The Relationship between Affective States and Dialog Patterns during Interactions with AutoTutor
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ID: 14163
Abstract: In an attempt to discover links between learning and emotions, this study adopted an emote-aloud procedure in which participants were recorded as they verbalized their affective states while interacting with an intelligent tutoring system, AutoTutor. Various characteristics and assessments of the participants’ interactions with AutoTutor were recovered by mining its log files. These interaction patterns were correlated to the affective states expressed by the participants. We identify significant correlations and speculate on their implications for the larger project of extending the AutoTutor system into a non-intrusive, affect-sensitive, intelligent tutoring system.
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