Detecting When Students Game the System, Across Tutor Subjects and Classroom Cohorts

  • Ryan Shaun Baker
  • Albert T. Corbett
  • Kenneth R. Koedinger
  • Ido Roll
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3538)

Abstract

Building a generalizable detector of student behavior within intelligent tutoring systems presents two challenges: transferring between different cohorts of students (who may develop idiosyncratic strategies of use), and transferring between different tutor lessons (which may have considerable variation in their interfaces, making cognitively equivalent behaviors appear quite different within log files). In this paper, we present a machine-learned detector which identifies students who are “gaming the system”, attempting to complete problems with minimal cognitive effort, and determine that the detector transfers successfully across student cohorts but less successfully across tutor lessons.

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References

  1. 1.
    Baker, R.S., Corbett, A.T., Koedinger, K.R.: Detecting Student Misuse of Intelligent Tutoring Systems. In: Proceedings of the 7th International Conference on Intelligent Tutoring Systems, pp. 531–540 (2004)Google Scholar
  2. 2.
    Baker, R.S., Corbett, A.T., Koedinger, K.R., Wagner, A.Z.: Off-Task Behavior in the Cognitive Tutor Classroom: When Students Game the System. In: Proceedings of ACM CHI 2004: Computer-Human Interaction, pp. 383–390 (2004)Google Scholar
  3. 3.
    Donaldson, W.: Accuracy of d’ and A’ as Estimates of Sensitivity. Bulletin of the Psychonomic Society 31(4), 271–274 (1993)MathSciNetGoogle Scholar
  4. 4.
    Maris, E.: Psychometric Latent Response Models. Psychometrika 60(4), 523–547 (1995)MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Stevens, R., Soller, A., Cooper, M., Sprang, M.: Modeling the Development of Problem-Solving Skills in Chemistry with a Web-Based Tutor. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 580–591. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Yu, L., Liu, H.: Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution. In: Proc. of the Intl. Conference on Machine Learning (ICML 2003), pp. 856–863 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ryan Shaun Baker
    • 1
  • Albert T. Corbett
    • 1
  • Kenneth R. Koedinger
    • 1
  • Ido Roll
    • 1
  1. 1.Human-Computer Interaction InstituteCarnegie Mellon UniversityPittsburghUSA

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