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Inducing Optimal Emotional State for Learning in Intelligent Tutoring Systems

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Intelligent Tutoring Systems (ITS 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3220))

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Abstract

Emotions play an important role in cognitive processes and specially in learning tasks. Moreover, there are some evidences that the emotional state of the learner correlated with his performance. Furthermore, it’s important that new Intelligent Tutoring Systems involve this emotional aspect; they may be able to recognize the emotional state of the learner, and to change it so as to be in the best conditions for learning. In this paper we describe such an architecture developed in order to determine the optimal emotional state for learning and to induce it. Based on experimentation, we have used the Naïve Bayes classifier to predict the optimal emotional state according to the personality and then we induce it using a hybrid technique which combines the guided imagery technique, music and images.

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References

  1. Abou-Jaoude, S., Frasson, C., Charra, O., Troncy, R.: On the Application of a Believable Layer in ITS. In: Workshop on Synthetic Agents, 9th International Conference on Artificial Intelligence in Education, Le Mans (1999)

    Google Scholar 

  2. Ahsen, A.: Guided imagery: the quest for a science. Part I: Imagery origins. Education 110, 2–16 (1997)

    Google Scholar 

  3. Bach, J.S.: Brandenburg Concerto No.2. In: Music from Ravinia series, New York, RCA Victor Gold Seal (1721) 60378-2-RG

    Google Scholar 

  4. Conati, C.: Probabilistic Assessment of User’s Emotions in Educational Games. Journal of Applied Artificial Intelligence 16, 555–575 (2002)

    Article  Google Scholar 

  5. Damasio, A.: Descartes Error. Emotion, Reason and the Human Brain. Putnam Press, New York (1994)

    Google Scholar 

  6. Estrada, C.A., Isen, A.M., Young, M.J.: Positive affect influences creative problem solving and reported source of practice satisfaction in physicians. Motivation and Emotion 18, 285–299 (1994)

    Article  Google Scholar 

  7. Eysenck, H.J., Eysenck, M.W.: Personality and individual differences. A natural science approach. Plenum press, New York (1985)

    Google Scholar 

  8. Francis, L., Brown, L., Philipchalk, R.: The development of an Abbreviated form of the Revised Eysenck Personality Questionnaire (EPQR-A). Personality and Individual Differences 13, 443–449 (1992)

    Article  Google Scholar 

  9. Gross, J.J., Levenson, R.W.: Emotion elicitation using films. Cognition and Emotion 9, 87–108 (1995)

    Article  Google Scholar 

  10. Idzihowski, C., Baddeley, A.: Fear and performance in novice parachutists. Ergonomics 30, 1463–1474 (1987)

    Article  Google Scholar 

  11. Isen, A.M.: Positive Affect and Decision Making. In: Handbook of Emotions, pp. 261–277. Guilford, New York (1993)

    Google Scholar 

  12. Mayer, J., Allen, J., Beauregard, K.: Mood Inductions for Four Specific Moods. Journal of Mental imagery 19, 133–150 (1995)

    Google Scholar 

  13. Nasoz, F., Lisetti, C.L., Avarez, K., Finkelstein, N.: Emotion Recognition from Physiological Signals for User Modeling of Affect. In: The 3rd Workshop on Affective and Attitude User Modeling, USA (2003)

    Google Scholar 

  14. Picard, R.W., Healey, J., Vyzas, E.: Toward Machine Emotional Intelligence Analysis of Affective Physiological State. IEEE Transactions onPattern Analysis and Machine Intelligence 23, 1175–1191 (2001)

    Article  Google Scholar 

  15. Reed, G.F.: Obsessional cognition: performance on two numerical tasks. British Journal of Psychiatry 130, 184–185 (1977)

    Article  Google Scholar 

  16. Rish, I.: An empirical study of the naive Bayes classifier. In: Workshop on Empirical Methods in AI (2001)

    Google Scholar 

  17. Rosic, M., Stankov, S., Glavinic, V.: Intelligent tutoring systems for asynchronous distance education. In: 10th Mediterranean Electrotechnical Conference, pp. 111–114 (2000)

    Google Scholar 

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© 2004 Springer-Verlag Berlin Heidelberg

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Chaffar, S., Frasson, C. (2004). Inducing Optimal Emotional State for Learning in Intelligent Tutoring Systems. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds) Intelligent Tutoring Systems. ITS 2004. Lecture Notes in Computer Science, vol 3220. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30139-4_5

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  • DOI: https://doi.org/10.1007/978-3-540-30139-4_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22948-3

  • Online ISBN: 978-3-540-30139-4

  • eBook Packages: Springer Book Archive

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