用教育理论定义8类沟通角色，分析团队协作中的角色动态变化。
Who Plays Which Role When? Communication Role Dynamics for Peer Recognition and Team Performance Prediction
- 基于教育理论构建8类沟通角色，标注6307条学生聊天记录。
- 角色多样性随项目进展提升，不同角色在不同阶段表现突出。
- 角色标签可预测同伴认可与团队绩效，适用于教育外场景。
团队角色为协作提供了可解释的视角，但现有计算研究多依赖领域特定人物设定或数据驱动聚类，缺乏理论基础。本文基于教育学文献操作化定义八类沟通角色，并对来自18个团队、55名学生、持续一学期的计算机科学课程项目中6,307条Slack消息进行标注。评估大模型是否能近似专家标签，实现可扩展的角色标注。利用这些角色标签，我们刻画了团队生命周期中的角色动态，发现不同角色在不同阶段达到高峰，且学生在项目推进过程中表现出更丰富的角色多样性。为验证角色构念的有效性，我们将其用于预测同伴认可，优于词法、对话和LLM提示基线。为进一步检验泛化能力，将相同角色构念应用于公开数据集DeliData，预测讨论后的团队绩效提升，结果再次超越已有方法。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。