Affiliations: Intelligent Systems Program & LRDC, 3939
O'Hara Street, University of Pittsburgh, Pittsburgh, PA, 15260, USA.
[email protected] | Computer Science Department & LRDC, 3939
O'Hara Street, University of Pittsburgh, Pittsburgh, PA, 15260, USA.
[email protected] | Robotics Institute & Project LISTEN, Carnegie
Mellon University, RI-NSH 4213, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA.
[email protected]
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 students 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.
