Computers & Education 62 (2013) 196–207

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Computers & Education

journal homepage: www.elsevier .com/locate/compedu

The relationships among group size, participation, and performanceof programming language learning supported with online forums

Ruey-Shiang Shaw*

Department of Information Management, Tamkang University, No. 151, Yingzhuan Rd., Danshui Dist., New Taipei City 25137, Taiwan

a r t i c l e i n f o

Article history:Received 23 July 2012Received in revised form30 October 2012Accepted 1 November 2012

Keywords:Cooperative/collaborative learningLearning communitiesProgramming and programming languagesTeaching/learning strategies

* Tel.: þ886 2 2625 1047; fax: þ886 2 2620 9737.E-mail address: rsshaw@mail.tku.edu.tw.

0360-1315/$ – see front matter � 2012 Elsevier Ltd.http://dx.doi.org/10.1016/j.compedu.2012.11.001

a b s t r a c t

This study examined the relationships among group size, participation, and learning performance factorswhen learning a programming language in a computer-supported collaborative learning (CSCL) context.An online forum was used as the CSCL environment for learning the Microsoft ASP.NET programminglanguage. The collaborative-learning experiment was performed with one large group and 15 smallgroups.A total of 120 students participated in this experiment as part of a half-semester ASP.NET programminglanguage course. The course contained an online forum for supporting the students’ social activities andparticipation. This study used a participation-weighted rate for different participation types. A ‘learningscore’ and a ‘learning satisfaction’ score were used to measure learning performance.The results of this study were as follows: (1) the online forum support aided collaborative learning,regardless of group size; (2) group sizes did not significantly influence learning scores directly butsignificantly influenced participation, and small groups had higher participation rates, which positivelyinfluenced learning scores; and (3) learning satisfaction using the online forum was higher than theaverage score. Small groups had higher learning satisfaction rates, and participation did not significantlyinfluence learning satisfaction.Due to this study’s results, we recommend that programs design instruction with small groups forteaching programming languages in online forums, support student-centered discussions, and encouragehigh levels of student participation to increase learning performance.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

Some studies have examined computer-supported collaborative learning (CSCL) as a way to improve the development of computationalprograms, increase learning outcomes, support collaboration in classrooms, and create e-learning environments (Dillenbourg, 1999; Stahl,Koschmann, & Suthers, 2006). Hsu, Chen, Chiu, and Ju (2007) also stated that a team’s software learning performance is strongly influencedby its collective efficacy and outcome expectations.

Students in collaborative learning environments must articulate their own points of view and also listen to the views of others to createknowledge and meaning together (Neo, 2003). Students working together in groups (i.e., collaborative learning) respect and highlightindividual groupmembers’ abilities and contributions. Collaborative learning emphasizes the sharing of authority and responsibility amongindividuals for the group’s actions. When learners in a collaborative situation share the same goal, they can learn from one another throughfocused group discussion. Puntambekar (2006) indicated that the main point of collaborative learning is the interaction between individualand team members because it allows knowledge sharing through different points of view.

Inmany studies of CSCL, group size has been identified as an important factor that requiresmore investigationwith respect to interaction(Strijbos, Martens, & Jochems, 2003). Previous studies reported mixed results associated with increases in group size in the context of CSCL(Easley, Devaraj, & Crant, 2003; Lim & Zhong, 2006; Mullen, Anthony, Salas, & Driskell, 1994). Shaw’s study used a class as a large group andindicated that in learning programming languages with online forum support, different types of participation influence learning perfor-mance (Shaw, 2012). He did not, however, test the influence of group size on learning programming languages.

All rights reserved.

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Based on Shaw’s participation types, which were derived from Social Learning Theory (Bandura, 1977), and group discussion activities,which were derived from the Social Interdependent Theory (Johnson & Johnson, 1989), this study focused on how group size influences“interdependence” in mutual sharing and learning. Therefore, the purpose of this study was to examine the influence that different groupsizes have on participation and learning performance using online forum tools to learn a programming language.

On the basis of the results of this study, the implications for improving the effectiveness of learning programming languages will beexplored in the discussion section and conclusion.

2. Related works and research framework

Previous research related to this study includes group size and participation studies. The research framework and hypotheses aredescribed in the following sections.

2.1. Group size

Social interdependence exists when the results of individuals’ efforts are affected by others’ actions (Johnson & Johnson,1989). The socialinterdependence theory perspective of cooperative learning presupposes that the way social interdependence is structured determines theway people interact with one another. Moreover, outcomes are the consequence of people’s interactions. Therefore, one of the elements thatmust be structured in the classroom is positive interdependence or cooperation. When this is accomplished, cooperation promotesinteraction, as the group members encourage and ease each other’s efforts to learn (Johnson, Johnson, & Holubec, 1998). Social interde-pendence theory predicts that cooperative groups will have higher levels of achievement than individuals working competitively orindividually (AbuSeileek, 2012; Deutsch, 1962; Johnson & Johnson, 1989, 2005). A number of researchers, however, have posited that groupsize is an important variable influencing group productivity for CSCL; therefore, group size is worthy of further investigationwith respect tointeraction (Strijbos et al., 2003).

Table 1 summarizes the research on the relationship between group size and performance. The studies presented in Table 1 showmixedresults associated with increases in group size in the context of computer-supported collaborations (Easley et al., 2003; Mullen et al., 1994).

Social interdependence theory, as originally formulated by Deutsch (1949), assumed the size of a group to be small with all participantsholding equal power. The theory emphasizes the collaborative outcomes determined by individuals’ interactions but did not consider thesize of the group important. The theory implies that as the size of the group increases, the need for members to be skilled in coordinatingtheir efforts also increases (Bertucci, Conte, Johnson, & Johnson, 2010). Therefore, group discussions in online forums derived from socialinterdependence theory and the influence of group size of CSCL for learning a programming language are the focus of this study.

2.2. Participation

Social learning assumes that human behaviors are influenced by a combination of environmental (social) and psychological factors.Bandura’s social learning theory emphasizes the importance of observing andmodeling the behaviors, attitudes, and emotional reactions ofothers (Bandura, 1977). Hrastinski (2009) concluded that “the implication of the theory of online learning as online participation isstraightforward: If we want to enhance online learning, we need to enhance online learner participation.” Several previous studies havestrongly emphasized the importance of active participation in education (e.g., Alavi, 1994; Astin, 1996; Pratton & Hales, 1986).

Morris, Finnegan, and Sz-Shyan (2005) found that influence factors of online participation (e.g., number of written discussion posts andseconds spent viewing content pages) result in better learning outcomes, such as final grades, number of discussion posts viewed, number of

Table 1Previous research on the relationships between group size and performance.

Sources Findings

Morgan, Coates, and Rebbin (1970) Team performance actually improved when one member was missing from 5-person teams, perhaps because membersbelieved their contributions were more necessary.

Seta, Paulus, and Schkade (1976), The size of the cooperative group has a positive impact on the level of achievement of group members.Thor (1976) Groups of 3 outperformed pairs and individuals, and pairs outperformed individuals. The larger the group, the more

each member learned.Fox (1985) As the size of the group increases, it becomes more difficult to monitor members’ behavior to detect norm violations

and apply meaningful sanctions.Schultz (1989) With fewer individuals, a small group may lack the ability to evaluate potential solutions.Jacobs and Ball (1996) The learning structure involved a large number of the group activities in ELT course books and encouraged learners to

make the most of collaboration.Ortiz, Johnson, and Johnson (1996) The larger the group, the more complex the teamwork, and the longer it may take for a group to be productive and

cohesive.Leidner and Fuller (1997) The effects of three factors (production blocking, evaluation apprehension, and social loafing) grow as group size

increases.Easley et al. (2003) The size of the team has an inverse relationship with team performance; production blocking, evaluation apprehension

and social loafing account for this decrease in performance and satisfaction.Liden et al. (2004) An increase in group size increases social loafing and coordination barriers among group members.Lim and Zhong (2006) As group size increases, the increase in individual anonymity makes it more difficult to assess each member’s contribution;

thus, individuals will withhold efforts and feel less motivated to participate.Bertucci et al. (2010) Cooperative learning in pairs and groups of 4 promoted higher achievement and greater academic support from peers

than did individualistic learning. Students working in pairs developed a higher level of social self-esteem than did studentslearning under the other conditions.

AbuSeileek (2012) Five student groups significantly outperformed other groups of 2–7 members on post-test communication skills.

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content pages viewed, and seconds spent viewing discussions. Previous studies found that when students feel that they have participatedeffectively, learning is most successful in an online environment (Bento & Schuster, 2003; Michinov, Brunot, Le Bohec, Juhel, & Delaval, 2011;Webster & Hackley, 1997).

Hrastinski (2009) recalled that online learning should not rely solely on the number of contributed postings as a measure of onlineparticipation. Shaw (2012) studied students participating in learning activities and interacting with others using an online forum. Heobserved participation behaviors in the online forum, tested their relationships with learning performance, and defined four types ofparticipation: Replier (R: Replies with solutions), Asker (A: Asks questions), Watcher (W: Browses others’ solutions and questions), and NoActivity (NA).

The core of social learning theory is learning through participation in society (Wenger, 1998). Shaw (2012) found that participation typesare also associated with significantly different learning scores and that the ‘Replier’ type is associated with superior learning scores in fourtypes of participation. Shaw’s study did not take group size into account and used a class of students as a large group. Therefore, the presentstudy extensively examined the influence among different group sizes, participation, and learning performance using online forum tools tosupport learning a programming language.

2.3. Research framework

Based on the discussion of related works above, the research framework for this study is shown in Fig. 1. The major objectives of thisstudy are to investigate the relationships among the size of online forum groups, online forum participation, and learning performance. Inthe context of learning a programming language supported by an online forum, we expect the impact of group size and participation toimprove learning performance to a statistically significant degree.

In this study, we used student-centered group discussions in online forums to support students learning the ASP.NET programminglanguage. The online forumswere accessed after class. After reviewing existing literature on groups from social interdependence theory andparticipation from social learning theory, we treated the size of discussion groups and participation in an online forum as important factorsin learning the programming language.

In the current study, we used a class for the large sized group and teams of 2–6 students as small sizes groups. We referred to Noss andHoyles (1996), who indicated that learners participating in online forums can express their own knowledge, explore the knowledge ofothers, and receive appropriate intrinsic and extrinsic feedback. We also referred to Shaw’s (2012) study that indicated the participationtypes of R (Replies), A (Asks), and W (Watches). We employed these participation types to compute participation with weighted rates.

This study used learning score and learning satisfaction assessments (Shaw, 2010; Sun & Cheng, 2007) tomeasure learning performance.This study used an online forum for supporting a business application program with Microsoft’s ASP.NET 4.0 course. E-learning is

a suitable means for learning programming languages (Chen & Shaw, 2006; Shaw, 2010). The hypotheses of this study are as follows:

H1: Different group sizes lead to significant differences in learning performance.H1a: Different group sizes lead to significant differences in learning scores.H1b: Different group sizes lead to significant differences in learning satisfaction.

H2: Different group sizes lead to significant differences in participation.H3: Participation has a significant positive effect on learning performance.

H3a: Participation has a significant positive effect on learning scores.H3b: Participation has a significant positive effect on learning satisfaction.

3. Experimental design

A quasi-experimental research method was used in this study. This study used the same content and materials in two classes ofa programming language course. According to Ross and Morrison (2004), it is neither practical nor feasible to assign subjects randomly totreatments. A common application in educational technology would be to expose two similar classes of students to alternative instructionalstrategies and compare them on designated dependent measures. This study refers to Wilkinson and Fung (2002), who used 2–6 membersas small groups. Due to the limitation of class size, this study used 2–6 students as small group sizes. Shaw (2012) used a class size as a largegroup; this study used 60 students in a class as a large group. Students in class A were used as a large group. Students in class B wererandomly assigned to groups of 2–6 students. This study recorded students’ participation activities and then evaluated and analyzed theirlearning performance. The participants and procedure, group sizes, participation, and learning performance designed for this study aredescribed in the following sections.

Fig. 1. Research framework.

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3.1. Participants and procedure

In this study, the subjects are second-year university students in an MIS (Management Information Systems) department who tooka Business Programming Design course using ASP.NETwith the VB programming language. An online forumwas set up to support the coursefor students’ interactive group discussions after class. In this experiment, a large portion of the course’s content is the ASP.NET Databaseapplication. There were two classrooms and a total of 120 valid participating students. The experiment was conducted between the end ofmidterm examinations and the final examination for a total of four weeks. The online forumwas used for four weeks. During the 2-h weeklycourse meetings, the instructor teaching the class referred to relevant course materials on Microsoft MSDN websites (e.g., Accessing Datawith ASP.NET, http://msdn.microsoft.com/en-us/library/6759sth4%28v¼vs.71%29.aspx) and the relevant book chapters of the ASP.NETdatabase application design. To control the effects of environmental and external factors, this experiment used the same second-yearundergraduate participants, the same instructor for both groups, and the same educational materials in both classes. Again, students inclass Awere used as a large group and students in class B were randomly assigned to groups of 2–6 students. The 60 students in class B weredivided into 15 groups, including five group size types. Therewas three of each type of groupwith 2–6 students. All groupmembers used theonline forum to discuss 10 programming exercises with other members of their group. The instructor posted the 10 programming exercisesbefore the experiment. The exercises were designed from the course content and adapted from examples on the MSDN websites. Anexample of one of the 10 programming exercises in the online forum is as follows:

[Q9]Design a Web Form for a book search that inputs a keyword from the book’s name then searches the books from Database tables anddisplays the cover page image for that book.

Students could freely interact with others within their group when discussing solutions to the exercises. They could post their answers,ask questions, and browse the exercise problems or others’ solutions. In this experiment, the online forum was used to support groupdiscussion. It was based on the student-centered concept of learning; the instructor does not participate in the discussion. The online forumused in this experiment was the Teaching and Learning Support Platform designed by IBM Lotus QuickPlace and provided by the Infor-mation Data Center (IDC) at the University. The experimental environment and online forum drew on the participation types experiment byShaw (2012). The online forum used in this study was linked to the student administration system of the University. When students log inwith their ID and password (assigned initially from IDC of the University), their browsing and actions were limited to their own group; theywere not allowed to browse the posts of other groups, their status was verified and their usage was recorded. The online forum used in thisstudy simulated a programmer’s collaborative community in the real world. Members of a programmer’s community post questions in anonline forum, seeking to solve problems collaboratively. Software programmers also join programmer clubs, forums, and blogs (e.g., ASP.NETforums, CodeAsp.Net, Wrox Programmer Forums, and CodeComments.com) to improve their understanding of programming problems, tolearnmore programming skills and knowledge, and to solve problems using suggestions fromothermembers. The online forum for studentsto interact and discuss with others is shown in Fig. 2. ‘R’, ‘A’, and ‘W’ represent the ‘Reply’, ‘Ask’, and ‘Watch’ types (from Shaw (2012)) thatwere marked by the instructor after the experiment. ST. 2 represents Student No. 2 in this group, and so on.

3.2. Group size

In this study, 120 s-year university students from two classes were selected from a total of 143 s-year university students to participate inthe experiment. Of the 120 students, 60 in class Awere assigned to a large group and 60 in class B were randomly assigned to groups of 2, 3,4, 5, or 6 members. Three of each category of group was formed, resulting in a total of 15 small groups in class B.

Fig. 2. Screen capture showing examples of participants’ posted messages and instructor’s marked types.

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3.3. Participation

Participation in online forums was often assessed using quantitative measures, such as the number of messages learners had posted ona discussion board (e.g., Mazzolini & Maddison, 2003). Romiszowski and Mason (2004) argued that infrequent contributors were “passiverecipients rather than actively engaged in learning.” Wenger (1998) claimed that participation is more than the total number of studentpostings in a discussion forum.

During the four-week duration of the study, students logged onto the online forum and browsed the 10 exercises. Students could practicethe solution program code and post results to share with other group members. Alternatively, they could merely look at others’ solutioncodes. They could then ask questions about the course content, exercises, or the solutions posted by other group members. After theexperiment, we used the participation types (R, A,W) tomark every post and count every student in large and small groups. Previous studieshave used the total number of posts to measure performance (e.g., Mazzolini & Maddison, 2003; Michinov et al., 2011, etc.). In this study, weobserved the behaviors of different participation types to analyze the students’ participation behaviors. We extended the work of Michinovet al. (2011), which simply counted the number of posted messages. To emphasize the contribution of different participation type postmessages, a weighted rate was designed to represent the effort of work and the contributions of groupmembers. ‘Replied Solution (R)’ postsrequire at least double the effort and contribution as ‘Ask Questions (A)’ posts, so the weighted rate of type ‘R’ is two times that of type ‘A’.The R and A types were identified by effectively posting messages and contributing to the discussion. The ‘Watch (W)’ type posted nomessages and did not contribute to the group; hence, the weighted rate for type ‘W’ was 0. According to the results, ‘Replied Solutions’required more effort than ‘Ask Questions’, and asking questions required more effort than ‘Browsing.’ The calculation of participation useda weighted rate for every student as follows:

Participation ¼X

Numbers of each participation type in 10 exercises*Weighted rate of each participation type

¼ Counts of ‘R’ type*2þ Counts of ‘A’ type*1þ Counts of ‘W ’ type*0

Shaw’s study includes another type, ‘NA (No Action)’, referring to students who did not participate in group discussions. We assigned theparticipation of those students a 0. The concept of weighted rates matched that of Jahang, Nielsen, and Chan (2010), who suggested thatquantity, equality, and sharing can be used as quantitative measures for evaluating small-group collaboration.

Because this study principally encourages the participation of online forum interactions, students are able to reply to solutions separatelyand multiple times for each exercise, meaning that students could not only reply to the partial or pieces solution but could also reply todifferent solutions for each exercise. For the rigorous classification and determination of the number of posts made by members of differentparticipation types, two research assistants double-checked the post categories and numbers of posts.

Finally, we designed a rule for obtaining bonus points that stood to increase a learner’s motivation to participate in online forumactivities. Points were awarded to students with higher motivational orientations who performed better on e-learning platforms (Zhu,Valcke, & Schellens, 2009).

3.4. Learning performance

In this study, learning performance was measured in terms of learning scores and learning satisfaction. The learning score refers to thefinal examination that tests students’ practical programming abilities. The evaluations referred to textbooks and course materials, examplesof ASP.NET from Microsoft MSDN Websites, and the 10 forum exercises provided (though not exact copies of these exercises). The coursecontent before the midterm examination (e.g., ASP.NET Web Server Controls) was considered to consist of the required skills to preparestudents for the material included in the final examination. The participants were second-year students without background knowledge ofASP.NET programming. To avoid students changing their behavior as a result of knowing theywere being studied (Hawthorne effect) (Franke& Kaul, 1978), this study used the midterm examination as the pre-test and the final examination as the post-test. The following is anexample of a midterm examination question:

“Design two DropDownList control items for a user to select one of the colleges in your university, and another to select one of thedepartments in the college you selected from the college DropDownList (the department DropDownList changes when the college selectionchanges). Then, present the Webpage of the department you selected.”

The following is an example of a practical question found on the final examination, extending the concepts from the above question fromthe midterm examination:

“Design a Web Form that uses the DropDownList control item for selecting a book name and displays the cover page image of this booksimultaneously when selecting one book.”

The learning satisfaction questionnaire used a 7-point Likert scale for five items based on research by Sun and Cheng (2007) and Shaw(2010). Examples of questionnaire items include the following:

“Using the online forum for discussing exercises with others in my ASP.NET programming language course made it easier to learn” and

“I am satisfied with my learning performance using the online forum for discussing exercises with others in this programming language course.”

4. Data analysis

Learning performance data included the learning scores and learning satisfaction measures that were analyzed for differences amonggroup sizes and participation after the experiment. The statistical methods applied include a Cronbach’s a analysis to test the internalconsistency of the learning satisfaction questionnaire, an analysis of covariance (ANCOVA) to test the differences in learning scores betweenthe large and small groups with the midterm examination scores as the covariance, a Mann–Whitney U Test and a one-way analysis of

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variance (ANOVA) to test the difference between group sizes and participation and group sizes and learning satisfaction, and a regressionanalysis to test the relationships between participation, learning scores and learning satisfactionmeasures. SPSS for MicrosoftWindowswasused for the analyses.

4.1. Reliability of learning satisfaction questionnaire

The reliability coefficient of Cronbach’s a for the constructs of the learning satisfaction questionnaire was 0.96, exceeding the “very high”reliability threshold of 0.90 (Nunnally, 1978).

4.2. Hypothesis testing

The averages of the participation rates, final examination scores, satisfaction by groupmembers and group sizes are shown in Table 2. Theaverages of the participation rates and learning scores for small groups are all higher compared to those of the large group.

4.2.1. Learning performance with different group sizesHypothesis H1 suggests that different group sizes lead to significant differences in learning performance. Learning performance was

further subdivided as learning score (H1a) and learning satisfaction (H1b). This research applies an analysis of covariance (ANCOVA) to testH1a and a one-way ANOVA to test H1b. In hypothesis H1 testing, group sizes were divided into two groups: large (60 students) and small (2-6 students in each group). In Table 2, the average final examination score for the small groups is higher than for the large group (46.27, 45.50)and the midterm examination score for the small groups is lower than for the large group (29.58, 32.77). An unusual situation is observed inthe 3-member group size in Table 2. This group size has the highest midterm examination score of all of the group sizes but the lowestparticipation, followed by the lowest final examination scores.

This study applies a one-way analysis of covariance (ANCOVA) to test H1a. Before hypothesis testing, we used a Kolmogorov–SmirnovTest (p ¼ 0.164 > 0.05) to examine the variables’ normal distribution. In assessing the homogeneity of our regression, the result from the F-test of the product terms for each group size and the midterm examination scores do not violate the regression’s assumption of homo-geneity (F ¼ 0.971, P ¼ 0.326 > 0.05). Therefore, a single rule for covariate-based adjustments of the dependent variable scores could beapplied to participants across the different group sizes. In other words, an interaction effect did not exist. Consequently, we can assess theeffects of group size on achievement (i.e., final examination score) controlling for the midterm examination score. The results of the H1aANCOVA F-test show that different group sizes did not cause significant differences in learning scores (F¼ 0.202, P¼ 0.654> 0.05). Using themidterm examination score as a covariance, the final examination score (i.e., the learning score) was not significantly different among thedifferent group sizes. Thus, these findings did not statistical support hypothesis H1a. The average learning scores for the large and smallgroups were 45.50 and 46.27, respectively.

This study applies a one-way ANOVA to test H1b. Before hypothesis testing, we used the Kolmogorov–Smirnov Test (p¼ 0.578> 0.05) toexamine the variables’ normal distribution. We also use Levene’s Test to examine the variances of the two populations. The results of theH1b F-test show that different group sizes lead to significant differences in learning satisfaction (F ¼ 1.813, P ¼ 0.027 < 0.05). The averagelevels of learning satisfaction in the large group and the small groups were 4.81 and 5.29, respectively. Therefore, hypothesis H1b wasstatistically supported, as the average level of learning satisfaction for the small groups was significantly higher than that of the large group.

4.2.2. Participation with different group sizesHypothesis H2 suggests that different group sizes lead to significant differences in participation. Before hypothesis testing, we used the

Kolmogorov–Smirnov Test (p ¼ 0.000 < 0.05) to determine that the variables are not in a normal distribution. We then used the Mann–Whitney U Test to examine the differences between the two group sizes. The results of the Mann–Whitney U Test show that different groupsizes lead to significant differences in participation (U ¼ 1151, p ¼ 0.001 < 0.05). The rate of participationwas weighted two times for the ‘R’type, and the group sizes were divided into two sets: the large group (60 students) and the small groups (2–6 students in each group). Theaverage rates of participation in the large group and the small groups were 2.77 and 6.35, respectively. Therefore, hypothesis H2 wasstatistically supported, and the average participation in the small groups is significantly higher than in the large group.

4.2.3. Participation and learning performanceHypothesis H3 suggests that participation is positively associated with learning performance. This hypothesis was further subdivided

into measures of learning scores (H3a) and learning satisfaction (H3b). The results of the regression analyses statistically supported H3a byshowing that participation was positively associated with the learning score (b ¼ 0.273, p ¼ 0.03 < 0.05) and was not positively associatedwith learning satisfaction (b ¼ 0.251, p ¼ 0.06 > 0.05). Thus, hypothesis H3a was statistically supported; higher participation rates arecorrelatedwith higher learning scores. Hypothesis H3bwas not statistically supported; high participation rates did not correlatewith higherlevels of learning satisfaction.

5. Discussion

In this study, we found that when learning a programming language is supported with an online forum, different group sizes influenceparticipation and learning performance. A summary of our findings is shown in Table 3. Our results support the notion that different groupsizes have significantly different participation rates and levels of learning satisfaction and that participation significantly influences learningscores. However, different group sizes did not significantly impact learning scores, and participation did not significantly influence learningsatisfaction.

From hypothesis H1a, we concluded that different group sizes are not associated with significantly different levels of learning perfor-mance with respect to learning scores in learning the ASP.NET programming language when assisted with an online forum. In testing thishypothesis, we used two group types: a class as a large group and a class separated into 15 smaller groups for discussions in the online

Table 2Numbers and averages of participation, final examination scores, satisfaction by group members and group size.

Groupsize(GS)

Group members(GM) (Group #)

R: Avg (counts foreach group)

A: Avg (counts foreach group)

Avg. participation(GM) (R*2 þ A)

Avg. participation(GS)

Avg. final exam.score (GM)(for each group)

Avg. final exam.Score by (GS)

Avg.satisfaction(GM)

Avg.satisfaction(GS)

Avg. mid. exam.Score (GM)(for each group)

Avg. mid. exam.Score (GS)

Large 60(Group 0)

0.88(53)

1.00(60)

2.77 2.77 45.50 45.50 4.81 4.81 32.77 32.77

Small 2(Group 1)

2.67(7)

2.00(6)

7.33 6.35 53.33(55)

46.27 5.80 5.29 23.00(15)

29.58

(Group 2) (7) (4) (62.5) (25)(Group 3) (2) (2) (42.5) (29)3(Group 4)

2.00(3)

1.22(0)

5.22 24.44(21.7)

5.33 33.56(46.3)

(Group 5) (6) (8) (25) (27.7)(Group 6) (9) (3) (26.7) (26.7)4(Group 7)

2.50(9)

1.16(8)

6.17 46.25(48.8)

4.97 31.67(36.3)

(Group 8) (10) (6) (57.5) (34)(Group 9) (11) (0) (32.5) (24.8)5(Group 10)

2.40(10)

2.33(6)

7.13 43.00(42)

5.53 25.33(34.2)

(Group 11) (10) (16) (49) (20.2)(Group 12) (16) (13) (38) (21.6)6(Group 13)

2.33(20)

1.39(12)

6.06 57.56(73.3)

5.28 31.94(30.3)

(Group 14) (4) (6) (46.8) (37)(Group 15) (18) (7) (52.5) (28.5)

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Table 3Summary of hypotheses tests results.

Hypothesis Results

H1: Different group sizes lead to significant differences in learning performance.H1a: Different group sizes lead to significant differences in learning scores. RejectH1b: Different group sizes lead to significant differences in learning satisfaction. Support

H2: Different group sizes lead to significant differences in participation. SupportH3: Participation has a significant positive effect on learning performance.H3a: Participation has a significant positive effect on learning scores. SupportH3b: Participation has a significant positive effect on learning satisfaction. Reject

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support forum. The results of ANCOVA testing for the two group types with respect to the learning score was not significant nor was theANCOVA testing for all 16 groups (one large group and 15 small groups) (F¼ 1.552, P¼ 0.101>0.05). The test for the 15 small groupswas alsonot significant (F¼ 1.242, P¼ 0.281>0.05), indicating that the large and small group sizes did not influence the learning scores directly. Theaverage final examination score, including the use of the online forum, was 45.88 out of a range of 0–100. The final average scores of previoustwo academic years were 40.32 and 41.69. The course content, course materials, and style of exercises remained consistent, and only theonline forum for practice and group discussion was added. Thus, the average learning score was higher with the use of the on-line forumcompared to the average learning score when an on-line forumwas not used. These results indicate that regardless of group size, the onlineforum effectively supports learning the ASP.NET programming language and raises the learning score.

Our results noted a relationship between sizes of online forum groups and learning performance for learning a programming language;group size did not significantly influence learning scores, which echoed and extended the research of Shaw (2012). He used a class as a largegroup to show that participation positively affects learning performance in learning a programming language when using an online supportforum. In this study, we used this experimental design framework and advanced it to show that group size did not directly or significantlyinfluence learning scores. According to the results, the 15 small groups were not significantly different. The comparison of final learningscores from three academic years showed the average learning score when using an online forumwas higher thanwhen an online forum isnot used. We concluded that building a student-centered collaborative environment with an online forum for students to interactivelydiscuss the exercises after class was effective for raising scores for learning a programming language, regardless of the size of the forumgroups.

From hypothesis H1b, we concluded that different group sizes significantly influenced learning satisfaction levels when using an onlinesupport forum. From the results, we observed that students in the small groups (5.29) had higher satisfaction rates than students in the largegroup (4.81). In testing this hypothesis, we used two group types that operated as H1b. Further ANOVA testing for the differences in learningsatisfaction among the 15 small groups were not significant (F ¼ 1.180, P ¼ 0.321 > 0.05), indicating that the two group types (large andsmall) lead to significant differences in learning satisfaction rates. The small groups had higher satisfaction rates compared to the largegroup. Differences among the small groups were not significant; regardless of the size of the small group (2–6 students), small groups weremore satisfied than the large group. Both of the two group types had average satisfaction scores that were significantly higher than the 7-point scale average value of 3.5 (Student’s One-Sample T-Test, t ¼ 13.862, P ¼ 0.000 < 0.001). Therefore, students in both group sizes weresatisfied with their experience using the online forum to support learning the ASP.NET programming language. The reasons for the highsatisfaction rate of the small groups may echo the findings of Fox (1985), who found that as the size of the group increases, monitoringmembers’ behavior to detect norm violations and apply meaningful sanctions becomes more difficult. The smaller the group is, the morevisible members’ efforts, thus accountability is greater. Reissing-Vasile (2005) concluded that small group discussion could be an effectivevehicle for exploring, deepening, negotiating, and making meaning effective. As groups become larger, fewer members attempt to activelycontribute to the joint efforts (Watson & Johnson,1972) andmembers are less likely to view their own contribution as being important to thegroup’s chances of success (Kerr, 1989; Olson, 1965). However, teamwork literature shows that the size of the team has an inverse rela-tionship with team performance; this decrease in performance and satisfaction is caused by production blocking, evaluation apprehension,and social loafing (Easley et al., 2003).

In summary, our findings support social interdependence theory in showing that interactive online forums are effective in supportinglearning the ASP.NET programming language for both large and small group sizes. Further, students in small groups are more satisfied thanthose in the large group (5.29 and 4.81, respectively).

From hypothesis H2, we concluded that different group sizes significantly influence participation rates in an online forum included tosupport learning. The students in small groups (6.35) had higher participation rates than students in the large group (2.77). Further analysisusing Kruskal–Wallis H tests showed that the differences in learning participation among group sizes of 2–6 students were not significant(H¼ 0.842, P¼ 0.936> 0.05), and theMann–Whitney U Test for pairs of small groups of 2–6 students were also not significant. Regardless ofthe size of the small group (2–6 students), small group students using the online forum to support learning the ASP.NET programminglanguage participated more than students in the large group; the average participation of every group size ranged from 5.22 to 7.33 (seeTable 2), all of which are higher than the large group. The results of testing might correspond to several previous studies, which suggest thatas group size increases, the increase in individual anonymity makes it more difficult to assess each member’s contribution. Individuals willwithhold efforts and feel less motivated to participate (Lim & Zhong, 2006). As a result, an increase in group size increases social loafing andcoordination barriers among group members (Liden, Wayne, Jaworski, & Bennett, 2004). Jacobs and Ball (1996) reported that the sizerecommended for most group activities is pairs or groups of three or four students, which “enhances the opportunities for each member toparticipate actively, and reduces the complexity of group management.” Social interdependence theory thus implies that as the size of thegroup increases, the need for members to be skilled in coordinating their efforts also increases (Deutsch, 1962; Johnson & Johnson, 1989,2005).

The average participation rates of small groups are all higher than large group (see Table 2).We observed postedmessages and found thatquantities of both ‘reply solution’ and ‘ask questions’ were higher in the small groups than in the large group. The interactive contents ofsmall group messages include more informal messages, e.g., “Hurry, Hurry, It is an emergency, could someone help me.,” “No, No, No, you

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keyed the program codes in wrong place, you should put the codes in.,” “Be of good cheer, everybody, this exercise should use databasefunction.,” “I totally cannot understand what is wanted in this exercise, I just knew..” Students in small groups might post messages andinteract with others more naturally and easily.

From hypothesis H3a, we concluded that participationwas associated positively on outcomes with respect to final scores in learning theASP.NET programming language when using the online forum, which meant that higher participation rates in the online forum to postanswers and ask questions lead to a higher learning score. The results echoed several previous studies, e.g., Hrastinski (2009), who claimedthat online participation drives online learning, and Shaw (2012), who proposed that learning a programming language when using anonline forumwith students’ active participation increases learning performance, as measured by student examination scores. In testing thishypothesis, we used a rate weighted by two times for counting ‘R’ type participation. Whenwe used one time-weighted rates for ‘R’ and onetime-weighted rates for ‘A’ type participation to evaluate posted messages, the results of the regression analyses did not support the claimthat participation was positively associated with learning scores (b ¼ 0.248, p ¼ 0.06 > 0.05). Whenwe used 1.5 time-weighted rates for ‘R’and one time-weighted rates for ‘A’ type participation to evaluate posted messages, the results of the regression analyses supported theclaim that participation was positively associated with learning scores (b ¼ 0.266, p ¼ 0.03 < 0.05). This result meant that weighting the ‘R’type activities would positively influence the learning scores. According to our observations of posted messages, the ‘R’ type messages wereat least double the contribution and effort than the ‘A’ type messages. Hence, we used the weighted rate of ‘R’ type messages at two timesthat of ‘A’ type messages in the calculation of participation. Weighting message types in this way emphasized the importance of ‘R’ typeparticipation. This finding was similar to that of Shaw (2012), who showed that participation types were also associated with significantlydifferent learning scores and that the ‘Replier’ type was associated with superior learning scores. The particular finding of 3-member groupsin Table 2 show very low final examination scores (24.44) in all three of these groups. Also notable is that the 3-member groups in Table 2had the highest midterm examination scores (33.56) of all the group sizes but the lowest participation (5.22) and the lowest final exam-ination score, suggesting that participation might influence learning scores.

Given the same incentive conditions for participation among all groups (additional bonus points for participation with differentparticipation type scoring) and observing the participation rate, final examination scores (in Table 2), and results of testing H3a, weconcluded that higher participation rates, especially the ‘R’ type, are associated with higher learning scores.

From Table 2, the learning score seems to be related to the numbers of ‘Reply solution (R)’-type posts. Although the number of ‘R’ posts ofGroup 6 (9) is more than that of other groups (e.g., Group 2 (7), Group 3 (2), etc.), from observing interactions of group members, there arepieces of solutions; however, without an entire solution being posted, themembers in the group cloud did not learn the solution completely.The ‘Asks questions (A)’ posts may be helpful for the direction of ‘R’ posts and the effectiveness of learning score. The number of ‘R’ posts ofGroup 7 (9) is less than that of Group 9 (11), but the number of ‘A’ posts and the performance of learning score of Group 7 (8, 48.8) are greaterthan those of Group 9 (0, 32.5), which may indicate that the appropriate ‘A’ posts can impact the ‘R’ posts and influence learning perfor-mance. However, the definition and appropriation of effective and quality participation could be studied in the future.

The summary of the results of testing hypotheses H1a, H2, and H3a showed that different group sizes did not significantly influencelearning scores directly but that group size significantly influenced participation and participation positively influenced learning scores.Wilkinson and Fung (2002) showed that interaction patterns and learning benefits differed between dyads (two members), small groups(three to six members) and large groups (seven or more members), especially if participation equality or shared products are required. Inthis study, one conclusion supported the claim that the learning performance of university students, actively engaged in learning, is superiorto that of passive recipients who browse others’ solutions to or questions about exercises. In the context of learning a programming languagewhile using an online forum, these results suggest that encouraging or motivating learners to participate actively in their forumswould lead

Table 4Examples of social interactions in large and small groups.

Group #/Group members Interactive contents of [Q8]: “Design a DropDownList to select the “Order No.” of Order table and link to Order Detail table, and then displaythe contents of Order Detail and total the quantities”

Group 0/60 ST A: I would like to ask does not give database this exercise can be done?ST B: Teacher hints a database for this exercise; you can create by yourself with the figure shown in this exercise.ST A: But just look at this figure, I don’t know the values of other no. orders, yeah.ST B: You can assume that w Anyway, the columns are the same. You can create two records for a DropDownList can choose.ST A: I understand, thank you.ST A: I find the database that teacher giving is below the topic.XDST C: Thank you for reminding.ST D: Thanks.ST E: You can also use the database established in class, Ah!ST F: Terrific.ST A: I don’t know how to do the AMT in this exercise, please teach me!. .

Group 4/3 ST X: According PPT can do here, but still cannot run? Who can able to use the DropDownList control item?ST X: How to use DropDownList to show table?ST Y: Yeah! It’s work! It’s work!...Oh, Ya.See the answers in the attached file.ST Z: So, how to use DropDownList to do it????? Quickly w teach me.

Group 15/6 ST M: Firstly, I type the Event by myself, finally I find there is a lightning symbol in Attribute of the Control Item, point inside aRowDataBound and double click will create the Event...ST N: OK OK w it’s running OK NOW!!!!! Thanks to ST. M w ww

ST M: I have built a database table, but cannot summary total, which control item should I use? Or directly type the instruction.w If I want to type an instruction, is it typed inside the GridView control item?ST N: I also build a database table, but I cannot find the Footer Row? w So, then I use instruction directly.ST O: I think it should use a control item, it seem no used instruction directly in class teaching. w I cannot give the answer of thisexercise, too.ST P: To type the programming codes in Event of Rowdatabound that should be OK ww

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to improved learning performance. Instructors, for example, could make rules or award bonus points to the learners who do exercises andshare solutions.

From hypothesis H3b, we concluded that participation rates are not associated with a positive influence on learning performance withrespect to levels of learning satisfaction in learning the ASP.NET programming language while using an online forum. The average value oflearning satisfaction is 5.05. The average value for every group is shown in Table 2 from 4.81 to 5.80, and the average value greater than 4.0 is73%. Using Student’s One-Sample T-Test to test with the 7-point scale average value of 3.5, the result was significant (t ¼ 13.862,P ¼ 0.000 < 0.001). This result shows that all students were significantly satisfied with their experience using the online forum to assist inlearning a programming language.

The social performance of social interactions relative to group size can be observed and analyzed, as reported in Table 4. It seems that thesocial performances of small groups are lower than those of large groups, but the average number of posts for one small-group exercise isgreater than that for the large-group exercise, and interactions in the small group include more informal messages (e.g., Quickly w Teachme.). Kreijns, Kirschner, and Jochems (2003) mentions some situations that could be observed from the interaction content in groups in thisexperiment. For example, in Table 4, ST A in a large group (Group0) posted a question and received a positive response; he/she may thentrust the group members and post another question. It was also observed that the large group may require more interaction than two smallgroups to build trust and group cohesion.

Observing students’ interactions with group members in this experiment may lead to some findings related to the social (psychological)dimensions of social interaction in collaborative learning, as suggested by Kreijns et al. (2003), to establish group cohesion, trust, respect,and belonging. This study focuses on behaviors and contributions related to learning performance with different types of participation; thesecond part of Kreijns et al.’s (2003) work on group formation related to social (psychological) dimensions of social performance could beconsidered in future studies.

6. Conclusion

In this study, we created an artificial environment using an online forum to support learning the ASP.NET programming language. Theforum acted as a community communication tool for programmers to support their learning of a programming language.

This study sought to experimentally test the relationships among group sizes, participation, and learning performance. We drew thefollowing conclusions from the experiment:

1. In the context of learning a programming language, an online support forum is helpful for learning performance regardless of group size.The group size has no significant influence on learning scores directly. As proposed in the social interdependence theory, groupdiscussion can lead to better performance.

2. In the context of learning a programming language while using an online forum, small group size positively influences learningparticipation and participation positively influences learning scores. Social learning theories stress that learning occurs throughinteraction with others, but small group sizes lead to higher participation and indirectly improve performance through participation.

3. Levels of learning satisfaction when using the online forum are significantly higher than average for all students, and the small groupshave higher levels of learning satisfaction than the large group. Participation has no significant influence on levels of learningsatisfaction.

In summary, this study strengthened and extended both the group discussion component of the social interdependence theory and theparticipation component of the social learning theory. Previous studies of relationships between group size and learning performanceyielded mixed results and were rarely focused on the relationships among group size, participation, and learning performance while usingan online forum for learning a programming language. We conclude that designing small groups for online support forums for learning theASP.NET programming language, using student-centered discussion, and encouraging students to actively participate in discussions (in thestyle of the ‘Reply answer’ participation type) will increase learning outcomes.

According to the conclusions of this study, our suggestions for future studies are as follows:

1. The particular situation of the 3-member group size in Table 2 and the number of small groups were relatively limited; therefore, thesegroup sizes could be investigated in future studies to better explain this situation.

2. Fåhræus (2001) suggested that efficient access to information and other necessary learning resources is a key factor in success. In thisstudy, we used IBM QuickPlace as the online forum tool to support learning. The influences of contemporary tools that students use forcommunication in efficient and popular ways (e.g., Facebook) should be studied further.

3. Fåhræus (2001) showed that trust is the most important element for success in collaborative learning. The influence of group size andgroup formulation on learners’ trust and participation in the online forum could be studied in the future.

4. In observing the content interaction in each group exercise, the difficulty of the exercise does not seem related to the quantities ofinteraction. For example, formedium difficulty Q10, there are 6 ‘R’ and 6 ‘A’ posts in the large group, but for high difficulty Q8, there are 6‘R’ and 8 ‘A’ posts. This observation shows that the difficulty perception between the instructor and students may also be different. Thisstudy emphasizes the number of posts made by members of different participation types. If exercise difficulty leads to more posts, thendifficulty issues can be studied in the future. From the social interactions point of view, the degree of maturity and kernel concept of ‘A’posts of Q8 is more than for Q10, an issue that could be statistically verified in the future.

5. Zhu et al. (2009) mentioned that students with higher motivational orientations perform better on e-learning platforms. This studydesigned a rule for obtaining bonus points for the purpose of increasing learners’ motivation to participate in online forum activities.This motivation rule was executed equally in the two group sizes. The results of H2 and Table 2 showed that students in small groupshave higher participation rates and had higher learning outcomes, shown by the final examination scores. Motivation to learn isa problem not only for CSCL but also for traditional face-to-face instruction. Keller (1983) indicated that the factors that influencemotivation are ARCS (Attention, Relevance, Confidence, and Satisfaction), and Pintrich, Smith, Garcia, and McKeachie (1991) suggested

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that the Motivated Strategies for Learning Questionnaire (MSLQ) should consider value components, expectancy components, andaffective components. Designs for increasing motivation and participation referring to ARCS and MSLQ should be studied further.

Acknowledgment

The authors would like to thank the reviewers for their constructive and invaluable comments.

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