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

Abstract

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. Our action selection engines have not yet been integrated into complete ITSs (this is the focus of future work), so we use simulated students to evaluate their capability to select rational tutorial actions that emulate the behaviors of human tutors. We also evaluate their capability to select tutorial actions quickly enough for real-world tutoring applications.