将面部情绪识别与医学模拟中的社会共享调节联系起来
Linking Facial Recognition of Emotions and Socially Shared Regulation in Medical Simulation
AI总结：
该研究利用跨模态分析比较医学模拟中新手与专家的面部情绪和SSRL互动，揭示二者调节策略差异，并为面向新手的定制化支持提供依据。
摘要：
计算机支持的模拟为医学培训提供了一种切实可行的替代方案。本研究考察在医学模拟培训情境中，由面部识别得到的情绪与社会共享学习调节（SSRL）互动的共现情况。研究采用跨模态分析（TMA），比较新手与专家学习者在协作式虚拟诊断任务中的情感和认知参与模式。结果显示，专家学习者的社会认知互动与高唤醒情绪（惊讶、愤怒）之间存在强关联，表明其投入是专注且费力的。相比之下，新手学习者的社会认知过程与快乐或悲伤之间联系更强，且SSRL模式连贯性较低，可能意味着分心或认知过载。对面部表情和话语等多模态数据进行的跨模态分析凸显了两组之间不同的调节策略，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。研究结果强调了情绪—调节动态在协作专长发展中的作用，并表明需要定制化脚手架来支持新手学习者的社会认知和情感参与。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
