Biometrics and Behavior Analysis for Detecting Distractions in e-Learning

TL;DR

This study employs computer vision and behavioral data to detect mobile phone usage during e-learning by analyzing head pose and physiological responses, achieving over 90% sensitivity in identifying phone interactions.

Contribution

It introduces a semi-supervised approach for detecting head pose deviations linked to phone usage, integrating behavioral and physiological data in e-learning contexts.

Findings

Head pose changes significantly during phone use.

Physiological responses vary with phone interaction.

Detection sensitivity exceeds 90%.

Abstract

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%.