Title: Linking Facial Recognition of Emotions and Socially Shared Regulation in Medical Simulation Show Chinese title

Title: 面部情绪识别与医学模拟中社会共享调节的关联

Abstract: 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. Show Chinese abstract

Abstract: 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。 本研究调查了在医学模拟培训情境中，基于面部识别的情绪与社会共享学习调节（SSRL）互动的共现情况。 使用跨模态分析（TMA），我们比较了新手学习者和专家学习者在协作虚拟诊断任务中的情感和认知参与模式。 结果表明，专家学习者表现出社会认知互动与高唤醒情绪（惊讶、愤怒）之间的强烈关联，表明其专注且努力的参与状态。 相比之下，新手学习者则表现出社会认知过程与快乐或悲伤之间的更强联系，SSRL模式的一致性较低，可能表明分心或认知超载。 对多模态数据（面部表情和话语）的跨模态分析突显了两组之间的不同调节策略，为医学教育中的计算机支持协作工作（CSCW）提供了方法论和实践上的见解。 我们的研究结果强调了情绪调节动态在协作专业知识发展中的作用，并建议需要量身定制的支持措施，以促进新手学习者社会认知和情感参与。