协作问题解决过程中认知情绪的有序网络分析
Ordered Network Analysis of Epistemic Emotions during Collaborative Problem Solving
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中文总结 AI 辅助
研究协作问题解决中认知情绪，用有序网络分析方法，探讨情感状态有序结构、不同报告方法及快慢组差异，揭示了仅描述性总结不明显的持续和转变模式，为开发支持CPS的AI系统提供依据。
中文摘要 AI 辅助
研究困惑和沮丧等情感状态在同地协作问题解决（CPS）过程中的持续和转变情况，对于理解认知情绪动态很重要。但由于该领域缺乏金标准事实，情感状态的准确识别仍具挑战性。本文通过回顾性线索回忆分析了在面对面CPS任务中收集的情感状态。使用有序网络分析（ONA），研究了情感状态的整体有序结构及其在自我捕捉和探测捕捉报告方法间的差异，以及快慢组对该有序结构不同方面的强调情况。发现ONA揭示了仅从描述性总结中不明显的持续和转变模式差异。特别是观察到一个稳定的认知核心连接着好奇心、乐观和困惑，不同报告方法强调状态间不同联系。快慢组分析表明，困惑和脱离接触的作用在协作中也显著变化，尤其是它们与冲突的关系。最后在协作背景下解释了研究结果，并讨论了其对开发支持CPS的人工智能系统的意义。
英文摘要
Investigating how affective states such as confusion and frustration persist and transition during co-situated collaborative problem solving (CPS) is important for understanding the dynamics of epistemic emotions. However, the accurate identification of affective states remain challenging as there is no gold-standard truth in this space. Here, we analyze affective states collected through retrospective cued-recall during an in-person CPS task. Using ordered network analysis (ONA), we examine (1) the overall ordered structure of affective states and how this structure differs across self-caught and probe-caught reporting methods, and (2) what aspects of this ordered structure are emphasized differently in slower and faster groups. We find that ONA reveals differences in persistence and transition patterns that are not apparent from descriptive summaries alone. In particular, we observe a stable epistemic core linking curiosity, optimism, and confusion, with different reporting methods emphasizing different connections among states. An analysis between faster and slower groups show that roles of confusion and disengagement also shift significantly during collaboration, particularly in their relationship to conflict. We interpret our findings in the context of collaboration and discuss their implications in developing AI systems that support CPS.
发表机构
- Colorado State University（科罗拉多州立大学）
- University of Houston（休斯顿大学）
机构由 AI 辅助整理，请以论文原文为准。
