A Time for Emoting: When Affect-Sensitivity Is and Isn't Effective at Promoting Deep Learning
A Time for Emoting: When Affect-Sensitivity Is and Isn't Effective at Promoting Deep Learning
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DOI：
10.1007/978-3-642-13388-6_29
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发表时间：
2010-06
期刊：
影响因子：
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通讯作者：
S. D’Mello;B. Lehman;Jeremiah Sullins;R. Daigle;R. Combs;Kimberly Vogt;Lydia Perkins;A. Graesser
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文献类型：
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作者：
S. D’Mello;B. Lehman;Jeremiah Sullins;R. Daigle;R. Combs;Kimberly Vogt;Lydia Perkins;A. Graesser
我们已经开发和评估了一个影响敏感版本的AutoTutor，基于对话的ITS，模拟人类导师。虽然原始的AutoTutor对学习者的认知状态敏感，但情感敏感的导师也对他们的情感状态做出反应。这种情感导师自动检测学习者的无聊，困惑，并通过监测会话线索，粗俗的肢体语言和面部特征的挫折。感知到的情感状态引导导师的反应，帮助学生调节他们的负面情绪。导师还通过其反应的言语内容以及具体教学代理人的面部表情和言语来合成影响。一项比较情感敏感和非情感导师的实验表明，情感导师改善了低域知识学生的学习，特别是在更深层次的理解。最后，我们讨论的条件下，情感敏感性是有效的，当它不是。
We have developed and evaluated an affect-sensitive version of AutoTutor, a dialogue based ITS that simulates human tutors. While the original AutoTutor is sensitive to learners’ cognitive states, the affect-sensitive tutor is responsive to their affective states as well. This affective tutor automatically detects learners’ boredom, confusion, and frustration by monitoring conversational cues, gross body language, and facial features. The sensed affective states guide the tutor’s responses in a manner that helps students regulate their negative emotions. The tutor also synthesizes affect via the verbal content of its responses and the facial expressions and speech of an embodied pedagogical agent. An experiment comparing the affect-sensitive and non-affective tutors indicated that the affective tutor improved learning for low-domain knowledge students, particularly at deeper levels of comprehension. We conclude by discussing the conditions upon which affect-sensitivity is effective, and the conditions when it is not.