Looking Ahead to Select Tutorial Actions: A Decision- Theoretic Approach
In IJAIED
14 (3)
Publication information
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. 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.
