通过可视化分析比较母语与非母语英语者在协作写作中的行为
Comparing Native and Non-native English Speakers' Behaviors in Collaborative Writing through Visual Analytics
AI总结：
本研究提出可视化分析工具COALA，通过显示不确定性、生成行为摘要和多粒度可视化，比较母语与非母语者在协作写作中的行为，并经用户研究验证其有效性。
摘要：
理解母语者（NS）与非母语者（NNS）之间的协作写作动态，对于提升协作质量和团队包容性至关重要。在本文中，我们与传播学研究者合作，开发了可视化分析解决方案，用于比较27个团队中162次写作会话中NS与NNS的行为。分析写作行为的主要挑战在于数据复杂性以及自动化方法引入的不确定性。为此，我们提出了COALA，一种新颖的可视化分析工具，通过显示作者聚类中的不确定性、利用大语言模型生成行为摘要，并在多个粒度上可视化与写作相关的操作，来提高模型的可解释性。我们通过领域专家（N=2+2）和相关经验的研究人员（N=8）的用户研究验证了COALA的有效性。我们展示了参与者使用COALA发现的见解，为未来AI辅助协作写作工具提出了功能建议，并讨论了分析写作之外协作过程的更广泛意义。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
