Collaboration Analysis Using Deep Learning

TL;DR

This paper presents a novel deep learning approach using Mask R-CNN to automatically analyze collaborative learning by extracting group interaction data from visual media, avoiding bias from self-reports.

Contribution

It introduces a new computer vision-based method for automated collaboration analysis in educational settings, improving accuracy over traditional questionnaires.

Findings

Successfully distinguished collaboration quality between treatment and control groups.

Demonstrated effectiveness of Mask R-CNN in detecting people and objects in educational videos.

Provided a scalable, unbiased alternative for collaborative learning assessment.

Abstract

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.