Publication: POLA: A student modeling framework for probabilistic on-line assessment of problem solving performance.

Conati, C., & VanLehn, K. (1996). POLA: a student modeling framework for Probabilistic On-Line Assessment of problem solving performance. In UM-96: Fifth International Conference on User Modeling  (pp. 75-82). Kailua-Kona, HI: User Modeling, Inc.

This paper presents POLA, a student modeling framework that performs probabilistic assessment of students' performance while they solve problems in introductory physics. Existing efforts toward probabilitic student modeling focus exclusively on performing knowledge tracing. With POLA we aim to turn OLAE, a system that performs probabilistic knowledge tracing, into a system that applies probabilistic reasoning to perform both knowledge and model tracing. POLA generates probabilistic predictions about the student's line of reasoning without using heuristics, even when the problem's solution space is large. An And/Or graph provides a compact representation of all the available solutions for a problem. A Bayesian network built incrementally from the And/Or graph and from the student's actions generates predictions about the solution that the student is following. At the end of the problem solving session the network provides an assessment of student's level of mastery of the physics knowledge involved in the problem's soution.

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