Procedural Help in Andes: Generating Hints Using a Bayesian Network Student Model

Abigail S. Gertner, Cristina Conati, Kurt VanLehn

One of the most important problems for an intelligent tutoring system is deciding how to respond when a student asks for help. Responding cooperatively requires an understanding of both what solution path the student is pursuing, and the student’s current level of domain knowledge. Andes, an intelligent tutoring system for Newtonian physics, refers to a probabilistic student model to make decisions about responding to help requests. Andes’ student model uses a Bayesian network that computes a probabilistic assessment of three kinds of information: (1) the student’s general knowledge about physics, (2) the student’s specific knowledge about the current problem, and (3) the abstract plans that the student may be pursuing to solve the problem. Using this model, Andes provides feedback and hints tailored to the student’s knowledge and goals.


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