Exploration of Factors in the Early Collaboration Phase Affecting Virtual Groups’ Overall Collaborative Learning Experiences

Abstract

Collaboration skills have become a critical competency for health care providers. This study aims to understand how individual student characteristics and group process in the early phase of group development are associated with students’ perception of their overall collaborative learning experience. The study was conducted in two consecutive semesters with a total of 86 health sciences undergraduate students participating. The results show that quality communication in the beginning phase of group development and health-care work experiences are two major factors associated with students’ perception of overall collaborative learning. The findings confirm the importance of the initial phase of collaboration. Faculty should deliberately design collaborative learning experiences that offer orientation in enhancing the interactions among group members and allow quality communication to proliferate.

Keywords

computer-supported collaborative learning quality communication health care education individual characteristics virtual group development

Introduction

There is a dramatic demographic change afoot in the United States. According to the Centers for Disease Control and Prevention’s report, The State of Aging & Health in America in 2013, multiple factors will create urgent demands on the numbers and the level of complexity of health care teams. First, “[t]wo factors - longer life spans and aging baby boomers - will combine to double the population of Americans aged 65 years or older during the next 25 years to about 2 million. By 2030, older adults will account for roughly 20% of the U.S. population” and “[m]ore than a quarter of all Americans and two out of every three older Americans have multiple chronic conditions, and treatment for this population accounts for 66% of the country’s health care budget” (Centers for Disease Control and Prevention, 2013, p. ii). These, and other demographic changes, have helped to encourage the growth of the American health care industry; and the industry, focusing more on “whole patient” care, has embraced interprofessional collaboration approach which connect professionals from various disciplinary backgrounds to address patient’s needs and provide better health care experiences. Research has shown that patients who receive health care from a health care system without a culture of collaboration tend to have a higher mortality rate (Kohn, Corrigan, & Donaldson, 2000). Interdisciplinary collaboration is essential to patient care, improves patients’ health outcomes, and reduces hospital stays, costs, and readmission rates (Baggs, Schmitt, Mushlin, Eldredge, Oakes, & Hutson, 1997; Curley, McEachern, & Speroff, 1998). While not all health care sites have fully adopted the interdisciplinary team and “whole person” approach to health care, the trend is gaining momentum, as the benefits to patients are more fully understood (Hutchinson, 2011; Interprofessional Education Collaborative Expert Panel, 2011).

Taking these trends into account, health sciences or prehealth professional education has incorporated an online learning component to the traditional face-to-face course delivery options. To respond to the trend, collaborative skill has been one of the core competencies in health care education (Verma et al., 2009). Instructors have also integrated collaborative learning into daily teaching practice, in order to cultivate future health care professionals with collaboration skills and to prepare a health care workforce capable of adapting and exceling in interdisciplinary teams (Fadel & Trilling, 2012; Roekel, n.d.; Ruiz, Mintzer, & Leipzig, 2006). These online courses allow busy health science students to complete educational requirements at their convenience and to engage in rich discussion and learning with students from diverse backgrounds. The collaborative experiences not only mirror the interactions that health science students will face in the workplace but also allow students to understand and appreciate the roles and responsibilities of the other health professions on the team, to learn team functionality techniques, and to develop interpersonal communication skills (Schuetz, Mann, & Everett, 2010).

Although students are embracing these new and exciting changes to the health care field, the most effective methods for delivering this education are still being developed, and instructors are finding that there are bumps in the road (Bromage, Clouder, Thistelwaite, & Gordon, 2010). For instance, students can face collaboration issues that break trust and result in ineffective learning (Kreijns, Kirschner, & Jochems, 2003; Näykki, Järvelä, Kirschner, & Järvenoja, 2014; Tseng & Yeh, 2013). To design online courses that fulfill professional requirements and support students’ collaborative learning, faculty working in the health sciences need to understand the characteristics of collaborative learning, as well as collaborative behaviors in online learning environments. Unfortunately, there is little research examining students’ collaborative behaviors within the health care education settings. Most educational studies within the health care context investigate instructional strategies such as interactivity, discussion, feedback, case methods, simulation and animation, and so on (Cook, Levinson, Garside, Dupras, Erwin, & Montori, 2010) and collaborative learning received relatively less attention.

Benefitting from the legacy of research on collaborative learning, research on virtual groups and computer-supported collaborative learning (CSCL) has been thriving since the 1990s. These studies have helped educators and administrators understand how group development, collaborative behaviors, and other factors may influence interaction among group members. Research has shown that the collaboration process involves several phases (Johnson, Suriya, Yoon, Berrett, & LaFleur, 2002; Kwon, Liu, & Johnson, 2014; Tuckman, 1965). In addition, Kwon et al.’s (2014) previous study found that certain behaviors, such as group regulative behaviors, are critical and should be demonstrated in a certain phase in order for the collaboration to proceed smoothly. However, questions remained on how other factors and processes can impact the collaboration process, as learners’ experience in health care work, background in online learning, and the impact of early group process on overall collaboration behaviors had not been explored. Guided by the findings of previous studies, and aiming to contribute to literature that helps educators design more effective collaborative learning experiences, this study focused on developing a deeper understanding of online learning and collaboration. This study aims to answer the following questions:

Are there differences between groups in terms of individual work experience, online learning experience, and group process in early collaboration?

How were the individuals’ work experience, online learning experience, and the group process in the early collaboration phase associated with student perceptions of the overall group collaboration?

The following section covers research focusing on factors that influence group members’ collaborative learning experiences. Although faculty support (Oakley, Felder, Brent, & Elhajj, 2004) and the type of tasks (Cohen, 1994; Hirokawa, 1990) play a critical role in engaging students in group collaboration, this review covers the individual factors and group process. The terms team and group are used interchangeably and represent the same concept in this study.

Literature Review

The Significance of Collaborative Skills

Collaborative learning has been a widely accepted teaching practice for decades in the professional education (Billings & Halstead, 2015; Neville, 2008; Ruiz et al., 2006). With the advancement of technology, using information technology to facilitate communication and enhance student learning has been a popular practice since the early 1990s. From early applications, such as e-mail and text-based instant messenger to the more recently developed web video-conferencing applications such as Skype, Adobe Connect, GoToMeeting, and Blackboard Collaborate, these applications allow users to communicate via text, audio, or video without the geographic constraints. Although these applications were initially created for personal or entertainment purposes, because of their potential, those working in the corporate and education fields quickly adapted these forms of computer-mediated communication to improve productivity and accessibility. Similarly, educators also saw the potential of these tools not just for enhancing communication and collaboration among students but also for fostering deeper levels of cognitive performance and preparation for the knowledge society (Resta & Laferrière, 2007). As a result of the positive potential of these communication tools and the educational benefits of collaborative learning strategies, many educators now include CSCL teaching strategies in their teaching practice.

However, implementing collaborative learning in teaching does not always go smoothly. In a study about collaboration within student groups, Kwon et al. (2014) found that the majority of groups were not demonstrating effective collaboration behaviors. Although educators expect positive interactions among students such as, active engagement, group regulation, supportive behaviors, and the co-constructing of knowledge, they often observe dysfunctional interactions or group regulation failures (Kwon et al., 2014). Other issues identified are as follows: Students resist working collaboratively (Barr, Dixon, & Gassenheimer, 2005), students experience group conflicts (Borg, Kembro, Notander, Petersson, & Ohlsson, 2011; Dool, 2007; Fiechtner & Davis, 1984; Harkins & Jackson, 1985; Roberts & McInnerney, 2007), and students report a lack of accountability and quality work, the existence of social loafers, and differences in expectations (Capdeferro & Romero, 2012). As a result, learners may not develop common ground and this prevents them from collectively constructing knowledge, leaving learners frustrated with collaboration experiences (Capdeferro & Romero, 2012).

To better understand the nature of group process and factors that affect collaboration process, researchers have looked into collaboration behaviors, examining group development and collaboration processes, and analyzing factors affecting the success of collaborative learning (Barbieri & Light, 1992; Bossche, Gijselaers, Segers, & Valcke, 2006). Collaborative learning is a complex ecological process influenced by individual factors, group members interactions, and also the collaborative tasks. In the following section, we will discuss the individual and group process factors.

Individual Factors: Gender, Aptitude, Prior Working Experiences, and Technology Competency

Individual factors refer to the characteristics that each member brings to the group such as gender, educational background, experience, and skills; and researchers have been exploring whether these have an impact on collaborative behaviors in virtual environments. The first factor is gender. Does gender play a role in how learners interact with their peers? Considering the content of messages, female students seem to be more socially affectionate and dependent on group members when compared with males (Rodino, 1997). In regard to participation in group discussions, there are no conclusive results in regard to gender. Wilson (2000) suggested that female and high aptitude students tend to more frequently communicate with their peers. But in Wang, Sierra, and Folger’s (2003) study, they found that there was no significant difference in terms of discussion styles and level of participation between males and females. Atai and Chahkandi (2012) also did not find gender differences in communication styles.

Aptitude is another strong determinant on students’ online collaborative performance. High achieving students tend to more actively engage in communication. Comparing with other students, high aptitude students post more discussion threads or messages during a collaboration process. These students are motivated to work hard in other activities also (Wilson, 2000).

In addition to their aptitude, students’ prior collaboration experiences could impact their perception, attitudes, and expectations of collaboration work and guide them on how to behave in the group (Lumsden, Lumsden, & Wiethoff, 2009). Reticence to participate in group work is one of the problems identified. For example, students could have worked with irresponsible group members previously and feel that working alone is more effective than working collaboratively. This negative experience would influence their motivation in future group work, and the reticence to participate is most often seen in collaborative learning (Barr et al., 2005; Roberts & McInnerney, 2007).

The level of learners’ competence with technology also impacts an individual’s collaboration experiences. Research has shown that a learner’s ability and confidence with online learning technology is positively associated with their satisfaction with their experience (Muilenburg & Berge, 2005). Group productivity will be seriously impacted if group members do not know how to use certain features built within an online learning environment (Curtis & Lawson, 2001). In addition to the aforementioned issues, the occurrence of technical glitches and of being in an environment with limited nonverbal social cues (Walther, 1992) can easily interrupt the group members’ communication processes (Palloff & Pratt, 1999).

The Group Collaboration Process

In addition to the individual factors mentioned in the previous section, research shows that interactions and processes taking place among group members are another critical factor impacting the overall collaboration process. Studies found that when compared with face-to-face groups in computer-mediated environments, virtual groups tend to be more task oriented and content focused (Dewiyanti, Brand-Gruwel, Jochems, & Broers, 2007; Janssen, Erkens, Kirschner, & Kanselaar, 2012; Johnson et al., 2002; Veerman, 2000; Veldhuis-Diermanse, 2002). They also share higher levels of conflict (Valacich, Sarker, Pratt, & Groomer, 2002) with fewer interactions and less information sharing among group members (Warkentin, Sayeed, & Hightower, 1997). Curtis and Lawson (2001) examined students’ online collaboration behaviors and found that virtual group members demonstrated common collaborative behaviors similar to face-to-face groups such as planning, contributing, and seeking input behaviors, but very few social interactions were found (5%). This lack of social interaction has been documented in other studies as well (Dewiyanti et al., 2007).

These studies have shown that it is the computer-mediated communication that casts the fundamental constraints on group member social interactions and group development. Kreijns et al. (2003) argue that instructors tend to ignore the need to facilitate group members’ social interactions and they may either take social interaction for granted or restrict social interaction to cognitive processes; this results in interactions among group members that are narrowly focused on the task and missing the social dimension. All of these findings indicate that virtual groups do not have an appropriate social space for fostering the development of the group.

Social space: The foundation of collaboration

Social space refers to the area where a group demonstrates their social psychological interactions, the area where members can develop group norms, cohesion, and trust. Researchers (Kreijns et al., 2003; Kwon et al., 2014) found that a sound social space is the prerequisite for a successful group. Studies have revealed how computer-mediated communication influences the establishment of a group’s social space. Lin, Standing, and Liu (2008) examined virtual group performance and found that communication directly influences the relationship and cohesion building, which can solidify a group’s coordination capability, and eventually impact the group performance and the satisfaction of learners. Although social interactions are not directly relevant to tasks, they are critical in developing group norms, cohesion, and trust which are the foundations of effective groups (Cohen & Bailey, 1997; Kreijns et al., 2003).

Researchers have argued that in a face-to-face environment when a group has just formed, group members usually take the opportunity to get to know each other, and to assess the personality, experience, and strengths of individual members. During this early collaboration phase, members usually engage in social interactions, get connected, decide on goals, define group norms (a shared understanding of what the groups is supposed to do), and decide on roles and responsibilities (Johnson et al., 2002). Within a computer-mediated communication environment, it is usually more difficult and takes longer time for students to get to know each other and develop norms. Even though group members would have introduced themselves in an earlier phase, the received information and interactions are not sufficient for group members to get to know one another’s personality and work style. This lack of knowledge can cause difficulties in the development of the critical components of an effective group such as trust, friendly group climate, group norms, and cohesion (Ocker, 2002). As one virtual group member commented: “It was tough getting together with the technology. It took a while to get used to—what took 10 min face-to-face took an hour online—it was hard to make decisions. Not having nonverbal cues made it harder” (Johnson et al., 2002, pp. 385–386). Because of the communication challenges, researchers found that within online environments, group development and building trust and cohesion, as well as decision-making takes longer time. Ocker (2002) also found that the virtual groups struggle in building group cohesion and relationships, even with video-conferencing tools enhancing communications among members.

Trust is another critical factor that affects group performance (Cascio, 2000) and social information exchange (Jarvenpaa & Leidner, 1999). Trust enables group members to depend on each other while reducing concern about their integrity or capability (Greenberg, Greenberg, & Antonucci, 2007). Similar to group norms and cohesion development, developing trust is challenging within an online environment. Walther and Bunz (2005) found, that for virtual groups, trust development is relatively complicated. For example, group members need to demonstrate certain behaviors such as frequent and explicit communication and sticking to deadlines to facilitate the development of trust. Although trust can arise, its development takes longer time (Bos, Olson, Gergle, Olson, & Wright, 2002; Greenberg et al., 2007; Wilson, Straus, & McEvily, 2006) and is difficult to sustain (Bos et al., 2002). This may be because “members may have no past on which to build, no future to reference, and may never even actually meet face-to-face” (Greenberg et al., 2007, p. 325). It is worth noting that the richer communication channels (e.g., video and audio conferencing) can enhance the trust development up to a level comparable to that of a face-to-face group, however virtual groups still face delays and fragile trust (Bos et al., 2002).

Group regulative behaviors

Ideally, groups are expected to reach high levels of collaboration. Collaboration is considered high level when members get involved in regulatory behaviors such as discussing, planning, coordinating, and evaluation (Cohen, 1994). Unfortunately, research has reported that not every group development is smooth and becomes successful. Some groups are effective in collaboration and achieve successful outcomes, but others may struggle and are not able to resolve the conflicts, eventually becoming unable to complete tasks effectively and efficiently (Kwon et al., 2014).

Janssen et al. (2012) analyzed group members’ collaboration behaviors and found that group members’ interactions can fall into four categories: discussion of information, regulation of task-related activities, regulation of social activities, and social activities. They found that group members devote the most time and effort to regulating tasks and monitoring task progress. Group members, however, devoted less attention to regulating the collaboration process. Research has shown that students tend to ignore or put little effort toward regulating the group collaboration process (Dewiyanti et al., 2007; Kwon et al., 2014; Janssen et al., 2012). Dewiyanti et al. (2007) found that the monitoring and regulating of group process did not happen naturally at the beginning of collaboration. Group regulation tasks can vary according to the group development phase and the progress of tasks (Kwon et al., 2014). Further, certain types of regulatory actions must be demonstrated at the right time to make a group effective. For example, in the early collaboration phase, group members need to focus on identifying tasks and goals, seeking group agreement, and planning group process (Kwon et al., 2014).

Previous studies have revealed what group members do in the early phase of group development and how critically this phase may impact the success of the collaboration. It seems that virtual group members have to rely on individual experiences and skills to guide them on how to behave in a group, before group norms, and trust and cohesion are established in the early phase. Unfortunately, it is still not clear how the relationship between individual factors and the early group process within this phase is connected to the overall collaboration process.

Method

Course Structure

This study was conducted in an online course at a land grant university in the Midwestern United States. The course focused on clinical ethics and was offered in Fall 2012 and Spring 2013. The clinical ethics course is a semester-based, 16-week long course aimed at undergraduate preprofessional health students. It was hosted under the Blackboard learning management system. This course was a requirement of a number of undergraduate prehealth professional programs. The course content was more medical humanities in nature than some of the other required courses.

A module was introduced each week. Modules in the course covered areas such as professionalism, patient–professional relationships, the social context of health care, and chronic health and end-of-life care. The students learned the six steps of ethical decision-making and ethical principles in an iterative and cumulative process—all of the steps were introduced in the beginning of the course, but each module typically added a new concept or new nuance to understanding and applying the steps and the principles.

For each module, the students had at least three expectations:

Participation in a weekly quiz that measured their knowledge of the concepts covered. The quizzes were taken independently.

Participation in a larger group discussion about a case introduced by the course instructor. In this group discussion activity, students were graded on their ability to assimilate and apply concepts from the readings into their responses. The discussion groups were assigned by the instructor.

Collaboration in the creation of the final project: the clinical ethics case. This assignment is the focus of the study. The co-writing of the clinical ethics case was being done through Blackboard Wikis. An orientation was offered to students so that students would understand the process of co-writing, the wiki environment, and the group building.

In the beginning of the semester, the students were assigned to a group to work on the final project: developing a clinical ethics case during Week 4 to Week 16. Each group has its own discussion board and wiki space to facilitate the development of the case and to develop personal connections. The creation of the case was broken down into small milestones such as selecting a medical topic, developing case study outlines, exploring each character’s perspectives, providing feedback to other group’s work, and finalizing their case writing. The clinical ethics case assignment asked each group member to take on a role of a stakeholder (e.g., the patient, the family members, physician, or nurse) and to write a clinical ethics case (story). The students chose a medical situation that held the possibility of multiple clinical and ethical options; and task of the group was to develop characters whose knowledge, experience, and personal beliefs could contribute to the final decision.

Participants

In this study, all students were from the Midwest, and most of the students were in their junior or senior year of study, but a few students were in earlier stages of their education. Some were entering the course with previous experience in health care, and others were newer to the health professions. The study was approved by the university institutional review board (#1205039) and did not involve any monetary compensation.

When combining two classes, 86 students participated in the study: 28 participants from Fall 2012 and 58 participants from Spring 2013. Among all participants, 84.9% were female and 15.1% were male. The average age of participants was 23.04 years, and the age was ranged from 19 to 45 years. The majority of participants (95.3%) reported that they had taken online courses previously. Among 85 participants, 59.3% of participants reported to have had health care-related work experiences. Several chi-square tests were performed to test if the two cohort groups were homogeneous. The tests found that between the fall and spring cohort groups, there was no statistical significance in terms of work experience x²(1, N = 85) = 2.272, p = .132; gender, x²(1, N = 86) = .627, p = .428; whether they took an online course previously, x²(1, N = 85) = .120, p = .729, or had online group experiences, x²(1, N = 85) = .050, p = .823.

Measurements

Students’ perception of the collaboration process

As mentioned, two independent surveys were conducted to observe the students’ perceptions of the group process at different times. The survey questions were developed by the authors and reviewed by the instructor to ensure face validity. The first survey investigated students’ group regulation in the early collaboration phase. Questions were developed to reflect four domains: initial leadership, quality communication, establishing group norms, and technology competence. About the initial leadership, students answered whether there was a leader initiating group tasks and coordinating group processes. The quality communication items evaluated whether students selected effective communication tools, shared common ground through the discussion forum, had frequent communications, and had efficient communication toward a group goal. The group norm questions assessed whether students decided individual roles, collaboration strategies, conflict management, and monitoring group process. The technical competency measured individuals’ skills and experience using the wiki tool. The first survey also examined students’ previous health care work experience and online group project experience in addition to demographic information.

The second survey investigated students’ overall evaluation of the group process at the end of the project. Fourteen items asked about students’ collaborative learning experience from an individual perspective and 21 items from a group perspective. Students’ perception about learning from the collaboration was asked through three items. The questions of the second survey were adopted from Kwon’s previous study (Kwon, Hong, & Laffey, 2013) and modified accordingly. All the questions of the two surveys were answered on 7-point Likert scales.

Procedures

At the beginning of the course, students received invitation e-mails that explained the study and invited them to join in this study. Students were informed that researchers would analyze their discussions and wiki content for research purposes. Beginning from Week 4, students started working on the clinical ethics case as a group (for the ethics case, see the description under the course structure section). On Week 8, after students got the opportunities to get familiar with their group members and their work style, then the first survey was administered through an online survey tool to ask their initial collaboration experiences. The second survey was administered at the end of course (16th week) to evaluate their overall group experiences. All surveys were administrated by the first author, who did not teach the course; and students’ names were replaced with research numbers in order to insure privacy.

Data Analysis

Ex post facto (after-the-fact) research design was employed to identify students’ early online collaboration behaviors that occurred naturally without an experimental treatment. A cluster analysis, based on the first survey, was conducted to identify groups. Based on the group membership, students’ overall collaborative experiences (second survey) were compared to confirm any difference between groups. To examine the effects of early collaboration factors on the group process in the later collaboration, a linear regression analysis was conducted. Typical assumptions for statistical tests had been tested, and no concerns were found.

Results

Early Group Process and Characteristics of Group Members

Table 1 illustrates the results of the survey, which asked about group process during the early collaboration phase and group members’ previous experience. A one-way analysis of variance (ANOVA) revealed that there were statistically significant differences between groups in the experience of health care work, F(17, 67) = 3.11, p = .000, and online group projects, F(17, 67) = 6.19, p = .000. We also found significant differences between groups in the quality of communication, F(17, 68) = 2.57, p = .003, and in establishing group norms, F(17, 65) = 2.59, p = .003.

Table 1.

Descriptive Results of Early Group Process and Background Information of Group Members.

Group	N	Health-care work experience		Online group experience		Technology competence		Initial leadership		Quality communication		Establishing group norm
Group	N	Yes	No	Yes	No	Mean	SD	Mean	SD	Mean	SD	Mean	SD
A	5	3	2	1	4	4.47	0.38	3.77	0.401	3.90	0.379	4.40	0.822
B	5	3	2	0	5	3.80	0.691	3.17	1.236	3.50	0.935	4.50	0.500
C	3	2	1	1	2	4.00	0.333	3.22	1.540	3.58	0.629	4.08	0.577
D	6	4	2	4	2	4.89	0.911	3.44	1.682	3.83	0.492	5.04	0.485
E	4	4	0	1	3	5.00	0.471	4.50	1.991	4.56	0.515	5.25	1.061
F	5	4	1	0	5	4.33	1.000	4.33	1.225	4.65	0.518	4.65	0.548
G	5	0	5	5	0	5.33	0.782	3.13	0.606	3.10	1.257	3.80	1.280
H	5	4	1	0	5	4.67	0.720	1.87	1.193	3.25	2.151	4.44	1.248
I	5	5	0	5	0	4.93	1.011	2.33	0.707	4.95	0.959	5.15	0.945
J	5	5	0	0	5	3.73	0.863	2.67	1.528	3.65	1.167	4.55	0.855
K	5	0	5	0	5	4.17	1.836	2.53	1.426	2.30	1.614	2.94	0.591
L	5	1	4	0	5	4.47	0.837	3.07	0.279	3.95	1.052	4.50	0.829
M	5	2	3	1	4	3.67	1.491	3.13	1.386	3.35	0.84	4.15	0.962
N	5	4	1	1	4	3.93	0.548	3.77	1.025	5.20	0.694	5.35	0.454
O	5	4	1	1	4	4.60	1.862	3.33	1.179	4.95	1.217	5.15	0.762
P	5	1	4	0	5	3.80	1.070	2.67	1.616	4.05	0.908	5.10	0.652
Q	4	2	2	0	4	5.08	1.287	3.25	0.877	5.63	0.777	5.13	0.854
R	3	3	0	0	3	3.56	1.347	2.25	1.792	3.50	2.336	5.53	0.937

Group A through F enrolled the clinical ethic course in Fall 2012 and Group G through R in Spring 2013. Statistical analysis revealed no significant difference between the cohorts except in the initial leadership, F(1, 84) = 9.71, p = .003. The year of enrollment was excluded from the following analysis. Group R consisted of four students but one did not participate in the survey asking previous health-care work experience and online group project experience.

Considering the post hoc analysis of the survey responses, the researchers decided to employ cluster analysis that could categorize groups into a small number of clusters based on the patterns of early group progress (technology competence, initial leadership, quality communication, and establishing group norms), previous health care work experience, and online group project experience. A hierarchical cluster analysis employed Ward’s method using squared Euclidean distance produced a two-cluster outcome: Cluster A includes Group B, C, G, H, J, K, L, M, P; Cluster B does Group A, D, E, F, I, N, O, Q, R. Each cluster consisted of 9 groups with 43 students in total.

Table 2 presents the mean scores and standard deviations on the survey responses based on the clusters. ANOVAs revealed that Cluster B had more health care work experience, F(1, 83) = 13.55, p = .000, and perceived higher initial leadership, F(1, 84) = 5.30, p = .024, quality communication, F(1, 84) = 20.35, p = .000, and establishing group norms, F(1, 81) = 16.74, p = .000. Based on the difference of the group process evaluated in the early collaboration phase, we titled Cluster B as more successful collaborators (MSC) and Cluster A as less successful collaborators (LSC).

Table 2.

Comparison Between Clusters on Early Collaboration and Previous Experience.

Questions	Cluster A			Cluster B			Comparisons
Questions	N	Mean	SD	N	Mean	SD	Comparisons
Health-care work experience	43	0.42	0.499	42	0.79	0.415	A < B F(1, 83) = 13.55, p = .000
Online group experience	43	1.84	0.374	42	1.69	0.468
Technical competence	41	4.18	1.088	42	4.56	1.055
Initial leadership	43	2.81	1.21	43	3.45	1.362	A < B F(1, 84) = 5.30, p = .024
Quality communication	43	3.41	1.256	43	4.56	1.106	A < B F(1, 84) = 20.35, p = .000
Establishing group norm	41	4.26	0.973	42	5.04	0.756	A < B F(1, 81) = 16.74, p = .000

Reflection on Group Process

Table 3 presents the questionnaires of the survey asking about group process after the collaboration. The survey examined students’ collaborative learning experiences from three aspects: individual, group, and learning perspectives. From the individual perspective, authors asked students how they felt about group members’ reliability and responsibility (positive interdependence, six items, α = .76), about encouraging atmosphere in a group (positive group climate, four items, α = .73), and about the progress of group work in a timely manner (time management, four items, α = .86). From the group perspective students evaluated the quality of their collaboration based on the following criteria:

How did group members support each other to achieve the goal? (supportive interactions, seven items, α = .88).

How were group members committed to group work? (commitment, six items, α = .90).

How did group members share their ideas and respect others? (collaborative meaning making, four items, α = .88).

How did group members share constructive critiques and negotiate different ideas? (negotiation of conflicts, four items, α = .81).

Table 3.

Survey Questionnaires Examining Collaborative Learning Experiences.

Individual perspective

Positive interdependence

• I felt that we depended on each other while working on this wiki project. • I felt that our group members relied on each other in accomplishing the group project. • I felt that I had to work collaboratively with my group members to complete this wiki project. • I felt that all of the group members felt equally responsible for completing this project. • I felt that our group divided up tasks among the members. • I think I would have done this wiki project more effectively if I were working on my own.^a

Positive group climate

• 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. • I felt as if the people in my group have good chemistry and we were friendly with each other. • I felt overall our group cooperated well in this project. • I feel comfortable seeking help from my fellow group members.

Time management

• I felt that our group members set up reasonable timelines to complete the tasks. • When we had a due date for a milestone, I was worried that my group would not get the work done in time.^a • I felt as if when I posted my contribution to the Wiki, I received feedback from my group members in a timely fashion. • I felt that we finished our wiki project early enough so that everyone had time to do a last revision/give feedback before it was due.

Group perspective

Supportive interactions

• We shared necessary materials and information with each other. • We worked in a manner for mutual benefits. • We provided needed feedback and comments to each other for the best interest of the group work. • We helped out each other’s when someone was in need. • I received valuable help from my members during the wiki project. • I provided valuable help to my members during the wiki project. • We encouraged each other’s effort on the group project.

Commitment

• We were committed to the group project. • We worked hard on the wiki project. • We were motivated to work on the wiki project. • We each shared a portion of the wiki project. • We participated equally in the wiki project. • We trusted each other to do what we were supposed to.

Collaborative meaning making

• We shared ideas with each other. • We were receptive to other member’s comments. • We were willing to revise our original contribution based on each other’s ideas or comments. • We respected each other’s suggestions and ideas.

Negotiation of conflict

• We challenged each other’s ideas or reasoning, so as to improve the quality of the work. • We were not afraid of raising different ideas or issues. • We were not afraid of challenging each other’s opinions. • We dealt constructively with challenges due to differences in opinions or perceptions.

Learning perspective

Social learning

• I learned something from my members which I could not learn if I worked alone. • Overall, group work in the wiki project helped me to learn clinical ethics. • I learned clinical ethics better because of the interaction between members during the wiki project.

Values were reversed.

Students also reflected on how much domain-specific knowledge they learned during the collaboration (social learning, three items, α = .88). The reflection questions guided students to imagine how it would be if they completed the project alone.

Table 4 presents the two clusters’ descriptive results of the survey asking about group process after the collaboration. Multivariate analyses of variance (MANOVAs) were conducted with two clusters: MSC and LSC, as an independent variable and with the survey response as dependent variables per each category. The MANOVA on the “individual perspective” showed significant differences between groups: Wilks’ λ = .65, F(3, 75) = 13.54, p = .00, η²= 0.35. Follow-up univariate analysis indicated significant difference between the clusters in the perception of “positive interdependence,” F(1, 77) = 15.16, p = .00, η²= 0.17, “positive group climate,” F(1, 77) = 11.31, p = .00, η²= 0.13, and “time management,” F(1, 77) = 41.49, p = .00, η²= 0.35. The finding revealed that the students in the MSC perceived their collaboration in more positive ways than those in the LSC from the individual perspective.

Table 4.

Descriptive Results of the Survey After Collaboration.

Category	Cluster A (n = 40)		Cluster B (n = 39)		F	partial η²
Category	Mean	SD	Mean	SD	F	partial η²
Individual perspective
Positive interdependence	4.38	1.069	5.25	.926	15.16**	.17
Positive group climate	4.13	1.381	5.04	.998	11.31**	.13
Time management	3.39	1.350	5.12	1.014	41.49**	.35
Group perspective
Supportive interactions	4.50	1.070	5.54	.769	24.45**	.24
Commitment	4.70	1.386	5.64	.859	13.08**	.15
Collaborative meaning making	5.16	.964	5.82	.916	9.86**	.11
Negotiation of conflict	4.60	.949	5.28	.923	10.48**	.12
Learning perspective
Social learning	4.00	1.561	4.81	1.715	4.85*	.06

Note. Authors named Cluster A as “Less Successful Collaborators” and Cluster B as “More Successful Collaborators” based on the survey results in the early collaboration period.

p < .05. **p < .01.

The MANOVA on the “group perspective” showed significant differences between the clusters: Wilks’ λ = .76, F(4, 74) = 5.96, p = .00, η²= 0.24. Follow-up univariate analysis indicated significant difference between the clusters in the perception of “supportive interactions,” F(1, 77) = 24.45, p = .00, η²= 0.24, “commitment,” F(1, 77) = 13.08, p = .00, η²= 0.15, “collaborative meaning making,” F(1, 77) = 9.86, p = .00, η²= 0.11, and “negotiation of conflict,” F(1, 77) = 10.48, p = .00, η²= 0.12. The finding revealed that the students in the MSC also perceived their collaboration in more positive ways than those in the LSC from the group perspective too.

An ANOVA on the “learning perspective” also revealed a significant difference between the clusters, F(1, 77) = 4.85, p = .03, η²= 0.06. The students in the MSC highly perceived that collaboration with peers helped them learn domain knowledge (clinical ethics).

Factors Affecting Group Process

Multiple linear regression analysis was used to develop a model for predicting subcategories of perceptions on the group process at the end of the collaboration from the early group process (technical competence, initial leadership, quality communication, and establishing group norms) and background information (health care work experience and online group experience). All of the analyses used a stepwise method. Multicollinearity among the independence variables had been checked, and variance inflation factors did not indicate any concern regarding the issue (all variance inflation factors were less than 1.5). Table 5 presents the results of the analysis.

Table 5.

Regression Models of Perceptions on Group Process.

Dependent variable	Significant predictor	β	t	p	Adjusted R²
Positive interdependence	Quality communication	.54	5.64	.000
					.286
Positive group climate	Quality communication	.39	3.63	.001
					.137
Time management	Quality communication	.61	6.72	.000
					.364
Supportive interactions	Quality communication	.63	7.46	.000
	Health-care work experience	.21	2.55	.013
					.504
Commitment	Quality communication	.45	4.40	.000
					.192
Collaborative meaning making	Establishing group norm	.31	2.51	.014
	Quality communication	.27	2.19	.032
					.245
Negotiation of conflicts	Quality communication	.56	6.24	.000
	Health-care work experience	.23	2.59	.011
					.431
Social learning	Quality communication	.37	3.53	.001
	Technical competence	.23	2.19	.032
					.223

It was found that “quality communication” explained a significant amount of the variance in the “positive interdependence,” F(1, 76) = 31.77, p = .00, R²_Adjusted = .286, the “positive group climate,” F(1, 76) = 13.20, p = .00, R²_Adjusted = .137, the “time management,” F(1, 76) = 45.11, p = .00, R²_Adjusted = .364, and the “commitment,” F(1, 76) = 19.33, p = .00, R²_Adjusted = .192. The analysis revealed that “quality communication” did significantly predict “positive interdependence,” β = .54, t(77) = 5.64, p = .00, “positive group climate,” β = .39, t(77) = 3.63, p = .00, “time management,” β = .80, t(77) = 6.71, p = .00, and “commitment,” β = .50, t(77) = 4.40, p = .00.

It was also found that both “quality communication” and “health care work experience” explained a significant amount of the variance in the “supportive interactions,” F(2, 75) = 40.14, p = .00, R²_Adjusted = .504, and the “negotiation of conflicts,” F(2, 75) = 30.22, p = .00, R²_Adjusted = .431. The analysis revealed that both “quality communication” and “health care work experience” did significantly predict “supportive interactions,” β = .63, t(77) = 7.46, p = .00; β = .21, t(77) = 2.55, p = .01, and “negotiation of conflicts,” β = .56, t(77) = 6.24, p = .00; β = .23, t(77) = 2.59, p = .01.

It was also found that both “establishing group norms” and “quality communication” explained a significant amount of the variance in the “collaborative meaning making,” F(2, 75) = 13.48, p = .00, R²_Adjusted = .245. They significantly predict the “collaborative meaning making” too (Norm: β = .56, t(77) = 6.24, p = .00; Communication: β = .23, t(77) = 2.59, p = .01).

It was also found that both “quality communication” and “technical competence” explained a significant amount of the variance in the “social learning,” F(2, 75) = 12.04, p = .00, R²_Adjusted = .223. They significantly predict the “social learning” too (Communication: β = .37, t(77) = 3.53, p = .00; Technical: β = .23, t(77) = 2.19, p = .03).

Discussion

This study examined student perceptions of group process in the early collaboration phase; and how they were associated with overall collaboration later with consideration of students’ educational and work backgrounds. The group process were evaluated in terms of quality communication, establishment of group norms, demonstration of initial leadership, and technical competence. In this project, quality communication was operationalized as follows: Whether students selected effective communication tools, shared common ground through the discussion forum, had frequent communications, and had efficient communication toward a group goal. Group norms encompass the following characteristics: students deciding on individual roles for tasks, discussing collaboration strategies, managing conflict, and discovering a system to monitor group process. Technical competency was assessed through individuals’ skills and experience with the wiki tool. Researchers also requested information about students’ previous health care-related work experience, as well as online group project experience. These questions were asked in order to investigate if these individual experiences were related to the group processes.

Results revealed that there were significant differences between groups in terms of the perceived group process and health care work experiences. Considering the difference, the researchers could identify two types of groups: more MSC vs. LSC. For the purposes of this study, success was identified by the quality of group process evaluated in terms of leadership, communication, and group norms. The MSC evaluated their group process more positively than the LSC. The analysis showed that the MSC had more health care work experience and initiated better group process by maintaining quality communication, establishing group norms, and demonstrating leadership in the early collaboration phase, while the LSC did poorly in all of these domains.

The study also examined students’ evaluation of group process at the end of the project period. A significant finding of the study was that the MSC had a more positive perception that they created a healthy social space, maintained positive and supportive interactions, coordinated members’ time and effort efficiently, and even managed conflict more effectively than the LSC believed that they did. As a consequence, the MSC are more likely to believe that they learned through their collaboration, as compared with the LSC. The result suggested that a group process formed in the early collaboration phase would affect later collaboration processes in a consistent direction.

A linear regression analysis confirmed the relationship and revealed that quality communication had close relationship with all aspects or dimensions of the group process in the later phase. It also suggested that students’ health care work experience was associated with supportive interactions and professional management of conflicts. In the following section, the two factors will be discussed in detail.

Quality Communication

The results indicate that quality communication was the most salient determinant that profoundly affected later group process. Unlike a face-to-face collaboration setting, the students in an online learning environment face challenges in every aspect of collaboration. The examples of these challenges include that group members reside in different time zones and have various daily life duties. Students’ implicit meanings can be misunderstood, due to the loss of facial expression or tone of voice in a written communication mode. They also have to deal with the difficulties in ensuring timely interactivities while using asynchronous communication tools. These constraints require students to spend more time and effort to maintain effective communication. Without proper communication, groups can become dysfunctional, which will lower the quality and efficiency of group process. Thus, establishing effective communication to mitigate some of the barriers of virtual communication, and establishing norms for the communication, is crucial to ensuring successful collaboration. In this sense, it was not surprising that quality communication played an important role during collaborative learning.

The functionality of communication among group members may include social-psychological (sharing personal information and stories and experiences and providing social support), cognitive processing (explicitly communicating expectations, explaining and clarifying questions, and seeking help from group members), and group regulative aspects (coordinating tasks, managing, generating strategies, monitoring tasks progress, and evaluation). Quality communications can foster cognitive processes, create healthy social spaces by developing trust, increase the awareness of peer’s commitment, and support the establishment of group norms (Lea & Spears, 1992; Walther, 1992; Wilson et al., 2006).

In this study, after assessing the requirements of the assignments and the needs of the group members, the students of the MSC thoughtfully chose tools that facilitated communication. After deciding the way to communicate, the MSC tried to ensure every group member was on the same page during discussion and maintained frequent communication. The findings of this study are consistent with other studies revealing a positive relationship between communication and group process such as relationship building, group cohesion, and group regulation (e.g., Lin et al., 2008).

The prominent influence of communication in this study can also be explained by the complexity of the task. In the current study, students developed a scenario dealing with clinical ethics in a health care work place. Developing authentic stories and considering various perspectives of different stakeholders required high cognitive demands that could not be handled by one person efficiently. When students carried out highly complex tasks, they are more likely to need support from each other to analyze the nature of the problem, to identify the problem, and to find possible solutions; whereas when dealing with simple tasks, they may work individually and do not need much planning, coordination, and communication among group members (Hirokawa, 1990). Thus, quality communication played an important role in facilitating “the integration of individual competences” and the “development of an effective strategy for arriving at a decision” in this study as Hirokawa (1990, p. 198) suggested.

The current study revealed that the quality of communication in early group process was closely associated with the quality of the whole collaboration process as evaluated at the end of the collaboration, including communication that encouraged positive interdependence, supportive interactions, effective group management, and perceived learning. The result was consistent with the argument that “different type of group regulation (are) required in different collaboration phases” (Kwon et al., 2014, p. 195). Kwon et al. (2014) suggested that a successful collaboration group spent considerable time on monitoring group process, sought group agreement, identified tasks and goals, and discussed better strategies in the early collaboration phase. These tasks require intensive communication among students, as was demonstrated by the intense interactivity of the group in that phase.

It is noteworthy that students in the less successful group evaluated their group process poorly, especially on collaborative meaning making and social learning, two factors that were associated with quality communication. Meaning making is a critical process in collaborative learning (Dillenbourg, 1999; Koschmann, 2002). It is natural and inevitable to have conflicts of opinions and misunderstandings of others’ during collaboration. However, group members will overcome the unavoidable obstacles if they establish an effective communication channel and group norms to engage in a negotiation process where they express their opinion toward task or knowledge and construct shared knowledge (Jonassen, 1994).

Previous Health Care Work Experience and Collaborative Learning

The results suggested that students’ work experience was an indicator for their supportive interaction and conflict negotiation behaviors during the group collaboration processes. This may be because that an individual who possesses more health care work experiences tends to have better collaborative skills.

As group-based or interdisciplinary collaboration practice has become a common practice in the U.S. health care system, health care professionals are expected to collaborate with people from different disciplines and with different stakeholders. In health care professionals’ day-to-day work, four major skills set were identified: collaboration, credibility, compassion, and coordination (Apker, Propp, Ford, & Hofmeister, 2006). That means, the nature of health care work requires quality interpersonal skills. These individuals are expected to be friendly, listen to others’ comments, and know how to and what to communicate. Further, these individuals should ideally also have group management skills, inquiry skills, conflict management skills, and presentation skills (Bosworth, 1994).

The current study suggested that the students’ health care work experience would facilitate them having supportive interactions and understanding how to negotiate conflicts during collaborative learning. It appeared that students who had health care group experience practiced professional interpersonal skills and this affected their attitude toward the collaboration in the online group project. The interpersonal skills seemed to be particularly beneficial to help them share different opinions and come to an agreement that was essential for quality collaboration.

Good Start Leads to Later Successful Collaboration

This study also found the critical role played by the initial phase of collaboration in the online group development and learning process as a whole. A good start is highly likely to lead to successful collaboration experiences overall. The study findings indicate that, when compared with the less successful group, the more successful group exhibited quality group processes, such as initial leadership, quality communication, and establishment of a group norm, and continued to enjoy the positivity throughout the whole collaboration process (see Table 4). It appeared that once the group laid down a good foundation and healthy social space, the group members followed the norms and put the most effort into creating quality tasks. On the other hand, the less successful group was never able to resolve their differences in expectations and work styles, and, as a result, the collaboration process buckled under the weight of the tension between community members created by the confusion and frustration they had experienced. In the end, some members of the less successful group stopped working and the others picked up the slack and finished the task. In a situation like this, it is likely that bad feelings and negativity would linger among group members, and their negative assessments of the experience appear to bear out this hypothesis.

The Limitations of This Study

Generalization of this study’s findings is limited. First, this study utilized a survey as the main data source. Although self-report questionnaire or survey is one of the major data source in CSCL study (Jeong, Hmelo-Silver, & Yu, 2014), this method has several limitations. For example, the self-report data tend to be biased or subjective. The response maybe subject to the way a question was designed. As Nunes et al. (2009) concluded from their review, self-report response may not reflect the truth due to possible recall bias, social desirability bias, and errors in self-observation.

Although this study identified the potential role that work experiences may have in students’ collaboration process, the survey data are not sufficient enough to verify students’ collaboration tasks in their workplace. Thus, we were not able to validate our assumption of the role of health care work experience in affecting group members’ collaboration behaviors. In addition, while this study observed and suggested there are desired collaboration patterns in the early collaboration phase, it did not prove how they were associated with the group process in the later phase. This study answered the question by examining the relations between two evaluations of different time periods.

The authors also would like to note that during the class session, the instructor did not give specific guidance on collaboration. Students have to rely on their own authority during the collaboration process, which may not be true in some teaching practices where faculty would provide guidance or intervene when needs arise.

Lastly, the readers should be cautious in concluding the findings as causal relationships. This study is exploratory and needs to be replicated in other settings and fields.

Conclusion and Implication

This study reveals the importance of quality communication and the possible combinational effect of health care work experience in the early phase of group collaboration. Overall, the finding supports the Lin et al. (2008) model of the role of communication plays in group process. Further, this study reaffirms the importance of communication in relationship and cohesion development, which indirectly affects the group coordination and performance. The findings also support the previous study, which revealed that successful collaboration groups efficiently coordinate group efforts according to the phases of collaboration (Kwon et al., 2014). For example, group members communicated effectively in order to establish group norms by identifying goals, assigning individual responsibilities, and reaching a consensus on decision making in an early collaboration phase.

In health care, it is commonly known that prevention is better than cure. This concept also applies to collaborative learning. This study finding suggests the criticality of the early collaboration phase as the time to form trust, group norms, and establish positive relationships with group members. Considering the interactions do not automatically happen among virtual group members (Liaw & Huang, 2000; Northrup, 2001) and often stay on the surface level with a tendency to lack discussion on group process or regulative behaviors (Dewiyanti et al., 2007; Veerman, 2000; Veldhuis-Diermanse, 2002), we suggest that instructors should provide appropriate educational interventions and facilitations to address these challenges. Instructors may structure the instruction in a way that allows the occurrence of personal connection and social interactions, which can foster a healthy social space and eventually increase teamwork satisfactory (Kreijns et al., 2003; Ku, Tseng, & Akarasriworn, 2013; Kwon et al., 2014). For example, instructors can embed virtual town hall meeting or self-introduction to the course to help students get personal connections and get familiar with the course. Further, instructor should facilitate group members to develop their communication etiquette, so that communication would foster group cognitive and collaboration processes. Group members should discuss what group behaviors are expected, how frequent a group should meet, and what communication technology the group would use, roles and responsibilities, group collaboration process, and so on. Communication is essential to collaboration, with quality communication exhibit in the early phase, groups may enjoy the lasting and successful collaboration process.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Ying-Hsiu Liu is an instructional designer at the University of Missouri – Columbia. Her research interests include computer-supported collaborative learning, learning technology integration, and blended learning.

Kyungbin Kwon is an assistant professor at the Indiana University. His research interests include computer-supported collaborative learning, metacognitive scaffoldings, and computational thinking.

LaShaune P. Johnson is an assistant professor at Creighton University. Her research interests include health disparities, cultural competence or health literacy training for health professionals, and online learning.

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