Influence of group member familiarity on online collaborative learning

https://doi.org/10.1016/j.chb.2008.08.010Get rights and content

Abstract

This study investigated the effects of group member familiarity during computer-supported collaborative learning. Familiarity may have an impact on online collaboration, because it may help group members to progress more quickly through the stages of group development, and may lead to higher group cohesion. It was therefore hypothesized that increased familiarity would lead to (a) more critical and exploratory group norms, (b) more positive perceptions of online communication and collaboration, (c) more efficient and positive collaboration, and (d) better group performance. To investigate these hypotheses, 105 secondary education students collaborated in groups of three. The results of this study indicate that higher familiarity led to more critical and exploratory group norm perceptions, and more positive perceptions of online communication and collaboration. Furthermore, in familiar groups students needed to devote less time to regulating their task-related activities. The expectation that familiarity would lead to better group performance was not confirmed. These findings imply that online educators pay attention to the effects group member familiarity has on online collaborative learning.

Introduction

Over the past 20 years, research on computer-supported collaborative learning (CSCL) has helped support the claim that collaborative activity among students can effectively be supported with computer technology. The accumulated knowledge concerning effective CSCL has also led to detailed design guidelines for CSCL (Kirschner et al., 2004, Kreijns et al., 2003, Strijbos et al., 2004). In spite of these design guidelines, researchers still experience problems when students collaborate using computer technology. These include for example, conflicts (Hobman, Bordia, Irmer, & Chang, 2002), communication difficulties (Daft and Lengel, 1986, Fuks et al., 2006), and shallow, uncritical discussions (Munneke, Andriessen, Kanselaar, & Kirschner, 2007). Although these problems may be caused by poor implementation of the design guidelines mentioned, it may also be the case that research has focused too little on potential moderators that can influence the effectiveness of CSCL (Hollingshead & McGrath, 1995), such as time spent on group work (e.g., one session versus prolonged group work), task type (e.g., open versus closed tasks), group size (e.g., small versus large groups), and group or student characteristics (e.g., estrangement versus familiarity of group members). For example, how well students know each other prior to their collaboration may have an impact on several aspects of their collaboration (Kiesler & Sproull, 1992). Ignoring such moderators may lead to inconsistent and contrasting results, making it very risky to draw generalizations. Furthermore, it is important identify factors that moderate the effectiveness of CSCL, because then they can be taken into account by educational designers enabling the design of more effective and enjoyable CSCL experiences.

The aim of this contribution is to examine the effect of one such potential moderator, namely group member familiarity. Kiesler and Sproull (1992) identified group member familiarity as an important factor to consider when designing CSCL. The effects of familiarity on group interaction and performance are related to aspects of Tuckman’s (1965) stages of group formation: Forming, storming, norming, and performing. It has been hypothesized that when group members know each other well, they will spend less time forming a coherent group, and will establish group norms more easily, and thus, reach the performing stage more quickly. This is thought to have beneficial effects for, among others, satisfaction with online collaboration and group performance (Adams, Roch, & Ayman, 2005). Furthermore, research has highlighted the importance of prior experiences of online collaborators (Carlson & Zmud, 1999). The more experience students have with, for example, the medium, their group members, or the task at hand, the more effectively they will be able to collaborate online. When students know their group members well, they have acquired knowledge about their partners that they can use to interpret partners’ messages, to identify their strengths and weaknesses, and to adapt their communication to their partners’ specific needs. Moreover, when students acquire knowledge about their partners they may develop deindividuating impressions of their group members (Walther, 1992), which may help them to overcome the inherent restrictions of the medium (e.g., lack of verbal cues, intonation of voice, and gestures). Also, it can be assumed that trust is higher in familiar group contexts (Carlson & Zmud, 1999). Research has shown that it takes some time for trust to develop among group members during online collaboration, while trust (or the lack thereof) has also been found to have effects on the collaborative process (Wilson, Straus, & McEvily, 2006). This all suggests that when students know their group members well, their online collaboration will be more efficient and effective.

Although only a small number of studies have investigated the impact of group member familiarity on CSCL (Adams et al., 2005, Mennecke, Hoffer, & Valacich, 1995, Mukahi & Corbitt, 2004, Orengo Castellá et al., 2000, Smolensky et al., 1990), researchers have demonstrated possible positive and negative consequences of increased familiarity among group members. For example, Adams et al. found that when group members knew each other better, their satisfaction with the group process increased, although their decision accuracy decreased. Similarly, Smolensky, Carmody, and Halcomb (1990) found that familiarity had a negative impact on students’ interactive behavior, which, in turn led to decreased group performance. In contrast, Mukahi and Corbitt found no relationship between familiarity and students’ collaborative activities.

An explanation for the mixed results may be the different operationalizations of familiarity (Adams et al., 2005). Adams et al., for example, following Gruenfeld, Mannix, Williams, and Neale (1996), asked students to rate familiarity with group members on a 4-point scale. Smolensky et al. (1990), on the other hand, did not measure familiarity directly but asked half of their participants to bring two friends to their experiment, so as to create familiar and unfamiliar groups, thus equating familiarity with friendship. In our opinion however, students can be familiar with each other without being friends. In this study, familiarity was operationalized by asking students, before the start of their collaboration, to indicate how well they knew the other group members. This way, the collaboration itself does not affect students’ judgments of familiarity. On the other hand, asking students to rate familiarity before the collaboration may draw attention to whether they worked with friends or strangers, which may influence students’ subsequent collaborative behavior.

Our study differed on several aspects from previous studies on familiarity. In contrast to other studies, students in our sample came from existing secondary education classes, thus most group members knew their teammates to a certain extent, although variations obviously existed. In other studies, (university) students were recruited from a pool of student volunteers (e.g., Adams et al., 2005). Additionally, the study presented here was carried out in an authentic educational context, in which students collaborated online for a longer period of time. In contrast, in other studies the effects of familiarity were often examined in a single online session, while students worked on group tasks with little or no relationship to the curriculum (e.g., Mennecke, Hoffer, & Valacich, 1995, Orengo Castellá et al., 2000). Furthermore, most studies that examined the role of familiarity during online collaboration focused on either students’ perceptions (e.g., their satisfaction with the collaborative process) or on students’ interactive behavior (e.g., use of negative speech). This study will focus on perceptions as well as behavior.

Thus, in order to extend the research findings concerning familiarity, this paper focuses on the effects of familiarity on (a) perceived group norms, (b) perceptions of online collaboration and communication, (c) students’ collaborative activities, and (d) group performance. The remainder of this introduction focuses on describing the possible effects familiarity may have on these four variables.

As groups include group members who are more familiar with one another, students may be more comfortable expressing disagreement (Gruenfeld et al., 1996). As such, familiarity may help group members to adopt critical or exploratory group norms instead of consensus norms (Postmes, Spears, & Cihangir, 2001). This is important because critical or exploratory group discussions have been shown to lead to more effective group work (Wegerif, Mercer, & Dawes, 1999). During critical group discussion, students do not hesitate to question each others’ opinions or to disagree with one another (Postmes et al.). Exploratory group discussions are similar to critical group discussions in the sense that students accept criticism from each other and discuss alternatives. In addition, these kinds of discussions should be held in a constructive manner. In other words, conflicts and disagreements are welcome, but group members should try to resolve them and come to an agreement (Di Eugenio et al., 2000, Erkens et al., 2005). Furthermore, during exploratory discussions group members share relevant information and encourage each other to participate (Wegerif et al., 1999). It is expected that familiar group members will be more likely to develop group norms which value critical or exploratory online discussions because they do not feel the social pressure to agree with other group members (Adams et al., 2005). Unfamiliar group members may be more prone to adapt to such pressure. These critical or exploratory versus consensual group norms will be developed in the norming stage of group formation (Tuckman, 1965). Thus, the following hypothesis may be formulated:

H1 Group member familiarity will contribute to more critical and exploratory group norms.

In familiar groups, group cohesion will likely be higher because group members feel more comfortable with the other members (Adams et al., 2005, Mennecke, Hoffer, & Valacich, 1995). Furthermore, when group members know each other better, they may be able to communicate and collaborate efficiently (Adams et al.). This will lead familiar group members to perceive their online communication and collaboration within their group as being more positive. Students may also perceive their communication and collaboration more positively in familiar groups because psychological safety is higher in these groups (Schepers et al., 2008, Van den Bossche et al., 2006). Indeed, studies by Mennecke, Hoffer, & Valacich, 1995, Adams et al., 2005, Stone and Posey, 2008 found more positive perceptions of communication and collaboration in familiar groups. Therefore, a second hypothesis will be investigated:

H2 Group member familiarity will lead to positive perceptions regarding the collaborative process.

As familiarity between group members increases, communication and coordination of collaboration may take less effort. For example, the transfer of information relevant to executing the task may be more efficient, and misunderstandings may be less likely to occur. This can be explained by the higher amount of knowledge available to familiar group members of other member’s skills, expertise and communication styles (Adams et al., 2005). Familiar group members may share a social history, making it easier to understand each other and know each other’s strengths and weaknesses. Similarly, familiarity may decrease the need for extensive regulation and coordination of task and group processes. Consequently, a third hypothesis will also be investigated.

H3 Group member familiarity will influence online collaborative activities. More specifically, transfer of information, regulation of task and group processes, and misunderstandings will decrease.

In light of the above, it is likely that the increased knowledge of group members’ skills and modes of interaction will help familiar groups outperform groups of strangers. For example, familiar groups will experience less process losses (e.g., misunderstandings) and be more inclined to pool information resources to effectively carry out the group task (Gruenfeld et al., 1996). Furthermore, if H1 is true, then familiar groups may hold more critical and exploratory group norms, which help them engage in argumentative interactions. Such argumentative interactions are likely to contribute to quality of the collaboration (Clark et al., 2007, Munneke et al., 2007, Weinberger and Fischer, 2006). Finally, collaboration may be more efficient because familiar groups do not need to devote as much time to regulating and coordinating task and group processes. Therefore, this study will address a fourth and final hypothesis:

H4 Group member familiarity will lead to better group performance.

Section snippets

Participants

The participants were students who came from five different history classes from two secondary schools. The total sample consisted of 105 eleventh-grade students (47 male, 56 female). The mean age of the students was 16.17 years (SD = .57, Min = 15, Max = 18). The participants were randomly assigned to 35 different 3-person groups. It is important to note that students were assigned to groups within their own class and did not collaborate with students from other classes or schools.

CSCL environment: Virtual Collaborative Research Institute

Group

Procedure

In total, the participating students worked eight, 50-min lessons on the inquiry task. During the lessons, each student worked on a separate computer in a computer lab. Students sat as far from their teammates as possible, in order to stimulate them to use to the VCRI-program to communicate with their other group members. Before the first computer lesson, students received information about the task and their group’s composition. Furthermore, students completed a pretest questionnaire,

Group norm perception

Table 3 shows the means and standard deviations of familiarity and the three measures of group norm perception, and their intercorrelations. As can be seen from this Table, students reported an average familiarity (M = 4.24, SD = 1.48) with their group members. Furthermore, familiarity correlated significantly with several dependent variables.

Because the data were nested (i.e., students worked in groups), and because there was interdependence between group members’ scores (i.e., group members

Conclusions and discussion

This study investigated the effect of familiarity on CSCL. The results indicate that familiarity influences several aspects of online collaboration. Because familiar group members may be more comfortable expressing their disagreement with their teammates, it was expected that higher familiarity would be associated with more critical and exploratory group norm perceptions (H1). This was confirmed as we found that familiar students reported their group norms to be more critical and exploratory

Acknowledgements

This study is part of the Computerized Representation of Coordination in Collaborative Learning (CRoCiCL) project. This project is funded by NWO, the Netherlands Organisation for Scientific Research under project number 411-02-121. Furthermore, the authors thank Jos Jaspers and Marcel Broeken for their technical assistance. Extra thanks to Alyda Griffioen for her work during the preliminary data analyses.

References (58)

  • V. Orengo Castellá et al.

    The influence of familiarity among group members, group atmosphere and assertiveness on uninhibited behavior through three different communication media

    Computers in Human Behavior

    (2000)
  • J. Schepers et al.

    Psychological safety and social support in groupware adoption: A multi-level assessment in education

    Computers and Education

    (2008)
  • M.A. Smolensky et al.

    The influence of task type, group structure and extraversion on uninhibited speech in computer-mediated communication

    Computers in Human Behavior

    (1990)
  • N.J. Stone et al.

    Understanding coordination in computer-mediated versus face-to-face groups

    Computers in Human Behavior

    (2008)
  • J.W. Strijbos et al.

    Designing for interaction: Six steps to designing computer-supported group-based learning

    Computers and Education

    (2004)
  • J.W. Strijbos et al.

    Content analysis: What are they talking about?

    Computers and Education

    (2006)
  • H. Van der Meijden et al.

    Face-to-face versus computer-mediated communication in a primary school setting

    Computers in Human Behavior

    (2005)
  • J. Van Drie et al.

    Effects of representational guidance on domain specific reasoning in CSCL

    Computers in Human Behavior

    (2005)
  • R. Wegerif et al.

    From social interaction to individual reasoning: An empirical investigation of a possible socio-cultural model of cognitive development

    Learning and Instruction

    (1999)
  • A. Weinberger et al.

    A framework to analyze argumentative knowledge construction in computer-supported collaborative learning

    Computers and Education

    (2006)
  • J.M. Wilson et al.

    All in due time: The development of trust in computer-mediated and face-to-face teams

    Organizational Behavior and Human Decision Processes

    (2006)
  • S.J. Adams et al.

    Communication medium and member familiarity: The effects on decision time, accuracy, and satisfaction

    Small Group Research

    (2005)
  • J.R. Carlson et al.

    Channel expansion theory and the experiential nature of media richness perceptions

    Academy of Management Journal

    (1999)
  • D.V. Cicchetti et al.

    A computer program for assessing specific category rater agreement for qualitative data

    Educational and Psychological Measurement

    (1978)
  • D.B. Clark et al.

    Analytic frameworks for assessing dialogic argumentation in online learning environments

    Educational Psychology Review

    (2007)
  • U. Cress

    The need for considering multilevel analysis in CSCL research: An appeal for the use of more advanced statistical methods

    International Journal of Computer-Supported Collaborative Learning

    (2008)
  • R.L. Daft et al.

    Organizational information requirements, media richness and structural design

    Management Science

    (1986)
  • Dennis, A. R., & Valacich, J. S. (1999). Rethinking media richness: Towards a theory of media synchronicity. Paper...
  • Di Eugenio et al.

    The agreement process: An empirical investigation of human–human computer-mediated collaborative dialogs

    International Journal of Human–Computer Studies

    (2000)
  • Cited by (110)

    View all citing articles on Scopus
    View full text