"Yes!": Using tutor and sensor data to predict moments of delight during instructional activities

Abstract

A long standing challenge for intelligent tutoring system (ITS) designers and educators alike is how to encourage students to take pleasure and interest in learning activities. In this paper, we present findings from a user study involving students interacting with an ITS, focusing on when students express excitement, what we dub "yes!" moments. These findings include an empirically-based user model that relies on both interaction and physiological sensor features to predict "yes!" events; here we describe this model, its validation, and initial indicators of its importance for understanding and fostering student interest. © 2010 Springer-Verlag.