Socializing the intelligent tutor: bringing empathy to computer tutors
Learning Issues for Intelligent Tutoring Systems
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SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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Communications of the ACM
Affective computing
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ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
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Affective interactions: the computer in the affective loop
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Predicting student emotions in computer-human tutoring dialogues
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Automatic prediction of frustration
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Toward an Affect-Sensitive AutoTutor
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Automatic detection of learner's affect from conversational cues
User Modeling and User-Adapted Interaction
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ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
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Emotions and Learning with AutoTutor
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ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Ranking feature sets for emotion models used in classroom based intelligent tutoring systems
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
A Framework for Designing Computer Supported Learning Systems with Sensibility
International Journal of e-Collaboration
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Inquiries into the link between affect and learning require robust methodologies to measure the learner's affective states. We describe two studies that utilised either an online or offline methodology to detect the affective states of a learner during a tutorial session with AutoTutor. The online study relied on self-reports for affect judgements, while the offline study considered the judgements by the learner, a peer and two trained judges. The studies also investigated the relationships between facial features, conversational cues and emotional expressions in an attempt to scaffold the development of computer algorithms to automatically detect learners' emotions. Both methodologies showed that boredom, confusion and frustration are the prominent affective states during learning with AutoTutor. For both methodologies, there were also some relationships involving patterns of facial activity and conversational cues that were diagnostic of emotional expressions.