Collaboration Analysis Using Deep Learning
This work addresses the problem of biased and noisy self-reported data in education research by providing an automated method for analyzing collaborative learning, though it is incremental as it applies existing computer vision techniques to a specific domain.
The paper tackled automated analysis of collaborative learning by using Mask R-CNN for object recognition from images and videos to extract group-working information, replacing self-reported questionnaires, and tested it on 33 dyads in a controlled study, showing it could recognize collaboration differences between treatment and control groups.
The analysis of the collaborative learning process is one of the growing fields of education research, which has many different analytic solutions. In this paper, we provided a new solution to improve automated collaborative learning analyses using deep neural networks. Instead of using self-reported questionnaires, which are subject to bias and noise, we automatically extract group-working information by object recognition results using Mask R-CNN method. This process is based on detecting the people and other objects from pictures and video clips of the collaborative learning process, then evaluate the mobile learning performance using the collaborative indicators. We tested our approach to automatically evaluate the group-work collaboration in a controlled study of thirty-three dyads while performing an anatomy body painting intervention. The results indicate that our approach recognizes the differences of collaborations among teams of treatment and control groups in the case study. This work introduces new methods for automated quality prediction of collaborations among human-human interactions using computer vision techniques.