Who Plays Which Role When? Communication Role Dynamics for Peer Recognition and Team Performance Prediction
For researchers studying team collaboration, this provides a scalable, theory-driven method to analyze communication roles and predict team outcomes, with demonstrated improvements over existing approaches.
This paper operationalizes a theory-grounded taxonomy of eight communication roles to analyze team dynamics, annotating 6,307 Slack messages from 55 students. Using LLM-based role annotation, they show that role dynamics vary over team lifecycles and that role constructs improve peer recognition prediction over baselines and generalize to predict team performance improvement in a public dataset.
Team roles offer an interpretable lens on collaboration, yet computational studies of roles often rely on domain-specific personas or data-driven clustering rather than theory-grounded taxonomies. We operationalize a taxonomy of eight communication roles grounded in education literature and annotate a corpus of 6,307 Slack messages from 55 students across 18 teams in a semester-long computer science course project. We evaluate whether LLMs can approximate expert labels, enabling scalable, taxonomy-driven role annotation. Using these role labels, we characterize role dynamics over teams' lifecycles, finding that different roles peak at different moments and that students enact a more diverse set of roles as projects progress. To evaluate the utility of our role constructs, we use them to predict peer recognition, outperforming lexical, conversational, and LLM-prompting baselines. To assess generalizability beyond the educational context, we apply the same role constructs to a public dataset (DeliData) to predict team performance improvement after deliberation, again exceeding prior performance.