Multimodal Affect Recognition in Intelligent Tutoring Systems

This paper concerns the multimodal inference of complex mental states in the intelligent tutoring domain. The research aim is to provide intervention strategies in response to a detected mental state, with the goal being to keep the student in a positive affect realm to maximize learning potential. The research follows an ethnographic approach in the determination of affective states that naturally occur between students and computers. The multimodal inference component will be evaluated from video and audio recordings taken during classroom sessions. Further experiments will be conducted to evaluate the affect component and educational impact of the intelligent tutor.