ITR: Monitoring Emotions while Students Learn with AutoTutor

本研究考察了大学生在复杂的学习环境中学习和推理过程中的情绪。大学生通过名为AutoTutor的智能辅导系统在网上学习入门计算机知识或概念物理。AutoTutor通过用自然语言与学习者互动并帮助他们使用模拟环境来帮助学习者构建解释，以回答困难的问题。AutoTutor有一个动画对话代理和一个对话管理工具，试图理解学习者的贡献并以适当的对话动作(简短的反馈、激励、提示、信息提示、断言、对学生问题的回答、行动建议、总结)做出回应。在这个学习过程中，通过将最先进的情感感应技术与AutoTutor相结合来监控学习者的情绪。困惑、沮丧、无聊、兴趣、兴奋和其他学习者情绪根据面部动作、身体姿势、对鼠标的压力、对话中的言语行为、对材料的掌握和互动的时机进行分类。一系列研究开发了情感感知技术，并测试了它们在对学习者情绪进行分类方面的有效性。第二条线的研究是调查学习成果和学习者的印象是否会受到AutoTutor对话动作的影响，这些动作受到学习者情绪状态的制约。这项研究将通过一个系统，以一种对学习者情绪敏感的方式促进对材料的深入学习，推动教育和自然语言对话技术的发展。监控学习者情绪的学习环境可能会更具激励性，并与学习者个人相关。

This research investigates emotions during the process of learning and reasoning while college students interact with complex learning environments. College students learn about introductory computer literacy or conceptual physics on the web by an intelligent tutoring system, called AutoTutor. AutoTutor helps learners construct explanations that answer difficult questions by interacting with them in natural language and by helping them use simulation environments. AutoTutor has an animated conversational agent and a dialog management facility that attempts to comprehend the learner's contributions and to respond with appropriate dialog moves (short feedback, pumps, hints, prompts for information, assertions, answers to student questions, suggestions for actions, summaries). The emotions of the learner are monitored during this learning process by integrating state-of-the-art affect sensing technology with AutoTutor. Confusion, frustration, boredom, interest, excitement, and other learner emotions are classified on the basis of facial actions, body posture, pressure on the mouse, speech acts in dialog, mastery of the material, and the timing of interactions. One strand of research develops the affect-sensing technologies and tests their validity in classifying the learner emotions. A second line of research investigates whether learning gains and learner impressions are influenced by dialog moves of AutoTutor that are constrained by the learner's emotional state. This research will advance education and natural language dialog technologies through a system that promotes deep learning of material in a fashion that is sensitive to the learners' emotions. A learning environment that monitors learner emotions is likely to be more motivating and personally relevant to the learner.