ITR: Monitoring Emotions while Students Learn with AutoTutor

ITR：使用 AutoTutor 监控学生学习时的情绪

• 批准号: 0325428
• 负责人: Arthur Graesser
• 金额: $ 125万
• 依托单位: University of Memphis
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-15 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0325428&HistoricalAwards=false
• 关键词: ITR; Monitoring; Emotions; while; Students

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.

本研究考察了大学生在复杂的学习环境中进行学习和推理过程中的情绪变化。 大学生通过一个名为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帮助学习者构建解释，通过自然语言与他们互动并帮助他们使用模拟环境来回答困难的问题。 AutoTutor有一个动画会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，泵，提示，信息提示，断言，学生问题的答案，行动建议，摘要）作出回应。 在学习过程中，通过将最先进的情感传感技术与AutoTutor相结合，监控学习者的情绪。 困惑，沮丧，无聊，兴趣，兴奋，和其他学习者的情绪进行分类的基础上面部动作，身体姿势，对鼠标的压力，对话中的言语行为，掌握的材料，和互动的时间。 其中一个研究发展了情感感知技术，并测试了它们对学习者情感分类的有效性。第二条线的研究调查是否学习收益和学习者的印象是由AutoTutor的对话移动的学习者的情绪状态的约束的影响。这项研究将通过一个系统推进教育和自然语言对话技术，该系统以一种对学习者情感敏感的方式促进材料的深度学习。 一个监控学习者情绪的学习环境可能更有激励性，并且与学习者个人相关。