This study examined whether a single ceiling-mounted camera could be used to capture fine-grained learning behaviours in co-located practical learning. In undergraduate nursing simulations, teachers first identified seven observable behaviour categories, which were then used to train a YOLO-based detector. Video data were collected from 52 sessions, and analyses focused on Scenario A because it produced greater behavioural variation than Scenario B. Annotation reliability was high (F1=0.933). On the held-out test set, the model achieved a precision of 0.789, a recall of 0.784, and an mAP@0.5 of 0.827. When only behaviour frequencies were compared, no robust differences were found between high- and low-performing groups. However, when behaviour labels were analysed together with spatial context, clear differences emerged in both task and collaboration performance. Higher-performing teams showed more patient interaction in the primary work area, whereas lower-performing teams showed more phone-related activity and more activity in secondary areas. These findings suggest that behavioural data are more informative when interpreted together with where they occur. Overall, the study shows that a single-camera computer vision approach can support the analysis of teamwork and task engagement in face-to-face practical learning without relying on wearable sensors.
@article{arxiv.2603.13679,
title = {Toward Scalable Co-located Practical Learning: Assisting with Computer Vision and Multimodal Analytics},
author = {Xinyu Li and Linxuan Zhao and Roberto Martinez-Maldonado and Dragan Gasevic and Lixiang Yan},
journal= {arXiv preprint arXiv:2603.13679},
year = {2026}
}