Towards an Integrated Specialized Learning Application (ISLA) to Support High Functioning ASD Children in Mathematics Learning

  • Aydée Liza MondragonEmail author
  • Roger Nkambou
  • Pierre Poirier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9307)


Autism spectrum disorder (ASD) is a neurological disorder affecting the way in which the brain processes information. It can affect all aspects of a person’s development. Autism is characterized by impairments in learning and communication, in the social interaction, imaginative ability as well as in repetitive and restricted patterns of behavior (Diagnostic and statistical manual of mental disorders: DSM-IV [12]). This research contributes to the advancement of intelligent tutoring systems by proposing a computational model in the field of specialized education in order to overcome the lack of individualized intervention, such as in the specialized education of individuals with autism. The affective intelligent tutoring system ISLA is an adaptive application evolving along with the learner’s needs. ISLA is unique and its contribution entails the model of accompaniment to help autistic children manage their emotions by analyzing the learning trace and considering the student’s current performance to respond accordingly to it during a mathematical learning situation such as addition.


Autism Affective intelligent tutoring systems Specialized education Personalized education Model of accompaniment 


  1. 1.
    Aleven, V., McLaren, B.M., Sewall, J., Koedinger, K.R.: The cognitive tutor authoring tools (CTAT): preliminary evaluation of efficiency gains. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 61–70. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Arroyo, I., Cooper, D.G. Burleson, W., Woolf, B.P., Muldner, K., Christopherson, R.: Emotion sensors go to school. In: Proceeding of AIED 2009, pp. 17–24, Brighton. IOS Press, Amsterdam (2009)Google Scholar
  3. 3.
    Azevedo, R.: Theoretical, methodological, and analytical challenges in the research of metacognition and self-regulation: a commentary. Metacognition Learn. 4, 87–95 (2009)CrossRefGoogle Scholar
  4. 4.
    Baron-Cohen, S., Leslie, A.M., Frith, U.: Does the autistic child have a theory of mind? Cognition 21(1), 37–46 (1985)CrossRefGoogle Scholar
  5. 5.
    Bloom, B.S.: The 2 sigma problem: the search for methods of group instruction as effective as one-to-one tutoring (1984)Google Scholar
  6. 6.
    Bruner, J.S.: On Knowing: Essays for the Left Hand. Harvard University Press, Cambridge (1966)Google Scholar
  7. 7.
    Bull, S., Kay, J.: Open learner models. In: Nkambou, R., Bourdeau, J., Mizoguchi, R. (eds.) Advances in Intelligent Tutoring Systems, pp. 318–338. Springer, Berlin (2010)Google Scholar
  8. 8.
    Centre for Disease Control (CDC): cdc.govGoogle Scholar
  9. 9.
    Chalfoun, P., Frasson, C.: Optimal affective conditions for subconscious learning in a 3D intelligent tutoring system. In: Jacko, J.A. (ed.) HCI International 2009, Part IV. LNCS, vol. 5613, pp. 39–48. Springer, Heidelberg (2009)Google Scholar
  10. 10.
    Shalom, D.B., Mostofsky, S.H., Hazlett, R.L., Goldberg, M.C., Landa, R.J., Faran, Y., McLeod, D.R., Hoehn-Saric, R.: Normal physiological emotions but differences in expression of conscious feelings in children with high-functioning autism (2006)Google Scholar
  11. 11.
    Das, J.P., Naglieri, J.A., Kirby, J.R.: Assessment of Cognitive Processes. Developmental disabilities monitoring (ADDM) (1994)Google Scholar
  12. 12.
    Diagnostic and statistical manual of mental disorders: DSM-IV. American Psychiatric Association (2000)Google Scholar
  13. 13.
    D’Mello, S.K., Craig, S.D., Gholson, B.F.S., Picard, R.W.: Interacting effects sensors in an intelligent tutoring systems. In: Affective Interactions: The Computer in the Affective Loop Workshop (2005)Google Scholar
  14. 14.
    Frith U.: Interacting minds. Biological Basis Review (1999)Google Scholar
  15. 15.
    Graesser, A.C., Moreno, K., Marineau, J., Adcock, A., Olney, A., Person, N.: AutoTutor improves deep learning of computer literacy: is it the dialogue or the talking head? In: Proceedings of Artificial Intelligence in education, pp 47–54. IOS Press, Amsterdam (2003)Google Scholar
  16. 16.
    Green, J., et al.: Parent-mediated communication-focused treatment in children with autism (2010)Google Scholar
  17. 17.
    Seip, J.: Teaching the autistic and developmentally delayed: A guide for staff training and development (1996)Google Scholar
  18. 18.
    Dautenhahn, K., Werry, I.: Towards Interactive Robots in Autism Therapy. University of Hertfordshire (2004)Google Scholar
  19. 19.
    Lajoie, S.P., Lesgold, A.: Apprenticeship training in the workplace: computer coached practice environment as a new form of apprenticeship. Mach. Mediated Learn. 3, 7–28 (1989)Google Scholar
  20. 20.
    Mitchell, P., Parsons, S., Leonard, A.: Using virtual technologies environments for teaching to adolescents with ASD (2007)Google Scholar
  21. 21.
    Mottron, L., Belleville, S.: A study of perceptual analysis in a high-level autistic subject with exceptional graphic abilities. Brain Cogn. 23, 279–309 (1993)CrossRefGoogle Scholar
  22. 22.
    National Center for Education Statistics (NCES)-2009-nces.ed.govGoogle Scholar
  23. 23.
    National Research Council: Educating Children With Autism. National Academy Press, Washington, DC (2001)Google Scholar
  24. 24.
    Nkambou, R.: Towards affective intelligent tutoring system. In: ITS 2006 Workshop on Motivational and Affective Issues in ITS, Taiwan (2006)Google Scholar
  25. 25.
    Ekman, P., Saron, C.D., Senulis, J.A., Friesen, W.V.: Approach/withdrawal and cerebral asymmetry: Emotional expression and brain physiology. Int. J. Pers. Soc. Psychol. (1992)Google Scholar
  26. 26.
    Postka, J., Massey, L.D., Mutter, S.A.: Intelligent Tutoring Systems, Lessons Learned. Laurence Erlbaum, Hillsdale (1988)Google Scholar
  27. 27.
    Picard, R.W.: Future affective technology for autism and emotion communication. Philos. Transa. R. Soc. B Biol. Sci. 364(1535), 3575–3584 (2009)CrossRefGoogle Scholar
  28. 28.
    Schraw, G (1998), Promoting general metacognitive awareness, Instructional Science 26Google Scholar
  29. 29.
    Skinner, B.F.: Verbal Behaviour. Prentice Hall, New York (1957)CrossRefGoogle Scholar
  30. 30.
    Tchetagni, J., Nkambou, R., Bourdeau, J.: A framework to specify a cognitive diagnosis component in ILES. J. Interact. Learn. Res. 17(3), 269–293 (2006)Google Scholar
  31. 31.
    Tindal, G.A., Marston, D.B.: Classroom-Based Assessment. Merrill, Columbus (1990)Google Scholar
  32. 32.
    Tomasello, M.: Joint attention as social cognition. In: Moore, C., Dunham, P.J. (eds.) Joint Attention: Its Origin and Role in Development, pp. 103–130. Cambridge University Press, Cambridge (1995)Google Scholar
  33. 33.
    VanLehn, K., Lynch, C., Schulze, K., Shapiro, J.A., et al.: The andes physics tutoring system: lessons learned. Int. J. Artif. Intell. Educ. 15(3), 678–685 (2005)Google Scholar
  34. 34.
    VanLehn, K.: The behaviour of tutoring systems. Int. J. Artif. Intell. Educ. 16(3), 227–265 (2006)Google Scholar
  35. 35.
    Woolf, B., Arroyo L., Muldner, K., Burleson, W., Cooper, D., Dolan, R., Christopherson, R.: The effect of motivational learning companions on low high achieving students and students with learning disabilities. In: International Conference on Intelligent Tutoring Systems, Pittsburgh (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Aydée Liza Mondragon
    • 1
    Email author
  • Roger Nkambou
    • 1
  • Pierre Poirier
    • 1
  1. 1.Université de Québec à Montréal (UQAM)MontrealCanada

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