This paper introduces an e-learning platform for the management of courses based on MOOCs, able to continuously monitoring student’s behavior through facial coding techniques, with a low computational effort client-side, and to provide useful insight for the instructor. The system exploits the most recent developments in Deep Learning and Computer Vision for Affective Computing, in compliance with the European GDPR. Taking as input the video capture by the webcam of the device used to attend the course, it: (1) performs continuous student’s authentication based on face recognition, (2) monitors the student’s level of attention through head orientation tracking and gaze detection analysis, (3) estimates student’s emotion during the course attendance. The paper describes the overall system design and reports the results of a preliminary survey, which involved a total of 14 subjects, aimed at investigating user acceptance, in terms of intention to continue using such a system

Facial coding as a mean to enable continuous monitoring of student’s behavior in e-Learning

Ceccacci, S.;Generosi, A.;Cimini, G.;Faggiano, S.;Giraldi, L.;Mengoni, M
2021-01-01

Abstract

This paper introduces an e-learning platform for the management of courses based on MOOCs, able to continuously monitoring student’s behavior through facial coding techniques, with a low computational effort client-side, and to provide useful insight for the instructor. The system exploits the most recent developments in Deep Learning and Computer Vision for Affective Computing, in compliance with the European GDPR. Taking as input the video capture by the webcam of the device used to attend the course, it: (1) performs continuous student’s authentication based on face recognition, (2) monitors the student’s level of attention through head orientation tracking and gaze detection analysis, (3) estimates student’s emotion during the course attendance. The paper describes the overall system design and reports the results of a preliminary survey, which involved a total of 14 subjects, aimed at investigating user acceptance, in terms of intention to continue using such a system
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/290141
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