作者：蒋如意*，刘清堂，马鑫倩，王登

出版刊物：International Journal of Computer-Supported Collaborative Learning

出版时间：2026年

内容摘要：

Collective emotions are macro-level phenomena arising from the emotional interactions among individuals in shared situations, which dynamically evolve through the process of interpersonal interaction. Although collective emotions are closely related to group learning performance and collaboration outcomes in computer-supported collaborative learning (CSCL), existing research often neglects the emotional states of nonspeakers and has insufficiently explored the interpersonal emotional interaction patterns among learners. In addition, the distinctive features of interpersonal interactions are rarely considered in the analysis of collective emotions’ evolution. The rapid advancement of AI technology enables more detailed and cost-effective emotion capture. This study uses AI to simultaneously capture the speakers’ verbal expression of emotions and the nonspeakers’ facial expression of emotions in a CSCL context. Using epistemic network analysis (ENA) and lag sequential analysis (LSA), this study systematically compares the interpersonal interaction patterns and temporal evolution of collective emotions between high- and low-performance groups. The results reveal significant differences between the groups: in terms of interpersonal interactions, the high-performance group typically exhibits neutral or cognitively engaged facial expressions from nonspeakers when the speaker expresses a positive viewpoint, whereas the low-performance group shows more confusion and negative emotions from the speaker, with nonspeakers frequently responding with affiliative smiles. In terms of temporal evolution, the high-performance group demonstrates a productive emotional transition path, while the low-performance group shows a negative emotional trajectory and a vicious cycle of negative emotions. This study fills a gap in the CSCL field regarding the interpersonal mechanisms of collective emotions and provides empirical evidence for precise teaching interventions and instructional design.

集体情绪是共享情境中个体间情感互动所产生的宏观层面现象，它通过人际互动过程动态演化。尽管集体情绪与计算机支持的协作学习（CSCL）中的小组学习绩效和协作成果密切相关，但现有研究往往忽视非说话者的情绪状态，对学习者之间的人际情感互动模式探讨不足。此外，集体情绪演化分析中很少考虑人际互动的独有特征。AI技术的快速发展使更细致且更具成本效益的情绪捕捉成为可能。本研究利用AI同步捕捉CSCL情境中说话者的言语情绪表达和非说话者的面部情绪表达。采用认知网络分析（ENA）和滞后序列分析（LSA），本研究系统比较了高绩效组与低绩效组在人际互动模式和集体情绪时间演化上的差异。结果揭示了组间显著差异：在人际互动方面，当说话者表达积极观点时，高绩效组通常表现出非说话者中性或认知投入的面部表情，而低绩效组则表现出说话者更多的困惑和负面情绪，非说话者则频繁以亲和性微笑回应。在时间演化方面，高绩效组展现出富有成效的情绪转换路径，而低绩效组则呈现负面情绪轨迹和负面情绪的恶性循环。本研究填补了CSCL领域关于集体情绪人际机制的空白，并为精准教学干预和教学设计提供了实证依据。