Abstract
Modeling learners’ emotional states is a promising tool for enhancing learning outcomes and tutoring abilities. In this paper, we present a new perspective of learner emotional modeling according to two fundamental dimensions, namely mental workload and engagement. We hypothesize that analyzing results from learners’ workload and engagement evolution can help Intelligent Tutoring Systems diagnose learners’ emotional states and understand the learning process. We demonstrate by an experiment involving 17 participants that learners’ mental workload and engagement are closely related to specific emotions with regard to different learning phases.
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References
Berka, C., Levendowski, D.J., Cvetinovic, M.M., et al.: Real-Time Analysis of EEG Indexes of Alertness, Cognition, and Memory Acquired With a Wireless EEG Headset. International Journal of Human-Computer Interaction 17, 151–170 (2004)
Pope, A.T., Bogart, E.H., Bartolome, D.S.: Biocybernetic system evaluates indices of operator engagement in automated task. Biological Psychology 40, 187–195 (1995)
Prinzel, L.J., Freeman, F.G., Scerbo, M.W.: A Closed-Loop System for Examining Psychophysiological Measures for Adaptive Task Allocation. International Journal of Aviation Psychology 10, 393–410 (2000)
Baker, R.S., Corbett, A.T., Koedinger, K.R.: Detecting Student Misuse of Intelligent Tutoring Systems. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 531–540. Springer, Heidelberg (2004)
Arroyo, I., Cooper, D.G., Burleson, W., et al.: Emotion Sensors Go To School. In: Proceeding of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling, pp. 17–25. IOS Press (2009)
D’Mello, S., Craig, S., Witherspoon, A., et al.: Automatic detection of learner’s affect from conversational cues. User Modeling and User-Adapted Interaction 18, 45–80 (2008)
Forbes-Riley, K., Rotaru, M., Litman, D.J.: The relative impact of student affect on performance models in a spoken dialogue tutoring system. User Modeling and User-Adapted Interaction 18, 11–43 (2008)
Wilson, G.F.: An analysis of mental workload in pilots during flight using multiple sychophysiological measures. Int. J. Aviat. Psychol. 12, 3–18 (2004)
Stevens, R.H., Galloway, T., Berka, C.: EEG-Related Changes in Cognitive Workload, Engagement and Distraction as Students Acquire Problem Solving Skills. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 187–196. Springer, Heidelberg (2007)
Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., Picard, R.: Affect-aware tutors: recognising and responding to student affect. Int. J. Learning Technology 4(3/4), 129–163 (2009)
Chaouachi, M., Jraidi, I., Frasson, C.: Modeling Mental Workload Using EEG Features for Intelligent Systems. User Modeling and User-Adapted Interaction, 50–61 (2011)
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© 2012 Springer-Verlag Berlin Heidelberg
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Chaouachi, M., Frasson, C. (2012). Mental Workload, Engagement and Emotions: An Exploratory Study for Intelligent Tutoring Systems. 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_9
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DOI: https://doi.org/10.1007/978-3-642-30950-2_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30949-6
Online ISBN: 978-3-642-30950-2
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