About this Journal Submit a Manuscript Table of Contents
Advances in Artificial Intelligence
Volume 2011 (2011), Article ID 384169, 13 pages
Research Article

Towards a Brain-Sensitive Intelligent Tutoring System: Detecting Emotions from Brainwaves

HERON Lab, Computer Science Department, University of Montreal, P.O. Box 6128, Centre Ville Montréal, QC, H3T-1J4, Canada

Received 14 May 2010; Accepted 21 February 2011

Academic Editor: Jun Hong

Copyright © 2011 Alicia Heraz and Claude Frasson. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. Harter, “A new self-report scale of intrinsic versus extrinsic orientation in the classroom: motivational and informational components,” Developmental Psychology, vol. 17, no. 3, pp. 300–312, 1981. View at Publisher · View at Google Scholar · View at Scopus
  2. R. Snow, L. Corno, and D. Jackson, “Individual differences in affective and cognitive functions,” in Handbook of Educational Psychology, D. C. Berliner and R. C. Calfee, Eds., pp. 243–310, Macmillan, New York, NY, USA, 1996.
  3. B. S. Bloom, Taxonomy of Educational Objectives. Handbook I: Cognitive Domain, David McKay, New York, NY, USA, 1956.
  4. M. Miserandino, “Children who do well in school: individual differences in perceived competence and autonomy in above-average children,” Journal of Educational Psychology, vol. 88, pp. 203–214, 1996.
  5. D. Stipek, Motivation to Learn: From Theory to Practice, Allyn and Bacon, Boston, Mass, USA, 3rd edition, 1998.
  6. M. E. Ford, Motivating Humans: Goals, Emotions, and Personal Agency Beliefs, Sage, London, UK, 1992.
  7. D. K. Meyer and J. C. Turner, “Discovering emotion in classroom motivation research,” Educational Psychologist, vol. 37, no. 2, pp. 107–114, 2002. View at Scopus
  8. C. Breazeal, Designing Sociable Robots, MIT Press, Cambridge, UK, 2003.
  9. C. Conati, “Probabilistic assessment of user's emotions in educational games,” Applied Artificial Intelligence, vol. 16, no. 7-8, pp. 555–575, 2002. View at Publisher · View at Google Scholar · View at Scopus
  10. S. D. Craig, A. C. Graesser, J. Sullins, and B. Gholson, “Affect and learning: an exploratory look into the role of affect in learning,” Journal of Educational Media, vol. 29, pp. 241–250, 2004.
  11. A. De Vicente and H. Pain, “Informing the detection of students motivational state : an empirical study,” in Proceedings of the 6th International Conference on Intelligent Tutoring Systems, S. A. Cerri, G. Gouarderes, and F. Paraguacu, Eds., pp. 933–943, Springer, Berlin, Germany, 2002.
  12. B. Kort, R. Reilly, and R. Picard, “An affective model of interplay between emotions and learning: reengineering educational pedagogy—building a learning companion,” in Proceedings IEEE International Conference on Advanced Learning Technology: Issues, Achievements and Challenges, T. Okamoto, R. Hartley, Kinshuk, and J. P. Klus, Eds., pp. 43–48, IEEE Computer Society, Madison, Wis, USA, 2001.
  13. M. R. Lepper and M. Woolverton, “The wisdom of practice: lessons learned from the study of highly effective tutors,” in Improving Academic Achievement: Impact of Psychological Factors on Education, J. Aronson, Ed., pp. 135–158, Academic Press, Orlando, Fla, USA, 2002.
  14. J. C. Lester, S. G. Towns, and P. J. FitzGerald, “Achieving affective impact: visual emotive communication in lifelike pedagogical agents,” The International Journal of Artificial Intelligence in Education, vol. 10, no. 3-4, pp. 278–291, 1999.
  15. D. J. Litman and K. Forbes-Riley, “Predicting student emotions in computer-human tutoring dialogues,” in Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, pp. 352–359, Association for Computational Linguistics, East Stroudsburg, Pa, USA, 2004.
  16. R. W. Picard, Affective Computing, MIT Press, Cambridge, UK, 1997.
  17. N. Wang, W. L. Johnson, R. Mayer, P. Rizzzo, E. Shaw, and H. Collins, “The politeness effect: pedagogical agents and learning gains,” in Artificial Intelligence in Education, C. Looi, G. McCalla, B. Bredeweg, and J. Breuker, Eds., pp. 686–693, IOS Press, Amsterdam, The Netherlands, 2005.
  18. I. Arroyo, D. G. Cooper, W. Burleson, B. Woolf, K. Muldner, and R. Christopherson, “Emotion sensors go to school,” in Proceedings of the 14th International Conference on Artificial Intelligence in Education, Brighton, UK, 2009.
  19. S. L. Norris and M. Currieri, “Performance enhancement training through neurofeedback,” in Introduction to Quantitative EEG and Neurofeedback, J. R. Evans and A. Abarbanel, Eds., Academic Press, New York, NY, USA, 1999.
  20. M. F. Bear, B. W. Connors, and M. A. Paradiso, Neuroscience: Exploring the Brain, Lippincott Williams & Williams, Baltimore, Md, USA, 2nd edition, 2001.
  21. D. S. Cantor, “An overview of quantitative EEG and its applications to neurofeedback,” in Introduction to Quantitative EEG and Neurofeedback, J. R. Evans and A. Abarbanel, Eds., Academic Press, San Diego, Calif, USA, 1999.
  22. A. Wise, The High Performance Mind, G. P. Putnam's Sons, New York, NY, USA, 1995.
  23. F. Angeleri, S. Butler, S. Glaquinto, and J. Majkowski, Analysis of the Electrical Activity of the Brain, John Wiley & Sons, New York, NY, USA, 1997.
  24. S. K. D'Mello, S. D. Craig, B. Gholson, S. Franklin, R. W. Picard, and A. C. Graesser, “Integrating affect sensors in an intelligent tutoring system,” in Proceedings of the Affective Interactions: The Computer in the Affective Loop Workshop at Intelligent User Interface 2005 (IUI '05), AMC Press, New York, NY, USA, 2005.
  25. P. J. Lang, M. M. Bradley, and B. N. Cuthbert, “International affective picture system (IAPS):affective ratings of pictures and instruction manual,” Tech. Rep. A-6, University of Florida, Gainesville, Fla, USA, 2005.
  26. B. McMillan, Pendant EEG, Pocket Neurobics, 2006, http://www.pocket-neurobics.com/.
  27. J. Lévesque, , Ph.D. thesis, 2006, BCIA-EEG.
  28. FIPA, “Foundation for Intelligent Physical Agents,” Specifications, 2009, http://www.fipa.org/.
  29. J. A. Mikels, B. L. Fredrickson, G. R. Larkin, C. M. Lindberg, S. J. Maglio, and P. A. Reuter-Lorenz, “Emotional category data on images from the international affective picture system,” Behavior Research Methods, vol. 37, no. 4, pp. 626–630, 2005. View at Scopus
  30. M. M. Bradley, M. Codispoti, B. N. Cuthbert, and P. J. Lang, “Emotion and motivation: defensive and appetitive reactions in picture processing,” Emotion, vol. 1, no. 3, pp. 276–298, 2001. View at Publisher · View at Google Scholar · View at Scopus
  31. R. R. McCrae and P. T. Costa Jr., “A Five-Factor Theory of personality,” in Handbook of Personality: Theory and Research, L. A. Pervin and O. P. John, Eds., 1999.
  32. T. F. Hawk and A. J. Shah, “Using learning style instruments to enhance student learning,” Decision Science Journal of Innovative Education, vol. 5, pp. 1–19, 2007.
  33. I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, San Francisco, Calif, USA, 2005.
  34. V. Belur and S. Dasarathy, Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques, 1991.
  35. C. Robson, Real Word Research: A Resource for Social Scientist and Practitioner Researchers, Blackwell, Hoboken, NJ, USA, 1993.
  36. W. J. Youden, How to Evaluate Accuracy. Materials Research and Standards, ASTM, 1961.
  37. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, San Mateo, Calif, USA, 1993.
  38. L. Breiman, “Bagging predictors,” Machine Learning, vol. 24, no. 2, pp. 123–140, 1996. View at Scopus
  39. T. W. Anderson and M. Anderson, An Introduction to Multivariate Statistical Analysis, John Wiley & Sons, New York, NY, USA, 3rd edition, 2003.