Body Movements for Communication in Group Work Classified by Deep Learning

Abstract

In recent years, interactive educational methods called “Active Learning” have often been introduced in educational institutions such as universities. However, active learning activities such as group work are sometimes difficult to evaluate because it is uncertain what types of outcome should be considered good or bad. To approach this problem, using sensors on a smartphone, we developed a support system visualizing situations such as group work activity or the degree of understanding of students. In this study, we focus on body movements that appear frequently in this group work and classify them to understand the group work situation more clearly. To classify body movements, we created a dataset consisting of 10 movements appearing in group work, such as “Nodding.” Using this dataset, we investigated whether the movements could be identified by the method of deep learning. As a result, it was found that body movements with few individual differences could be identified with relatively high accuracy.