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Biometrics and Behavior Analysis for Detecting Distractions in E-Learning

XXVI INTERNATIONAL SYMPOSIUM ON COMPUTERS IN EDUCATION, SIIE 2024(2024)

Univ Autonoma Madrid | UCL

Cited 0|Views12
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
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Key words
biometrics,head pose,machine learning,multi-modal learning,online learning,phone usage
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要点】:本文利用计算机视觉技术检测在线学习期间学生的异常头部姿态,提出了一种基于行为数据的新方法来识别手机使用事件,并验证了手机使用对学习注意力和生理反应的影响。

方法】:研究采用行为数据,通过分析学习者在MOOC学习期间的行为和生理响应,特别是注意力和心率,来评估手机使用对学习的影响,并使用Webcam图像估计头部姿态事件。

实验】:研究对120名学习者在MOOC学习过程中的行为进行了监测,通过识别头部姿态的变化来检测手机使用事件,实验结果使用了一个敏感度超过90%的半监督方法,并标记出头部姿态的异常事件供人工审查。