对比母语与非母语者协作写作行为，发现差异并提升团队包容性。
Comparing Native and Non-native English Speakers' Behaviors in Collaborative Writing through Visual Analytics
- 用可视化工具分析27个团队162次写作会话中的行为模式。
- 通过不确定性展示和大模型摘要，提升分析可解释性。
- 适合教育研究、AI协作工具设计者参考。
理解母语者（NS）与非母语者（NNS）在协作写作中的行为动态，对提升合作质量与团队包容性至关重要。本文与沟通研究者合作，开发了视觉分析工具，用于比较27个团队共162次写作会话中NS与NNS的行为。分析面临数据复杂性和自动化方法带来的不确定性挑战。为此，我们提出新工具 extsc{COALA}，通过显示作者聚类的不确定性、利用大语言模型生成行为摘要，并在多粒度上可视化写作相关操作，增强模型可解释性。通过领域专家（N=2+2）和有经验的研究者（N=8）的用户研究，验证了 extsc{COALA}的有效性。参与者借助该工具获得关键洞察，提出了未来AI辅助协作写作工具的改进方向，并讨论了该方法在写作之外协作过程分析中的广泛意义。
原文摘要 · Abstract (English)
Understanding collaborative writing dynamics between native speakers (NS) and non-native speakers (NNS) is critical for enhancing collaboration quality and team inclusivity. In this paper, we partnered with communication researchers to develop visual analytics solutions for comparing NS and NNS behaviors in 162 writing sessions across 27 teams. The primary challenges in analyzing writing behaviors are data complexity and the uncertainties introduced by automated methods. In response, we present \textsc{COALA}, a novel visual analytics tool that improves model interpretability by displaying uncertainties in author clusters, generating behavior summaries using large language models, and visualizing writing-related actions at multiple granularities. We validated the effectiveness of \textsc{COALA} through user studies with domain experts (N=2+2) and researchers with relevant experience (N=8). We present the insights discovered by participants using \textsc{COALA}, suggest features for future AI-assisted collaborative writing tools, and discuss the broader implications for analyzing collaborative processes beyond writing.
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