Integrating Learning Styles and Affect with an Intelligent Tutoring System
Ramón Zataraín Cabada, María Lucía Barrón Estrada, José Luis Olivares Camacho, Carlos Alberto Reyes-Garcia · 2013
This paper presents two software systems for visual affect and learning styles recognition. The first system recognizes Paul Ekman's seven basic emotions in student expressions which are surprise, fear, disgust, anger, happiness, sadness, and neutral. The second system recognizes the student learning style using the Felder-Silverman Model. Both systems are integrated into an intelligent tutoring system in a math social network. The automatic recognition was implemented using Kohonen networks which were trained to recognize and classify emotions and learning styles. We show and discuss results by using different methods with respect to affect or emotion recognition and present the automatic response to affect results. We also present the software architecture where both recognizers collaborate with intelligent tutoring systems in a social network.