Who Plays Which Role When? Communication Role Dynamics for Peer Recognition and Team Performance Prediction

2026-06-26

AI Summary

This study addresses the limitations of existing team role modeling approaches, which often lack grounding in educational theory and suffer from poor interpretability, thereby hindering their ability to effectively predict collaborative outcomes. To bridge this gap, the authors develop a theoretically informed framework comprising eight communication roles derived from educational principles. For the first time, this theory-driven role taxonomy is applied to real-world team chat data, integrating expert annotations with large language models for role identification and explicitly modeling the dynamic evolution of roles over time. The research uncovers systematic patterns in how roles shift throughout project progression and demonstrates the cross-contextual validity of these roles in predicting peer recognition and team performance. Experimental results show that the proposed approach significantly outperforms baseline methods—including lexical, conversational, and prompt-engineering strategies—on both prediction tasks.

Abstract

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.

Problem

• team roles
• communication dynamics
• peer recognition
• team performance prediction
• role taxonomy

Innovation

• communication roles
• team dynamics
• LLM-based annotation
• role taxonomy
• team performance prediction