Estimation of Student's Engagement Using a Smart Chair
Kazuaki Nomura, Motoi Iwata, Olivier Augereau, Koichi Kise · 2018
In a classroom, it is important for teachers to grasp students' engagement in order to lecture effectively. However, to grasp students' engagement is too difficult in the situation where there are so many students. Therefore, the purpose of this study is to grasp the students' engagement by a chair with a pressure mat, not by teachers. We recorded students' upper body pressure distribution while they were taking e-learning lectures. We recorded 145 lectures in total. Then we extracted 56 features for each lecture, selected proper features and trained classifiers to determine whether he or she was engaged in the lecture. As a result, the average accuracy was 75.2% for student-dependent. This result shows it is possible to predict student's engagement automatically, and it will help teachers to give lectures more effective.