Advertisement

The Effect of Adapting Feedback Generality in ITS

  • Brent Martin
  • Antonija Mitrovic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4018)

Abstract

Intelligent tutoring systems achieve much of their success by adapting to individual students. One potential avenue for personalization is feedback generality. This paper presents two evaluation studies that measure the effects of modifying feedback generality in a web-based Intelligent Tutoring System (ITS) based on the analysis of student models. The object of the experiments was to measure the effectiveness of varying feedback generality, and to determine whether this could be performed en masse or if personalization is needed. In an initial trial with a web-based ITS it appeared that it is feasible to use a mass approach to select appropriate concepts for generalizing feedback. A second study gave conflicting results and showed a relationship between generality and ability, highlighting the need for feedback to be personalized to individual students’ needs.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Anderson, J.R., Corbett, A.T., Koedinger, K.R., Pelletier, R.: Cognitive Tutors: Lessons Learned. Journal of the Learning Sciences 4(2), 167–207 (1995)CrossRefGoogle Scholar
  2. 2.
    Koedinger, K.R., Mathan, S.: Distinguishing Qualitatively Different Kinds of Learning Using Log Files and Learning Curves. In: Mostow, J., Tedesco, P. (eds.) Proc. Workshop on Data Mining of Student Logs at ITS 2004, Maceio, Brazil, pp. 39–46 (2004)Google Scholar
  3. 3.
    Martin, B.: Constraint-Based Modelling: Representing Student Knowledge. New Zealand Journal of Computing 7(2), 30–38 (1999)Google Scholar
  4. 4.
    Mitrovic, A.: An Intelligent SQL Tutor on the Web. Artificial Intelligence in Education 13(2-4), 173–197 (2003)Google Scholar
  5. 5.
    Mitrovic, A., Martin, B., Mayo, M.: Using evaluation to shape ITS design: Results and experiences with SQL-Tutor. User Modelling and User Adapted Interaction 12(2-3), 243–279 (2002)zbMATHCrossRefGoogle Scholar
  6. 6.
    Newell, A., Rosenbloom, P.S.: Mechanisms of skill acquisition and the law of practice. In: Anderson, J.R. (ed.) Cognitive skills and their acquisition, pp. 1–56. Lawrence Erlbaum Associates, Hillsdale (1981)Google Scholar
  7. 7.
    Ohlsson, S.: Constraint-Based Student Modeling. In: Greer, J., McCalla, G. (eds.) Student Modeling: The Key to Individualized Knowledge-Based Instruction, pp. 167–189. Springer, Heidelberg (1994)Google Scholar
  8. 8.
    Zapata-Rivera, J.D., Greer, J.E.: Interacting with Inspectable Bayesian Student Models. Artificial Intelligence in Education 14(2), 127–163 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Brent Martin
    • 1
  • Antonija Mitrovic
    • 1
  1. 1.Intelligent Computer Tutoring Group, Department of Computer Science and Software EngineeringUniversity of CanterburyChristchurchNew Zealand

Personalised recommendations