Group regulation and social-emotional interactions observed in computer supported collaborative learning: Comparison between good vs. poor collaborators

Computers & Education 78 (2014) 185e200

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Computers & Education journal homepage: www.elsevier.com/locate/compedu

Group regulation and social-emotional interactions observed in computer supported collaborative learning: Comparison between good vs. poor collaborators Kyungbin Kwon a, *, Ying-Hsiu Liu b, 1, LaShaune P. Johnson c, 2 a b c

Indiana University, Instructional Systems Technology, 201 North Rose Avenue, Bloomington, IN 47405, USA University of Missouri, 507 Clark Hall, Columbia, MO 65211, USA Creighton University, 2500 California Plaza, Hixson-Lied Science Building, Rm 202, Omaha, NE 68178, USA

a r t i c l e i n f o

a b s t r a c t

Article history: Received 19 December 2013 Received in revised form 8 May 2014 Accepted 5 June 2014 Available online 13 June 2014

This study explored what social interactions students exhibited during collaborative learning, and analyzed how the social interactions evolved in a computer-supported collaborative learning (CSCL) environment. Six groups (n ¼ 28) from an undergraduate online course were observed during a semester. Students' interactions were analyzed in two perspectives: group regulation and socioemotional. Cluster analysis was conducted to identify collaboration patterns of the groups. The analysis identified three collaborator clusters: one good and two poor. The good collaborators (named Early Active Collaborator) demonstrated: (1) intensive interactions among group members in the early collaboration phase, (2) positive socio-emotional interactions continuously, and (3) adaptive selections of group regulatory behaviors. The others showed dormant interactions throughout the projects and least socio-emotional interactions (named Passive Task-oriented Collaborator) and did not coordinate group process in a timely manner (named Late Collaborator). Comparisons of the interaction pattern and instructor intervention were discussed. © 2014 Elsevier Ltd. All rights reserved.

Keywords: Computer-mediated communication Collaborative learning Teaching/learning strategies Distributed learning environments

1. Introduction With the aging of the American population and the growth of life-sustaining technologies, health science professionals will need to be able to quickly handle unexpected medical and ethical challenges; and with the growing emphasis on patient-centered care and care coordination, they are increasingly called upon to work on interdisciplinary teams. Within these teams, they will be asked to provide health care and to negotiate difficult ethical questions. In the Institute of Medicine's, 2001 report, “Crossing the Quality Chasm”, the authors emphasized the importance of interdisciplinary healthcare teams in facing the challenges of a quickly evolving American health care system. Within this call for interdisciplinarity in care, comes also a call for increasingly interdisciplinary educational opportunities for preprofessional health sciences students (Institute of Medicine, 2001). Courses required across the undergraduate health sciences curriculum, such as the clinical ethics courses featured in this study, offer an opportunity for the “interprofessional collaborative process”, which allows for the reinforcement of theories and practices integral to interdisciplinary health care administration. In recent years, efforts to strengthen this interprofessional collaboration in students have been studied and several models have been developed. Many models feature problem-based learning or case studies to encourage interdisciplinary collaboration (Solomon & Salfi, 2011). In this study, a group of interdisciplinary health sciences students were tasked with creating an ethical case study and creating an interdisciplinary, patient-centered solution to the ethical dilemma. Clinical ethics courses are standard in most health sciences programs, and offer, according to Schonfeld and Spetman (2007), pre-professional students critical thinking skills that will allow them to solve unexpected situations.

* Corresponding author. Tel.: þ1 812 856 8450; fax: þ1 812 856 8239. E-mail addresses: [email protected] (K. Kwon), [email protected] (Y.H. Liu), [email protected] (L.P. Johnson). 1 Tel.: þ1 573 882 2095. 2 Tel.: þ1 402 280 2042. http://dx.doi.org/10.1016/j.compedu.2014.06.004 0360-1315/© 2014 Elsevier Ltd. All rights reserved.

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As the numbers of online asynchronous health sciences courses grow, it is important to discover how best to offer online opportunities to develop the building blocks needed for later interprofessional collaboration and to acquire the critical thinking skills required to handle difficult ethical situations. While many instructors have tried to improve students' collaboration skills by providing collaboration opportunities through a group project, students' actual collaboration is below expectation in many cases. Considering the deficit of non-verbal expression, a time lag between conversations, inept collaboration skills in online environments, and different time zone, one cannot guarantee effective collaborative learning without proper interventions or guides (Salomon & Globerson, 1989). For this reason, many types of instructor's interventions and collaboration tools were invented, such as visualization of group process to establish group awareness (Bodemer, 2011; Dehler, Bodemer, Buder, & Hesse, 2011; Phielix, Prins, Kirschner, Erkens, & Jaspers, 2011; Sangin, Molinari, Nüssli, & Dillenbourg, 2011), self and peer assessments on group process (Dochy, Segers, & Sluijsmans, 1999; McLeod, Liker, & Lobel, 1992; Phielix, Prins, & Kirschner, 2010; Phielix et al., 2011), and metacognitive guidance (Kwon, Hong, & Laffey, 2013). The positive outcomes of the intervention were well demonstrated by the products of or satisfaction with collaboration. However, these results are limited in their ability to explain how the intervention works, and what behaviors can be expected in a collaborative learning situation. In this sense, direct observation of collaborative behavior is required. To evaluate students' collaboration and to identify effective collaborative interactions, there is a need to know what types of behavior students exhibit when interacting with group members, and what types of behavior are helpful in coordinating group work and in encouraging others. Classifying students' behaviors will illustrate social interactions employed for collaboration. In addition, observation on how the social interactions evolve along with the collaborative process will give a holistic picture of in-group dynamics. Further, comparisons of behavior patterns between good and poor groups will explain which behaviors could enhance group collaboration. This knowledge will allow instructors to design more effective interventions and to diagnose the collaboration process. As mentioned in Dufner, Park, Kwon, and Peng (2002), it takes several weeks to complete a group task and to develop the skills needed for successful collaboration. To fully explore and understand student collaboration behaviors, we purposefully observed groups over a thirteen week period, using multiple sources of data. In sum, the purposes of this study were to explore the ways in which pre-professional health sciences students collaboratively accomplished tasks offered in a computer-supported collaborative learning (CSCL) environment and to identify behaviors that led to group regulation and/or socio-emotional interactions. The study also aimed to classify desirable collaboration patterns which were demonstrated by students. These goals would be achieved by observing online discussion exchanged throughout a group project and eliciting students' responses on their collaboration experience. 2. Theoretical framework 2.1. Group regulation To achieve common goals, a group needs to coordinate group efforts and resources in effective ways. By analogy with self-regulation, the group coordination behavior can be identified as group regulation. While number of agents and scope of actions are different, self- and group regulation share characteristics in that both require the following behaviors: identifying goals and tasks, monitoring process, and € ffel, Van der Meijden, Staarman, & Janssen, 2005; Saab, 2012). As the ability to regulate evaluating strategies and outcomes (De Jong, Kollo one's learning process plays an important role in individual learning (Zimmerman & Schunk, 2001), the quality of collaborative learning heavily relies on the competency to continuously coordinate group process (Erkens, Jaspers, Prangsma, & Kanselaar, 2005), time management (Xu, Du, & Fan, 2013), individual responsibility and social interdependence (D. Johnson, Johnson, & Smith, 2007), and high interactivity among members (Brewer & Klein, 2006; Cohen, 1994). If these group regulatory behaviors were not coordinated properly, one might not expect positive outcomes from collaboration and, in the worst case, students would experience social loafing: deliberate less , Williams, & Harkins, 1979; effort for collaboration of less capable or apathetic students (Karau & Williams, 1993; Kerr & Bruun, 1983; Latane Salomon & Globerson, 1989); the sucker effect: reduction in effort not to take over other peers' responsibilities (Kerr, 1983; Salomon & Globerson, 1989); or a failure at group coordination (Barron, 2003; Kruger, 1993). Compared to learning individually, students who work in collaboration with group members need another unique group regulatory behavior: sharing common ground. In a CSCL context, for example, students ask other students for opinions and update group process with others in order to keep everyone on the same page (Janssen, Erkens, Kirschner, & Kanselaar, 2012). Communicating one's strengths, weaknesses, and preferences allows students to choose effective collaboration strategies. Announcing when a member will be off-line will reduce misunderstanding among students about one's absence or nonresponse. If students have different ideas or suggestions, they need to negotiate them (Kirschner, Beers, Boshuizen, & Gijselaers, 2008). The timing for maintaining common ground can vary, depending on tasks and group members. However, early group regulation usually enhances the establishment of shared common ground, and this reduces the need for maintaining group effort later (Lajoie & Lu, 2012). These behaviors are indispensable ingredients for regulating group work. Students, however, in many cases do not seem to exhibit the skills as expected (e.g., Gunawardena, Lowe, & Anderson, 1997; Puntambekar, 2006). As Salomon and Globerson (1989) expressed, “Teams just do not always function as well as they could or as well as one would have expected them to” (p. 90). In addition, students using computer-mediated communication (CMC) for collaboration usually spend more time and effort than ones in a face-to-face setting to regulate group work (van der Meijden & Veenman, 2005). All of these group regulatory behaviors should be initiated, guided and encouraged through group members' autonomy and/or through instructor's intervention. For these reasons, observing students' collaborative behaviors in an online learning environment is beneficial to describe which group regulatory behaviors (do not) occur, and to identify which ones affect the success of and satisfaction on collaboration. 2.2. Socio-emotional interaction A socio-emotional interaction refers to actions relevant to the expression of one's emotion in a social context such as “getting to know each other, committing to social relationship, developing trust and belonging, and building a sense of on-line community” (Kreijns,

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Kirschner, & Jochems, 2003, p. 342). As group regulation is an important cognitive interaction necessary to keep a group functional and moving toward common goals, socio-emotional interaction plays a critical role in creating a sound social space where a student “facilitates and reinforces social interaction and, in turn, influences the effectiveness of collaborative learning” (Kreijns, Kirschner, Jochems, & Van Buuren, 2004, p. 169). If the group regulation is fuel of an engine, the socio-emotional interaction is the motor oil that lubricates movement of members and protects them from friction. Socio-emotional interactions are closely associated with cognitive interactions (Liaw & Huang, 2000), formation and sharing a sense of community (Gunawardena, 1995; Wegerif, 1998), satisfaction on collaboration (Bulu, 2012), and learning outcomes (Richardson & Swan, 2003). While students participate in collaborative learning, they need processes to build interpersonal relationships, positive group climate, trust among group members, and a sense of community; all which underlie socio-emotional interactions. When students know group members well, they tend to have more positive collaborative experiences, and they even can express disagreement easily, which encourages critical, divergent and exploratory opinions (Janssen, Erkens, Kirschner, & Kanselaar, 2009). Feeling of belonging to a group and connectedness with members affects students' motivation to and engagement in collaboration (So & Brush, 2008). Further, socio-emotional interactions are interconnected with group regulation behaviors. Well-organized group coordination is one of the important factors for establishing positive group climate, which boosts helpful interactions among members (Kwon et al., 2013). When students share a sense of positive interdependence through group coordination, they are willing to support each other and feel less anxiety toward collaboration. Individual accountability from members and their commitment toward quality work are important to develop trust within a group (Tseng & Yeh, 2013). Students are expected to follow a group schedule and carry out their responsibility when they share a group norm. If, however, one violates the group norm without a proper excuse to other members, they might easily disbelieve the person; and it could also affect the group coordination that follows. As Kreijns et al. (2003) suggested an “important attribute of group cohesion is mutual trust amongst group members”. One unmotivated member's irresponsible behavior can cause the failure of building mutual trust, which spoils group climates and makes group process be rough. McMillan (1996) viewed a sense of community as “a spirit of belonging together, a feeling that there is an authority structure that can be trusted, an awareness that trade, and mutual benefit come from being together, and a spirit that comes from shared experiences that are preserved as art” (p. 315). His brief but thorough description implies sense of community is the final product of successful collaborative learning, which students want to have and instructors desire to cultivate. All socio-emotional interactions as well as group regulatory behaviors mutually affect the sense of community. From the socio-emotional perspective, “emotional safety”, which triggers self-disclosure and intimacy; “feeling of belonging”, which indicates acceptance of a group and implies one's willingness to commitment for the group; and “trust”, which is established on group norms and individual accountability, are prerequisites of sense of community (McMillan, 1996; McMillan & Chavis, 1986). In CSCL, where interactions rely highly on asynchronous text-based communication, socio-emotional interactions can be restricted. In a face-to-face context, lots of social information can be delivered through nonverbal expressions such as facial expression, gesture, body language, touch, and eye contact. The nonverbal communication affects interpersonal relationships either positively or negatively. For example, while getting closer, lovers pay more attention to the other's nonverbal messages. Sometimes you may reveal (or be discovered) your actual opinion through a facial expression. However, all of this meaningful information is invisible in CSCL and students exchange social messages via written text. For this reason, socio-emotional interaction should be explicitly expressed verbally and this can reduce its interactivity or lose context. Regretfully, instructors often limit their intervention or guide to task-related interactions such as group regulation and cognitive inquiry behaviors (Kreijns et al., 2003; Van Leeuwen, Janssen, Erkens, & Brekelmans, 2013). When instructors assume that “social interaction automatically takes place” (Kreijns et al., 2003, p. 336), students might be psychologically isolated or struggle with the lack of social interaction and confess, “One of the biggest drawbacks is the distances apart from each other. You can't get together and discuss over coffee or exchange information. This hampers getting it all together” (Johnson, Suriya, Yoon, Berrett, & La Fleur, 2002, p. 389). All of these studies shed light on the dynamics of collaboration and lay the foundation of further investigation on and understanding of interactions among students in CSCL. The current study asked students to work on a complex task by collaboratively writing a clinical ethics scenario which required group members to get actively involved in planning, translating and reviewing processes (Flower & Hayes, 1981). The researchers observed student's group dynamics from two perspectives, group regulation and socio-emotional interaction, to answer the following research questions: 1. What kinds of group regulation and socio-emotional behaviors are involved in during the collaboration process? 2. How do the behaviors change during the group project? What types of patterns evolved while the project progressed? 3. What is a desirable or undesirable pattern for successful collaboration? 3. Method 3.1. Course context The study was conducted in an undergraduate clinical ethics online course, offered at a Midwestern land grant university. The course was designed to facilitate undergraduate health sciences students' ability to critically evaluate the ethical principles that undergird healthcare and to apply of field-specific codes of ethics to common clinical dilemmas. The course was in the sixteen week semester format and was hosted under Blackboard, a learning management system offers ready-touse instructional tools. To facilitate the group collaboration, discussion forums for group communication and wikis for collaborative writing a clinical ethics scenario were offered by the instructor. The course was divided into fifteen learning modules that were aligned with the “six step process of ethical decision-making” proposed by Purtilo and Doherty (2010). For instructional purposes, this number of modules allowed the instructor to sequentially present the topic and to scaffold ethical reasoning skills and technological skills to be slowly introduced. Furthermore, the slower pace allowed the instructor to regularly assess the students' understanding of concepts and provided regular opportunity to provide supplemental materials if there was confusion. In each of the modules, a major theme was introduced to

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students. The students were assigned readings from textbooks, journals or non-academic (typically patient-centered) websites. Weekly assessment of the students' understanding of this material was done through weekly quizzes and weekly discussion board forums. 3.2. Participants Participants were 31 students who enrolled in the course in Fall 2012. Three students (from group 3 and 5) dropped out the course, and their data were excluded in the study. Among 28 students, 25 were female and 3 were male. Students' average age was 27.46, and ranged from 21 to 45. Twenty-five students were majoring in health sciences, and 3 students had not declared a major. Most students have worked in health-related fields, and 8 students have not worked in a health-related field. Most of the students had experienced online courses, except for one. Students had taken 8.36 online courses, on average. However, students had limited experience in online group work; only 7 out of 28 students had online group project experiences, and only 2 of them had used a wiki tool for the group project. The participants were randomly assigned by the instructor to one of six groups that consisted of five to six members. The random assignment attempted to achieve heterogeneous grouping with respect to prior work experience, acquaintance among member, online learning experience and gender (See Table 1). 3.3. Group project and procedures In this study, students were required to work as a group to develop a clinical ethics scenario, which was the focus of this study. In this semester-long project, each group was required to create a clinical ethics case scenario and take on the role of one of the stakeholders in the case. This project could be characterized as an open and dynamic project that involved both individual and collaborative efforts, and required members to obtain a thorough understanding of all stakeholders' roles, and the complexity of ethical decision making. To develop the scenario, group members were supposed to involve themselves in intensive negotiation and communication. Students were expected to use what they learned in the class, such as the theories and “Six Step Process of Ethical Decision Making” to guide them develop the scenario. To orient students to the use of the wiki tool, these students participated in a “Sandbox” practice, which allowed them to practice posting/editing/commenting. Additionally, this activity also provided students an opportunity to get familiar with the features of wiki and the concept of co-writing. Video tutorials were created to reinforce the basic skills required to work with the wiki platform. Each group had an assigned wiki space, but it was open to other groups. In order to facilitate group work, each group also had its own private discussion forum which allowed students to communicate and discuss their work. The group project consisted of eight Milestones (M1 through M8) divided into group or individual tasks (See Table 2). These eight Milestones served as accumulative building blocks for the final projectdeach assignment was progressively more detailed and required more critical thinking, resulting in a completed clinical ethics scenario and analysis. Beginning from week four, group members were involved in selecting a topic (M1), developing an outline of the topic (M2), creating involved characters and its assign members (M3). In the middle of the project, the students were asked to collaborate on assembling their individual work (M4) and to visit other groups' case scenarios and provide constructive feedback (M5). Each group was given opportunity to revise and polish their scenario based on the feedback provided by their classmates (M6). The students were asked to revisit their characters' roles and reflect on whether their view had changed in terms of character's choice and understanding of the ethical principles (M7). In the last milestone, group members revised and polished their scenario and completed the story writing (M8). The instructor evaluated the group's case scenario based on rubrics developed previously. 3.4. Measurements 3.4.1. Coding scheme on types of discussion Student discussions were collected throughout the semester (13 out of 16 week-long semester). The discussion messages exhibited collaborative behaviors, which were great sources of data for assist in understanding how students coordinated group work and shared social interactions. Based on the guidelines offered by Chi (1997) and Graneheim and Lundman (2004), the process of coding scheme development and content analysis followed the steps: (1) Developing a coding scheme. The development of coding scheme was a recursive process. After reading the discussion messages, the researchers met and discussed the coding scheme that each individual researcher identified independently until they reached a

Table 1 Demographic information of groups. Group

1

2

3

4

5

6

Total

Numbera Gender Female Male Age* # of Online course taken* Online group project experience Yes No Health care work experience Yes No

5

5

3

6

4

5

28

5

5

3

27.2 (7.8) 14 (25.8)

28.7 (5.5) 2.3 (1.2)

3 1 33.5 (8.3) 12.3 (9.4)

5

25.6 (6.0) 4.6 (3.3)

4 2 28.2 (6.3) 10.3 (6.0)

23.2 (2.3) 4.6 (3.4)

25 3 27.5 (6.5) 8.4 (11.7)

1 4

5

1 2

4 2

1 3

5

7 21

3 2

3 2

2 1

4 2

4

4 1

20 8

*Note: Mean (SD). a Initially, all groups had 5 members except group 4 that had 6. However 2 students from group 3 and 1 student from group 5 dropped the course.

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Table 2 Description of Milestones. Week

Milestone Title

Type

Task description

4

M1. Select a topic M2. Develop scenario outline

Group task

Select a topic idea and provide a description of it.

Group task

M3. Reflect on ethical consideration M4. Understand who WE are.

Individual task

Individual task

Develop story outlines by answering questions: Who are the characters? What is the conversation about? What is the story set up? What is the background of the story? Where it takes place? Why the conversation takes place? Write a reflection on the character you are representing, what ethical considerations that would impact on the character's role and choosing ethical principle. Summarize the findings of the characters in the story, connecting them to the ethical issue you are addressing. Each group member need to understand rest of group members' character's role. What possible steps these characters may take. Read one other group's story, and provide constructive feedback.

Group task

Revise the scenario based on classmates' feedback.

Individual task

Revisit the role and reflect on whether your view has changed in terms of character's choice and understanding of the ethical principles. Polish your story and complete the story writing.

6

8 9

12

M5. Critique and feedback M6. Revise case scenario M7. Reflect on view changes M8. Wrap up and commentary

13 14 16

Group task

Group task

Note: For all group tasks students were encouraged to use group discussion forum. However, they rarely used it for Milestone 1 and 6 for the characteristics of the tasks. For the reason, discussion messages only from Milestone 2, 4 and 8 were included in the analysis.

consensus. They then went back and used the coding scheme to analyze pre-determined discussion messages. During the practice, they were mindful in using these codes and took notes on difficulties and questions caused by the coding scheme. After the practice, they met again to discuss issues raised by these codes, and revised the coding scheme accordingly. The code-then-revise processes repeated several times until all of them satisfied with the coding scheme. Based on the literature review and the recursive coding scheme refinement process, two categories, group regulatory and socioemotional behavior, and 14 subcategories were developed to analyze discussion messages (See Table 3). Group regulatory behaviors refer to discussions involved in coordinating members' joint effort to achieve common goals. These behaviors include scheduling group work or setting up a due date for a task, dividing labor among group members, identifying tasks to do, discussing effective strategies, monitoring group processes, sharing member's strength and weakness so that the group members can match their talent to the task and contribute to the task in maximal manner, and appraisal of both group product and process. Socio-emotional behaviors refer to discussions Table 3 Types of online discussion on the group project. Category

Subcategory

Code

Definition of behavior

Example

Group regulatory behaviors

Scheduling

SCHDL

Dividing Labor

DV_LBR

Task

TASK

Strategy

STRTG

Schedule group work by checking available time or setting due dates Divide labors or specify a person's responsibility on a task Identify tasks to be completed by acknowledging goals or requirements of a project Inquire about effective ways to coordinate group process and to achieve goals

Open-self

Opn_SLF

Share individual strength, weakness, preference, situation to enhance group awareness

Monitoring Group Process

MONITOR

Acknowledge group progress by checking and sharing what has been done

Group Agreement

AGREE

Seek other member's feedback to reach group agreement and to establish group norms

Evaluation

EVAL

Evaluate group product or/and group process

Emotional Expression

EMOTION

Encouragement

ENCRG

Forming Sense of Community

COMMUNITY

Express feeling about members and group work, such as thanks, sorry, excited or worried, etc. Encourage others by praising what's done well or by cheering up Share personal issues and/or feeling of belonging resulted in developing social bonding

I work Tuesday morning though so I will try to finish on Monday night! I will do the doctor if you want to do the mother :) I need to proof read and see if I can convert it from Word to Wiki so it looks better. As far as our group goes I think we need some leadership … I would suggest that for the next 4 milestones we have leaders. I am not a huge fan of writing papers unless it is a subject I enjoy. . It takes me forever to really get going on them. I have already done the ‘what’ part, so I think once we do those other ones, we will have what we need. I also had put a part where I said “we may want to” can you guys look at this and see if you agree we should use teamwork in this way All our schedules are different so it makes the communication process take longer. Milestone 2 is supposed to be an extension of milestone 1, with a total of 2700 words between the two. So, I don't think that repeating things from one is a problem. so I apologize for my random absences.

Socio-emotional behaviors

Other

OTHER

Actions which does not match with any other categories.

Great work everyone! Great team work! I'm thankful everyone fulfills their responsibility to the group and understands they aren't alone in the craziness of life! :)

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expressing one's emotion or encouraging others such as gratitude, sympathy, apology, excitement, worry, recognition, and encouraging note, which result in establishing sense of community. (2) Segmentation. Graneheim and Lundman (2004, p. 106) defined idea units as the “constellation of words or statements that related to the same central meaning”. Researchers divided a discussion message into idea units that contain one meaning matched with the subcategories. In this study, the idea unit served as a unit of analysis. (3) Coding and ensuring inter-rater reliability. After all the discussion messages were divided into idea units, two independent coders conducted coding based on the coding scheme. Idea units that did not fit to any of the subcategories were coded “other” and were excluded from the analysis. Researchers examined discussion threads and intentionally selected samples which contained enough messages and were representative of Milestones 2, 4 and 8. Two coders separated the messages into idea units (53% of total idea units) unanimously. They coded the idea units independently and reached outstanding (category: Kappa ¼ .89) and substantial (subcategory: Kappa ¼ .78) agreement (Landis & Koch, 1977). The initial discrepancy was resolved after negotiation between the coders and a researcher. Based on the sufficient interrater consistency, rest of idea units were coded by one coder. (4) Representation and identifying patterns of behavior (meaning making). After completing the coding, statistics techniques were used to visualize patterns that merged from data.

3.4.2. Surveys on group members' perception on group process Two independent surveys were conducted to observe students' perceptions (or evaluation) of group process at different times. The first survey was answered at the end of Milestone 2. It investigated students' group regulation in the early collaboration phase. Questions were developed in reflection of three domains: strategy, communication, authorship (See Appendix 1). Strategy requested if a group considered strategies for collaboration or identifies individual responsibility (2 questions, Cronbach's a ¼ .51). Communication asked if ways of communication were effective (3 questions, Cronbach's a ¼ .61). Authorship checked what students felt about authorship and responsibility on their writing (2 questions, Cronbach's a ¼ .80). The survey questions were developed by the authors for the study and were not validated previously. The low reliability of the measures might due to the small number of questions which could underestimate the relationships between the measures and the variables of interest (Schmitt, 1996). The second survey investigated students' feeling on the group process at the end of the project (M8). Ten questions addressed group climates, interdependence on each other, equal contributions, and overall impression with excellent internal consistency (Cronbach's a ¼ .91). The questions were adopted from Kwon et al.'s (2013) work and modified accordingly (See Appendix 1). All the questions of two surveys were answered on 7-point Likert scales. 3.4.3. Evaluation on group performance Group products were evaluated by the instructor on the following criteria: scenario summary should include all of the characters and the summary of the dilemma (30%); scenario should have proper citations, have done complete sentences and have done proofreading (20%); each character should make a critical contribution to creating a realistic ethical scenario (10%). As outcomes, students should outline all of the options for the characters involved and list possible constraints. A decision for the characters needs to be made based on the consideration (40%). Grades were distributed within a week after each Milestone was done. Instructor's comments on the grades and group process were included. For this study, only numeric grades were analyzed as an indicator of group productivity. To all of the groups, the instructor offered an email that included summative feedback and an explanation of the group's score. For the first group assignment, in particular, the instructor gave detailed comments to the entire group about strategies for evenly distributing labor and made suggestions for how to work collaborativelydand also gave feedback to individual group members. When it was requested by a student, or the instructor deemed it necessary, the instructor also emailed individual group members with feedback or suggestions. 3.5. Research design and data analysis Due to the nature of research purpose, ex post facto (after-the-fact) research design was employed to identify students' online collaboration behaviors occurred naturally without an experimental treatment. In order to observe students' online collaborative behavior, messages posted on a discussion board were collected and analyzed based on the coding scheme developed by researchers (See Table 3). All discussion units of the messages were coded along two dimensions: category and subcategory. Descriptive analysis illustrated how many collaborative behaviors were demonstrated on the discussion board. After observing collaboration, a cluster analysis was employed to explore if there were different patterns of collaborative behaviors among groups. Based on a post hoc analysis of group discussion activities in terms of timing and types of behaviors (group regulation and socio-emotional), three groups were categorized. Further analysis was conducted based on the groups extracted from the analysis. 4. Results 4.1. Overview of group discussion Table 4 describes the number of messages posted and word counts of them. Overall each group posted 20 messages (SD ¼ 13.43) per Milestone. In average one message contained 60.3 words. Number of messages varied across groups, which was considered in the following analysis.

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Table 4 Number of messages and word counts on group discussion. Group

1 2 3 4 5 6

(n (n (n (n (n (n

¼ ¼ ¼ ¼ ¼ ¼

Milestone 2

5) 5) 3) 6) 4) 5)

Milestone 4

Milestone 8

Total

Message

Word

Message

Word

Message

Word

Message

Word

4 16 9 24 52 10

210 1520 449 2600 3626 230

6 13 21 20 25 25

421 529 1404 930 810 1683

14 42 3 40 16 16

1099 1477 844 2148 715 762

24 71 33 84 93 51

1730 3526 2697 5678 5151 2675

Contents of group discussion were examined by categorizing discussion units into the coding scheme discussed in the method section. Table 5 shows average number of two discussion types: group regulation and socio-emotional behaviors on each Milestone. Analysis of variances (ANOVAs) on the total mean number of discussion units per category revealed significant difference between groups (group regulation: F(5, 22) ¼ 2.70, p ¼ .047, partial h2 ¼ 0.24; socio-emotional: F(5, 22) ¼ 2.79, p ¼ .043, partial h2 ¼ 0.39). Post-hoc analysis indicated Group5 showed more group regulation than Group1 and Group6. Group5 also showed more socio-emotional behaviors than Group1, Group2, Group3, and Group6. ANOVAs on two discussion behaviors for each Milestone also showed significant differences between groups with different patterns (group regulation in Milestone 2: F(5,22) ¼ 3.22, p ¼ .025; socio-emotional in Milestone 2: F(5,22) ¼ 3.30, p ¼ .023; group regulation in Milestone 4: F(5,22) ¼ 3.46, p ¼ .019; socio-emotional in Milestone 4: F(5,22) ¼ 3.66, p ¼ .015; group regulation in Milestone 8: F(5,22) ¼ 2.50, p ¼ .062; socio-emotional in Milestone 8: F(5,22) ¼ 1.48, p ¼ .236). 4.2. Cluster analysis on group discussion Based on the previous results of overall pattern of discussion activities, the researchers decided to employ cluster analysis (Single linkage procedure with squared Euclidean distances) to explore whether there were different patterns of group discussion activities in terms of timing (Milestone 2, 4 and 8) and types of behaviors (group regulation and socio-emotional). Groups were categorized according to their similarity with respect to the frequency of behavior types in accordance with three project periods. To categorize groups, six variables were selected independently from three Milestones divided by two behavior types. In order to equalize impact of six variables on determining clusters, standardized scores were used. The dendrogram (Fig. 1) illustrates a three cluster solution: C1 ¼ Group2 and 4; C2 ¼ Group1, 3, and 6; C3 ¼ Group5. Fig. 2 describes the profiles of these groups. Group5 in C3 showed high frequency of group regulation as well as socio-emotional behaviors in the early collaboration phase (M2). One can see a clear trend that both group regulation and socio-emotional behaviors decreased as the group project progressed. However, it is worth noting that even the smallest number of behaviors was not less than average of other groups. This cluster was named Early Active Collaborator (EAC). Group2 and 4 in C1, in contrast with C3, were passive in the early collaboration phases (M2 and M4) but got quite active at the last phase. During the first half of the collaboration period, their interaction rates were below the average. However, they showed two times more interactions than the other passive group at the end of the group project. The interaction rate at the last period was far more than EAC. This cluster was named Late Collaborator (LC). Other groups in C2 showed the least collaborative behaviors in the group discussion overall. Interestingly, they shared the least socioemotional behaviors in all collaboration phases. Although they seemed to be active in the middle of collaboration (M4), their socioemotional behaviors were still lower than average. This cluster was named Passive Task-oriented Collaborator (PTC). 4.3. Pattern of discussion With respect to the research question as to whether different collaboration patterns of groups can be identified, we examined subcategories of two behaviors types of each cluster. Table 6 and Table 7 describe average number of subcategories across the Milestones. Table 5 Mean number of discussion units coded as group regulation and socio-emotional behaviors. Group

Milestone 2 GR

1 (n ¼ 5) 2 (n ¼ 5) 3 (n ¼ 3) 4 (n ¼ 6) 5 (n ¼ 4) 6 (n ¼ 5)

1.8 (2.49) 11.2 (8.96) 7.67 (12.42) 9 (12.74) 34.25 (29.14) 2.8 (3.42)

Milestone 4 SE

GR

0.6 (0.89) 0.8 (0.84) 1 (1.73) 4 (4.29) 8.75 (8.18) 0.4 (0.89)

3.4 (3.65) 4.6 (3.05) 13.67 (2.31) 7.83 (8.04) 12 (7.16) 14 (3.24)

Note: Values enclosed in parentheses represent standard deviation. GR ¼ Group Regulation, SE ¼ Socio-emotional behavior.

Milestone 8 SE 0 (0) 0.4 (0.55) 0.33 (0.58) 2 (2.28) 3.25 (1.26) 3.2 (2.59)

Total

GR

SE

GR

SE

4.4 (4.83) 16.4 (4.28) 5 (3.61) 14.83 (12.69) 11 (5.35) 6.8 (2.59)

0.6 (1.34) 1.8 (0.84) 1 (1) 2 (1.79) 1.25 (0.96) 0.4 (0.55)

9.6 (9.71) 32.2 (8.53) 26.33 (7.77)

1.2 (2.17) 3 (1.58) 2.33 (2.52) 8 (7.72) 13.25 (10.24) 4 (3.32)

31.67 (31.59) 57.25 (30.5) 23.6 (5.18)

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Fig. 1. Cluster analysis: dendrogram.

Multivariate analyses of variance (MANOVAs) were conducted with three clusters as an independent variable, and with the eight subcategories of group regulation as dependent variables per each collaboration phase. The MANOVA on the Milestone 2 revealed significant differences between clusters, Wilks' l ¼ .26, F(7, 20) ¼ 2.20, p ¼ .03, partial h2 ¼ 0.49. Follow-up univariate analysis indicated that EAC showed more group regulation than other two clusters on Dividing Labor, Task, Strategy, Monitoring Group Process, Group Agreement, and Evaluation. MANOVA on the Milestone 4 revealed significant differences between groups: Wilks' l ¼ .27, F(7, 20) ¼ 2.12, p ¼ .03, partial h2 ¼ 0.49. Follow-up univariate analysis indicated that PTC showed more Scheduling than other two clusters; more Evaluation than LC. Interestingly, EAC showed more Monitoring Group Process than other two clusters even decreased overall number of behaviors, F(2. 25) ¼ 6.57, p ¼ .01, partial h2 ¼ 0.35. MANOVA on the Milestone 8 revealed significant differences between clusters, Wilks' l ¼ .12, F(7, 20) ¼ 4.25, p ¼ .00, partial h2 ¼ 0.65. Follow-up univariate analysis indicated that LC showed more Strategy, Monitoring Group Process, and Group Agreement than PTC; EAC and LC showed more Task than PTC; EAC showed more Evaluation than other groups. To identify a pattern of effective collaborative learning, one might want to note the EAC's group regulation across the Milestones. In early phase (M2), EAC was very active in all group regulatory behaviors compared to other two clusters. A close examination of subcategories revealed that EAC paid significantly more attention on Monitoring Group Process (F(2, 25) ¼ 6.89, p ¼ .00, partial h2 ¼ 0.41), Group Agreement (F(2, 25) ¼ 8.75, p ¼ .00, partial h2 ¼ 0.41), Task (F(2, 25) ¼ 9.28, p ¼ .00, partial h2 ¼ 0.43) and Strategy (F(2, 25) ¼ 9.98, p ¼ .00, partial h2 ¼ 0.44) than others. The following quotes extracted from EAC's M2 discussion show how they regulated group work.

Fig. 2. Profiles of the clusters: group regulation (top) and social-emotional behaviors (bottom). Note: LC ¼ Late Collaborator, PTC ¼ Passive Task-oriented Collaborator, EAC ¼ Early Active Collaborator.

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Table 6 Average number of group regulation observed during the group project. Cluster Milestone 2 LC (n ¼ 11) PTC (n ¼ 13) EAC (n ¼ 4) Comparison Milestone 4 LC (n ¼ 11) PTC (n ¼ 13) EAC (n ¼ 4) Comparison Milestone 8 LC (n ¼ 11) PTC (n ¼ 13) EAC (n ¼ 4)

SCHDL

DV_LBR

TASK

STRTG

Opn_SLF

MONITOR

AGREE

EVAL

1 (1.26) 0.23 (0.44) 2 (1.41) EAC > PTC

0.73 (1.27) 0.15 (0.38) 2.5 (2.65) EAC > LC,PTC

1.55 (1.92) 0.46 (1.39) 6.25 (5.12) EAC > LC,PTC

1.09 (1.22) 0.23 (0.44) 4.25 (3.86) EAC > LC,PTC

0.82 (0.98) 0.77 (1.42) 1 (1.41)

1.27 (1.85) 0.92 (1.98) 7.25 (7.23) EAC > LC,PTC

2.64 (2.66) 0.62 (0.96) 7.25 (6.13) EAC > LC,PTC

0.91 (1.58) 0.15 (0.38) 3.75 (3.3) EAC > LC,PTC

0.73 (1.01) 2.62 (2.43) 0 (0) PTC > LC,EAC

0.18 (0.6) 0 (0) 0 (0)

1.36 (1.5) 0.77 (1.17) 2.25 (2.63)

1.36 (1.43) 0.92 (0.95) 1.25 (1.26)

0.73 (1.19) 1.31 (2.06) 1.5 (1.73)

0.91 (1.04) 1.69 (1.6) 4.25 (2.63) EAC > LC,PTC

1.09 (1.51) 1.54 (1.51) 2 (0.82)

0 (0) 1 (1.15) 0.75 (0.96) PTC > LC

1.64 (1.29) 1.46 (1.76) 0.25 (0.5)

0.73 (1.01) 0.15 (0.38) 0.5 (1)

2 (1.61) 0.38 (0.77) 2.5 (1.73) EAC, LC > PTC

2 (2.37) 0.46 (0.66) 1.75 (0.96) LC > PTC

1.09 (1.38) 0.62 (0.65) 0.5 (0.58)

3.73 (2.37) 1.23 (1.09) 1.75 (2.36) LC > PTC

4.36 (3.32) 1 (1.08) 2 (1.41) LC > PTC

0 (0) 0.15 (0.38) 1.75 (0.96) EAC > LC,PTC

Comparison

Note: Values enclosed in parentheses represent standard deviation. Comparisons between groups were conducted at the 0.05 alpha level. Index: SCHDL ¼ Scheduling, DV_LBR ¼ Dividing Labor, TASK ¼ Task, STRTG ¼ Strategy, Opn_SLF ¼ Open-self, MONITOR ¼ Monitoring Group Process, AGREE ¼ Group agreement, EVAL ¼ Evaluation.

 Monitoring group process: “When I was going through the outline to add Dr. Thomas James why section I noticed we totally left that section out of the outline.”; “Can we specify somewhere what all still needs to be done by who?”  Group agreement: “I would like for us all to be able to look over the final product and give a thumbs up reply before we agree that it's complete. What do you think?”; “Just realized as I was about to do [omitted]. Do you think it's okay for me to change that around?”  Task: “From what I am seeing, I (student1) need to add my part under “why”. Student2 also needs to add her part there.”  Strategy: “I think this would be so much easier if we were all sitting together and figuring it out.”; “I saved the old version onto a word document, in case we want to go back to that.” These group regulatory behaviors were expected to support EAC to establish group norm by sharing ideas on group process; to share group awareness by monitoring current group process; to identify group goals and tasks to do; to consider better strategies for effective collaboration especially at the beginning of collaboration. Contrasts to EAC, the other groups were not successful to regulate group work in their early collaboration phase and this negatively affected the next collaboration. The following quotes extracted from PTC and LC in M4 discussion imply conflict of group regulation which was not observed from EAC. “but in my defense I had no idea that the meeting was happening. I know it's my fault because I had to go 2e3 days without internet but I am sorry.” PTC 01

Table 7 Average number of socio-emotional behaviors observed during the group project. Cluster Milestone 2 LC (n ¼ 11) PTC (n ¼ 13) EAC (n ¼ 4) Comparison Milestone 4 LC (n ¼ 11) PTC (n ¼ 13) EAC (n ¼ 4) Comparison Milestone 8 LC (n ¼ 11) PTC (n ¼ 13) EAC (n ¼ 4) Comparison

EMOTION

ENCRG

COMMUNITY

1.73 (2.94) 0.46 (0.88) 3.75 (4.35)

0.64 (1.21) 0.15 (0.38) 2.25 (2.06) EAC > LC,PTC

0.18 (0.4) 0 (0) 2.75 (2.75) EAC > LC,PTC

1 (1.41) 1.31 (2.18) 1.25 (0.96)

0.18 (0.6) 0 (0) 1 (0) EAC > LC,PTC

0.09 (0.3) 0 (0) 1 (0.82) EAC > LC,PTC

0.82 (0.75) 0.62 (0.96) 0.25 (0.5)

1 (0.89) 0 (0) 0.5 (0.58) LC > PTC

0.09 (0.3) 0 (0) 0.5 (0.58) EAC > LC,PTC

Note: Values enclosed in parentheses represent standard deviation. Comparisons between groups were conducted at the 0.05 alpha level. Index: EMOTION ¼ Emotional Expression, ENCRG ¼ Encouragement, COMMUNITY ¼ Forming Sense of Community.

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“I did try to schedule meetings, and set things up, but like 3 people never really showed, never really did things till later.” PTC 02 “I think that we should have talked about fixing this before we went to the professor. We aren't in high school anymore, sometimes things come up.” PTC 03 “What is going on with the wiki? I have had no contact from anyone on this and it is due tomorrow.” LC 01 “I didn't see this until just before noon, but I got mine turned in” LC 02 EAC changed their focus accordingly as the group project progressed. In Milestone 4, they kept paying attention to monitoring their current group process, while other group regulatory behaviors required in the early phase, such as Group Agreement and Strategy, decreased. In the last phase (Milestone 8), they paid relatively more attention to checking requirements of the project and evaluating group products and group process by reflecting on their collaborative journey. The following quotes illustrate how EAC evaluate the group process at the end of the project. “Though I think we communicated very well along the way. When I filled out the survey I was thinking we had been emailing since we worked together so well. Lol” “First, I want to say working on a group project online is not an easy thing to do and I would also like to take the time to say, I believe I have had the greatest group ever!!” Another set of MANOVAs was conducted with three clusters as an independent variable and with the number of three subcategories of socio-emotional behaviors as dependent variables per each collaboration phase. The MANOVA on the Milestone 2 revealed significant differences between clusters, Wilks' l ¼ .44, F(2, 25) ¼ 3.91, p ¼ .00, partial h2 ¼ 0.34. Follow-up univariate analysis indicated that EAC showed more socio-emotional behaviors than other two clusters on Encouragement (F(2, 25) ¼ 5.80, p ¼ .01, partial h2 ¼ 0.32) and Forming Sense of Community (F(2, 25) ¼ 12.60, p ¼ .00, partial h2 ¼ 0.50). MANOVA on the Milestone 4 revealed significant differences between clusters, Wilks' l ¼ .31, F(2, 25) ¼ 6.06, p ¼ .00, partial h2 ¼ 0.44. Follow-up univariate analysis indicated that EAC kept showing more socio-emotional behaviors than other two clusters on Encouragement (F(2, 25) ¼ 10.58, p ¼ .00, partial h2 ¼ 0.46) and Forming Sense of Community (F(2, 25) ¼ 13.74, p ¼ .00, partial h2 ¼ 0.52). MANOVA on the Milestone 8 revealed significant differences between clusters, Wilks' l ¼ .43, F(2, 25) ¼ 4.03, p ¼ .00, partial h2 ¼ 0.35. Follow-up univariate analysis indicated that EAC showed more Forming Sense of Community (F(2, 25) ¼ 5.04, p ¼ .02, partial h2 ¼ 0.29) than other two clusters; LC showed more Encouragement (F(2, 25) ¼ 8.28, p ¼ .00, partial h2 ¼ 0.40) than PTC. The following quotes extracted from EAC show how they socially interacted with each other.  Encouragement: “Student 3 you are definitely putting in a lot of effort here!”; “What you put is great student 1!”  Forming sense of community: “Like I said, I'm thankful everyone fulfills their responsibility to the group and understands they aren't alone in the craziness of life!)”; “We definitely do have a great group! Even with our many other obligations!” The results clearly demonstrated that EAC encouraged group members continuously and formed a sense of community during the group project. In contrast to the group regulation, EAC kept relatively high proportion of encouragement and sense of community throughout the group project. 4.4. Perception on group process Students' evaluation on their collaboration was asked twice: after Milestone 2 and 8. Table 8 and Table 9 describe survey responses of each cluster. ANOVAs on Strategy and Communication revealed significant difference between clusters (Strategy: F(2, 25) ¼ 5.36, p ¼ .012, partial h2 ¼ 0.30; Communication: F(2, 22) ¼ 7.57, p ¼ .003, partial h2 ¼ 0.41). A Tukey post-hoc test indicated EAC gave higher scores on Strategy than PTC. EAC also more positively evaluated their collaboration on the Communication than LC and PTC. There were no statistically significant differences between clusters on the Authorship (p ¼ .55). The results revealed that EAC appraised effective ways to collaborate and communicated more frequently in an effective way at the early phase of the group project.

Table 8 Survey responses after Milestone 2.

Strategy Communication Authorship

LC

PTC

EAC

Comparison

4.41 (0.66), n ¼ 11 3.61 (0.90), n ¼ 11 3.85 (1.23), n ¼ 10

3.81 (0.93), n ¼ 13 3.88 (0.56), n ¼ 11 4.23 (1.42), n ¼ 13

5.38 (1.11), n ¼ 4 5.56 (0.96), n ¼ 3 3.38 (1.89), n ¼ 4

EAC > PTC EAC > LC, PTC

Note: The survey was responded on a 7-point Likert scale. 1: worst; 7: best. Values enclosed in parentheses represent standard deviation.

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Table 9 Survey responses after Milestone 8.

Positive group climate Positive interdependence Schedule

LC (n ¼ 9)

PTC (n ¼ 12)

EAC (n ¼ 4)

Comparisons

0.26 (0.88) 0.15 (1.43) 0.12 (1.39)

0.48 (0.96) 0.02 (0.68) 0.11 (0.64)

0.86 (0.68) 0.28 (0.8) 0.6 (0.87)

EAC > PTC

Note: Survey responses were converted into standardized scores after the PCA. Values enclosed in parentheses represent standard deviation.

After the group project, students' perception on their collaboration was examined. An exploratory factor analysis (EFA) with a varimax rotation was performed on the 10 items of the survey. A principle component analysis (PCA) was employed to extract factors. As a result three factors were identified: “positive group climate” (question 4~9), “positive interdependence” (question 1~3) and “schedule” (question10), which accounted for 83.15% of the variance. ANOVA on “positive group climate” revealed significant difference between clusters, F(2, 22) ¼ 3.92, p ¼ .035, partial h2 ¼ 0.26. A Tukey post-hoc test indicated EAC perceived higher positive group climate than PTC. There were no statistically significant differences between clusters on “positive interdependence” (p ¼ .78) and “schedule” (p ¼ .44). The results revealed that EAC felt members were reliable and friendly, and perceived they had good chemistry and shared accountability. 4.5. Group productivity Products of each Milestone graded by the instructor were analyzed to examine group productivities. Table 10 describes mean grades of clusters from three Milestones. For being graded in different scale originally, grades of each Milestone were rescaled into 10 points in total to be compared with each other. Because the values of EAC violated normality and equality of variances with other groups, statistical analysis on the group productivity was not conducted. However, as Table 10 illustrated, EAC got perfect grades except for on Milestone 4. The instructor used the same grade result as a proxy for shared accountability based on a number of observations, which seemed to be supported by the group wiki data. The wiki feature allows the instructor to view individual student contributions to the wiki, specifically word count contributed by each student, number of contributions and number of times logged on by each student, which enables the instructor to compare students' individual activity, and take this into account when grading the assignments. In cases where the uneven distribution was obvious, the instructor gave the lagging students different grades than the larger group, along with a message warning the student to consider their contributions in future assignments. While the penalties for weaker participation varied, and were rarely (if ever) as harsh as giving zero points to a student, the observed pattern of individual grades separate from the group grade can be understood as evidence of a failed group process. In the EAC group, the group process worked well enough to keep each member engaged, challenged, and committed throughout the course. For example, during the discussion with group members on selecting the scenario topic, one of the EAC group members was able speak about her personal experience and decision-making about the subject of the clinical ethics assignment. Because her group's encouragement to speak reflect on her experience and link it to theories learned in the course, she felt that she was able to translate her personal experiences into a realisticsounding contribution to the assignment; latter, she stated that the group experience was “therapeutic for me in many ways!”. Individual contribution to collaboration measured by the number of messages supported the notion and excluded the possibility that a few very dedicated members achieve the result (numbers of messages posted by each student were 13, 20, 29, and 31; mean ¼ 23.25, SD ¼ 8.34). With the other groups, the group process was much more fractured and resulted in irregular patterns of contributions and poorer product (mean score of final project by LC, PTC and EAC respectively were 7.75, 7.92 and 10). 5. Discussion and conclusion The main findings of the study can be summarized as follows. In an online group collaboration setting where minimal instructor's direct intervention was given, we could identify three different collaboration patterns in consideration of collaborative action types and timing. The most desirable collaboration pattern, which we called Early Active Collaborator (EAC), was demonstrated by one group which had intensive interactions among group members in the early collaboration phase. This group demonstrated socio-emotional interactions continuously, while it scaled down group coordination activities gradually. Regrettably, most groups fell into ill-advised collaboration patterns: (1) showed dormant interactions throughout the projects and least socio-emotional interactions, Passive Task-oriented Collaborator (PTC); (2) rushed at the end of the project without a clear group coordination plan, Late Collaborator (LC). It is valuable to examine the desirable collaboration pattern compared to the ill-advised ones from a multi-dimensional perspective. 5.1. Different type of group regulation required in different collaboration phases While examining EAC's group regulatory actions, one may realize that their focus of group coordination changed as group project developed. In the early group collaboration phase, they spent more than 70% of their effort on the following actions: monitoring group Table 10 Grades of the group project.

Milestone 2 Milestone 4 Milestone 8

LC (n ¼ 11)

PTC (n ¼ 13)

EAC (n ¼ 4)

8.17 (0.72) 9.25 (0.26) 7.75 (0.79)

8.92 (1.02) 9.62 (0.51) 7.92 (0.76)

10.00 (0) 9.00 (0) 10.00 (0)

Note: Values enclosed in parentheses represent standard deviation.

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process, seeking group agreement, identifying tasks and goals, and considering better strategies. Once these activities were completed and a group norm was established, considerably less effort was needed for these in later phase of the collaboration. In contrast, the LC did not participate in these actions at the early collaboration phase; instead they adopted them towards the end of the project. Additionally, there were a small number of incidences where the EAC also paid attention to setting a schedule and a responsibility for each member. These group regulations were observed from the EAC significantly higher than other groups only from Milestone 2, but not for other Milestones. This finding suggests that certain type of regulatory actions should take place in the early collaboration phase to ensure a successful collaboration. Setting a schedule and individual accountability was time-sensitive behavior in that it was indispensable to plan collaboration and to set up group norms at the beginning of the group project. A high proportion of monitoring group processes was observed continuously from the EAC. They regularly shared what portions of the assignment that they had completed and identified what was left to be done. While they did not have direct feedback or a guide about monitoring group process from the instructor, they tried to maintain group awareness by sharing the individual member's progress with all other members. It was clear that the autonomous group awareness action enhanced efficiency of group coordination and reduced noise of collaboration. At the end of the project, the unique group regulation observed only with the EAC and not with the LC was evaluation of group product and group process. It was significant that, while the LC paid attention to other metacognitive actions, which were usually required in the early collaboration phase, the EAC conducted group evaluation at the right time. The evaluation was positive in general and detailed in assessing each individual's efforts and group processes. This participation in ongoing in-group evaluations reveals that the EAC kept in mind the group reflection process while simultaneously monitoring group processes. Based on the results, one can say that EAC's group coordination was strategic, scheduled in advance, and monitored in group. 5.2. Socio-emotional interaction, fruit of good collaboration The study clearly illustrated that most successful group (EAC) encouraged team members constantly and formed sense of community by sharing personal issues and developing social bonding. While conducting this analysis, the researchers questioned why (or how) good collaboration could result in better perception on collaboration and group productivity. The concepts of social interdependence theory and social capital might help explain why the most successful groups were able to create environments where individual accountability, trust, and sense of community flourished. Social capital refers to “features of social organization such as networks, norms, and social trust that facilitate coordination and cooperation for mutual benefit” (Putnam, 1995, p. 67). Francis Fukuyama (1996) states that social capital develops from trust. As stated earlier, trust is also integral in forming a sense of community. So, as the successful group created group norms and positive habits during the group regulation processes, social capital emerged. The World Bank further identifies elements of social capital that are useful in understanding how positive socio-emotional collaboration is connected to productive assignment collaboration (The World Bank, n.d.). Those elements are: groups and networks; trust and solidarity; collective action and cooperation; social cohesion and inclusion; and information and communication. Two of the elements, in particular, trust and solidarity (the elements that “foster greater cohesion and more robust collective action”) and collective action and cooperation (which looks at the “ability of people to work together toward resolving communal issues”), are elements demonstrably embraced by the successful group (EAC). The achieved social capital creates the “we're all in this together” rhetoric that appears to be influential to the outcomes for the group. Connected to the social capital, is the concept of social interdependence. Developed out of the field of educational psychology, the theory of social interdependence uses socio-emotional behaviors to connect sociological theories such as social capital and educational theories such as group regulation. Johnson et al. (2007) state “Social interdependence exists when the accomplishment of each individual's goals is affected by the actions of others” (p. 16). They go further to explain the role of psychological elements to the idea of social interdependence. The most relevant psychological element is promotive interaction; in the EAC group we see examples of it, which Johnson and his colleagues define as: “individuals encouraging and facilitating each other's efforts to complete tasks, achieve, or produce in order to reach the group's goals. It consists of a number of variables, including mutual help and assistance, exchange of needed resources, effective communication, mutual influence, trust, and constructive management of conflict.” (p. 17). The other side of this coin is oppositional interaction, which contains elements discouragement and obstruction (Johnson et al., 2007). While there did not appear to be direct and intentional obstruction of others in the LC and PTC group, being a poor communicator, or completing a task at the very last minute (so that it cannot be reviewed by all group members), was indirectly obstructive to the collaboration process and destructive to the sense of trust needed for social capital. These two destructive occurrences were regular topics of the later semester emails from the groups and a sign that group regulation had failed. In summary, while it is not completely clear whether good social capital, promotive interaction, and social interdependence are products of or elements of good collaboration, it is quite clear that when researchers reflect on the in-group and student-instructor communications, the development of good socio-emotional interactions is shaped by shared accountability and well-organized group regulation. The LC and PTC group's poor group regulation have created obstacles for developing group trust and have prevented the group from establishing social capital, which produced bad fruits and left a bad taste in the mouths of the students. 5.3. Instructor intervention In this study, the instructor chose not to read group discussion boards to determine progress or to intervene with problems. Instead, the instructor offered detailed feedback to groups and individuals after the submission of an assignment, and provided advice and feedback when requested. In order to establish group interdependence, in the beginning of the course, the instructor emphasized that individual grades for the project were influenced by the performance of the entire group. She also encouraged groups to begin early with organizing roles and responsibilities for completing the tasks of the assignments. Therefore, one can assume that students established goal interdependence and received initial guidance for collaboration.

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Instructor's intervention was elicited by students' request as the project progressed. In most cases, students contacted the instructor (usually via email) and requested clarification on the assignment, and in a few cases, requested her to read drafts of papers. Although the researchers did not count the frequency of instructor's interactions, the instructor confirmed that the most frequent interaction with students was “cognitive activity” related to task content and this was consistent with Van Leeuwen et al.'s (2013) findings that revealed instructor interventions during CSCL. It revealed that students did not request instructor's intervention when they did not encounter a group regulation problem rather they tended to rely on instructor for clarifying course contents or assignments. As expected, the frequency and level of detail of feedback decreased as the semester progressed and the students became more comfortable with the project, except the LC group. LC requested instructor's intervention when they faced group regulation issues. Some of their requests for clarifications and complaints about disruptive collaboration came later in the semester and near the due dates of assignments, signaling a breakdown in the group processes. It is also noteworthy that during the first half of collaboration period, LC not only exhibited less-than-average group regulatory actions, their socio-emotional actions were also greatly less than average, which gave LC group members less opportunity to form interpersonal relationships, to establish a group norms and to set individual responsibility. LC's later complaints about disruptive collaboration affirmed the importance of early instructor intervention on group regulation. The needs of instructor's intervention varied, based on group coordination competency and timing of intervention. While LC requested instructor's direct intervention on their group regulation, EAC asked only clarification on assignment (cognitive activities). In the early collaboration phase, EAC established a group norm and trust through well-organized group regulation, so they needed a minimal intervention from the instructor. However, in LC's case, as the researchers discussed, they required instructor intervention on group regulation as well as socio-emotional aspect at the end of the collaboration. Van Leeuwen et al. (2013) also found that instructor's intervention types varied at different time and between groups. In both studies, many instructor interventions were reactions to students' request. It meant instructors could be flexible to students' collaboration situation and could give tailored feedback. However, for the afterward intervention, like a prescription after death, the instructor might lose a chance to guide students to be more strategic on collaboration. Teacher presence in the early collaboration phase might encourage positive aspects of the collaboration process (Molenaar, Roda, van Boxtel, & Sleegers, 2012) and reduce negative aspects, such as lack of communication or low level of individual accountability (Tseng & Yeh, 2013). 5.4. Implications As highlighted in the study, autonomous strategic group regulation and continuous socio-emotional interactions are crucial for successful collaborative learning in an online learning environment. The observed behavioral patterns between group regulation and socioemotional interactions advances understanding of human interactions in the CSCL: while all of the groups perceived positive interdependence in the group project, in the end, only one group was able to successfully regulate group process and establish a sense of community based on mutual trust. Many other groups did (or could) not initiate strategic collaboration and failed to ensure individual accountability, which resulted in broken trust. Shared group regulatory actions, such as dividing labor, identifying tasks, scheduling and monitoring group process ensures, or at least enhances, individual accountability as well as supporting behaviors; and this establishes mutual trust (e.g., Fransen, Kirschner, & Erkens, 2011). The result reveals that sharing positive interdependence among group members is necessary, but not sufficient for successful online collaboration. For students inexperienced in group collaboration, they may believe that they will achieve a common goal (i.e., completion of an assignment) merely through positive interdependence (or, in lay terms, a “we're all in this together” approach). They may have attributed early group successes to “good chemistry”, without a clear understanding of how to ensure success with each new group. While early establishment of a sense of community is key, it alone may not produce the desired result. Successful groups exhibit both the traits of positive interdependence and the regulation of group work. Therefore, it is vital that students equip themselves with the strategies needed to develop group regulation, and use those strategies early in their group interactions. The results of the study also suggest practical implications for instructors. First, instructors can consider instilling in the students the importance of group awareness from the very start of the course. For instance, instructors might require that the students fill out the “getting to know you” and “times unavailable” forms (Oakley, Felder, Brent, & Elhajj, 2004). These forms will allow instructors to first introduce the broad importance of group awareness, by highlighting the diversity of schedules among its members. Additionally, the “getting to know you” form may allow students to find common ground (through shared hobbies, interests, and experiences) with which they can develop positive, non-assignment social interactions. Secondly, group collaboration is more than merely schedule coordination. With this in mind, instructors can help the groups regulate members' efforts by providing broad frameworks with which students need to follow to complete their assignments correctly. In the Oakley et al. (2004) article, the authors introduce the “team policies” form, which outlines the responsibilities and roles needed for successful group completion of an assignment. Similarly, Shank (2011) suggests developing team agreements that allow instructors to confirm that all team members have an understanding of their requirements. Shank allows for instructors to help groups refine and provide feedback for groups in their efforts to make these agreements. Once the class begins, it will be quite difficult for instructors to monitor all of the events in the group. However, if at the beginning of the course, they take the preventative approach to group work by introducing policies and agreements, they may find that they will have fewer times during the semester where they finds themselves needing to help groups repair broken trust and re-distribute uneven workloads. The early introduction of these coordination processes will allow the instructor to provide scaffolding for good group collaboration. This scaffolding is particularly important for students who are new to (online or in-person) group work. The early introduction of these assignments also allows the instructor a view into potential group conflictsdif there are groups who struggle to complete these more simple assignments, the instructor may be able to provide other tools to assist the groups, perhaps a synchronous virtual group meeting where the instructor helps facilitate the process. Once the class gets underway, the instructor's focus must change. The instructor can be responsive to student's requests, but more than likely, it will largely focus on the elaboration of content or on a check of task completion rather than support for quality collaboration or evaluation on group process (Van Leeuwen et al., 2013). Enhancing group awareness can be a good practical alternative to direct instructor intervention. Phielix et al. (2011), for example, implemented a peer feedback tool that provided a visualization of group cognitive and social behaviors rated by students and a reflection tool that simulated them to co-reflect on their group process. The results of the study revealed

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that students who used these tools in the early phases of collaboration affected their social and cognitive behaviors. The positive effects were shown by the students “having more influence, being friendlier, more cooperative, more productive and making contributions of higher quality (p. 1099e1100)”. As suggested by the study, providing opportunities to monitor/evaluate group process can encourage autonomous group regulation and socio-emotional interactions. As mentioned in the introduction, interdisciplinary interaction and group work are vital to the health care field. The health sciences classroom offers an excellent opportunity for the instructor to help students develop the skills necessary to adapt to the changing health work force. 6. Limitation The study has several limitations. First the number of participants was limited to the small numbers of students. The study included only 28 students from 6 groups. However, their group dynamic was observed from discussion forums consisted of 973 idea units, considerably large amount of data to validate findings. In addition, multiple measurements: surveys on group collaboration and course grades were included in the analysis of group dynamics, which triangulated data sources and enhanced credibility (Webb, Campbell, Schwartz, & Sechrest, 1966). Even so, a strength of the study is a holistic capture of students' natural interactions developed over a semester. While the study is limited in size (in terms of student enrollment), it advances the field with its longitudinal approach by shifting the field of study from the “snapshot” approach to understanding interactions, to a longer term, almost ethnographic approach to understanding the development of socio-emotional and cognitive aspects of interactions among students. Second, collaboration setting was also limited to asynchronous communication such as online discussion and wiki. Students would select different communication strategies based on available resources. They, for example, might show different collaboration patterns if they had alternative synchronous communication tools such as audio chat or desktop-sharing. As group dynamic is in flux and many extraneous variables, such as group size, characteristics of members, task, communication tools, and collaboration period, affect students' interactions, it is difficult to oversee actual feature of online collaboration in one time. Although the study ensured validity of results by conducting content analysis on the quite amount of discussion and triangulating through two surveys and course grades, more participants, focus group interview and observation from various collaboration settings are required for future studies. Third, interpretation of the first survey responses needs to be done with caution due to the low reliability although their face validity was ensured. One also needs to consider the gender imbalance (89% of female) of the sample. Men and women may have different interaction patterns in an online collaboration setting (e.g., Large, Beheshti, & Rahman, 2002; Rovai & Baker, 2005). 7. Conclusion From this study, ideal collaborative interaction patterns were extracted from a successful group: intensive interactions in an early collaboration phase, timely selected group regulatory behaviors, and establishment of sense of community. Regretfully, most groups did not exhibit these behaviors as expected. The results support the argument that students (even college students) are not well cultivated to be effective collaborators and they may suffer from collaboration by simply exposed to or forced to collaborative learning (Kreijns et al., 2003; Salomon & Globerson, 1989). The findings propose intensive instructor guides in an early collaboration phase and scaffolding on timesensitive collaborative behaviors for successful group regulation. Building positive group climate, trust, and sense of community will be possible based on the sound group regulation. Further research would be requested on ways to facilitate collaborative behaviors while encouraging students' autonomy and ways to cultivate high level of competence in collaboration. Appendix 1. Survey questionnaire Survey in an early collaboration Constructs

Item description

Strategy

Q1. In the very beginning, we communicated about who will work on what. Q2. As a group, we have come up with strategies on how to make the collaboration more smoothly. Q3*. I wish that our group would have communicated more frequently. Q4. I feel that our communication is effective in moving the group towards project outcomes. Q5. I feel that the use of group discussion forum helps every group member get on the same page. Q6. I feel that I have the ownership of the whole writing process/written product. Q7. I feel that it is my responsibility to ensure the quality of the whole wiki writing.

Communication

Authorship

Note. * values were reversed.Survey at the end of collaboration Constructs

Item description

Interdependence

Q1. I felt that we depended on each other while working on this wiki project. Q2. I felt that our group members relied on each other in accomplishing the group project. Q3. I felt that I had to work collaboratively with my group members to complete this wiki project. Q4. I felt that all of the group members felt equally responsible for completing this project. Q5. Because I felt as if my group members were reliable and easy to get along with, I felt less stress and anxiety about working in a group. Q6. I felt as if the people in my group have good chemistry and we were friendly with each other. Q7. I felt overall our group cooperated well in this project. Q8. I didn't feel comfortable asking my fellow group members a question. Q9. I felt that our group divided up tasks among the members. Q10. I felt that our group members set up reasonable timelines to complete the tasks.

Group climate

Scheduling

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Appendix 2. Summary of clinical ethics scenario Students outline the medical issue covered an ADHD diagnosis in an elementary-aged child. Diagnosis comes after behavioral issues in school. Ethical dilemma: The parent can only make one choice among several potential options (all of them are ethical). Mother of child is deciding if she will chose medication to treat the child's ADHD. Mother wants behavioral issues to stop, but are concerned about the potential side effects of medication (loss of appetite, weight loss, insomnia, headaches, stomach aches, dizziness, depression and mood changes). Scenario scenes: Mother has conference with child's teacher to discuss options and parents are referred to pediatrician. Mother has meeting with pediatrician, who has done additional assessments on child. Pediatrician suggests combination therapy and medication. Next scene, mother is meeting with therapist. Students outline the possible outcomes from various choicesddo nothing, do therapy alone, do medication alone, do combinationdand their consequences. Students describe the choice the mother makes, and then outline the decisionmaking approachesd“utilitarian” and “deontological”. Students discuss the ethical principles needed to be upheld by the care providers (therapist and pediatrician): “veracity”. Teacher also must abide by the “veracity” approach. Students then wrote a commentary that explained the history of the scenario family; outlining personal beliefs and social determinants of health that have brought the family to this crossroads. These details helped to influence the decision upon which the group ultimately decided. Note: The summary of clinical ethic scenario is provided to provide readers with more detail of the projects. The scenario was developed by the Early Active Collaborator (group 5) and the course instructor summarized it. References Barron, B. (2003). When smart groups fail. Journal of the Learning Sciences, 12(3), 307e359. Bodemer, D. (2011). Tacit guidance for collaborative multimedia learning. Computers in Human Behavior, 27(3), 1079e1086. Brewer, S., & Klein, J. (2006). Type of positive interdependence and affiliation motive in an asynchronous, collaborative learning environment. Educational Technology Research & Development, 54(4), 331e354. Bulu, S. T. (2012). Place presence, social presence, co-presence, and satisfaction in virtual worlds. Computers & Education, 58(1), 154e161. Chi, M. T. (1997). Quantifying qualitative analyses of verbal data: a practical guide. The Journal of the Learning Sciences, 6(3), 271e315. Cohen, E. (1994). Restructuring the classroom: conditions for productive small groups. Review of Educational Research, 64(1), 1e35. € ffel, B., Van der Meijden, H., Staarman, J. K., & Janssen, J. (2005). Regulative processes in individual, 3D and computer supported cooperative learning contexts. De Jong, F., Kollo Computers in Human Behavior, 21(4), 645e670. Dehler, J., Bodemer, D., Buder, J., & Hesse, F. W. (2011). Guiding knowledge communication in CSCL via group knowledge awareness. Computers in Human Behavior, 27(3), 1068e1078. Dochy, F., Segers, M., & Sluijsmans, D. (1999). The use of self-, peer and co-assessment in higher education: a review. Studies in Higher Education, 24(3), 331e350. Dufner, D. K., Park, Y.-T., Kwon, O., & Peng, Q. (2002). Asynchronous team support: perceptions of the group problem solving process when using a cybercollaboratory. Paper presented at the System Sciences, 2002. HICSS. In Proceedings of the 35th Annual Hawaii International Conference on. Erkens, G., Jaspers, J., Prangsma, M., & Kanselaar, G. (2005). Coordination processes in computer supported collaborative writing. Computers in Human Behavior, 21(3), 463e486. Flower, L., & Hayes, J. R. (1981). A cognitive process theory of writing. College Composition and Communication, 32(4), 365e387. Fransen, J., Kirschner, P. A., & Erkens, G. (2011). Mediating team effectiveness in the context of collaborative learning: the importance of team and task awareness. Computers in Human Behavior, 27(3), 1103e1113. Fukuyama, F. (1996). Trust: The social virtues and the creation of prosperity. New York, N.Y: Simon and Schuster. Graneheim, U. H., & Lundman, B. (2004). Qualitative content analysis in nursing research: concepts, procedures and measures to achieve trustworthiness. Nurse Education Today, 24(2), 105e112. Gunawardena, C. N. (1995). Social presence theory and implications for interaction and collaborative learning in computer conferences. International Journal of Educational Telecommunications, 1(2/3), 147e166. Gunawardena, C. N., Lowe, C. A., & Anderson, T. (1997). Analysis of a global online debate and the development of an interaction analysis model for examining social construction of knowledge in computer conferencing. Journal of Educational Computing Research, 17(4), 397e431. Institute of Medicine. (2001). Crossing the quality chasm: A new health system for the 21st century. Washington, DC: The National Academies Press. Janssen, J., Erkens, G., Kirschner, P. A., & Kanselaar, G. (2009). Influence of group member familiarity on online collaborative learning. Computers in Human Behavior, 25(1), 161e170. Janssen, J., Erkens, G., Kirschner, P. A., & Kanselaar, G. (2012). Task-related and social regulation during online collaborative learning. Metacognition and Learning, 7(1), 25e43. Johnson, D., Johnson, R., & Smith, K. (2007). The state of cooperative learning in postsecondary and professional settings. Educational Psychology Review, 19(1), 15e29. Johnson, S. D., Suriya, C., Yoon, S. W., Berrett, J. V., & La Fleur, J. (2002). Team development and group processes of virtual learning teams. Computers & Education, 39(4), 379e393. Karau, S., & Williams, K. (1993). Social loafing: a meta-analytic review and theoretical integration. Journal of Personality and Social Psychology, 65(4), 681e706. Kerr, N. (1983). Motivation losses in small groups: a social dilemma analysis. Journal of Personality and Social Psychology, 45(4), 819e828. Kerr, N., & Bruun, S. (1983). Dispensability of member effort and group motivation losses: free-rider effects. Journal of Personality and Social Psychology, 44(1), 78e94. Kirschner, P. A., Beers, P. J., Boshuizen, H. P. A., & Gijselaers, W. H. (2008). Coercing shared knowledge in collaborative learning environments. Computers in Human Behavior, 24(2), 403e420. Kreijns, K., Kirschner, P. A., & Jochems, W. (2003). Identifying the pitfalls for social interaction in computer-supported collaborative learning environments: a review of the research. Computers in Human Behavior, 19(3), 335e353. Kreijns, K., Kirschner, P. A., Jochems, W., & Van Buuren, H. (2004). Determining sociability, social space, and social presence in (A)synchronous collaborative groups. CyberPsychology & Behavior, 7(2), 155e172. Kruger, A. C. (1993). Peer collaboration: conflict, cooperation, or both? Social Development, 2(3), 165e182. Kwon, K., Hong, R., & Laffey, J. M. (2013). The educational impact of metacognitive group coordination in computer-supported collaborative learning. Computers in Human Behavior, 29(4), 1271e1281. Lajoie, S. P., & Lu, J. (2012). Supporting collaboration with technology: does shared cognition lead to co-regulation in medicine? Metacognition and Learning, 7(1), 45e62. Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159e174. Large, A., Beheshti, J., & Rahman, T. (2002). Gender differences in collaborative web searching behavior: an elementary school study. Information Processing & Management, 38(3), 427e443. , B., Williams, K., & Harkins, S. (1979). Many hands make light the work: the causes and consequences of social loafing. Journal of Personality and Social Psychology, 37(6), Latane 822e832. Liaw, S., & Huang, H. (2000). Enhancing interactivity in web-based instruction: a review of the literature. Educational Technology, 40(3), 41e45. McLeod, P. L., Liker, J. K., & Lobel, S. A. (1992). Process feedback in task groups: an application of goal setting. The Journal of Applied Behavioral Science, 28(1), 15e41. McMillan, D. W. (1996). Sense of community. Journal of Community Psychology, 24(4), 315e325. McMillan, D. W., & Chavis, D. M. (1986). Sense of community: a definition and theory. Journal of Community Psychology, 14(1), 6e23. Molenaar, I., Roda, C., van Boxtel, C., & Sleegers, P. (2012). Dynamic scaffolding of socially regulated learning in a computer-based learning environment. Computers & Education, 59(2), 515e523.

200

K. Kwon et al. / Computers & Education 78 (2014) 185e200

Oakley, B., Felder, R. M., Brent, R., & Elhajj, I. (2004). Turning student groups into effective teams. Journal of Student Centered Learning, 2(1), 9e34. Phielix, C., Prins, F. J., & Kirschner, P. A. (2010). Awareness of group performance in a CSCL-environment: effects of peer feedback and reflection. Computers in Human Behavior, 26(2), 151e161. Phielix, C., Prins, F. J., Kirschner, P. A., Erkens, G., & Jaspers, J. (2011). Group awareness of social and cognitive performance in a CSCL environment: effects of a peer feedback and reflection tool. Computers in Human Behavior, 27(3), 1087e1102. Puntambekar, S. (2006). Analyzing collaborative interactions: divergence, shared understanding and construction of knowledge. Computers & Education, 47(3), 332e351. Purtilo, R. B., & Doherty, R. (2010). Ethical dimensions in the health professions (5 ed.). St. Louis, MO: Saunders. Putnam, R. D. (1995). Bowling alone: America's declining social capital. Journal of Democracy, 6(1), 65e78. Richardson, J., & Swan, K. (2003). Examining social presence in online courses in relation to students?perceived learning and satisfaction. Journal of Asynchronous Learning Networks, 7(1), 68e88. Rovai, A. P., & Baker, J. D. (2005). Gender differences in online learning. Quarterly Review of Distance Education, 6(1), 31e44. Saab, N. (2012). Team regulation, regulation of social activities or co-regulation: different labels for effective regulation of learning in CSCL. Metacognition and Learning, 7(1), 1e6. Salomon, G., & Globerson, T. (1989). When teams do not function the way they ought to? International Journal of Educational Research, 13(1), 89e99. Sangin, M., Molinari, G., Nüssli, M.-A., & Dillenbourg, P. (2011). Facilitating peer knowledge modeling: effects of a knowledge awareness tool on collaborative learning outcomes and processes. Computers in Human Behavior, 27(3), 1059e1067. Schmitt, N. (1996). Uses and abuses of coefficient alpha. Psychological Assessment, 8(4), 350e353. Schonfeld, T. L., & Spetman, M. K. (2007). Ethics education for allied health students: an evaluation of student performance. Journal of Allied Health, 36(2), 77e80. Shank, P. (2011). The online learning idea book : 95 proven ways to enhance technology-based and blended learning. San Francisco: Pfeiffer. So, H., & Brush, T. (2008). Student perceptions of collaborative learning, social presence and satisfaction in a blended learning environment: relationships and critical factors. Computers & Education, 51(1), 318e336. Solomon, P., & Salfi, J. (2011). Evaluation of an interprofessional education communication skills initiative. Education for Health, 24(2), 616. The World Bank. (n.d.). Measuring the Dimensions of social capital. Retrieved 6/17/2014 from: http://go.worldbank.org/TC9QT67HG0. Tseng, H. W., & Yeh, H.-T. (2013). Team members' perceptions of online teamwork learning experiences and building teamwork trust: a qualitative study. Computers & Education, 63, 1e9. van der Meijden, H., & Veenman, S. (2005). Face-to-face versus computer-mediated communication in a primary school setting. Computers in Human Behavior, 21(5), 831e859. Van Leeuwen, A., Janssen, J., Erkens, G., & Brekelmans, M. (2013). Teacher interventions in a synchronous, co-located CSCL setting: analyzing focus, means, and temporality. Computers in Human Behavior, 29(4), 1377e1386. Webb, E. J., Campbell, D. T., Schwartz, R. D., & Sechrest, L. (1966). Unobtrusive measures: Nonreactive research in the social sciences. Chicago: Rand McNally. Wegerif, R. (1998). The social dimension of asynchronous learning networks. Journal of Asynchronous Learning Networks, 2(1), 34e49. Xu, J., Du, J., & Fan, X. (2013). “Finding our time”: predicting students' time management in online collaborative groupwork. Computers & Education, 69, 139e147. Zimmerman, B. J., & Schunk, D. H. (2001). Self-regulated learning and academic achievement: Theoretical perspectives (2nd ed.). Mahwah, NJ: Erlbaum.