Toward Scalable Co-located Practical Learning: Assisting with Computer Vision and Multimodal Analytics
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915862265462784 |
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| author | Li, Xinyu Zhao, Linxuan Martinez-Maldonado, Roberto Gasevic, Dragan Yan, Lixiang |
| author_facet | Li, Xinyu Zhao, Linxuan Martinez-Maldonado, Roberto Gasevic, Dragan Yan, Lixiang |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_13679 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Toward Scalable Co-located Practical Learning: Assisting with Computer Vision and Multimodal Analytics Li, Xinyu Zhao, Linxuan Martinez-Maldonado, Roberto Gasevic, Dragan Yan, Lixiang Human-Computer Interaction Computer Vision and Pattern Recognition 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. |
| title | Toward Scalable Co-located Practical Learning: Assisting with Computer Vision and Multimodal Analytics |
| topic | Human-Computer Interaction Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.13679 |