User Modeling and User-Adapted Interaction
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
IEEE Transactions on Affective Computing
The impact of system feedback on learners' affective and physiological states
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
AutoTutor: an intelligent tutoring system with mixed-initiative dialogue
IEEE Transactions on Education
ACM SIGAPP Applied Computing Review
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Multimodal approaches are increasingly used for affect detection. This paper proposes a model for the fusion of physiological signal that measure learners' heart activity and their facial expressions to detect learners' affective states while students interact with an Intelligent Tutoring System (ITS). It studies machine learning and fusion techniques that classify the system's automated feedback from the individual channels and their feature level fusion. It also evaluates the classification performance of fusion models in multimodal systems, identifying the effects of fusion over the individual modalities.