360-Degree Cameras vs Traditional Cameras in Multimodal Learning Analytics: Comparative Study of Facial Recognition and Pose Estimation

Multimodal Learning Analytics (MMLA) has emerged as a powerful approach within the computer-supported collaborative learning community, offering nuanced insights into learning processes through diverse data sources. Despite its potential, the prevalent reliance on traditional instruments such as tripod-mounted digital cameras for video capture often results in suboptimal data quality for facial expressions and poses captured, which is crucial for understanding collaborative dynamics. This study introduces an innovative approach to overcome this limitation by employing 360-degree camera technology to capture students' facial and body features while collaborating in small working groups. A comparative analysis of 1.5 hours of video data from both traditional tripod-mounted digital cameras and 360-degree cameras evaluated the efficacy of these methods in capturing facial action units (AUs) and face and body keypoints. The use of OpenFace revealed that the 360-degree camera captured high-quality facial features more effectively than the traditional method, significantly enhancing the reliability of facial feature detection. Similarly, OpenPose analysis demonstrated that the 360-degree camera substantially improved the capture of complete body keypoints compared to the traditional setup. These findings suggest that integrating 360-degree camera technology in MMLA can provide richer data for analyzing affect and engagement in collaborative learning environments. Future research will focus on refining this technology to further enhance our understanding of the learning process.