情感面部识别与医学模拟中社会共享调节的关联研究 (Linking Facial Recognition of Emotions and Socially Shared Regulation in Medical Simulation)

Computer-supported simulation enables a practical alternative for medical training purposes. This study investigates the co-occurrence of facial-recognition-derived emotions and socially shared regulation of learning (SSRL) interactions in a medical simulation training context. Using transmodal analysis (TMA), we compare novice and expert learners' affective and cognitive engagement patterns during collaborative virtual diagnosis tasks. Results reveal that expert learners exhibit strong associations between socio-cognitive interactions and high-arousal emotions (surprise, anger), suggesting focused, effortful engagement. In contrast, novice learners demonstrate stronger links between socio-cognitive processes and happiness or sadness, with less coherent SSRL patterns, potentially indicating distraction or cognitive overload. Transmodal analysis of multimodal data (facial expressions and discourse) highlights distinct regulatory strategies between groups, offering methodological and practical insights for computer-supported cooperative work (CSCW) in medical education. Our findings underscore the role of emotion-regulation dynamics in collaborative expertise development and suggest the need for tailored scaffolding to support novice learners' socio-cognitive and affective engagement.

翻译：计算机支持的模拟为医学培训提供了一种实用的替代方案。本研究探讨了在医学模拟培训情境中，面部识别所推导的情感与社会共享学习调节（SSRL）交互之间的共现关系。通过跨模态分析（TMA），我们比较了新手与专家学习者在协作虚拟诊断任务中的情感与认知参与模式。结果显示，专家学习者的社会认知互动与高唤醒度情感（惊讶、愤怒）之间存在强关联，表明其专注且投入的参与状态。相比之下，新手学习者的社会认知过程与快乐或悲伤情感的联系更为紧密，且其SSRL模式连贯性较低，这可能意味着注意力分散或认知超载。对多模态数据（面部表情与话语）的跨模态分析揭示了两组间不同的调节策略，为医学教育中的计算机支持协同工作（CSCW）提供了方法论与实践启示。我们的研究结果强调了情绪调节动态在协作性专业技能发展中的作用，并表明需要针对性的支架式支持以促进新手学习者的社会认知与情感参与。