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Abstract
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.
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Authors and Affiliations
Department of Computer Science, North Carolina State University, Raleigh, North Carolina, USA
Joseph F. Grafsgaard, Kristy Elizabeth Boyer & James C. Lester
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- Kristy Elizabeth Boyer
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- James C. Lester
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Editors and Affiliations
University of Montpellier & CNRS LIRMM, 161 rue Ada, 34095, Montpellier, France
Stefano A. Cerri
NASA and Florida Institute for Human and Machine Cognition, Human Centered Computing - Intelligent Systems Division, 94035, Moffett Field, CA, USA
William J. Clancey
Department of Applied Informatics and Multimedia, Technological Educational Institute of Crete, School of Applied Technology, Stavromenos, P.O.Box 1939, 71004, Heraklion, Crete, Greece
Giorgos Papadourakis
Neoanalysis Ltd., Marni 56, 10437, Athens, Greece
Kitty Panourgia
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© 2012 Springer-Verlag Berlin Heidelberg
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Grafsgaard, J.F., Boyer, K.E., Lester, J.C. (2012). Toward a Machine Learning Framework for Understanding Affective Tutorial Interaction. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds) Intelligent Tutoring Systems. ITS 2012. Lecture Notes in Computer Science, vol 7315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30950-2_7
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