A Decision-Theoretic Approach for Selecting Tutorial Discourse Actions

Abstract

We propose a decision-theoretic architecture for selecting tutorial discourse actions. DT Tutor, an action selection engine which embodies our approach, uses a dynamic decision network to consider the tutor's objectives and uncertain beliefs in adapting to the changing tutorial state. It predicts the effects of the tutor's discourse actions on the tutorial state, including the student's internal state, and then selects the action with maximum expected utility. We illustrate our approach with prototype applications for diverse domains: calculus problem-solving and elementary reading. Formative off-line evaluations assess DT Tutor's ability to select optimal actions quickly enough to keep a student engaged.