Predicting learning from student affective response to tutor questions

Abstract

Modeling student learning during tutorial interaction is a central problem in intelligent tutoring systems. While many modeling techniques have been developed to address this problem, most of them focus on cognitive models in conjunction with often-complex domain models. This paper presents an analysis suggesting that observing students’ multimodal behaviors may provide deep insight into student learning at critical moments in a tutorial session. In particular, this work examines student facial expression, electrodermal activity, posture, and gesture immediately following inference questions posed by human tutors. The findings show that for human-human task-oriented tutorial dialogue, facial expression and skin conductance response following tutor inference questions are highly predictive of student learning gains. These findings suggest that with multimodal behavior data, intelligent tutoring systems can make more informed adaptive decisions to support students effectively.