On Modeling the Affective Effect on Learning

  • Arunkumar Balakrishnan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7080)


This paper presents an approach to modeling the affective state of a student. The model represents the learning process used by the student including the affective state. The student modeling is built upon an ontology of machine learning strategies. This paper describes how the ontology is extended to include affective knowledge. The recommended learning strategies for a situation are ordered based on the affective state of the learner.


Student modeling affective modeling integrated machine learning 


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  1. 1.
    Balakrishnan, A.: Development of an ontology of learning strategies and its application to generate open learner models. In: IEEE Proceedings of the 8th International Conference on Machine Learning and Applications 2009, USA (2009)Google Scholar
  2. 2.
    Astleitner, H.: Designing emotionally sound interaction: The FEASP approach. Instructional Science 28(3), 169–198 (2000)CrossRefGoogle Scholar
  3. 3.
    Bartneck, C.: Integrating the OCC model of emotions in Embodied characters. In: Workshop on Virtual Conversational Characters: Applications, Methods and Research Challenges (2002)Google Scholar
  4. 4.
    Blanchard, E.G., Volfson, B., Hong, Y.-H., Lajoie, S.P.: Affective Artificial Intelligence in education: From detection to adaptation. In: Int. Conference on AI in Education (2009)Google Scholar
  5. 5.
    Brown, J.S., VanLehn, K.: Repair theory: A generative theory of bugs in procedural skills. Cognitive Science 4, 379–426 (1980)CrossRefGoogle Scholar
  6. 6.
    Burton, R.: Diagnosing bugs in a simple procedural skill. In: Sleeman, D.H., Brown, J.S. (eds.) Intelligent Tutoring Systems. Academic Press (1982)Google Scholar
  7. 7.
    Conati, C.: Probabilistic assessment of user’s emotions in educational games. Journal of Applied Artificial Intelligence 16(7-8), 555–575 (2002); Managing cognition and affect in HCICrossRefGoogle Scholar
  8. 8.
    Forbus, K., Gentner, D.: Learning physical domains: Toward a theoretical framework. In: Anderson, J.R., et al. (eds.) Machine Learning 2 -An Artificial Intelligence Approach, ch. 12. Morgan Kaufmann (1986)Google Scholar
  9. 9.
    Graesser, A.C., McDaniel, B., Chipman, P., Witherspoon, A., D’Mello, S.: Detection of emotions during learning with AutoTutor. In: The Proceedings of the Cognitive Science Society. Erlbaum, Mahwah (2006)Google Scholar
  10. 10.
    Jaques, P.A., Viccari, R.M.: A BDI Approach to Infer Student’s Emotions in Intelligent Learning Environments. In: Lemaître, C., Reyes, C.A., González, J.A. (eds.) IBERAMIA 2004. LNCS (LNAI), vol. 3315, pp. 901–911. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  11. 11.
    John, S.B., Van Lehn, K.: Repair theory: A generative theory of bugs in procedural skills. Cognitive Science 4(4), 379–426 (1980)CrossRefGoogle Scholar
  12. 12.
    Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement Learning: A survey. Journal of Artificial Intelligence Research 4, 237–285 (1996)Google Scholar
  13. 13.
    Mota, S., Picard, R.W.: Automated posture analysis for detecting learner’s interest level. In: Proceedings of Computer Vision and Pattern Recognition, Workshop on HCI, pp. 724–736 (2003)Google Scholar
  14. 14.
    Pat, L., James, W., Stellan, O.: Rules and principles in cognitive diagnosis. In: Frederiksen, N., et al. (eds.) Diagnostic Monitoring of Skill and Knowledge Acquisition. Lawrence Erlbaum Associates Publishers (1990)Google Scholar
  15. 15.
    Picard, R.W., Papert, S., Bender, W., Blumberg, B., Breazeal, C., Cavallo, D., Machover, T., Resnick, M., Roy, D., Strohecker, C.: Affective learning – a manifesto. BT Technology Journal 22(4) (2004)Google Scholar
  16. 16.
    Robison, J., McQuiggan, S., Lester, J.: Developing Empirically Based Student Personality Profiles for Affective Feedback Models. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010. LNCS, vol. 6094, pp. 285–295. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    Shen, L., Wang, M., Shen, R.: Affective e-learning: Using “emotional” data to improve learning in pervasive learning environment. Journal of Educational Technology and Society 12(2) (2009)Google Scholar
  18. 18.
    Tecuci, G., Kodratoff, Y.: Apprenticeship Learning in Imperfect Domain Theories. In: Kodratoff, Y., Michalski, R.S. (eds.) Machine Learning: An Artificial Intelligence Approach, vol. 3. Morgan Kaufmann (1990)Google Scholar
  19. 19.
    Van Lehn, K.: Learning one subprocedure per lesson. Artificial Intelligence 31, 1–40 (1987)CrossRefGoogle Scholar
  20. 20.
    Van Lehn, K.: Towards a theory of impasse-driven learning. In: Mandl, H., Lesgold, A. (eds.) Learning Issues for Intelligent Tutoring Systems. Springer, Heidelberg (1988)Google Scholar
  21. 21.
    Yanwen, W., Tingting, W., Xiaonian, C.: Affective modeling and recognition of learning emotion: Application to E-learning. Journal of Software 4(8) (2009)Google Scholar
  22. 22.
    Yasmin, H., Juliata, N., Enrique, S., Gustava, A.-F.: Incorporating an affective model to an intelligent tutor for mobile robotics. In: 36th ASEE/IEEE Frontiers in Education Conference. IEEE (2006)Google Scholar
  23. 23.
    Hernández, Y., Sucar, L.E., Conati, C.: An Affective Behavior Model for Intelligent Tutors. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 819–821. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Arunkumar Balakrishnan
    • 1
  1. 1.Department of Computer Technology and ApplicationsCoimbatore Institute of TechnologyCoimbatoreIndia

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