Estimation of student's engagement based on the posture
Kazuaki Nomura, Motoi Iwata, Olivier Augereau, Koichi Kise · 2019
It is important for teachers to grasp students' engagement in order to improve the quality of lectures. However, in the e-learning environment, there is no teacher to grasp the students' engagement and it may cause ineffective learning. The purpose of this study is to grasp the students' engagement by using a pressure mat and web camera. We recorded students' postural data, that is upper body pressure distribution and upper body pose, during e-learning lectures. Then we extracted 38 features from upper body pressure distribution and 33 features from upper body pose for every minute, selected proper features and trained classifiers to estimate whether he or she was engaged in the lecture. As a result, the average accuracy was 79.3% for student-dependent estimation. This result shows it is possible to predict the student's engagement automatically.