Affective Behavior in Intelligent Tutoring Systems for Virtual Laboratories

One of the most important highlights of personal tutoring is that of recognizing the student’s affective state and reacting accordingly by expressing the pedagogical movements in an affectively suitable way. In this paper, we propose a model for an affective tutor. The intelligent tutor integrates an affective student model based on the OCC cognitive model of emotion, with a cognitive student model, within a virtual laboratory for teaching robotics. The main contribution of this work is in the affective behavior model, which integrates the information from the student cognitive and affective state, and the tutorial situation, to decide the best pedagogical action. We use Bayesian networks and we propose the use of a decision network with a utility measure on learning. By using the decision network, the tutor will select the best pedagogical and affective response given the current state of the student. We present some initial examples of possible affective and pedagogical responses under different situations.