Adapting to When Students Game an Intelligent Tutoring System

Ryan Baker (1) , Kenneth R. Koedinger (2) , Albert T. Corbett (2) , Angela Z. Wagner (2) , Shelley Evenson (3) , Ido Roll (2) , Meghan Naim (4) , Jay Raspat (4) , Joseph E. Beck (5)

1 Learning Sciences Research Institute
2 Human-Computer Interaction Institute
3 School of Design
4 North Hills Junior High
5 Machine Learning Department [Carnegie Mellon Univ.]

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.

Mots clés: game systems

Domaines: Environnements Informatiques pour l'Apprentissage Humain

Ryan Baker, Kenneth R. Koedinger, Albert T. Corbett, Angela Z. Wagner, Shelley Evenson, et al.. Adapting to When Students Game an Intelligent Tutoring System. International Conference on Intelligent Tutoring Systems, 2006, Jhongli, Taiwan. ⟨hal-00190177⟩