Tenth International Conference On Advances In Computing, Control And Networking - ACCN 2020
Author(s) : Fumiko Harada, Hiromitsu Shimakawa, Yuto Hattori
In this study, we propose the method estimating collaborative state by the similarity of motion characteristics and emotion characteristics changes. The group learning based on collaboration among students are introduced in many university classes. However, it may not function as a group and teachers evaluate only result of group learning. Our method focusses on similarity of motion and emotion characteristics change. The change points are extracted from these time series data by applying singular spectrum transformation (SST). In addition, the similarity points of change extracted by comparing the change points among students. The collaborative states are estimated in a certain period from the number of similarity points of change using machine learning. The experimental result indicates this method can identify between non-collaborative state and collaborative state. The method facilitates teachers to evaluate and guide the groups.