (Step 4.1) and revise their answers (Step 2) until a consistent answer is
provided (Step 4). The consistent answer is then verified (Step 5). When the answer is correct, the next question is
shown (Step 6). If the answer is incorrect, the system will check whether it is the first incorrect answer or not (Step
5). If it is the first incorrect answer, the group members have to discuss (Step 4.1) the question again (Step 2). If there
is a second incorrect answer, the system skips to the next question so as to prevent the students from repeatedly
guessing the answer.
Figure 2. Online collaborative learning strategy
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The students in the control group were grouped randomly and used the conventional face-to-face paper-based
collaborative learning strategy as shown in Figure 3. The test paper is distributed to each group of students (Step 1) for
discussion (Step 2). In the process, the students share and discuss the questions to come up with a common answer
depending only on the English knowledge of the group members (Step 3). They continue to the next question (Step 4 )
until the test paper is handed in (Step 5).
Figure 3. Face-to-face paper-based collaborative learning strategy
The interface design of online learning system
The home page of the online collaborative learning platform system was designed using PHP and Dreamweaver.
After logging in, the system displays the test frame as shown in Figure 4, where the functions of the online
collaborative learning platform are introduced. The frame is divided into several areas as shown below.
Figure 4. System contents page of the online collaborative learning platform
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Block 1 is the question area, displaying the TOEIC question. After answering the question, the students can select
“next” for the system to verify the correctness of their answers. If their answers are correct, the next question is
shown. Block 2 displays the answering conditions of the group members, where they can be viewed by other
students. The system verifies the correctness of the answers only when the answers of the group members are
consistent; otherwise, the group members are required to discuss and revise their answers so as to come up with a
consistent answer.
The students directly click on the answer in Block 3 to instantaneously update the answer in Block 2. Block 4
provides the discussion area for the group, where the dialogues and the time for answering the question are recorded.
In Block 5, the students can choose some common message to quickly respond with comments such as “I don’t know
the answer” and “Please answer the question quickly,” to their peers. Block 6 is the message area, where the students
type personal messages for collaborative learning. Block 7 shows the list of the online group members.
Experiment design
The first independent variable is the learning-based grouping strategy (homogeneous, heterogeneous or random). The
second independent variable is the collaborative mode (online or face-to-face). The dependent variable is the
students’ learning achievement, while the control variables are the instructor, the learning content, and the prompts
provided to the students.
The participants and experiment procedure
A total of 48 graduate students aged 23~25 in a university were selected as the experimental subjects. There were 12
Divergers (hereinafter referred to as Div.), 12 Accommodators (Acc.), 12 Convergers (Con.), and 14 Assimilators
(Ass.) grouped into 4 homogeneous groups of the same learning style (Experimental group A), 4 heterogeneous
groups with different learning styles (Experimental group B), and 4 random groups which did not consider learning
style (the Control group), as shown in Figure 5.
Experimental group A
(Homogeneous)
Div.
Con.
Ass.
Acc.
Control group
(Random)
4 random groups
without considering
learning styles
Div., Acc.
Con., Ass.
Div., Acc.
Con., Ass.
Div., Acc.
Con., Ass.
Div., Acc.
Con., Ass.
Experimental group B
(Heterogeneous)
Figure 5. The grouping method of the three groups
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The experiment procedure for the collaborative learning activity is shown in Figure 6. These three groups then took
the pre-test for 20 minutes. Experimental groups A and B further utilized the online English collaborative learning
platform three times, with 10 English questions practiced each time, for group discussion and learning. The learning
period for these groups was not restricted so as to assess the effects of learning period on learning performance. The
students could spend almost 120 minutes to finish ten questions. After completing the learning activities, the
experimental post-test was given for 20 minutes, aiming to test the enhancement in the students’ English learning
achievement.
students
Learning style
15 mins
Learning style test
Homogeneous
Heterogeneous
Random
(four students in a group)
(four students in a group)
(four students in a group)
Experimental
group A
Experimental
group B
Control group
Experimental
process design
pre-test
Online English collaborative
learning platform
20 mins
Traditional paperbased learning
post-test
120 mins
(10 questions for
each 3 times)
20 mins
Figure 6. Experiment design
Research tools
The English test questions were acquired from the book of “New TOEIC Grammar Tests” written by Chin, published
by International Books (Chin, 2012). 32 pre-test questions were randomly selected from the book focusing on
English grammar to understand the students’ level of English for TOEIC. Another 32 post-test questions were also
randomly selected from the book. The aim was to understand the effects of learning style-based grouping
collaborative learning on students’ English grammar and vocabulary. Students in the two Experimental groups as
well as in the Control group used the same English test questions as their learning material.
Two analyses in non-parametric tests were utilized for the experimental data analyses. The Kruskal-Wallis one-way
analysis of variance by ranks (H test) is suitable for the comparison of more than three groups with fewer test
samples which applied ranks to analysis of variance. The Mann-Whitney test (U test) is suitable for two independent
samples, and aims to test two independent groups with the same parameters. Especially, research with fewer samples
would apply such an analysis instead of the t-test for testing the differences between two groups (Conover, 1989).
After completing the learning activities, the chat room dialogues were processed for message coding and sequential
analysis, using the message coding proposed by Hou, Sung, and Chang (2009) to analyze the students’ behaviors and
attitudes in the chat rooms. The different kinds of behavior and an example of each are shown in Table 1.
C1
C2
Table 1. Message coding in chat rooms
Behavior
Example
Learner: “hello～ I am user1.”
Peers knowing each other
Learner: “This question should be past tense so the
Discussion and comparison
answer is B！”
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C3
Proposing solutions or related information
C4
C5
Looking for solutions
Irrelevant response to the question
Learner: “Wrong！ The prompt shows that it should
be a noun.”
Learner: “Is the answer A?”
Learner: “Hurry up！”
Results
Prior knowledge of pre-test
Before the learning activities, the pre-test was given to test the differences in the basic English knowledge of the 48
students. The Kruskal-Wallis (H test) analysis shows that the mean and the standard deviation for Experimental
group A were 42.75 and 25.41 (the lower level presenting the lower scores); for Experimental group B they were
38.82 and 23, and for the Control group they were 42.59 and 25.09, respectively. The significance level p = 0.868 ＞
0.05 showed that the scores of these three groups did not reach significant difference, indicating that the students’
basic English knowledge was similar prior to the experiment.
According to the H test, the mean and the standard deviation for Divergers were 33.75 and 5.75, for Accommodators
they were 34.5 and 4.88, for Convergers they were 47.25 and 11.50, and for Assimilators, they were 46.5 and 11.88,
respectively. The significance level p = 0.062 ＞ 0.05 indicated no significant difference, meaning that the basic
English knowledge of students of the four different learning styles in Experimental group A were similar.
The same process was performed for Experimental group B and it was found with the H test that the mean and the
standard deviation for Divergers were 45 and 9.38, for Accommodators they were 45 and 9.38, for Convergers, 41.25
and 8.25, and for Assimilators, they were 38.25 and 7.00, respectively. The significance level p = 0.877 ＞ 0.05
revealed that the four groups did not reach significant difference; thus, the students’ basic English knowledge in
Experimental group B was also similar.
The post-tests of three groups of students
Based on the Kruskal-Wallis (H test) analysis (in Table 2), the significance level p = 0.000 < 0.05 presented
significant difference among these three groups. Post hoc analysis showed that Experimental groups A and B
performed better than the Control group, providing evidence that the online English collaborative learning platform
could assist the students in terms of their learning performance.
Utilizing the Mann-Whitney (U test) for pair comparison, the mean and standard deviation for Experimental group A
were 73.63 and 14.80, and for Experimental group B they were 65.38 and 14.17, respectively. The significance level
p = 0.196 > 0.05 presented no significant difference in the learning performance of these two groups, both of which
used the online collaborative learning platform.
(1)
(2)
(3)
Table 2. H test analysis of the post-test of English basic competence
N
Mean
SD
Mean Rank.
df
p
Homogeneous groups
16
73.63
14.80
34.00
2
0.000
Heterogeneous groups
16
65.38
14.17
28.94
Random groups
16
42.32
9.72
10.56
Post hoc
(1)>(3)
(2)>(3)
The post-test results of homogeneous and heterogeneous groups
The learning performance of the homogeneous and heterogeneous groups and the suitability of the learning styles for
such grouping are discussed in this section. According to the Kruskal-Wallis (H test) analysis, Accommodators and
Convergers revealed better scores than Divergers and Assimilators in the homogeneous groups in Experimental
group A, as shown in Table 3. From the Post hoc analysis, the significance level p = 0.009 < 0.05 shows that
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Accommodators and Convergers outperformed Divergers and Assimilators; that is, the students with different Kolb’s
Learning Styles exhibited significant differences in their learning performance in the homogeneous groups.
(a)
(b)
(c)
(d)
Table 3. H test analysis of the post-test in the homogeneous groups (Experimental group A)
Homogeneous group
N
Mean
SD
Mean Rank df
p
Post hoc
Divergers
4
60.25
9.74
3.88
3
0.009
Accommodators
4
86.25
9.75
12.50
(b)(c)>
Convergers
4
85.25
6.71
12.50
(a)(d)
Assimilators
4
62.75
8.46
5.13
According to the Kruskal-Wallis (H test) analysis, similar scores were revealed for the different learning styles in the
heterogeneous groups in Experimental group B, as shown in Table 4. The significance level p = 0.501 > 0.05
presented no significant difference for Kolb’s Learning Style in the learning performance of the heterogeneous
groups.
Table 4. H test analyses of the post-test in the heterogeneous groups (Experimental group B)
Heterogeneous groups
Mean
N
Mean
SD
df
Rank
1
2
3
4
Div.
Div.
Div.
Div.
4
69.50
20.42
9.00
3
Acc.
Acc.
Acc.
Acc.
4
68.75
15.43
5.50
Con.
Con.
Con.
Con.
4
68.50
10.12
10.38
Ass.
Ass.
Ass.
Ass.
4
54.75
6.65
9.13
Diverger (Div.), Accommodator (Acc), Converger (Con.), and Assimilator (Ass.)
p
0.501
From the experimental data, the mean and the standard deviation of Divergers in the homogeneous group were 60.25
and 9.74, while they were 69.5 and 20.42 in the heterogeneous group, respectively. The significance level p = 0.686
> 0.05 showed no significant difference in the learning performance of Divergers in these two types of groups.
Nevertheless, Divergers’ scores in the heterogeneous group were higher than those in the homogeneous group. The
mean and the standard deviation of Accommodators in the homogeneous group were 86.25 and 9.75, and 68.75 and
15.43 in the heterogeneous group, respectively. The significance level p = 0.200 > 0.05 revealed no significant
difference in the learning performance of Accommodators in these two types of groups. However, the
Accommodators’ scores in the homogeneous group were higher than those in the heterogeneous group.
The mean and the standard deviation of Convergers in the homogeneous group were 85.25 and 6.71, and 68.50 and
10.12 in the heterogeneous group, respectively. The significance level p = 0.057 > 0.05 presented no significant
difference in the learning performance of Convergers in these two types of groups. However, the scores between the
homogeneous and heterogeneous groups showed differences in that the Convergers’ scores in the homogeneous
group were higher than those in the heterogeneous group. The mean and the standard deviation of Assimilators in the
homogeneous group were 62.75 and 8.46, and 54.75 and 6.65 in the heterogeneous group, respectively. The
significance level p = 0.486 > 0.05 revealed no significant difference in the learning performance of Assimilators in
these two types of groups. Nonetheless, Assimilators’ scores in the homogeneous group were higher than those in the
heterogeneous group.
Rather than having students cooperatively complete learning tasks, in this study students had to answer the same
question based on consistent ideas and concepts so that the effect of drawing on the strong points of the different
learning styles would not appear. Relatively, students with different learning styles collaboratively learning and
negotiating for consistent answers would not necessarily present the effect of drawing on their strong points. On the
other hand, students with the same learning style could easily communicate, negotiate, and achieve a consistent
answer. Thus, as the results show in Table 2, although the learning performance of the homogeneous group was not
significantly different from that of the heterogeneous group, the mean of the post-test in the homogeneous group
outperformed that of the heterogeneous group.
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The time period of learning process
In this study, the average learning period was 20~25 minutes for Experimental group A, 35~40 minutes for
Experimental group B, and 25~30 minutes for the Control group. It was found that Experimental group A
(homogeneous group) had a shorter learning period; therefore, a consistent answer could be rapidly achieved during
the collaborative learning. Regarding the Control group with the paper-based collaborative learning, they needed to
refer to the prompts from the paper-based reference book; therefore, they repeatedly answered the question when
they gave an incorrect answer, so the students’ learning period in the control group was longer than that of
Experimental group A. Experimental group B (heterogeneous group) had the longest learning period, meaning that
they spent more time on the collaborative discussion in order to achieve a consistent answer. In contrast, we found
that students with the same learning style could easily communicate, negotiate, and rapidly achieve a consistent
answer.
Sequential analysis
After the students finished using the online learning platform, the communication messages from the chat rooms
were coded. Those messages related to self-introduction or knowing each other were coded as C1 (“Peers knowing
each other”), messages related to discussion of the question or comparison of members’ ideas were coded as C2
(“Having discussion”), messages proposing solutions were coded as C3 (“Proposing solutions”), messages seeking the
answer were coded as C4 (“Looking for solutions”), and messages containing unrelated responses were coded as C5
(“Irrelevant response”). Scorer Reliability Analysis of the coding revealed that the Spearman-Brown Coefficient =
0.937. The proportions of message codes in the homogeneous group are shown in Table 5. From the proportion data,
Accommodators and Convergers produced more messages, about 34.87(%) and 33.72(%), respectively, of the total
number of messages in the homogeneous group, and among these two types of groups, C3 (Proposing solutions) and
C4 (Looking for solutions) presented high proportions. Consequently, Accommodators and Convergers seem to be
more suitable for learning in homogeneous groups. The proportions of message codes in the heterogeneous group are
listed in Table 6. The proportions of the codes among the various groups were close, and C5 (Irrelevant response)
appeared with the highest proportion, suggesting that more communication and negotiation were required for the
heterogeneous group, thus influencing their performance. The results are consistent with the conclusions in Table 2,
Table 3 and Table 4 in that the mean of the post-test in the homogeneous group outperformed that of the
heterogeneous group. Accommodators and Convergers presented better learning performance in the homogeneous
group, whereas they showed no significant difference for the learning performance in the heterogeneous group.
Table 5. Proportions of message codes in the homogeneous groups (Experimental group A)
C1 (%)
C2 (%)
C3 (%)
C4 (%)
C5 (%)
Number of
Homogen
message
Peers knowing
Having
Proposing
Looking for
Irrelevant
eous
(%)
each other
discussions
solutions
solutions
response
Div.
0.69
0.46
5.31
0.92
11.09
18.48
Acc.
0.92
1.62
11.09
9.70
11.55
34.87
Con.
0.92
1.62
12.24
7.85
11.09
33.72
Ass.
0.46
1.39
3.00
0.46
7.62
12.93
Diverger (Div.), Accommodator (Acc), Converger (Con.), and Assimilator (Ass.)
Table 6. Proportions of message codes in the heterogeneous groups (Experimental group B)
C1 (%)
C2 (%)
C3 (%)
C4 (%)
C5 (%)
Number of
Heterogen
message
Peers knowing
Having
Proposing
Looking for
Irrelevant
eous
(%)
each other
discussions
solutions
solutions
response
Group I
0.71
1.95
4.80
2.84
15.63
25.93
Group II
0.36
1.07
2.84
1.07
14.56
19.89
Group III
0.53
3.20
7.99
2.13
14.39
28.24
Group IV
0.36
4.44
5.86
2.84
12.43
25.93
The message codes were further analyzed using the Sequential Analysis approach proposed by Hou, Sung, and Chang
(2009). Sequential Analysis describes the message code sequence with the significance (Z) of the Z fraction binomial
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test to present the learning behaviors with a message code transfer diagram. Each circle in the diagram represents a
message code. Each thicker line represents the level of the significance where the significance Z > 1.96 and the arrow
points in the direction of the transfer. The learning behaviors of the homogeneous and heterogeneous groups are
described below in terms of their message code transfer diagrams.
From the message code transfer diagram, as shown in Figure 7, Diverger students in the homogeneous group
continuously focused on C3 (“Proposing solutions”) with the significance Z = 5.88. Diverger students in the
homogeneous group did indeed often propose their own opinions and ideas. For example, user_1 said, “I think the
answer is variety.” Sometimes, C3 (“Proposing solutions”) would be transferred to C5 (“Irrelevant response”) with the
significance Z = 4. For example, user_3 said, “Hurry up!” However, the significance of transferring from C5
(“Irrelevant response”) to C3 (“Proposing solutions”) reached Z = 2.74. Since Divergers tended to propose their own
opinions, the students would transition between C3 (“Proposing solutions”) and C5 (“Irrelevant response”) before
coming up with a consistent answer.
As shown in Figure 7, Assimilator students in the homogeneous group showed the significance Z = 6.08 for C5
(“Irrelevant response”) and it appears that they spent more time thinking. Moreover, Assimilator students in the
homogeneous group presented the significance Z = 2.81 on the transition from C2 (“Having discussions”) to C3
(“Proposing solutions”), revealing a two-way connection line. Apparently, Assimilator students in the homogeneous
group would not only think independently, but would also occasionally discuss with others. For example, user_8 said,
“Wrong! The prompt shows…” Finally, both the Diverger and Assimilator groups showed a high occurrence of C5
(“Irrelevant response”) to the extent that their learning performance was affected.
Figure 7. Message code transfer diagrams for the homogeneous Diverger and Assimilator groups
Accommodators tend to listen to their peers’ opinions while learning. As shown in Figure 8, Accommodators in the
homogeneous group showed the significance Z = 2.2 for transferring from C2 (“Having discussions”) to C3
(“Proposing solutions”). In this case, the Accommodator group proposed dialogues related to correct answers after
discussion with their peers. For example, user_16 said, “The question should be… so the answer is B!”
Accommodator students in the homogeneous group therefore tended to listen to their peers’ ideas. Besides,
Accommodator students in the homogeneous group would transfer from C3 (“Proposing solutions”) to C5 (“Irrelevant
response”) with the significance Z = 2.2. For instance, user 14 said, “I will follow your answers.” However, the
students would transfer from C5 (“Irrelevant response”) back to C3 (“Proposing solutions”) with the significance Z =
2.78. Since Accommodators tended to listen to their peers’ ideas and opinions, the dialogues transferred between C3
(“Proposing solutions”) and C5 (“Irrelevant response”) before the peers’ ideas and opinions were expressed.
Moreover, as shown in Figure 8, Convergers in the homogeneous groups would continuously repeat C3 (“Proposing
solutions”) and C4 (“Looking for solutions”) statements with the significance Z = 4.66 and 3.34, respectively. It
appeared as a one-way connection line with the significance Z = 2.02. Consequently, Converger students in the
homogeneous groups were actually brave in the face of problems and did directly propose questions for discussion
with their peers. For example, user_9 said, “Is the answer D?” Moreover, Converger students in the homogeneous
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group would transition from C3 (“Proposing solutions”) to C5 (“Irrelevant response”) with the significance Z = 4.22.
They would deviate from the topic when waiting for others’ answers, but would return to the topic quickly. For
instance, user_10 said, “Hurry up.” They therefore transferred between C3 (“Proposing solutions”) and C5 (“Irrelevant
response”). Finally, both the Accommodator and the Converger groups presented the characteristic of Doing, meaning
that the transition among various statuses appeared on the message transfer chart, which presented the active
interaction among peers and fewer occurrences of C5 (“Irrelevant response”). From the sequential analysis results for
the homogeneous group, we observe that the learning performance of the Accommodator and Converger groups were
better than that of the Diverger and Assimilator groups, which was consistent with the observation shown in Table 3.
Figure 8. Message code transfer diagrams for the homogeneous Accommodator and Converger groups
Regarding the heterogeneous groups, students with different learning styles learned together in each heterogeneous
group (as shown in Figure 9). The learning behaviors in the heterogeneous groups mostly transitioned from C3
(“Proposing solutions”) to C5 (“Irrelevant response”). For instance, user_21 said, “The answer is B”, but then user_24
said, “I don’t know!” However, most students in the heterogeneous groups would transition from C5 (“Irrelevant
response”) back to C3 (“Proposing solutions”), forming a two-way connection line. These four heterogeneous groups
showed high occurrences of C5 (“Irrelevant response”) with the significance Z = 19.94, 30.15, 10.49, and 15.84,
respectively. Thus, students in the heterogeneous groups were likely to present their own opinions because of their
different learning styles. The discussion period was therefore prolonged as it was difficult to reach a consensus.
Besides, the heterogeneous groups’ occurrence of C5 (“Irrelevant response”) was higher than that of the homogeneous
groups. Consequently, the learning performance of the heterogeneous groups showed no significant difference, which
is consistent with the conclusion shown in Table 4.
Figure 9. Message code transfer diagram for the heterogeneous groups
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Discussion and conclusions
Research on learning styles has been greatly emphasized, as students’ learning styles affect their learning intentions.
Thus, this study implemented the English collaborative learning system to investigate the effects of learning stylebased grouping. The collaborative learning method adopted in this study required the students to solve the same
question with only one answer which they should arrive at through discussion and negotiation in order to reach a
consistent answer. Due to this approach, the effect of drawing on the strong points of students with different learning
styles would therefore not necessarily appear as they had to negotiate a consistent answer.
In terms of the suitable way of using Kolb’s learning style for grouping students, there was no significant difference
in the learning performance of Experimental groups A and B. However, the students in Experimental group A with
homogeneous learning styles had more consistent opinions, so the time required to complete the learning activities
was shorter than that of Experimental group B, the groups made up of students with heterogeneous learning styles.
Furthermore, the learning performance of Accommodators and Convergers when working together in homogeneous
groups was better, as these two kinds of learners tend to positively enjoy interacting and discussing problems with
others, according to Kolb’s Learning Styles. In Experimental group B, the learning performance in the heterogeneous
group does not reveal notable differences. It is speculated that students with different learning styles show different
learning attitudes and spend a lot of time on communication and negotiation, with the result that more occurrences of
C5 (“Irrelevant Response”) affected their learning performance.
This study aimed to compare the interaction patterns of the students assigned to collaborative learning groups based
on the two learning style-based grouping strategies. The sequential analysis showed that both Convergers and
Accommodators in the homogeneous groups would properly transfer to “Having Discussions” or “Looking for
solutions” and actively interact with their peers; thus, they are suitable for working together in a homogeneous group.
Moreover, Divergers and Assimilators produced more instances of “Irrelevant Response” so that their learning
performance was less impressive. In the heterogeneous group, the students are likely to hold their own opinions
because of different learning methods and do not easily reach a consensus, so that their learning performance was
less significant. Besides, the occurrences of “Irrelevant Response” were higher than for the homogeneous group. As a
result, providing learners with a suitable learning environment and proper learning methods would assist them in
enhancing their learning performance and interest.
To sum up, the findings of this study are helpful to those researchers who try to develop grouping method based on
students’ learning style or conduct English online learning activities in the future. Although the learning performance
of the homogeneous groups was not significantly different from that of the heterogeneous groups, the mean of the
post-test in the homogeneous groups outperformed that of the heterogeneous groups. This is speculated that students
with the same learning style could easily negotiate to achieve a consistent answer, and thus improve their learning
performance. Our finding is consistent with the observation that students with the same learning style can avoid
undesirable conflict and can easily achieve a consensus solution (Adán-Coello et al., 2011). It should be noted that
arriving at the right answer more efficiently or quickly does not necessarily mean that greater learning took place;
therefore, further studies are needed to examine whether deeper learning have occurred by employing some
measuring tools of higher order thinking.
On the other hand, it has been widely debated as to whether learning styles exist, or have any significant impact on
learning, implying that further studies and discussions are needed to examine the effectiveness of the learning stylebased grouping strategy. The authors of this study are glad to provide the data of the experiment upon requests.
Acknowledgements
This study is supported in part by the National Science Council of the Republic of China under contract number NSC
102-2221-E-031 -002 and NSC 102-2511-S-031 -001.
296
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