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 [email protected] 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.

翻译：本研究探讨了单个天花板安装摄像头是否能够用于捕捉共址实践学习中的细粒度学习行为。在本科护理模拟教学中，教师首先识别出七个可观察的行为类别，随后基于这些类别训练了YOLO检测器。研究共采集52场模拟教学视频数据，由于场景A比场景B产生更显著的行为差异，分析主要聚焦于场景A。标注可靠性较高（F1=0.933）。在预留测试集上，模型达到0.789的精确率、0.784的召回率以及0.827的[email protected]指标。当仅比较行为频率时，高绩效组与低绩效组未呈现显著差异。然而，当结合空间情境分析行为标签时，两组在任务执行与协作表现上均显示出明显差异：高绩效团队在主要工作区域表现出更多患者互动行为，而低绩效团队则呈现更多手机相关活动及次要区域活动。这些发现表明，行为数据与其发生空间位置结合解读时具有更高信息价值。总体而言，本研究证明单摄像头计算机视觉方法可在不依赖可穿戴传感器的情况下，有效支持面对面实践学习中团队协作与任务参与度的分析。