In this article, we explore computer vision approaches to detect abnormal head pose during e-learning sessions and we introduce a study on the effects of mobile phone usage during these sessions. We utilize behavioral data collected from 120 learners monitored while participating in a MOOC learning sessions. Our study focuses on the influence of phone-usage events on behavior and physiological responses, specifically attention, heart rate, and meditation, before, during, and after phone usage. Additionally, we propose an approach for estimating head pose events using images taken by the webcam during the MOOC learning sessions to detect phone-usage events. Our hypothesis suggests that head posture undergoes significant changes when learners interact with a mobile phone, contrasting with the typical behavior seen when learners face a computer during e-learning sessions. We propose an approach designed to detect deviations in head posture from the average observed during a learner's session, operating as a semi-supervised method. This system flags events indicating alterations in head posture for subsequent human review and selection of mobile phone usage occurrences with a sensitivity over 90%.

翻译：本文探讨了利用计算机视觉方法检测在线学习期间异常头部姿态的研究，并介绍了移动设备使用对学习效果影响的实证分析。我们基于120名学习者在参与慕课学习过程中采集的行为数据，重点研究了手机使用事件对行为与生理反应（特别是注意力、心率和冥想度指标）在使用前、使用期间及使用后的影响规律。同时，我们提出一种通过慕课学习期间摄像头采集的图像序列来估计头部姿态事件的方法，用以检测手机使用行为。我们的研究假设认为：当学习者操作手机时，其头部姿态会发生显著变化，这与在线学习过程中面对电脑时的典型行为模式形成鲜明对比。为此，我们设计了一种半监督检测方法，通过识别学习者头部姿态相对于课程期间平均姿态的偏离程度，实现对异常姿态事件的自动标注。该系统能以超过90%的灵敏度标记出头部姿态变化事件，为后续人工复核与手机使用事件的筛选提供有效支持。