  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%.
