Concentration Estimation in E-Learning Based on Learner's Facial Reaction to Teacher's Action

Ryosuke Kawamura, Kentaro Murase

Abstract: In video-based learning, estimating the level of concentration is important for increasing the efficiency of learning. Facial expressions during learning obtained with a Web camera are often used to estimate concentration because cameras are easy to install. In this work, we focus on how learners react to video contents and propose a new method which is based on the Jaccard coefficient calculated from learner's facial reactions to teacher's actions. We conduct experiments and collect data in a Japanese cram school. Analysis of our collected data shows a weighted-F1 score of 0.57 for four levels of concentration classification, which is higher than the accuracy obtained with the methods based on learner's facial expression alone. The results indicate that our method can be effective for concentration estimation in an actual learning environment.