自动化语音处理对协作学习检测影响大，精度可接受但语句减少26.5%
Dude, where's my utterance? Evaluating the effects of automatic segmentation and transcription on CPS detection
- 用自动转录与分割替代人工，评估对协作行为识别的影响
- 自动处理后检测性能接近人工标注，但语句数量减少26.5%
- 适合教育AI开发人员关注数据粒度对模型效果的影响
协作问题解决（CPS）标记捕捉有效团队合作的关键特征，如聚焦任务、避免打断和生成建设性想法。一个可靠的CPS检测系统可帮助教师识别小组是否陷入困境或展现出有效协作。此类系统依赖由多个组件组成的自动化流程。本文评估了自动转录与语音分割这两个关键环节对CPS检测的影响。在公开的权重任务数据集（WTD）上，使用自动转录与分割方法的检测性能与人工标注数据相当；然而，自动分割使语句数量减少了26.5%，影响了数据粒度。本文讨论了该发现对开发支持课堂协作学习的AI工具的意义。
原文摘要 · Abstract (English)
Collaborative Problem-Solving (CPS) markers capture key aspects of effective teamwork, such as staying on task, avoiding interruptions, and generating constructive ideas. An AI system that reliably detects these markers could help teachers identify when a group is struggling or demonstrating productive collaboration. Such a system requires an automated pipeline composed of multiple components. In this work, we evaluate how CPS detection is impacted by automating two critical components: transcription and speech segmentation. On the public Weights Task Dataset (WTD), we find CPS detection performance with automated transcription and segmentation methods is comparable to human-segmented and manually transcribed data; however, we find the automated segmentation methods reduces the number of utterances by 26.5%, impacting the the granularity of the data. We discuss the implications for developing AI-driven tools that support collaborative learning in classrooms.
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