Abstract
Affective reasoning has been the subject of increasing attention in recent years. Because negative affective states such as frustration and anxiety can impede progress toward learning goals, intelligent tutoring systems should be able to detect when a student is anxious or frustrated. Being able to detect negative affective states early, i.e., before they lead students to abandon learning tasks, could permit intelligent tutoring systems sufficient time to adequately prepare for, plan, and enact affective tutorial support strategies. A first step toward this objective is to develop predictive models of student frustration. This paper describes an inductive approach to student frustration detection and reports on an experiment whose results suggest that frustration models can make predictions early and accurately.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
André, E., Mueller, M.: Learning affective behavior. In: Proceedings of the 10th International Conference on Human-Computer Interaction, pp. 512–516. Lawrence Erlbaum, Mahwah, NJ (2003)
Bandura, A.: Self-efficacy: The exercise of control. Freeman, New York (1997)
Beal, C., Lee, H.: Creating a pedagogical model that uses student self reports of motivation and mood to adapt ITS instruction. In: Workshop on Motivation and Affect in Educational Software, in conjunction with the 12th International Conference on Artificial Intelligence in Education (2005)
Blaylock, N., Allen, J.: Corpus-based, statistical goal recognition. In: Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, Acapulco, Mexico, pp. 1303–1308 (2003)
Burleson, W., Picard, R.: Affective agents: Sustaining motivation to learn through failure and a state of stuck. In: Proceedings of the ITS Workshop of Social and Emotional Intelligence in Learning Environments, Maceio, Alagoas, Brazil (2004)
Conati, C., Mclaren, H.: Data-driven refinement of a probabilistic model of user affect. In: Tenth International Conference on User Modeling. New York, NY, pp. 40–49 (2005)
de Vicente, A., Pain, H.: Informing the detection of the students’ motivational state: an empirical study. In: Proceedings of the 6th International Conference on Intelligent Tutoring Systems, pp. 933–943. Springer, New York (2002)
Gale, A., Sampson, G.: Good-Turing frequency estimation without tears. Journal of Quantitative Linguistics 2(3), 217–237 (1995)
Gratch, J., Marsella, S.: A domain-independent framework for modeling emotion. Journal of Cognitive Systems Research 5(4), 269–306 (2004)
Johnson, L., Rizzo, P.: Politeness in tutoring dialogs: Run the factory, that’s what I’d do. In: 7th International Conference on Intelligent Tutoring Systems, Maceio, Brazil, pp. 67-76 (2004)
Lang, P.: The emotion probe: Studies of motivation and attention. American Psychologist 50(5), 285–372 (1995)
Lazarus, R.: Emotion and Adaptation. Oxford University Press, New York (1991)
McQuiggan, S., Lester, J.: Learning empathy: A data-driven framework for modeling empathetic companion agents. In: Proceedings of the 5th International Conference on Autonomous Agents and Multiagent Systems, Hakodate, Japan, pp. 961–968 (2006)
McQuiggan, S., Lester, J.: Diagnosing self-efficacy in intelligent tutoring systems: An empirical study. In: Proceedings of the 8th International Conference on Intelligent Tutoring Systems, Jhongli, Taiwan, pp. 565–574 (2006)
Mitchell, T.: Machine Learning, McGraw-Hill, OH (1997)
Mott, B., Lee, S., Lester, J.: Probabilistic goal recognition in interactive narrative environments. In: Proceedings of the Twenty-first National Conference on Artificial Intelligence, Boston, MA, pp. 187–192 (2006)
Mott, B., Lester, J.: Narrative-centered tutorial planning for inquiry-based learning environments. In: Proceedings of the 8th International Conference on Intelligent Tutoring Systems, Jhongli, Taiwan, pp. 675–684 (2006)
Ormrod, J.: Educational Psychology: Developing Learners, 4th edn. Prentice Hall, Upper Saddle River, NJ (2002)
Paiva, A., Dias, J., Sobral, D., Aylett, R., Woods, S., Hall, L., Zoll, C.: Learning by feeling: evoking empathy with synthetic characters. Applied Artificial Intelligence 19, 235–266 (2005)
Picard, R.: Affective Computing. MIT Press, Cambridge, MA (1997)
Porayska-Pomsta, K., Pain, H.: Providing cognitive and affective scaffolding through teaching strategies: applying linguistic politeness to the educational context. In: Seventh International Conference on Intelligent Tutoring Systems, Maceio, Alagoas, Brazil, pp. 77–86 (2004)
Prendinger, H., Ishizuka, M.: The empathic companion: a character-based interface that addresses users’ affective states. Applied Artificial Intelligence 19, 267–285 (2005)
Seligman, M., Walker, E., Rosenhan, D.: Abnormal psychology, 4th edn. W.W. Norton & Company, Inc, New York (2001)
Smith, C., Lazarus, R.: Emotion and adaptation. In: Pervin (ed.) Handbook of Personality: theory & research, pp. 609–637. Guilford Press, NY (1990)
Witten, I., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufman, San Francisco, CA (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
McQuiggan, S.W., Lee, S., Lester, J.C. (2007). Early Prediction of Student Frustration. In: Paiva, A.C.R., Prada, R., Picard, R.W. (eds) Affective Computing and Intelligent Interaction. ACII 2007. Lecture Notes in Computer Science, vol 4738. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74889-2_61
Download citation
DOI: https://doi.org/10.1007/978-3-540-74889-2_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74888-5
Online ISBN: 978-3-540-74889-2
eBook Packages: Computer ScienceComputer Science (R0)