EduBrowser : a multimodal automated monitoring system for co-located collaborative learning

Abstract

Majority of learning analytics systems are designed to monitor and analyze students’ online interactions during collaborative learning. In the case of co-located collaborative learning, student interactions take place in the physical space as well as online. While existing learning management systems provide specific logs and snapshots of students’ online responses that are automatically captured, the potential of insights that can be derived from students’ non-digital face-to-face interactions during collaborative discourse remains untapped. In this paper, we propose an architecture for data acquisition and processing from co-located face-to-face collaborative learning, designed to be scalable beyond dyadic and triadic collaborative learning and across different curricula. We outline the system design, current experience of deployment across 4 sessions of co-located collaborative learning sessions, as well as brief examples of acquired data.