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Roles are one of the most important concepts in understanding humansociocognitive behavior. During group interactions, members take on differentroles within the discussion. Roles have distinct patterns of behavioralengagement (i.e., active or passive, leading or following), contributioncharacteristics (i.e., providing new information or echoing given material),and social orientation (i.e., individual or group). Different combinations ofthese roles can produce characteristically different group outcomes, beingeither less or more productive towards collective goals. In onlinecollaborative learning environments, this can lead to better or worse learningoutcomes for the individual participants. In this study, we propose andvalidate a novel approach for detecting emergent roles from the participants'contributions and patterns of interaction. Specifically, we developed a groupcommunication analysis (GCA) by combining automated computational linguistictechniques with analyses of the sequential interactions of online groupcommunication. The GCA was applied to three large collaborative interactiondatasets (participant N = 2,429; group N = 3,598). Cluster analyses and linearmixed-effects modeling were used to assess the validity of the GCA approach andthe influence of learner roles on student and group performance. The resultsindicate that participants' patterns in linguistic coordination and cohesionare representative of the roles that individuals play in collaborativediscussions. More broadly, GCA provides a framework for researchers to explorethe micro intra- and interpersonal patterns associated with the participants'roles and the sociocognitive processes related to successful collaboration.

Group Communication Analysis: A Computational Linguistics Approach for Detecting Sociocognitive Roles in Multi-Party Interactions