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Article

Computer Vision and Human Behaviour, Emotion and Cognition Detection: A Use Case on Student Engagement

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KU Leuven, Faculty of Psychology and Educational Sciences, 3000 Leuven, Belgium
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KU Leuven, Imec Research Group Itec, 8500 Kortrijk, Belgium
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University of Antwerp, Department of Computer Science, Internet Data Lab (IDLab), 2000 Antwerpen, Belgium
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KU Leuven, Department of Electrical Engineering, research group on Processing Speech and Images (PSI), 3000 Leuven, Belgium
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CIREL—Centre Interuniversitaire de Recherche en Education de Lille (ULR 4354), 59650 Villeneuve-d’Ascq, France
*
Author to whom correspondence should be addressed.
Academic Editors: Heui Seok Lim, Danial Hooshyar, Kyu Han Koh and Michael Voskoglou
Mathematics 2021, 9(3), 287; https://doi.org/10.3390/math9030287
Received: 23 December 2020 / Revised: 20 January 2021 / Accepted: 27 January 2021 / Published: 1 February 2021
(This article belongs to the Special Issue Artificial Intelligence in Education)
Computer vision has shown great accomplishments in a wide variety of classification, segmentation and object recognition tasks, but tends to encounter more difficulties when tasks require more contextual assessment. Measuring the engagement of students is an example of such a complex task, as it requires a strong interpretative component. This research describes a methodology to measure students’ engagement, taking both an individual (student-level) and a collective (classroom) approach. Results show that students’ individual behaviour, such as note-taking or hand-raising, is challenging to recognise, and does not correlate with students’ self-reported engagement. Interestingly, students’ collective behaviour can be quantified in a more generic way using measures for students’ symmetry, reaction times and eye-gaze intersections. Nonetheless, the evidence for a connection between these collective measures and engagement is rather weak. Although this study does not succeed in providing a proxy of students’ self-reported engagement, our approach sheds light on the needs for future research. More concretely, we suggest that not only the behavioural, but also the emotional and cognitive component of engagement should be captured. View Full-Text
Keywords: student engagement; synchronous hybrid learning; computer vision student engagement; synchronous hybrid learning; computer vision
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    Link: https://osf.io/q45ft/
    Description: Supplementary files (e.g. an anonymised dataset, and some parts of the computer vision code) may eventually be shared in case the manuscript would be accepted.
MDPI and ACS Style

Vanneste, P.; Oramas, J.; Verelst, T.; Tuytelaars, T.; Raes, A.; Depaepe, F.; Van den Noortgate, W. Computer Vision and Human Behaviour, Emotion and Cognition Detection: A Use Case on Student Engagement. Mathematics 2021, 9, 287. https://doi.org/10.3390/math9030287

AMA Style

Vanneste P, Oramas J, Verelst T, Tuytelaars T, Raes A, Depaepe F, Van den Noortgate W. Computer Vision and Human Behaviour, Emotion and Cognition Detection: A Use Case on Student Engagement. Mathematics. 2021; 9(3):287. https://doi.org/10.3390/math9030287

Chicago/Turabian Style

Vanneste, Pieter, José Oramas, Thomas Verelst, Tinne Tuytelaars, Annelies Raes, Fien Depaepe, and Wim Van den Noortgate. 2021. "Computer Vision and Human Behaviour, Emotion and Cognition Detection: A Use Case on Student Engagement" Mathematics 9, no. 3: 287. https://doi.org/10.3390/math9030287

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