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Bi

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Grant support

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.

Analysis of institutional authors

Daza, RobertoAuthorCobos, RuthAuthorMorales, AythamiAuthorFierrez, JulianAuthor

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September 15, 2024
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Proceedings Paper
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Biometrics and Behavior Analysis for Detecting Distractions in e-Learning

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

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

Affiliations

UCL, London, England - Author
Univ Autonoma Madrid, Sch Engn, Madrid, Spain - Author

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%.

Keywords

BiometricsHead poseLearningMachine learningMulti-modal learningMultimodalOnline learningPhone usagPhone usage

Quality index

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-16:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 6 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: United Kingdom.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Becerra, Alvaro) .

the author responsible for correspondence tasks has been Becerra, Alvaro.