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The relationship between emotions and learning was investigated by tracking the affective states that college students experienced while interacting with AutoTutor, an intelligent tutoring system with conversational dialogue. An emotionally responsive tutor would presumably facilitate learning, but this would only occur if learner emotions can be accurately(More)
We explored the reliability of detecting a learner’s affect from conversational features extracted from interactions with AutoTutor, an intelligent tutoring system (ITS) that helps students learn by holding a conversation in natural language. Training data were collected in a learning session with AutoTutor, after which the affective states of the learner(More)
This study investigated facial features to detect the affective states (or emotions) that accompany deep-level learning of conceptual material. Videos of the participants' faces were captured while they interacted with AutoTutor on computer literacy topics. After the tutoring session, the affective states (boredom, confusion, delight, flow, frustration, and(More)
The relationship between emotions and learning was investigated by tracking the emotions that college students experienced while learning about computer literacy with AutoTutor. AutoTutor is an animated pedagogical agent that holds a conversation in natural language, with spoken contributions by the learner. Thirty students completed a multiple-choice(More)
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(More)
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