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%.

研究中使用计算机视觉技术检测在线学习过程中异常的头部姿态，并介绍了手机使用对学习过程的影响的研究。通过收集来自 120 位学习者在 MOOC 学习过程中的行为数据，研究聚焦于手机使用事件对行为和生理反应（特别是注意力、心率和冥想）的影响，并提出了一种通过摄像头图像来估计头部姿态事件以检测手机使用事件的方法。本研究的假设是，当学习者与手机交互时，头部姿态会发生显著变化，与学习者面对计算机时的典型行为形成对比。我们提出了一种半监督方法，用于检测学习者会话期间头部姿态与平均姿态的偏差，并对发现的头部姿态异常事件进行标记，敏感度超过 90%。