Looking Ahead to Select Tutorial Actions: A Decision-Theoretic Approach

R. Charles Murray (1) , Kurt Vanlehn (1) , Jack Mostow (2)

1 LRDC - Learning Research and Development Center
2 The Robotics Institute

We propose and evaluate a decision-theoretic approach for selecting tutorial actions by looking ahead to anticipate their effects on the student and other aspects of the tutorial state. The approach uses a dynamic decision network to consider the tutor's uncertain beliefs and objectives in adapting to and managing the changing tutorial state. Prototype action selection engines for diverse domains – calculus and elementary reading – illustrate the approach. These applications employ a rich model of the tutorial state, including attributes such as the student's knowledge, focus of attention, affective state, and next action(s), along with task progress and the discourse state. For this study, neither of our action selection engines had been integrated into a complete ITS, so we used simulated students to evaluate their capabilities to select rational tutorial actions that emulate the behaviors of human tutors. We also evaluated their capability to select tutorial actions quickly enough for real-world tutoring applications. (http://aied.inf.ed.ac.uk/members04/archive/Vol_14/Murray/Murray04.html)

Keywords: bayesian networks; decision theory

Domains: Computer Science [cs]; Technology for Human Learning

R. Charles Murray, Kurt Vanlehn, Jack Mostow. Looking Ahead to Select Tutorial Actions: A Decision-Theoretic Approach. International Journal of Artificial Intelligence in Education, 2004, 14, pp.235-278. ⟨hal-00197308⟩