CVPE: A Computer Vision Approach for Scalable and Privacy-Preserving Socio-spatial, Multimodal Learning Analytics

Abstract

Capturing data on socio-spatial behaviours is essential in obtaining meaningful educational insights into collaborative learning and teamwork in co-located learning contexts. Existing solutions, however, have limitations regarding scalability and practicality since they rely largely on costly location tracking systems, are labour-intensive, or are unsuitable for complex learning environments. To address these limitations, we propose an innovative computer-vision-based approach – Computer Vision for Position Estimation (CVPE) – for collecting socio-spatial data in complex learning settings where sophisticated collaborations occur. CVPE is scalable and practical with a fast processing time and only needs low-cost hardware (e.g., cameras and computers). The built-in privacy protection modules also minimise potential privacy and data security issues by masking individuals’ facial identities and provide options to automatically delete recordings after processing, making CVPE a suitable option for generating continuous multimodal/classroom analytics. The potential of CVPE was evaluated by applying it to analyse video data about teamwork in simulation-based learning. The results showed that CVPE extracted socio-spatial behaviours relatively reliably from video recordings compared to indoor positioning data. These socio-spatial behaviours extracted with CVPE uncovered valuable insights into teamwork when analysed with epistemic network analysis. The limitations of CVPE for effective use in learning analytics are also discussed.