Online Course Student Collaboration Literature: A Review and Critique

Abstract

University online course enrollment continues to rise at a rate higher than that of traditional, face-to-face university education, and several benefits exist to creating a collaborative online course environment. Therefore, a need exists to critically consider existing research about small group work in online courses. The present article provides a meta-synthesis of 41 articles related to this topic. This meta-synthesis includes a review of literature followed by a discussion of critiques and directions for future research about online course student collaboration. Findings from this meta-synthesis include a lack of consistent definitions within literature about student collaboration online, methodological issues in existing empirical studies, and the lack of interdisciplinary contribution to online course small group literature.

Keywords

online student group collaboration review

The current trend in education shows a steady increase in distance education and online course enrollment (Allen & Seaman, 2015; Instructional Technology Council, 2014). Enrollment in distance education has increased between 4.7% and 9% every year since 2009 (Instructional Technology Council, 2014), with some years as high as a 20% increase (Allen & Seaman, 2015). Enrollment in online courses has grown at a rate higher than that of traditional face-to-face enrollment although the growth rate has slowed in recent years (Allen & Seaman, 2015). Currently, 70.7% of degree-granting institutions offer online classes, and more than 95% of degree-granting institutions with enrollment of 5,000 or more students report offering distance learning opportunities (Allen & Seaman, 2015).

A classic stigma of online courses is that they involve individualized, task-oriented work that tends to be lonely (Kerka, 1996). Early researchers worried that distance learning was associated with isolation, lack of immediacy, and student independence, rather than interaction (Besser & Donahue, 1996). Although independent learning is possible for many adult learners, a sense of distance among students and instructors can negatively affect learning (Moore, 1980). Learning outcomes are enhanced by online courses, compared with face-to-face courses, when learning occurs collaboratively, rather than individually (Means, Toyama, Murphy, Bakia, & Jones, 2010). In other words, optimal learning through distance education can be achieved in an interactive setting. Learning is best achieved through social, rather than individual, meaning-making (Vygotsky, 1978).

Student engagement is a critical aspect of successful learning. Distance learners are less engaged in active and collaborative learning than traditional face-to-face students (Chen, Gonyea, & Kuh, 2008). The presence of collaboration and community in an online environment contributes to deeper student learning (Chapman, Ramondt, & Smiley, 2005). The ability for learners to express their uncertainties, ask questions, and engage in dialogue in online community environments contributes to deep learning. Although a meta-analysis of 50 study effects comparing online to face-to-face learning found that learning outcomes were generally more successfully accomplished by online students than face-to-face students (+.20), these effect sizes were dependent on whether learning was collaborative or independent (Means et al., 2010). Mean effect sizes were extremely small for independent learning online (+.05), while they were much larger with collaborative learning environments online (+.25), pointing to the advantage of collaborative group work in an online learning environment.

The consistent growth of distance education and the importance of establishing collaboration in an online learning environment both warrant a closer evaluation of student collaboration in online courses. A review focused specifically on online course group literature benefits both the distance education literature and the small group literature. In distance education, the most recent review of findings encompasses studies published from 2000 through 2009, and limits its focus to 18 studies that analyzed students’ text communication messages within the course (Jahng, 2012b). The present review goes beyond text communication within a course to better understand group formation and group interaction processes within online courses. Small group literature, in general, has found that greater participation in group settings is linked to greater member satisfaction (Porter, 1965). The importance of collaboration and participation is evident in face-to-face groups (Porter, 1965), but much more can be understood about small group communication within an online course environment by conducting a thorough review and critique of existing literature. Findings from the present critique can be used to better understand how groups work in an online environment, in general, which could apply to other small groups either forming or interacting in an online environment.

A review of studies of online course groups further benefits the small group literature by providing findings from bona fide groups over time. For instance, studies of team learning are rarely longitudinal and often incorporate experimental designs, limiting understanding of ongoing fluctuations of learning in groups (Erhardt, Gibbs, Martin-Rios, & Sherbloom, 2016). A similar concern exists in group research on virtual environments (Sivunen & Hakonen, 2011). Research on online course groups presents an opportunity to learn from groups in their established environment and to examine data over time in the form of multiweek course sessions. Furthermore, online course groups have varying degrees of interdependence, purposes, and tasks. Some may more closely resemble organizational teams and others more emergent online groups such as Wikipedia editors (Black, Wesler, Cosley, & DeGroot, 2011). Thus, findings from online course group research could be more readily applied and tested in other group contexts.

This article is a critical synthesis of existing literature concerning online group work. To clarify the scope of the present article, several terms deserve definition. First, the review and critique focuses on student small group collaboration in online classes. For this review, collaboration is defined as the interdependent contributions of group members toward a goal. Collaboration, in its present conceptualization, can involve either group discussions or group projects in online courses. The review also strictly focuses on collaboration within small groups, so the terms collaboration and small group work are used interchangeably. The first portion of the review of literature is organized around group formation processes, such as how groups are formed, optimal group size, and roles within groups. The second portion of the review of literature places focus on interactional processes within work groups, defined as, “members’ interdependent acts that convert inputs to outcomes through cognitive, verbal, and behavioral activities directed toward organizing taskwork to achieve collective goals” (Marks, Mathieu, & Zaccaro, 2001, p. 357). It should be recognized that interactional processes differ from emergent states in that emergent states focus on the “cognitive, motivational, and affective states of teams” (Marks et al., 2001, p. 357), while processes describe actions and behaviors. For example, while a trait like cohesiveness is considered an emergent state, conflict resolution is considered an interactional process. Overall, literature about online small group work was collected, reviewed, and organized into group formation and group interaction processes. A review of existing literature on a variety of topics within online group work is followed by a criticism of trends in the literature and recommendations for directions of future research on online student collaboration.

Method

When it comes to published literature about online learning, there is no shortage. Means et al. (2010) identified over 1,000 empirical studies about online learning published between 1996 and July 2008. Although only a fraction of these studies covers student collaboration in online courses, once a foundation of existing research is available to describe a phenomenon the subject can benefit from a thorough synthesis of existing findings. Because of the qualitative nature of some bodies of research and the need for parsimonious distribution of findings to practitioners, a meta-synthesis can help create a better understanding of a phenomenon (Zimmer, 2006). Because of the value in meta-synthesis, the present article will synthesize two categories of online group work research, followed by a criticism of existing literature and identification of future research directions involving online student collaboration. Although a quantitative meta-analysis might be preferable for gaining a synthesized understanding of findings from past studies, the present study’s focus on literature returned studies using a variety of methodologies, many of which were not quantitative. Because studies use a variety of methodologies, sufficient statistics are not always collected or available, creating an opportunity for a more inclusive method of reviewing literature through a qualitative meta-synthesis.

Search Parameters and Selection Criteria

Because online course group work is a topic with relevance spanning multiple disciplines, multiple electronic databases were searched by all authors during spring of 2015 for pertinent peer-reviewed articles. Specifically, databases searched included Education Resource Complete, PsycINFO, and Communication and Mass Media Complete, using the following keywords/terms: online, student, course, collaboration, and group. All searches were conducted with the individual terms and with selected terms grouped into phrases (e.g., “online course”) to more easily identify the most relevant search results. Further searches using the same keywords were conducted with specific publications relevant to the topic (i.e., Small Group Research and Communication Education). Finally, broader searches of Google Scholar and related agency reports, such as the U.S. Department of Education, as well as reference lists of relevant articles, were used to round out the search for online course group literature. These searches were limited to articles published in English, though articles were not limited to a specific region of the world.

The majority of articles in the initial search were prescriptive in nature, focused on providing pedagogical advice and how-to approaches that varied in terms of how much the advice was rooted in previous literature. As these articles did not include empirical study of the topic, they were excluded from analysis. The remaining results contained 95 articles. All authors reviewed the abstracts for these articles and determined additional exclusion criteria to maintain a clear focus on studies of student groups in online courses. In some cases, articles discussed independent online communities or training groups, which may differ from their higher education counterparts. Only articles that examined online groups at the university level (undergraduate and/or graduate students) were included in the analysis. Furthermore, some studies of online course discussion focused on online discussions among the class as a whole, rather than at a small group level. Articles involving online discussion among the class as a whole were excluded unless class discussion was being used as a comparison variable (e.g., examining how discussion differs in small groups compared with discussion among the class as a whole). Finally, as the primary goal of this meta-synthesis is to examine groups in online course contexts, studies that focused on the role of the instructor and/or used instructor behavior as a key variable were excluded. The final parameters for inclusion—empirical studies of small groups in university-level online courses not involving instructor roles—yielded 41 articles for review. See the appendix for a summary of included articles, including information about theoretical frameworks and methodologies used in each article. Analysis of the articles revealed two major themes in existing online course group research: group formation and group interaction processes. Each major theme and corresponding subthemes are discussed in the following sections.

Group Formation

Existing research considers three major aspects of group formation: optimal group size, the assembly of student work groups, and the roles in each group.

Group Size

The first step in forming groups in an online course is to determine the optimal size of the group. Few researchers have attempted to pinpoint a specific optimal number of students in an online work group, and some articles pointing to an optimal group size are based on anecdotal description, rather than empirical evidence (e.g., Rovai, 2000). When groups were made up of between two and seven students, groups of five outperformed all other options for group size (AbuSeileek, 2012). This optimal size is argued to be due to the opportunity for individuals to contribute while still leaving opportunity to learn from others’ contributions (AbuSeileek, 2012). Although a small sample of researchers have attempted to pinpoint an exact optimal number, many researchers have compared and contrasted small and large online work and discussion groups (e.g., Hamann, Pollock, & Wilson, 2012; Kim, 2013), although findings have not necessarily been consistent. For example, small groups sometimes benefit student learning in online courses (e.g., Hamann et al., 2012; Kim, 2013; Shaw, 2013). When it comes to participation, larger groups show less participation in the reading of online posts (Hewitt & Brett, 2007). Similarly, small group discussions encourage more frequent visits to discussion boards and more interactive posts, compared with larger group discussions (Kim, 2013). Smaller groups tend to participate more, which also contributes to greater learning, as evidenced by higher final exam scores (Shaw, 2013). Students significantly prefer small online discussion with about 10 students over entire-class discussion boards (Hamann et al., 2012). Students participating in small groups in their online course express positivity toward the ease of communicating with group members in a small group setting (Koh, Barbour, & Hill, 2010).

Some articles report advantages of small groups in online courses, whereas others report advantages to large work groups in online courses (Hew & Cheung, 2011; Hewitt & Brett, 2007; Lim & Zhong, 2006). When it comes to participation, large groups have access to more posts and the posts tend to be shorter in length (Hewitt & Brett, 2007), and the larger the group, the longer the duration of the discussion (Hew & Cheung, 2011). Higher level knowledge is more likely to be constructed in larger groups because of more diversity in posts (Hew & Cheung, 2011). Large groups with leaders tend to outperform small groups, likely because the leaders are able to facilitate discussions, resolve conflicts, and prevent subgroups from forming within the group (Lim & Zhong, 2006). Overall, group size research shows conflicting results, and optimal group size in online students’ work groups has not been empirically determined by existing research.

Assembly of the Group

After determining group size, it is also important to gain an understanding of the optimal way student groups in online classes should be formed. Several strategies exist to split students up into work groups in an online environment (Dascalu, Bodea, Lytras, Ordonez de Pablos, & Burlacu, 2014).

First, groups can be determined solely based on the learners’ preferences. Although students express more satisfaction when they are able to choose their group than when groups are randomly assigned (Sadeghi & Kardan, 2015), finding individuals who are similar or work well together might be particularly difficult in an online environment (Vercellone-Smith, Jablokow, & Friedel, 2012). Lawrence-Slater (2006) asked students to include a brief autobiography and photograph in the online discussion to make informed decisions about who to include in their work groups. After use of this strategy, 85% of learners were able to find a group to work with, and students showed satisfaction with the group formation process, overall (Lawrence-Slater, 2006). When it comes to group success, groups formed solely based on learner preferences show a mean assessment score higher than those formed randomly (Sadeghi & Kardan, 2015). Online course discussion group cliques that form based on learners’ own selections often exhibit a diverse range of cognitive styles among group members (Vercellone-Smith et al., 2012). Although cognitive gaps have been found to cause conflict in face-to-face groups, learners in these groups tended to continue to choose to work with one another with no apparent conflict, leading to the conclusion that the online environment may reduce the prevalence of conflict in cognitively diverse groups (Vercellone-Smith et al., 2012).

The second strategy for grouping students into work groups for online classes is based on the instructor’s choice (Dascalu et al., 2014). Although randomly assigning students to groups seems to be the most efficient method, student satisfaction with these groups is generally much lower than when students choose groups themselves (Sadeghi & Kardan, 2015). Interestingly, students who were given the opportunity to choose their own groups provided feedback that they would have liked more instructor intervention to form the groups (Koh et al., 2010). Aside from student satisfaction, the average assessment scores on tasks within collaborative learning environments were lowest for groups formed randomly, compared with other group formation strategies (Sadeghi & Kardan, 2015).

Group formation can be either aimed at creation of heterogeneous or homogeneous groups (Abnar, Orooji, & Taghiyareh, 2012). Some researchers tend to advocate for homogeneous groups (e.g., Lawrence-Slater, 2006), whereas other researchers advocate for heterogeneous groups (e.g., Lim & Zhong, 2006). Although culturally heterogeneous groups tend to withhold opinions, they have the ability to perform better than homogeneous groups when they have an appointed leader to encourage participation (Lim & Zhong, 2006). One possible explanation is that mediated learning systems with textual communication help create understanding of diverse group members, resulting in more acceptance of heterogeneity within the group. However, online students participating in group work interact more with people of their same culture (Stepanyan, Mather, & Dalrymple, 2014). A common argument for homogeneity within online work groups is the ability to group individuals with similar learning styles (Brauer & Schmidt, 2012) or interests (Lawrence-Slater, 2006). Like group size, the assembly of online student work groups is still debated, and an optimal strategy for group assembly is not agreed upon in existing literature.

Group Roles

Group roles were often the subject of existing literature about group formation in that sometimes roles were assigned to group members, while other times group roles emerged throughout the group work process. Williams, Morgan, and Cameron (2011) advocated for a balance between allowing roles to emerge naturally and assigning roles to group members. One role, in particular, that emerges in online groups is the role of leader.

Several studies have considered the efficacy of assigning group roles to students in online group discussions (e.g., De Wever, Schellens, Van Keer, & Valcke, 2008; De Wever, Van Keer, Schellens, & Valcke, 2010; Wise & Chiu, 2014). In general, when roles are assigned in online discussion groups, students find that the roles provide needed structure to the group, and students are more likely to read others’ posts (Wise, Saghafian, & Padmanabhan, 2012). A number of roles have continually emerged in the research as particularly beneficial to online student work groups. First, the role of student facilitator, sometimes called moderator, has been found to be effective in a variety of learner behaviors, including contributing to knowledge and providing procedural guidance (Hew & Cheung, 2011). Students who are assigned a summarizing role show greater breadth of consideration for posts within the discussion during weeks they are appointed to the role (Wise & Chiu, 2014) and higher levels of knowledge construction (Schellens, Van Keer, & Valcke, 2005). However, students’ perceptions of the summarizer role only found it moderately valuable (Wise et al., 2012).

Aside from assigned roles within online group discussions, some existing research focuses on emergent roles within groups (Morgan, Cameron, & Williams, 2009; Williams et al., 2011; Yeh, 2010). Roles often emerge naturally in a group, rather than being explicitly assigned, and students often see the benefit of having clear roles in the group (Morgan et al., 2009). Emergent roles that are consistent among studies include group members who provide information or opinions, roles that negatively affect group performance, such as not meeting deadlines or trying to take control, and the leader (Morgan et al., 2009; Williams et al., 2011; Yeh, 2010).

Leadership is a role that tends to emerge throughout the process of completing the group work, and group members often look to the leader’s actions as indication for norms or expectations within the group (Morgan et al., 2009). The acceptance of a leader within an online course work group depends on the size and makeup of the group (Lim & Zhong, 2006). Leaders have more influence in the group than other members, often becoming the dominant group member, which is not positively viewed by small groups, but is welcomed by large, diverse groups.

Group Interaction Processes

In addition to group size, formation, and roles, a number of the online course group studies examine the interaction that takes place among group members over the course of their projects. In a review of 18 education studies of small group communication in online courses, the majority focused on some aspect of the group process, with nearly one fourth specifically dealing with collaboration (Jahng, 2012b). Some studies explore the factors that influence individual perspectives, such as attitudes toward group work, willingness to collaborate, and self-regulating behaviors with regard to managing online group work (e.g., Cheng & Chu, 2016; Du, Xu, & Fan, 2013; Xu, Du, & Fan, 2013, 2014); In addition, a few studies explore the role that individual and cultural differences play in students’ group work behaviors, emotion management, and attitudes (Du, Ge, & Xu, 2015; Du, Zhao, Xu, & Lei, 2016). However, this particular group of studies focuses more on what individuals bring into the group, save for two moderate effects at the group level in a study of emotion management among Chinese online student groups (Xu, Du, & Fan, 2015). The majority of studies in this category focus on specific behaviors and patterns of interaction in the online group context. Social factors in interaction, such as social loafing and free riding, social presence, and creating a sense of community, have also been examined in the context of online class groups.

Quantity of Interaction

At the most basic level, collaboration has been studied in terms of quantity. In addition to exploring reasons for collaborating, Chiong and Jovanovic (2012) studied the number of online course discussion board posts students made in their groups to categorize the students as active, borderline, or inactive participants. Initially, the researchers assigned students to groups at random, but “remixed” the groups in the middle of the unit to group the most active participants together. The new group configurations essentially resulted in a rich get richer effect in that the new groups consisting of primarily active members became more active in terms of number of posts. Replacing active members with borderline and/or inactive members in other groups did not spur existing members to step up and become more active in their place. Although reconfiguring group members based on previous discussion post activity may not lead inactive participants to increase their number of posts, the addition of students from another culture may positively affect the quantity of discussion posts in online course groups (Wresch, Arbaugh, & Rebstock, 2005).

Quantitative analysis of online student group communication is not always based on number of discussion board posts, however. Social network analysis (SNA) quantifies the density and centrality of connections among group members, as well as the formation of cliques, to identify characteristic patterns in the group. These network connections become denser and interconnected when group collaboration is dynamic and opportunistic, rather than fixed (Siqin, van Aalst, & Chu, 2015) or when the instructor participates with the group (Ergün & Usluel, 2016). Another categorization method involves the total number of words shared by each member in the group (Jahng, Nielsen, & Chan, 2010). Jahng et al. (2010) also analyzed quantity of words posted as one of three indices of small group collaboration levels: quantity, equality, and shareness. Equality is defined as how equally all group members made contributions to the project discussion, while shareness is the amount of open communication among members (i.e., percentage of words that were sent to all members of the group). All three indices are needed for groups to maximize collaboration and, by extension, positive outcomes. However, other group factors exist that can serve to facilitate or hinder collaboration. Facilitating factors include a collaborative approach, high team spirit, and lots of social communication, whereas hindering factors include indecisiveness, lack of leadership, technical problems, personal conflicts, and a late start (Jahng, 2013).

Differences in group size also affect quantity of interaction in online groups. For instance, students tend to take more active roles in smaller group discussions, even those who were comparatively inactive in a large group setting such as a class-wide discussion board (Jahng et al., 2010). Although a larger, class-wide group may generate a higher quantity of posts overall, smaller group discussions generated higher participation rates (number of posts and hits, or views, per post) and more in-depth posts that encouraged interaction among group members (Kim, 2013).

Patterns of Interaction

Researchers studying online course groups acknowledge that when it comes to interaction, quantity is not necessarily synonymous with quality, and understanding online group process requires going beyond the number of posts. The types of discussion items group members post and the behavior patterns that emerge provide additional important insights. For instance, interpersonal content can lead to increased interactive participation in online groups (Beuchot & Bullen, 2005). On a larger scale, online group member behaviors can be classified into facilitating factors, conflicting factors, or one of four activity subsystems: production, exchange, distribution, and consumption (Choi & Kang, 2009). Analysis of online group communication on the course website indicated that although high- and low-performing groups demonstrated some similar patterns in these subsystems, differences later in the project suggested that lower performing groups were slower to collaborate. Oliveira, Tinoca, and Pereira’s (2011) analysis also compared a high- and low-performing group and found that some patterns were unique to the high-performing group (e.g., clarification of focus, collaboration, revision) while others were unique to the low-performing group (e.g., struggle, anxiety). The low-performing group was slower to interact as well, suggesting more collaborative groups performed better in assigned tasks (Thompson & Ku, 2006). Higher performing groups that engage in more constructive discourse also generate higher level ideas and questions (Siqin et al., 2015), and these more collaborative groups move through phases of interaction that more closely mirror existing models based on face-to-face group processes (Jahng, 2012a). Essentially, research to date has found distinct characteristics and interaction patterns in high-performing online student groups compared with low-performing groups, and that in particular, speed of initiating collaboration is a differentiating factor identified in multiple studies. Identifying differences between high- and low-performing groups in online courses helps instructors make adjustments in the course to facilitate the characteristics and patterns common to high-performing groups. Such adjustments benefit the groups as a whole in terms of project performance, but can positively affect individual students in the group as well. Students who are active participants and contribute high-level ideas in these collaborative group interactions benefit by exhibiting correspondingly higher levels of knowledge on the topic (Siqin et al., 2015).

Social Loafing and Free Riding

The Ringelmann effect notes that as group sizes increase, individual workload decreases (Kravitz & Martin, 1986), which can also be viewed through the lens of social loafing. Free riding happens when individuals share the same benefits as other group members but bear a disproportionate amount of work (Piezon & Donaldson, 2005). As tasks become less meaningful, individuals are more likely to engage in social loafing (Karau & Williams, 1993). Perceived injustice, and uneven distribution of rewards, also increase social loafing (Karau & Williams, 1993; Liden, Wayne, Jaworski, & Bennett, 2004). Group member domination, perceptions of unequal reward distribution, and unfairness of group evaluations are additional factors associated with increased perceptions and self-reported incidents of social loafing in online class groups (Piezon & Ferree, 2008).

Social Presence

Social presence has been defined and redefined countless times, with each definition focusing on a different piece of the concept (e.g., Gunawardena & Zittle, 1997; Short, Williams, & Christie, 1976). Some definitions of social presence tend to focus on the social affordances of the technology, or the ability to transmit important interpersonal cues, such as nonverbal communication (Short et al., 1976). Others focus on the perceptions of interactants, defining social presence as “the degree with which a person is perceived as a ‘real person’ in mediated communication” (Gunawardena & Zittle, 1997, p. 9). Similarly, another definition notes social presence as, “a measure of the feeling of community that the learner experiences in an online environment” (Tu & McIsaac, 2002, p. 131). Focusing on the sender of messages, Rourke, Anderson, Garrison, and Archer (1999) conceptualized social presence as “the ability of learners to project themselves socially and affectively into a community of inquiry” (p. 50). The inconsistencies in conceptualizing social presence may seem slight, but minor differences in defining a construct lead to inconsistent measurement of the concept overall (Biocca, Harms, & Burgoon, 2003; Lowenthal, 2010).

Although social presence research did not originate in studying educational interactions, the growth of online learning (Allen & Seaman, 2015; Instructional Technology Council, 2014), and the emphasis on learning as a social process (Vygotsky, 1978), has resulted in heavy reliance on social presence research in online education settings (Lowenthal, 2010). Definitions focusing on the technological affordances of online courses point to social presence as a qualifier of creating a sense of community for students. Technologies affording a larger variety and richer cues, such as nonverbal information and communication of emotion, have been associated with the benefits of more social presence in an online educational setting (Bayram, 2013; Draus, Curran, & Trempus, 2014; Miller & Redman, 2010). Richer media, such as use of video, is associated with more student satisfaction in online courses than less rich media, such as text-based discussion boards (Bayram, 2013). Use of video is, more specifically, associated with closer connection and more positive evaluations of the course instructor (Draus et al., 2014; Miller & Redman, 2010) and more satisfaction with the overall course (Draus et al., 2014), but not necessarily more positive attitudes toward the subject of the course (Miller & Redman, 2010). While online courses typically use asynchronous, text-based communication methods, the use of richer media like video have been associated with increased perceptions of learning (Bayram, 2013), and Draus et al. (2014) suggested the possibility of an overall increase in grades by 3.2%, although the increase was not statistically significant. Students often feel a lack of connection to their online classmates and many students have yet to see the connection between creating interpersonal relationships online and creating academic relationships online (Newberry, 2001). When individuals form friendships in their online class groups, it creates a warm atmosphere and sense of community, which increases individual accountability and interdependence among group members. Individual accountability may decrease an individual’s social loafing (Piezon & Ferree, 2008; Wang, 2009). Surface-level communication has also been shown to effectively create a warm environment and elevate social presence (Wade, Cameron, Morgan, & Williams, 2011). Similarly, “off-topic” conversations (e.g., two participants discussing a movie in the group chat, or group members complaining about the difficulty of using the chat itself) also help create a sense of community and connect group members (Beuchot & Bullen, 2005; Paulus, 2009). These topics were generally surface-level, connecting the group members in the moment rather than fostering interpersonal relationships (Beuchot & Bullen, 2005; Paulus, 2009). Both interpersonal and surface-level connections have been shown to elevate social presence in online class groups.

Emotional communication develops relationships and a sense of community in online groups. Individuals often experience extreme emotions in online classes (O’Regan, 2003). Explicit use of emotional communication (e.g., emoticons) helped create relationships among group members, created a sense of community, and elevated social presence (Robinson, 2013), which in turn could increase individual accountability and interdependence, and decrease instances of social loafing in online class groups (Piezon & Ferree, 2008; Wang, 2009).

Critique and Future Directions

The large body of existing research has yielded many insights into patterns of formation and interaction processes in online course groups. However, gaps in the current literature remain, offering opportunities for further exploration and clarification. The following critique identifies three main areas to be addressed: conflicting conceptualizations and results, methodological issues, and a lack of diverse disciplinary perspectives.

Conflicting Conceptualizations and Results

Within the review of literature, a lack of consistent construct definitions was evident. For example, a comprehensive definition of social presence does not yet exist in the current literature, and further research should be done to solidify the definition. Current definitions focus on different aspects of social presence (e.g., Gunawardena & Zittle, 1997; Oztak & Brett, 2011; Rourke et al., 1999; Short et al., 1976) rather than a holistic picture of the concept. The lack of a comprehensive conceptualization of social presence hinders scholars’ ability to fully operationalize and measure this concept in terms of online class groups, and leads to a lack of understanding of the ways social presence can influence, and be influenced by, group work online, as well as specific student learning outcomes. Future research should focus on the creation of an inclusive conceptualization of social presence. Additional analyses of existing studies should be done to create a comprehensive list of current social presence definitions to create a more inclusive definition of the concept.

Studies have also produced several conflicting findings. For example, existing research on group size has led to conflicting results. Some scholars argue that small groups lead to more participation, learning, and satisfaction (e.g., Kim, 2013; Koh et al., 2010; Shaw, 2013), whereas others posit that large groups perform better, learn more, and discuss longer than small groups (e.g., Hew & Cheung, 2011; Hewitt & Brett, 2007; Lim & Zhong, 2006). Interestingly, methodological differences can be observed in those studies that support small groups compared with those studies that support large groups. Studies supporting the use of small groups tend to be qualitative in nature and often involve the coding of discussion behaviors. For example, Kim (2013) coded discussions into four categories, including independent, quasi-interactive, interactive (elaborate), and interactive (negotiating). However, with the exception of some articles (e.g., Hew & Cheung, 2011), the studies supporting large groups tend to be quantitative in nature. For example, Hewitt and Brett (2007) used quantitative indicators of participation, such as the number of discussions opened, the number of discussions read, and the length of contributions to the discussion. Because of the contradictions among studies and among methodologies, optimal group size is still unknown, and future research should continue exploring group size and its effect on other group processes to determine if an optimal group size exists. Inconsistent findings lead to inconsistent advice for future students and instructors to follow when putting together groups in online classes. If an optimal group size does in fact exist, future research has the opportunity to further our understanding of what that is and how it affects other aspects of group processes, helping future students and instructors set groups up for success.

Furthermore, studies that specifically investigate learning outcomes often operationalize learning outcomes differently. Education research, in general, has struggled with determining the best measurement of cognitive learning (McCroskey, Richmond, & McCroskey, 2006). More specifically, research measuring learning in online settings has used a variety of measurements. Some studies focus on end of semester exam score (Shaw, 2013), others look to student satisfaction (Koh et al., 2010), the specific number of posts (Chiong & Jovanovic, 2012; Wresch et al., 2005), or simply note that students reach higher levels of learning (Hew & Cheung, 2011) as evidence of student engagement and learning. Each of these methods has its advantages and disadvantages (McCroskey et al., 2006). For example, although course grades seem like a fairly objective measurement, many factors aside from cognitive learning contribute to course grades, such as attendance, motivation, and participation. This lack of clearly defined learning outcomes can make it difficult to compare studies to determine best practices for online classes. Future development of specific learning outcomes and ways to operationalize those outcomes across studies would be beneficial to understand and develop best practices for online course group work to aid in student learning.

Theoretical and Methodological Issues

Articles included in this review drew from multiple theoretical frameworks, including social presence, input–process–output (IPO), and community of inquiry (COI). Most commonly, articles referenced social constructivist principles. However, several articles did not articulate clear theoretical bases for their studies, merely using past topic research as a framework or citing computer-supported collaborative learning (CSCL), more of a pedagogical approach than a theoretical framework. A clearer articulation of theory in this line of research would facilitate more focused knowledge building regarding online course groups.

In addition, methodological issues in the current body of literature warrant consideration for future research. Although empiricism was one of the criteria for selection in this review, many articles seemed more concerned with using theory or existing research to prescribe particular instruction design or teaching methods, rather than providing empirical evidence for the effectiveness of practices (e.g., Annand, 2011; Newberry, 2001). Better than no empirical evidence at all, articles commonly utilized case studies (e.g., Lawrence-Slater, 2006), content analyses of online course content, and interview data (e.g., Koh et al., 2010). Although qualitative research is often valuable for its ability to capture a rich description of individuals’ points of view (Denzin & Lincoln, 2011), results cannot necessarily be generalized to other contexts. Future research about small group work in online courses would benefit from adopting more experimental designs. Few existing articles currently use this research design, despite its ability to provide understanding of causality between variables.

One particular benefit that could come of an experimental design is the possibility of better understanding what input variables cause the output variable of greater learning in online group work. Currently, much of the research tends to focus on affective, rather than cognitive, outcomes of online group work. Creating understanding of outcome variables like student satisfaction is valuable, especially for concerns like student retention. However, another important aspect of course work, actual student learning, is neglected in existing literature. Only a fraction of the literature reviewed looked toward student learning as an outcome variable (e.g., Sadeghi & Kardan, 2015; Shaw, 2013). If online collaboration truly contributes to deeper student learning (Chapman et al., 2005), researchers should focus studies of the details of collaboration on the outcome variable of learning. For example, it would be beneficial to better understand the group size or composition that leads to greater learning, or if social presence or sense of community actually contributes to student learning in online class groups. In addition, studies done to further understanding of specific learning outcomes, and ways to operationalize those outcomes, would be beneficial.

Another critique of the methodology of existing articles about online group work has to do with the samples used. When empirical studies were employed, their samples were often not generalizable to most learners. Many studies used graduate student learners as the sample (e.g., Koh et al., 2010; Paulus, 2009; Xu et al., 2015). Common samples also included instructional design or education majors or students enrolled in instructional design or education courses (e.g., Schellens et al., 2005). Hew and Cheung (2011) even claimed that their use of students studying education as their sample was a strength of their study, citing the possibility of minimizing the risk of confounding variables from students studying other disciplines. This is problematic for several reasons. First, graduate students pursuing advanced degrees do not necessarily participate in learning the same way typical undergraduate students participate. Next, students enrolled in instructional design or education courses are exposed to instruction about course design within their courses. These students may have a deeper understanding of course design and instructional methods, such as designing group work, that more general student samples would not have. This may have affected their participation and responses within the study. Overall, much of the existing literature may not be generalizable to the general student population because of its use of convenience sampling.

Diverse Disciplinary Perspectives

An additional limitation of the existing online course group research is the largely homogeneous disciplinary perspective. Although a handful of the articles reviewed came from interdisciplinary group and technology publications, the vast majority of studies of online course groups are written by scholars in education, published in education-focused journals and, as previously noted, rely primarily on data from students in educational technology courses. Small group research is an area of study that has benefited from multiple disciplinary perspectives, including psychology, sociology, management, and communication, but the same cannot be said about research focusing on small student groups in online courses. While education scholars clearly and rightfully have a vested interest in studying this particular type of group, scholars in other disciplines can offer different approaches and ideas for examining online course group formation and interaction. Communication scholars are in a particularly strong position to offer additional insights on group interaction processes using computer-mediated communication (CMC) research in other contexts. The social information processing perspective, for example, posits that time is a critical element of relationship development in CMC because interpersonal relationship development through CMC requires the exchange of more messages to reach the same closeness levels achieved through face-to-face interaction (Walther, 1992). As online course groups face comparable issues in terms of relationship development, particularly if placed in semester-long groups, social information processing provides a theoretical framework with potential to yield valuable practical insights into groups’ social online interaction patterns. In addition, communication climate in online student groups has yet to be explicitly examined and could have important impacts on group processes. For instance, psychologically safe communication climates can mitigate potential negative effects of virtuality for organizational teams (Gibson & Gibbs, 2006). Furthermore, although instructional communication research tends to incorporate the role of the instructor more prominently than the publications on which the present review focused, instructional communication researchers’ understanding of classroom dynamics could certainly be applied and explored in online contexts as well. For instance, Kaufmann, Sellnow, and Frisby (2016) recently developed an Online Learning Climate Scale (OLCS) that includes items assessing interaction and collaboration among students in the course. Communication researchers might compare differences in OLCS perceptions and group outcomes, or use the OLCS as a starting point to assess student perceptions of the online group process. Lack of interdisciplinary viewpoints not only limits the scope of existing research but also presents a promising and potentially fruitful opportunity for scholars in a variety of disciplines to explore further, as a continued need exists to connect research regarding learning, technology, and small groups (Keyton, 2016).

Conclusion

Online education continues to grow, as does the need for effective team collaboration. As such, online course groups have been the focus of many studies and publications in recent years. Although the existing literature has provided a number of insights on group formation, roles, interaction, collaboration, and social factors in online student groups, several limitations exist. Through a meta-synthesis of existing literature on student collaboration in online courses, it became evident that current literature presents conflicting results and conceptual definitions, is notably lacking in true empirical data, and is mainly written by, and for, education scholars. Future research has ample opportunity to address these limitations and provide a more thorough understanding of online course group processes, yielding beneficial practical applications for current and future online course instructors and students.

Footnotes

Appendix

Articles Included in Critical Synthesis About Student Group Work in Online Courses.

Authors (year)	Theoretical framework/method	Findings
AbuSeileek (2012)	Past topic research/quantitative	Groups in a foreign language course scored higher in individual accountability mode than positive interdependence mode; groups of five significantly outperformed groups of other sizes.
Beuchot and Bullen (2005)	Social presence, interpersonality, and impersonality/mixed methods	Interpersonality online associated with higher participation and more in-depth group discussions.
Chapman, Ramondt, and Smiley (2005)	Constructivist learning theory/qualitative	Collaborative learning environments encourage learners to disclose their uncertainties, which is more likely to happen in a stronger community, leading to greater learning for all individuals in the group.
Cheng and Chu (2016)	Theory of planned behavior/quantitative	Students’ past experience and behavior positively influence their subjective norms, attitudes, and perceived behavioral control of collaborating online, which in turn positively influences their intentions to collaborate online for group projects.
Chiong and Jovanovic (2012)	Evolutionary game theory/qualitative	Reworking groups midway through the course to put the most active participants together led to more activity in those groups, but did not encourage inactive members in other groups to become active.
Choi and Kang (2009)	Activity theory/qualitative	Lower performing groups were slower to collaborate than higher performing groups. In both groups, the majority of learner behaviors were related to expending efforts and using the knowledge of the community around them to achieve their learning goal and production outcomes.
De Wever, Schellens, Van Keer, and Valcke (2008)	CSCL/quantitative	Students enacted all five roles in online groups as they were assigned: source searcher, theoretician, summarizer, moderator, and starter.
De Wever, Van Keer, Schellens, and Valcke (2010)	Constructivist principles/content analysis	Students in the tutor-supported discussion groups reached significantly higher levels of knowledge construction than students completing tasks with only defined role support.
Du, Xu, and Fan (2013)	Interest influences/quantitative	Most variance in online group work interest occurred at the individual student level. In particular, learning-oriented reasons positively associated with group work interest. At the group level, learning-oriented reasons, as well as peer-oriented reasons and monitoring motivation, significantly predict interest.
Ergün and Usluel (2016)	Social network analysis	Six groups analyzed; overall lowest network density occurred in the first week of group discussion and highest occurred in the week the instructor participated in the group.
Hamann, Pollock, and Wilson (2012)	Past topic research/quantitative	Small discussion groups received higher student satisfaction and critical thinking skills than class-wide online discussions.
Hew and Cheung (2011)	Interaction analysis model/mixed methods	Group size, but not discussion duration, positively related to frequency of higher level knowledge construction. Group discussion facilitators, who more frequently give comments or opinions, show appreciation, encourage people to contribute, and summarize, also relate to more frequent occurrences of higher level knowledge construction.
Hewitt and Brett (2007)	Past topic research/quantitative	Larger classes wrote more notes per student on average, but notes were longer and students read higher percentages of notes in smaller classes.
Jahng (2012a)	Grounded theory/qualitative	All groups had two working phases and three decision-making points and experienced temporal development patterns aligning with aspects of the traditional group development model and punctuated equilibrium model, but groups in the study seemed to align more closely with the latter model.
Jahng (2013)	Small group collaboration model/mixed methods	Identifies facilitating factors (e.g., a collaborative approach, high team spirit and lots of social communication) and hindering factors (e.g., indecisiveness, lack of leadership, technical problems, personal conflicts, and a late start) related to quantity, quality, and shareness collaboration indices.
Jahng, Nielsen, and Chan (2010)	IPO model, COI/mixed methods	Identified three indices for evaluating small group collaboration: quantity, quality, and shareness (degree of open communication between members); also found that inactive participants in whole-group discussions became more active in small group discussions and the most active participants in each group were female.
Kim (2013)	Past topic research/mixed methods	Small group discussions showed higher rates of participation (posts and hits), interactive-negotiating, and interactive-elaborating posts, than class-wide discussions.
Koh, Barbour, and Hill (2010)	Social presence and social interaction/qualitative	Students identified lack of communication and accountability as issues in group work online and suggested strategies for course design and group work processes that instructors could facilitate to improve online collaboration.
Lawrence-Slater (2006)	Case study	Students who submitted a photo in their required profiles were more active online (which in turn related positively to learning outcomes) and demonstrated higher quality in some aspects of their work.
Lim and Zhong (2006)	Media synchronicity theory/quantitative	Leaders facilitated higher performance in culturally diverse groups
Morgan, Cameron, and Williams (2009)	Past topic research/qualitative	Identified five themes for how students carried out social tasks in online groups: respect, “be nice,” follow the rules/follow the leader, communication, and defined roles and expectations.
Oliveira, Tinoca, and Pereira (2011)	Grounded theory/qualitative	More and less successful groups differed in patterns of work and social presence; more successful group patterns indicated a more collaborative and trusting nature.
Paulus (2009)	Theory of common ground/computer-mediated discourse analysis	Groups talked more about off-topic issues than course concepts and established common ground through being explicitly responsive, responsible, and relational.
Piezon and Ferree (2008)	Social loafing and organizational justice/quantitative	Dominance negatively related to individual group contributions and positively to reports of social loafing.
Robinson (2013)	Sociocognitive model of self-regulated learning and COI/qualitative	Group work (both face-to-face and online) is a source of emotion. Unlike face-to-face work in which emotions are easily noted, online groups must be much more detailed and overt textually to communicate emotions in online course groups.
Schellens, Van Keer, and Valcke (2005)	Social constructivist principles; information processing approach to learning/qualitative	Higher individual student posts and a positive attitude toward the learning environment led to higher levels of knowledge construction. Knowledge construction levels decreased for the more complex tasks, and there were virtually no differences in knowledge construction based on role condition (though students who served in the summarizer role did report significantly higher levels of knowledge construction than students in other roles or students without roles).
Shaw (2013)	Social learning theory and social interdependence theory/quantitative	Different group sizes have significantly different participation rates and levels of learning satisfaction, and participation significantly influences learning scores. However, different group sizes did not significantly affect learning scores, and participation did not significantly influence learning satisfaction.
Siqin, van Aalst, and Chu (2015)	CSCL/mixed methods	Opportunistic collaboration led to more ideas, and higher level ideas, but interactions were scattered and made little use of metacognition. Active participation and contribution of high-level ideas positively correlated with students’ domain knowledge.
Stepanyan, Mather, and Dalrymple (2014)	Probabilistic social network analysis	Participants of shared cultures tended to interact with each other; participants with a high number of responses tend to receive even more. Network cohesion increases over time.
Thompson and Ku (2006)	Mixed-methods case study	Major challenges to online collaboration included ineffective communication, conflict among group members, and negative attitudes. More collaborative groups produced better quality projects and had more positive attitudes toward online collaborative learning. Social loafing occurred in each group.
Vercellone-Smith, Jablokow, and Friedel (2012)	Kirton’s adaption-innovation theory/mixed methods	Students’ selection of online communication partners did not correlate with cognitive style; members of the core groups exhibited higher clique membership.
Wade, Cameron, Morgan, and Williams (2011)	Past topic research/quantitative	Students’ perceptions of caring and concern affected trust in online groups, but interpersonal relationships with their group members did not. Males reported less participation and more negative reports of group chats.
Wang (2009)	CSCL/case study	Friendship and meaningful learning tasks helped to promote individual accountability and positive interdependence.
Williams, Morgan, and Cameron (2011)	Past topic research/qualitative	Identified four emerging themes in the process of role formation (testing the waters, apologies as being nice, tag—you’re it, and struggling to find one’s role), and informal roles that evolve (leader, wannabe, spoiler, agreeable enabler, coattails, and supportive worker) when roles are not assigned.
Wise, Saghafian, and Padmanabhan (2012)	Past topic research/mixed methods	Role descriptions elicited particular functions from assigned group members; group members found the use of roles valuable, particularly with regard to providing structure to the group.
Wise and Chiu (2014)	Past topic research/quantitative	Synthesizing and summarizing roles increased students’ listening behaviors in online discussion groups during their weeks assigned to those roles, but the listening behaviors were only weakly sustained when students no longer had those roles.
Wresch, Arbaugh, and Rebstock (2005)	Rovai’s four attributes of community/quantitative	Adding students with different cultural backgrounds to online discussion groups increased discussion participation overall, but not to the extent expected; also discusses cultural differences in expectations regarding participation.
Xu, Du, and Fan (2013)	Self-regulated learning/quantitative	Most variance in emotion management came at the individual level, influenced by full-time student status, feedback, learning-oriented reasons, arranging the environment, monitoring motivation, and help seeking.
Xu, Du, and Fan (2014)	Self-regulated learning/quantitative	Studied Chinese students for cultural comparison. Emotion management was positively associated with feedback and learning-oriented reasons at the group level, and monitoring motivation, learning-oriented reasons, feedback, peer-oriented reasons, arranging the environment, and the number of previous online courses at the individual level; findings indicated some cultural differences compared with Xu et al.’s (2013) test of U.S. students.
Xu, Du, and Fan (2015)	Self-regulated learning/quantitative	Feedback, help seeking, online group work interest, and affective attitude relate positively to group work management (regulating behaviors).
Yeh (2010)	Past topic research/mixed methods	Explores behaviors and corresponding roles that emerge in online groups; the most common roles were information providers, opinion providers, and troublemakers.

Note. CSCL = computer-supported collaborative learning; IPO = Input–process–output; COI = community of inquiry.

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Maura R. Cherney is a PhD student at the University of Wisconsin–Milwaukee, USA. Her research focuses on communication technology use and its effects in interpersonal and educational contexts.

Michelle Fetherston recently received her PhD from the University of Wisconsin–Milwaukee, USA. Her research focuses on communication related to work and careers, particularly career exploration and preparation in higher education.

Lauren Jean Johnsen is a PhD student at the University of Missouri, USA. Her research focuses on how family members communicate to make sense of stigmatized family structures, topics, and identities.

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