Abstract
We have constructed a learning agent that models student behavior at a high level of granularity for a mathematics tutor. Rather than focusing on whether the student knows a particular piece of knowledge, the learning agent determines how likely the student is to answer a problem correctly and how long he will take to generate this response. To construct this model, we used traces from previous users of the tutor to train the machine learning agent. This agent used information about the student, the current topic, the problem, and the student’s efforts to solve this problem to make its predictions. This model was very accurate at predicting the time students required to generate a response, and was somewhat accurate at predicting the likelihood the student’s response was correct. We present two methods for integrating such an agent into an intelligent tutor.
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References
J. Anderson. Rules of the Mind. Lawrence Erlbaum Associates, Hillsdale, NJ, 1993.
I. Arroyo, J. E. Beck, K. Schultz, and Woolf. Piagetian psychology in intelligent tutoring systems. In Proceedings of the Ninth International Conference on Artificial Intelligence in Education, 1999.
Carole R. Beal, Beverly P. Woolf, Joseph Beck, Ivon Arroyo, Klaus Schultz, and David M. Hart. Gaining confidence in mathematics: Instructional technology for girls. In Proceedings of International Conference on Mathematics/Science Education and Technology, 2000.
J. E. Beck, M. Stern, and B.P. Woolf. Using the student model to control problem difficulty. In Proceedings of the Seventh International Conference on User Modeling, pages 277–288, 1997.
J. E. Beck and B. P. Woolf. Using a learning agent with a student model. In Proceedings Third International Conference on Intelligent Tutoring Systems, 1998.
Joseph E. Beck and Beverly P. Woolf. Learning to teach: A machine learning architecture for making teaching decisions. In Proceedings of the Seventeenth National Conference on Artificial Intelligence, 2000.
Bark Cheung Chiu and Geoffrey I. Webb. Using decision trees for agent modeling: Improving prediction performance. User Modeling and User Adapted Interaction, 8(1–2):131–152, 1998.
A. S. Gertner, C. Conati, and K. VanLehn. Procedural help in ANDES: Generating hints using a Bayesian network student model. In Fifteenth National Conference on Artificial Intelligence, pages 106–111, 1998.
K. R. Koedinger, J. R. Anderson, W. H. Hadley, and M. A. Mark. Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education, 8:30–43, 1997.
Thomas N. Meyer, Todd M. Miller, Kurt Steuck, and Monika Kretschmer. A multi-year large-scale field study of a learner controlled intelligent tutoring system. In Proceedings of the Ninth International Conference on Artificial Intelligence in Education, pages 191–198, 1999.
M. Quafafou, A. Mekaouche, and H.S. Nwana. Multiviews learning and intelligent tutoring systems. In Proceedings of SeventhWorld Conference on Artificial Intelligence in Education, 1995.
J.A. Self. Bypassing the intractable problem of student modelling. In C. Frasson and G. Gauthier, editors, Intelligent Tutoring Systems: at the Crossroads of Artificial Intelligence and Education, pages 107–123, Norwood, NJ, 1990.
R.S. Sutton and A.G. Barto. An Introduction to Reinforcement Learning. MIT Press, 1998.
K. VanLehn, S. Ohlsson, and R. Nason. Applications of simulated students: An exploration. Journal of Artificial Intelligence in Education, 5(2):135–175, 1994.
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Beck, J.E., Woolf, B.P. (2000). High-Level Student Modeling with Machine Learning. In: Gauthier, G., Frasson, C., VanLehn, K. (eds) Intelligent Tutoring Systems. ITS 2000. Lecture Notes in Computer Science, vol 1839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45108-0_62
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DOI: https://doi.org/10.1007/3-540-45108-0_62
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