Adapting to When Students Game an Intelligent Tutoring System

  • Ryan S. J. d. Baker
  • Albert T. Corbett
  • Kenneth R. Koedinger
  • Shelley Evenson
  • Ido Roll
  • Angela Z. Wagner
  • Meghan Naim
  • Jay Raspat
  • Daniel J. Baker
  • Joseph E. Beck
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4053)

Abstract

It has been found in recent years that many students who use intelligent tutoring systems game the system, attempting to succeed in the educational environment by exploiting properties of the system rather than by learning the material and trying to use that knowledge to answer correctly. In this paper, we introduce a system which gives a gaming student supplementary exercises focused on exactly the material the student bypassed by gaming, and which also expresses negative emotion to gaming students through an animated agent. Students using this system engage in less gaming, and students who receive many supplemental exercises have considerably better learning than is associated with gaming in the control condition or prior studies.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ryan S. J. d. Baker
    • 1
  • Albert T. Corbett
    • 2
  • Kenneth R. Koedinger
    • 2
  • Shelley Evenson
    • 3
  • Ido Roll
    • 2
  • Angela Z. Wagner
    • 2
  • Meghan Naim
    • 4
  • Jay Raspat
    • 4
  • Daniel J. Baker
    • 5
  • Joseph E. Beck
    • 6
  1. 1.Learning Sciences Research InstituteUniversity of NottinghamNottinghamUK
  2. 2.Human-Computer Interaction InstituteCarnegie Mellon UniversityPittsburghUSA
  3. 3.School of DesignCarnegie Mellon UniversityPittsburghUSA
  4. 4.North Hills Junior HighPittsburghUSA
  5. 5.Department of PediatricsUniversity of Medicine and Dentistry of New JerseyNew BrunswickUSA
  6. 6.Machine Learning DepartmentCarnegie Mellon UniversityPittsburghUSA

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