Affective Dialogue Ontology for Intelligent Tutoring Systems: Human Assessment Approach

摘要

Researchers has focused on integrate the dialogue to learning systems, such as Intelligent Tutoring Systems. To date, the researchers have focused on make more intelligent, accuracy and faster this systems. However, they become cold. So, it is important that these systems consider affective aspects when they bring feedback to the students because affective aspects and motivation have been shown to improve learning in face-to-face scenarios. This work introduces a novel ontology which models aspects of student, tutor and dialogue to bring an affective feedback. We conducted a human assessment approach to evaluate the proposed ontology. We design an instrument derived from ontological components (concepts and relations). We collected 24 responses of professors of undergraduate programs. Most of the domain experts, more than the 70 %, were agree with the relations proposed in the affective dialogue ontlogy proposed in this work. The results suggest that the proposal will serve as useful guidelines for the design of Intelligent Tutoring Systems, particularly to dialogue modules.