A Decision-Theoretic Tutor for Analogical Problem Solving

Abstract

We describe a decision-theoretic tutor that helps students learn from Analogical Problem Solving (APS), i.e., from problem-solving activities that involve worked-out examples. This tutor incorporates an innovative example-selection mechanism that tailors the choice of example to a given student so as to trigger studying behaviors that are known to foster learning. The mechanism relies on a two-phase decision-theoretic process, as follows. First, a probabilistic user model corresponding to a dynamic Bayesian network simulates how a given student will use an example to solve a problem and what she will learn from doing so. Second, this simulation is quantified via an expected utility calculation, enabling the tutor to select the example with the highest expected utility for maximizing learning and problem-solving outcomes. Once an example is presented to a student, the user model generates an assessment of the student’s APS, enabling the selection mechanism to have up to date information on the student. Our empirical evaluation shows that this selection mechanism is more effective than standard selection approaches for fostering learning from APS. Here, we provide a comprehensive technical description of the example-selection mechanism, as well as an overview of its evaluation and a discussion of some of the challenges related to our decision-theoretic approach.