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HumanCAIC (TED2021-131787B-I00 MICINN), SNOLA (RED2022-134284-T), e-Madrid-CM (S2018/TCS-4307), IndiGo! (PID2019-105951RB-I00), TEA360 (PID2023-150488OB-I00, SPID202300X150488IV0), BIO-PROCTORING (GNOSS, Agreement Ministerio de Defensa-UAM-FUAM dated 29-03-2022), and Catedra ENIA UAM-VERIDAS en IA Responsable (NextGenerationEU PRTR TSI-100927-2023-2). Roberto Daza is supported by a FPI fellowship from MINECO/FEDER.

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Biometrics and Behavior Analysis for Detecting Distractions in e-Learning

Publicado en:26th International Symposium On Computers In Education, Siie 2024. - 2024-01-01 (), DOI: 10.1109/SIIE63180.2024.10604582

Autores: Becerra, Alvaro; Irigoyen, Javier; Daza, Roberto; Cobos, Ruth; Morales, Aythami; Fierrez, Julian; Cukurova, Mutlu

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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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