Modeling Student Performance to Enhance the Pedagogy of AutoTutor
The Tutoring Research Group from the University of Memphis has developed a pedagogically effective Intelligent Tutoring System (ITS), called AutoTutor, that implements conversational dialog as a tutoring strategy for conceptual physics. Latent Semantic Analysis (LSA) is used to evaluate the quality of student contributions and determine what dialog moves AutoTutor gives. By modeling the students’ knowledge in this fashion, AutoTutor successfully adapted its pedagogy to match the ideal strategy for students’ ability.
Unable to display preview. Download preview PDF.
- 3.Foltz, P.W. (1996). Latent semantic analysis for text-based research. Behavior Research Methods, Instruments, and Computers, 28, 197–202.Google Scholar
- 6.Lepper, M. R., Woolverton, M., Mumme, D.L., & Gurtner, J.L. (1991). Motivational techniques of expert human tutors: Lessons for the design of computer-based tutors. In S.P. Lajoie & S.J. Derry (Eds.), Computers as cognitive tools (pp. 75–105). Hillsdale, NJ: Erbaum.Google Scholar
- 8.Person, N. K., Graesser, A. C., Kreuz, R. J., Pomeroy, V. & the Tutoring Research Group. (2000). Simulating human tutor dialog moves in AutoTutor. Submitted to International Journal of Artificial Intelligence in Education.Google Scholar
- 10.Person, N. K., Klettke, B., Link, K., Kreuz, R. J., & the Tutoring Research Group (1999). The integration of affective responses into AutoTutor. Proceeding of the International Workshop on Affect in Interactions (pp. 167–178). Siena, Italy.Google Scholar
- 13.Wiemer-Hastings, P., Graesser, A. C., Harter, D., & the Tutoring Research Group (1998). The foundations and architecture of AutoTutor. Proceedings of the 4th International Conference on Intelligent Tutoring Systems (pp. 334–343). Berlin, Germany: Springer-Verlag.Google Scholar