Motivational Processes

  • Benedict du Boulay
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6738)


A motivationally intelligent tutor should determine the motivational state of the learner and also determine what caused that state. Only if the causation is taken into account can an efficient pedagogic strategy be selected to find an effective way to maintain or improve the learner’s motivation. Thus we argue that motivation is more constructively thought of as a process involving causation rather than simply as a state. We describe methods by which this causality might be determined and suggest a range of pedagogic tactics that might be deployed as part of an overall pedagogic strategy.


motivation pedagogy feelings expectancies and values 


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  1. 1.
    Forgas, J.P.: Affect and Cognition. Perspectives on Psychological Science 3, 94–101 (2008)CrossRefGoogle Scholar
  2. 2.
    du Boulay, B., Avramides, K., Luckin, R., Martinez-Miron, E., Rebolledo-Mendez, G., Carr, A.: Towards Systems That Care: A Conceptual Framework based on Motivation, Metacognition and Affect. International Journal of Artificial Intelligence and Education 20(3), 197–229 (2010)Google Scholar
  3. 3.
    Boekaerts, M.: Understanding Students’ Affective Processes in the Classroom. In: Schutz, P.A., Pekrun, R. (eds.) Emotion in Education, pp. 37–56. Acadmic Press, Burlington (2007)CrossRefGoogle Scholar
  4. 4.
    Wigfield, A., Eccles, J.S.: Expectancy–Value Theory of Achievement Motivation. Contemporary Educational Psychology 25, 68–81 (2000)CrossRefGoogle Scholar
  5. 5.
    Zimmerman, B.J.: Investigating Self-Regulation and Motivation: Historical Background, Methodological Developments, and Future Prospects. American Educational Research Journal 45, 166–183 (2008)CrossRefGoogle Scholar
  6. 6.
    Arroyo, I., Cooper, D.G., Burleson, W., Woolf, B.P., Muldner, K., Christopherson, R.: Emotion Sensors Go to School. In: Dimitrova, V., Mizoguchi, R., du Boulay, B., Grasser, A. (eds.) Artificial Intelligence in Education. Building Learning Systems that Care: from Knowledge Representation to Affective Modelling, Vol. Frontiers in AI and Applications 200, pp. 17–24. IOS Press, Amsterdam (2009)Google Scholar
  7. 7.
    D’Mello, S., Graesser, A., Picard, R.W.: Toward an affect-sensitive AutoTutor. IEEE Intelligent Systems 22, 53–61 (2007)CrossRefGoogle Scholar
  8. 8.
    Zeman, J., Klimes-Dougan, B., Cassano, M., Adrian, M.: Measurement Issues in Emotion Research With Children and Adolescents. Clinical Psychology: Science and Practice 14, 377–401 (2007)Google Scholar
  9. 9.
    Zakharov, K., Mitrovic, A., Johnston, L.: Towards Emotionally-Intelligent Pedagogical Agents. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. L. (eds.) ITS 2008. LNCS, vol. 5091, pp. 19–28. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Kleinsmith, A., De Silva, P.R., Bianchi-Berthouze, N.: Recognizing Emotion from Postures: Cross-Cultural Differences in User Modeling. In: Ardissono, L., Brna, P., Mitrovic, A. (eds.) UM 2005. LNAI, vol. 3538, pp. 50–59. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Conati, C., Chabbal, R., Maclaren, H.: A Study on Using Biometric Sensors for Monitoring User Emotions in Educational Games. In: Proceedings of the Workshop Assessing and Adapting to User Attitude and Affects: Why, When and How? 9th International Conference on User Modeling, UM 2003 (2003)Google Scholar
  12. 12.
    D’Mello, S.K., Craig, S.D., Witherspoon, A., McDaniel, B., Graesser, A.: Automatic detection of learner’s affect from conversational cues. User Modeling and User-Adapted Interaction 18, 45–80 (2008)CrossRefGoogle Scholar
  13. 13.
    Baker, R., Walonoski, J., Heffernan, N., Roll, I., Corbett, A., Koedinger, K.: Why Students Engage in “Gaming the System” Behaviours in Interactive Learning Environments. Journal of Interactive Learning Research 19, 185–224 (2008)Google Scholar
  14. 14.
    Baker, R.S.J.d., Rodrigo, M. M.T., Xolocotzin, U.E.: The Dynamics of Affective Transitions in Simulation Problem-Solving Environments. In: Paiva, A., Prada, R., Picard, R.W. (eds.) ACII 2007. LNCS, vol. 4738, pp. 666–677. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 15.
    Muldner, K., Burleson, W., VanLehn, K.: “Yes!”: Using Tutor and Sensor Data to Predict Moments of Delight during Instructional Activities. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 159–170. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Pekrun, R., Goetz, T., Titz, W., Perry, R.P.: Academic Emotions in Students’ Self-Regulated Learning and Achievement: A Program of Qualitative and Quantitative Research. Educational Psychologist 37, 91–105 (2002)CrossRefGoogle Scholar
  17. 17.
    Larsen, J.T., McGraw, A.P., Mellers, B.A., Cacioppo, J.T.: The Agony of Victory and Thrill of Defeat Mixed Emotional Reactions to Disappointing Wins and Relieving Losses. Psychological Science 15, 325–330 (2004)CrossRefGoogle Scholar
  18. 18.
    Graesser, A., Chipman, P., King, B., McDaniel, B., D’Mello, S.: Emotions and Learning with AutoTutor. In: Luckin, R., Koedinger, K.R., Greer, J. (eds.) Proceeding of the 2007 Conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts that Work, vol. Frontiers in AI and Applications 158, pp. 569–571. IOS Press, Amsterdam (2007)Google Scholar
  19. 19.
    Dweck, C.S., Chiu, C.-y., Hong, Y.-y.: Implicit Theories and Their Role in Judgments and Reactions: A Word From Two Perspectives. Pscychological Inquiry 6(4), 267–285 (1995)CrossRefGoogle Scholar
  20. 20.
    Conati, C., Zhou, X.: Modeling Students’ Emotions from Cognitive Appraisal in Educational Games. In: Cerri, S.A., Guy, G., Paraguacu, F. (eds.) ITS 2002. LNCS, vol. 2363, pp. 944–954. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  21. 21.
    Baker, R.S.J.d., D’Mello, S.K., Rodrigo, M.M.T., Graesser, A.C.: Better to be frustrated than bored: The incidence, persistence, and impact of learners’ cognitive–affective states during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies 68, 223–241 (2010)CrossRefGoogle Scholar
  22. 22.
    Balaam, M., Luckin, R., Good, J.: Supporting affective communication in the classroom with the Subtle Stone. International Journal of Learning Technology 4, 188–215 (2009)CrossRefGoogle Scholar
  23. 23.
    Hull, A., du Boulay, B.: Scaffolding Motivation and Metacognition in Learning Programming. In: Dimitrova, V., Mizoguchi, R., du Boulay, B., Grasser, A. (eds.) Artificial Intelligence in Education. Building Learning Systems that Care: from Knowledge Representation to Affective Modelling, vol. Frontiers in AI and Applications 200, pp. 755–756. IOS Press, Amsterdam (2009)Google Scholar
  24. 24.
    Weber, G., Brusilovsky, P.: ELM-ART: An Adaptive Versatile System for Web-based Instruction. International Journal of Artificial Intelligence in Education 12, 351–384 (2001)Google Scholar
  25. 25.
    van Zijl, M.: Towards a Motivationally Intelligent Pedagogical Agent. Department of Computer Science and Software Engineering. University of Canterbury, Christchurch (2010)Google Scholar
  26. 26.
    Graesser, A.C., Chipman, P., Haynes, B.C., Olney, A.: AutoTutor: an intelligent tutoring system with mixed-initiative dialogue. IEEE Transactions on Education 48, 612–618 (2005)CrossRefGoogle Scholar
  27. 27.
    del Soldato, T., du Boulay, B.: Implementation of Motivational Tactics in Tutoring Systems. International Journal of Artificial Intelligence in Education 6, 337–378 (1995)Google Scholar
  28. 28.
    Puntambekar, S., du Boulay, B.: Design of MIST – A System to Help Students Develop Metacognition. In: Murphy, P. (ed.) Learners, Learning & Assessment, pp. 245–257. Paul Chapman Publishing, London (1999)Google Scholar
  29. 29.
    Avramides, K., du Boulay, B.: Motivational Diagnosis in ITSs: Collaborative, Reflective Self-Report. In: Dimitrova, V., Nizoguchi, R., du Boulay, B., Graesser, A. (eds.) Artificial Intelligence in Education. Building Learning Systems that Care: From Knowledge Representation to Affective Modelling, vol. Frontiers in AI and Applications 200, pp. 587–589. IOS Press, Amsterdam (2009)Google Scholar
  30. 30.
    Lepper, M.R., Woolverton, M., Mumme, D.L., Gurtner, J.: Motivational techniques of expert human tutors: Lessons for the design of computer-based tutors. In: Lajoie, S., Derry, S. (eds.) Computers as Cognitive Tools, pp. 75–105. Lawrence Erlbaum Associates, Hillsdale (1993)Google Scholar
  31. 31.
    Lehman, B., Matthews, M., D’Mello, S., Person, N.: What Are You Feeling? Investigating Student Affective States During Expert Human Tutoring Sessions. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S.L. (eds.) ITS 2008. LNCS, vol. 5091, pp. 50–59. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  32. 32.
    du Boulay, B.: Towards a Motivationally-Intelligent Pedagogy: How should an intelligent tutor respond to the unmotivated or the demotivated? In: Calvo, R.A., D’Mello, S.K. (eds.) Affective Prospecting, Explorations in the Learning Sciences. Instructional Systems and Performance Technologies, vol. 3. Springer, New York (2011)Google Scholar
  33. 33.
    Pintrich, P.: Motivation and Classroom Learning. Handbook of Psychology: Educational Psychology 7, 103–122 (2003)Google Scholar
  34. 34.
    Keller, J.M.: Motivational design of instruction. In: Reigluth, C.M. (ed.) Instructional design theories and models: An overview of their current status, pp. 386–434. Lawrence Erlbaum, Hillsdale (1983)Google Scholar
  35. 35.
    Bandura, A.: Self-efficacy: The exercise of control. Freeman, New York (1997)Google Scholar
  36. 36.
    Gama, C.: Metacognition in Interactive Learning Environments: The Reflection Assistant Model. In: Lester, J.C., Vicari, R.M., Paraguacu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 668–677. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Benedict du Boulay
    • 1
  1. 1.Human Centred Technology Research Group, School of InformaticsUniversity of SussexBrightonUK

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