Adapting to when students game an intelligent tutoring system

  • Authors:

  • 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

  • Affiliations:

  • Learning Sciences Research Institute, University of Nottingham, Nottingham, UK;Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA;Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA;School of Design, Carnegie Mellon University, Pittsburgh, PA;Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA;Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA;North Hills Junior High, Pittsburgh, PA;North Hills Junior High, Pittsburgh, PA;Department of Pediatrics, University of Medicine and Dentistry of New Jersey, New Brunswick, NJ;Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
  • Year:
  • 2006

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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.