”Mirror, Mirror, on the Wall” - Promoting Self-Regulated Learning using Affective States Recognition via Facial Movements
”Mirror, Mirror, on the Wall” - Promoting Self-Regulated Learning using Affective States Recognition via Facial Movements
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DOI：
10.1145/3532106.3533500
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Si Chen;Yixin Liu;Risheng Lu;Yuqian Zhou;Yi-Chieh Lee;Yun Huang
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作者：
Si Chen;Yixin Liu;Risheng Lu;Yuqian Zhou;Yi-Chieh Lee;Yun Huang
先前的研究表明，自我调节学习的情感状态可以用来改善学习者的认知过程和学习结果。然而，很少有研究探讨使用面部动作来检测学习者的情感状态对自我调节学习的影响。在这项工作中，我们设计、实现并评估了Mirror：一个在基于视频的学习中应用面部表情识别来支持学习者反思的自我调节学习工具。我们进行了两项研究，以确定用户需求(12名参与者)和评估该工具(16名参与者)。结果表明，观看视频后，参与者通过不同的反思过程受益于使用Mirror，例如通过自我观察来更深入地了解他们的学习经历，通过自我判断来归因于他们的学习影响。同时，我们也发现了几个伦理问题，例如，用户处理人工智能不确定性的代理，对基于结果的人工智能的反应性，对“积极的”人工智能结果的过度依赖，以及人工智能知情决策的公平性。
Prior research suggests that affective states of self-regulated learning can be used to improve learners’ cognitive processes and their learning outcomes. However, little research explored the effect of using facial movements to detect learners’ affective states on self-regulated learning. In this work, we designed, implemented, and evaluated Mirror: a self-regulated learning tool that applies facial expression recognition to support learners’ reflections in video-based learning. We conducted two studies to identify user needs (with 12 participants) and to evaluate the tool (with 16 participants). The results show that, after watching a video, participants benefited from using Mirror through different reflection processes, e.g., gaining a deeper understanding of their learning experiences through self-observation and attributing causes for their learning affects through self-judgment. Meanwhile, we also identified several ethical concerns, e.g., users’ agency of handling the uncertainty of AI, reactivity towards outcome-based AI, over-reliance on “positive” AI results, and fairness of AI informed decision-making.