Analyzing roles of individuals in small-group collaboration processes

Abstract

The papers in this issue present innovative approaches to analyzing the roles of individuals in small-group collaborations supported by computer technologies. In reading these articles, you may find it interesting to consider the ways in which their methods conceptualize the relationship of collaborative group learning to the roles of its individual participants. Taken together, these studies envision and explore a space of possible strategies for analyzing the multi-level phenomena of collaborative learning, sometimes coding utterances of individuals and at other times characterizing group trajectories. They each push the boundaries of CSCL research in various ways. Although they can be read as primarily proposing analytic procedures, they also contribute to theory and technology. Perhaps highlighting their nuanced stances on the issue of unit-of-analysis in probing learning data can help to reveal their contributions to the advance of CSCL as a vision and as a field.It is often difficult to determine where overall progress is being made in CSCL research and practice. Statistical indicators in comparative reviews tend to be overwhelmed by the diversity of theories and methodologies applied in research and by the variety of pedagogies adopted in practice. In both researcher and teacher communities, there are new participants entering with training in traditional disciplines as well as long-time participants still working within old paradigms. Folk theories derived from common sense linger on and may obscure the visibility of innovations in scientific theory, methodology or pedagogy.Folk theories of minds and learning still influence classroom practice. According to Bruner (1996) and Bereiter (2002), teachers' pedagogy is often deeply affected by everyday intuitive Intern.

Group communication analysis: A computational linguistics approach for detecting sociocognitive roles in multiparty interactions

“…Several studies have emphasized the importance of roles in group interactions (Dillenbourg, 1999;Hoadley, 2010;Jahnke, 2010;Marcos-Garcia, Martinez-Mones, & Dimitriadis, 2015;Sarmiento & Shumar, 2010;Smith Risser & Bottoms, 2014;Spada, 2010;Stahl, Law, Cress, & Ludvigsen, 2014;Strijbos & De Laat, 2010;Volet et al, 2017). Recent work on scripted or assigned roles shows that the assignment of specific roles facilitates collaborative awareness (Strijbos, Martens, Jochems, & Broers, 2004), team discourse and performance (Gervits, Eberhard, & Scheutz, 2016;Xie, Yu, & Bradshaw, 2014), and the depth of knowledge co-construction by the group (De Wever, Keer, Schellens, & Valcke, 2010;Gu, Shao, Guo, & Lim, 2015).…”

Formation of reciprocal appreciation patterns in small groups: an agent-based model

“…The model results suggest that many aspects of forming dyadic and triadic patterns that are recognized by research on collaboration, information sharing and communication patterns (Hogan et al 1999;Enyedy 2003;Barron 2003;van Boxtel et al 2000;Schwarz and Linchevski 2007;Sangin et al 2011;Stahl et al 2014) can be traced back to strong reciprocation, which in the case of low or moderate competitive comparisons of appreciation lead to egalitarian triads but in the presence of strong competitive comparisons lead to strong dyadic patterns.The notion that cyclic patterns are absent and transitive patterns are only featured weakly in the present model is due to strong reciprocation, which suppresses their formation. The fact that such patterns have not been reported in studies of collaborative discourse and communication suggests that reciprocation based on appreciation is a strong candidate for explaining the formation of the reciprocated dyadic and triadic patterns reported here.…”

“…ref. (Hogan et al 1999;Enyedy 2003;Barron 2003;van Boxtel et al 2000;Schwarz and Linchevski 2007;Sangin et al 2011;Stahl et al 2014) and references therein]. For completeness, however, indirect triadic interaction (denoted as T) is studied by including a third agent k so that peer-appreciation κ ik is updated following the rule in Eq.…”

“…Strong reciprocation also means that social patterns based only on one-directional ties are rare when ties are based on affection, trust or appreciation (Yoon et al 2013;Block 2015;Rivera et al 2010). Patterns similar to affection and trust based interaction are also found in small groups in information sharing and communication (Hogan et al 1999;Enyedy 2003;Barron 2003), task-related collaboration (van Boxtel et al 2000;Schwarz and Linchevski 2007;Sangin et al 2011;Stahl et al 2014), group performance (Lusher et al 2014), and co-regulation in group learning (Volet et al 2009). Studies that focus on co-regulation and peer-to-peer interaction in small groups indicate that in these cases, strongly reciprocated dyadic and triadic patterns of interaction are characteristics of successful and high achieving groups (Hogan et al 1999;Barron 2003;Volet et al 2009).…”

Measuring the Impact of Interdependence on Individuals During Collaborative Problem-Solving

“…When researchers focus on individuals in collaborative settings, the team is often treated as a contextual variable that needs to be controlled for in order to isolate the processes and performance of individuals (Stahl, Law, Cress, & Ludvigsen, 2014). Such models obfuscate any details about the moment-to-moment interactions between the individual and their team, how individuals may depend on one another for information, and how they may influence one another over time.…”