Adapting to When Students Game an Intelligent Tutoring System
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.
KeywordsIntelligent Tutor System Learn Gain Cognitive Tutor Human Tutor Problem Step
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- 1.Aleven, V.: Helping Students to Become Better Help Seekers: Towards Supporting Metacognition in a Cognitive Tutor. Paper presented at German-USA Early Career Research Exchange Program: Research on Learning Technologies and Technology-Supported Education, Tubingen, Germany (2001)Google Scholar
- 3.Arroyo, I., Woolf, B.: Inferring learning and attitudes from a Bayesian Network of log file data. In: Proceedings of the 12th International Conference on Artificial Intelligence in Education, pp. 33–40 (2005)Google Scholar
- 5.Baker, R.S., Roll, I., Corbett, A.T., Koedinger, K.R.: Do Performance Goals Lead Students to Game the System? In: Proceedings of the International Conference on Artificial Intelligence and Education (AIED 2005), pp. 57–64 (2005)Google Scholar
- 7.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
- 8.Beck, J.: Engagement tracing: using response times to model student disengagement. In: Proceedings of the 12th International Conference on Artificial Intelligence in Education (AIED 2005), pp. 88–95 (2005)Google Scholar
- 9.Cheng, R., Vassileva, J.: Adaptive Reward Mechanism for Sustainable Online Learning Community. In: Proc. of the International Conference on Artificial Intelligence in Education, pp. 152–159 (2005)Google Scholar
- 10.Klawe, M.M.: Designing Game-based Interactive Multimedia Mathematics Learning Activities. In: Proceedings of UCSMP International Conference on Mathematics Education (1998)Google Scholar
- 11.Microsoft Corporation, Microsoft Office 97. Seattle, WA: Microsoft Corporation (1997)Google Scholar
- 12.Mostow, J., Aist, G., Beck, J.E., Chalasani, R., Cuneo, A., Jia, P., Kadaru, K.: A La Recherche du Temps Perdu, or As Time Goes By: Where Does the Time Go in a Reading Tutor That Listens? In: Cerri, S.A., Gouardéres, G., Paraguaçu, F. (eds.) ITS 2002. LNCS, vol. 2363. Springer, Heidelberg (2002)CrossRefGoogle Scholar
- 13.Murray, R.C., van Lehn, K.: Effects of Dissuading Unnecessary Help Requests While Providing Proactive Help. In: Proc. of the International Conference on Artificial Intelligence in Education, pp. 887–889 (2005)Google Scholar