  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.
