Detecting Guessed and Random Learners’ Answers through Their Brainwaves

  • Alicia Heraz
  • Claude Frasson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5535)


This paper describes an experiment in which we tried to predict the learner’s answers from his brainwaves. We discuss the efficiency to enrich the learner model with some electrical brain metrics to obtain some important information about the learner during a test. We conducted an experiment to reach three objectives: the first one is to record the learner brainwaves and his answers to the test questions; the second is to use machine learning techniques to predict guessed and random answers from the learner brainwaves; the third is to implement an agent that transmits the prediction results to an Intelligent Tutoring System. 21 participants were recruited, 45827 recording were collected and we reached a prediction accuracy of 96%.


Intelligent Tutoring System Brainwaves Learning Guess 


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  1. 1.
    Ahn, H.I., Teeters, A., Wang, A., Breazeal, C., Picard, R.W.: Stoop to Conquer: Posture and affect interact to influence computer users’ persistence. In: Paiva, A.C.R., Prada, R., Picard, R.W. (eds.) ACII 2007. LNCS, vol. 4738, pp. 582–593. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Anderson, J.R.: Tailoring Assessment to Study Student Learning Styles. In: American Association for Higher Education, vol. (53), p. 7 (2001)Google Scholar
  3. 3.
    Boyer, K.E., Phillips, R., Wallis, M., Vouk, M., Lester, J.: Balancing Cognitive and Motivational Scaffolding in Tutorial Dialogue. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 239–249. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    D’Mello, S., Jackson, T., Craig, S., Morgan, B., Chipman, P., White, H., Person, N., Kort, B., el Kaliouby, R., Picard., R.W., Graesser, A.: AutoTutor Detects and Responds to Learners Affective and Cognitive States. In: Workshop on Emotional and Cognitive Issues at the International Conference of Intelligent Tutoring Systems, Montreal, Canada, June 23-27 (2008)Google Scholar
  5. 5.
    Grandbastien, M., Labat, J.M.: Environnements informatiques pour l’apprentissage humain. Éditions, Lavoisier, Paris, France (2006)Google Scholar
  6. 6.
    Heraz, A., Daouda, T., Frasson, C.: Decision Tree for Tracking Learner’s Emotional State predicted from his electrical brain activity. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 822–824. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Heraz, A., Razaki, R., Frasson, C.: Using machine learning to predict learner emotional state from brainwaves. In: ICALT, Niigata, Japan (2007)Google Scholar
  8. 8.
    Heraz, A., Frasson, C.: Predicting the Three Major Dimensions of the Learner’s Emotions from Brainwaves. International Journal of Computer Science (2007)Google Scholar
  9. 9.
    Kapoor, A., Ahn, H.I., Picard, R.W.: Mixture of Gaussian Processes for Combining Multiple Modalities. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds.) MCS 2005. LNCS, vol. 3541, pp. 86–96. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Kort, B., Reilly, R., Picard, R.W.: An Affective Model of Interplay Between Emotions and Learning: Reengineering Educational Pedagogy-Building a Learning Companion. In: Proceedings of International Conference on Advanced Learning Technologies (ICALT 2001), Madison, WI (August 2001)Google Scholar
  11. 11.
    Lang, P.J., Bradley, M.M., Cuthbert, B.N.: International affective picture system (IAPS): Affective ratings of pictures and instruction manual. Technical Report A-6. University of Florida, Gainesville, FL (2005)Google Scholar
  12. 12.
    McMillan Bruce (2006),
  13. 13.
    Nkambou, R., Héritier, V.: Facial expression analysis for emotion recognition in ITS. In: ITS 2000 workshop on Emotional Intelligence proceedings (2004)Google Scholar
  14. 14.
    Norris, S.L., Currieri, M.: Performance enhancement training through neurofeedback. In: Evans, J.R., Abarbanel, A. (eds.) Introduction to Quantitative EEG and Neurofeedback. Academic Press, London (1999)Google Scholar
  15. 15.
    Wenger, E.: Artificial Intelligence and Tutoring Systems. Morgan Kaufmann, Los Altos (1987)Google Scholar
  16. 16.
    Witten Ian, H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Alicia Heraz
    • 1
  • Claude Frasson
    • 1
  1. 1.HERON Lab, Computer Science DepartmentUniversity of MontréalCanada

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