  Roles are one of the most important concepts in understanding human
sociocognitive behavior. During group interactions, members take on different
roles within the discussion. Roles have distinct patterns of behavioral
engagement (i.e., active or passive, leading or following), contribution
characteristics (i.e., providing new information or echoing given material),
and social orientation (i.e., individual or group). Different combinations of
these roles can produce characteristically different group outcomes, being
either less or more productive towards collective goals. In online
collaborative learning environments, this can lead to better or worse learning
outcomes for the individual participants. In this study, we propose and
validate a novel approach for detecting emergent roles from the participants'
contributions and patterns of interaction. Specifically, we developed a group
communication analysis (GCA) by combining automated computational linguistic
techniques with analyses of the sequential interactions of online group
communication. The GCA was applied to three large collaborative interaction
datasets (participant N = 2,429; group N = 3,598). Cluster analyses and linear
mixed-effects modeling were used to assess the validity of the GCA approach and
the influence of learner roles on student and group performance. The results
indicate that participants' patterns in linguistic coordination and cohesion
are representative of the roles that individuals play in collaborative
discussions. More broadly, GCA provides a framework for researchers to explore
the micro intra- and interpersonal patterns associated with the participants'
roles and the sociocognitive processes related to successful collaboration.
