On-line student modeling for coached problem solving using Bayesian networks

Authors:

Cristina Conati
Intelligent Systems Program
University of Pittsburgh
e-mail: conati@pogo.isp.pitt.edu

Abigail Gertner
Learning Research and Development Center
University of Pittsburgh
e-mail: gertner+@pitt.edu

Kurt VanLehn
Learning Research and Development Center
University of Pittsburgh
e-mail: vanlehn+@pitt.edu

Marek J. Druzdzel
Decision Systems Laboratory
School of Information Sciences
and Intelligent Systems Program
University of Pittsburgh
e-mail: marek@sis.pitt.edu

Abstract:

This paper describes the student modeling component of Andes, an Intelligent Tutoring System for Newtonian physics. Andes' student model uses a Bayesian network to do long-term knowledge assessment, plan recognition and prediction of students' actions during problem solving. The network is updated in real time, using an approximate anytime algorithm based on stochastic sampling, as a student solves problems with Andes. The information in the student model is used by Andes' Help system to tailor its support when the student reaches impasses in the problem solving process. In this paper, we describe the knowledge structures represented in the student model and discuss the implementation of the Bayesian network assessor. We also present a preliminary evaluation of the time performance of stochastic sampling algorithms to update the network.