Evaluating a Probabilistic Model for Affective Behavior in an Intelligent Tutoring System

Abstract

We have developed an affective behavior model (ABM) for intelligent tutoring systems, so the tutor considers the affective state as well as the pedagogical state of the student. The ABM first establishes the affective state based on the OCC cognitive model of emotions, represented as a dynamic Bayesian network. Then, the tutor selects the best tutorial action according to the pedagogical and affective state of the student. For this it uses a dynamic decision network that was designed based on the opinions of a group of experienced teachers. The ABM has been integrated to an intelligent tutor for mobile robotics. A pilot study with a group of 20 students shows that the affective and tutorial actions selected by the tutor are in general in agreement with the students’ expectations.