ITR: Monitoring Emotions while Students Learn with AutoTutor

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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.