Search for: All records

The educational data mining community has extensively investigated affect detection in learning platforms, finding associations between affective states and a wide range of learning outcomes. Based on these insights, several studies have used affect detectors to create interventions tailored to respond to when students are bored, confused, or frustrated. However, these detector-based interventions have depended on detecting affect when it occurs and therefore inherently respond to affective states after they have begun. This might not always be soon enough to avoid a negative experience for the student. In this paper, we aim to predict students' affective states in advance. Within our approach, we attempt to determine the maximum prediction window where detector performance remains sufficiently high, documenting the decay in performance when this prediction horizon is increased. Our results indicate that it is possible to predict confusion, frustration, and boredom in advance with performance over chance for prediction horizons of 120, 40, and 50 seconds, respectively. These findings open the door to designing more timely interventions.