Sidney K. D'Mello, Scotty D. Craig, Barry Gholson, Stan Franklin, Rosalind Picard, Arthur C. Graesser

Abstract

This project augments an existing intelligent tutoring system (AutoTutor) that helps learners construct explanations by interacting with them in natural language and helping them use simulation environments. The research aims to develop an agile learning environment that is sensitive to a learner’s affective state, presuming that this will promote learning. We integrate state-of-the-art, nonintrusive, affect-sensing technology with AutoTutor in an endeavor to classify emotions on the bases of facial expressions, gross body movements, and conversational cues. This paper sketches our broad theoretical approach, our methods for data collection and evaluation, and our emotion classification techniques. Keywords Affective states, emotions, learning, intelligent tutoring systems, AutoTutor