Evaluating an Affective Student Model for Intelligent Learning Environments

Abstract

We are developing an affective model for intelligent tutoring systems; thus, the tutor considers the affective state as well as the knowledge state of the student to give instruction to students. An important component of the affective model is the affective student model. This last one is rooted on the OCC cognitive model of emotions and the five-factor model, and it is represented as a dynamic Bayesian network. The personality traits, goals and knowledge state are considered to establish the student affect. The affective model has been integrated to an intelligent learning environment for learning mobile robotics. We conducted an initial evaluation of the affective student model with a group of 20 under graduate and graduate students to evaluate the affective student model. Results are encouraging since they show a high agreement between the affective state established by the affective student model and the affective state reported by the students.