A dynamic mixture model to detect student motivation and proficiency
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Predicting facial indicators of confusion with hidden Markov models
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Characterizing the effectiveness of tutorial dialogue with hidden markov models
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
Monitoring affect states during effortful problem solving activities
International Journal of Artificial Intelligence in Education
An extensible micro-world for learning in the data networking professions
Information Sciences: an International Journal
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Affect and cognition intertwine throughout human experience. Research into this interplay during learning has identified relevant cognitive-affective states, but recognizing them poses significant challenges. Among multiple promising approaches for affect recognition, analyzing facial expression may be particularly informative. Descriptive computational models of facial expression and affect, such as those enabled by machine learning, aid our understanding of tutorial interactions. Hidden Markov modeling, in particular, is useful for encoding patterns in sequential data. This paper presents a descriptive hidden Markov model built upon facial expression data and tutorial dialogue within a task-oriented human-human tutoring corpus. The model reveals five frequently occurring patterns of affective tutorial interaction across text-based tutorial dialogue sessions. The results show that hidden Markov modeling holds potential for the semi-automated understanding of affective interaction, which may contribute to the development of affect-informed intelligent tutoring systems.