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Confusion

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Overview: 

This project explores interactions between cognition and emotion during the learning of scientific methods in the context of a computer tutoring environment.  The primary focus is on the relations between impasses, cognitive disequilibrium, and confusion. Confusion is an emotion that correlates with learning gains because it is diagnostic of cognitive disequilibrium, a state that occurs when learners face obstacles to goals, contradictions, incongruities, anomalies, conflicts, and system breakdowns. Cognitive equilibrium is normally restored after thought, reflection, problem solving and other effortful cognitive activities. Therefore, pedagogical tactics that challenge, perplex, and productively confuse learners are stimulating alternatives to the typical information delivery systems that promote shallow knowledge in the comfort zone of the learner, but rarely deep comprehension. However, the cognitive-affective amalgamation of cognitive disequilibrium and confusion does not directly guarantee deep learning, but rather is a promising opportunity for promoting deep learning. This 3-year project develops tutorial interventions that induce, track, and regulate confusion and cognitive disequilibrium in the minds of learners. There are three specific objectives: (1) To promote deep learning by developing tutorial interventions that experimentally induce impasses, cognitive disequilibrium, and the resulting confusion; (2) to integrate sensing devices and signal processing algorithms that detect and track the associated confusion; and (3) to develop affect-sensitive pedagogical strategies that are designed to help learners regulate their confusion. The three objectives will be realized by augmenting an existing Intelligent Tutoring System, called Operation ARIES! (Acquiring Research Investigative and Evaluative Skills) with technologies that are designed to provide automated affective and cognitive assessment and an intelligent handling of emotions.  The tutorial trialogues of ARIES will be enhanced by integrating state-of-the-art sensing devices that identify affective states during learning (confusion, frustration, boredom, flow/engagement, etc) on the basis of the dialogue history, conversational cues, facial expressions, and body posture. 
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