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Student Procedural Knowledge Inference through Item Response Theory

  • Manuel Hernando
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6787)

Abstract

This paper describes our research lines that focus on modeling and inferring student procedural knowledge in Intelligent Tutoring Systems. Our proposal is to apply Item Response Theory, a well-founded theory for declarative knowledge assessment, to infer procedural knowledge in problem solving environments. Therefore, we treat the problems as tests and the steps of problem solving as options (or choices) in a question. An important feature of our system is that it is not only based on an expert analysis, but also on data-driven techniques so that it can collect the largest amount of students’ problem solving strategies as possible.

Keywords

Student modeling procedural knowledge Item Response Theory 

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References

  1. 1.
    Anderson, J.R., Corbett, A.T., Koedinger, K.R., Pelletier, R.: Cognitive Tutors: Lessons learned. The Journal of the Learning Sciences 4(2), 167–207 (1995)CrossRefGoogle Scholar
  2. 2.
    Anderson, J.R.: The architecture of cognition. Harvard University Press, Cambridge (1983)Google Scholar
  3. 3.
    Anderson, J.R., Boyle, C.F., Corbett, A.T., Lewis, M.W.: Cognitive modeling and intelligent tutoring. Artif. Intell., 7–49 (1990)Google Scholar
  4. 4.
    Bisanz, J., LeFevre, J.: Strategic and non strategic processing in the development of mathematical cognition. In: Bjorklund, D. (ed.) Children’s Strategies: Contemporary Views of Cognitive Development, pp. 213–244. Lawrence Erlbaum Associates, Inc., Hillsdale (1990)Google Scholar
  5. 5.
    Bloom, B.S.: The search for methods of group instruction as effective as one-to-one tutoring. Educational Leadership 13(8), 4–17 (1984)Google Scholar
  6. 6.
    Conati, C., Gertner, A.S., Vanlehn, K., Druzdzel, M.J.: On-line student modeling for coached problem solving using bayesian networks, pp. 231–242. Springer, Heidelberg (1997)Google Scholar
  7. 7.
    Dodd, B.G., Ayala, R.J.D., Koch, W.R.: Computerized adaptive testing with polytomous items. Applied Psychological Measurement (19), 5–22 (1995)Google Scholar
  8. 8.
    Embretson, S.E., Reise, S.P.: Item response theory for psychologists, 1st edn. Lawrence Erlbaum, Mahwah (2000)Google Scholar
  9. 9.
    Guzmán, E., Conejo, R.: A model for student knowledge diagnosis through adaptive testing. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 12–21. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Guzmán, E., Conejo, R., Pérez-de-la Cruz, J.L.: Adaptive testing for hierarchical student models. User Modeling and User-Adapted Interaction 17(1-2), 119–157 (2007)CrossRefGoogle Scholar
  11. 11.
    Nkambou, R., Mephu Nguifo, E., Couturier, O., Fournier-Viger, P.: Problem-solving knowledge mining from users’ actions in an intelligent tutoring system. In: Bozapalidis, S., Rahonis, G. (eds.) CAI 2007. LNCS, vol. 4728, pp. 393–404. Springer, Heidelberg (2007)Google Scholar
  12. 12.
    Rittle-Johnson, B., Koedinger, K.R.: Designing Knowledge Scaffolds to Support Mathematical Problem Solving, ch. 23(3), pp. 313–349. Routledge, New York (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Manuel Hernando
    • 1
  1. 1.Dpto. Lenguajes y Ciencias de la Computación.Universidad de Málaga.MálagaSpain

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