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

Pieter Vanneste, José Oramas, Thomas Verelst, Tinne Tuytelaars, Annelies Raes, Fien Depaepe and Wim Van den Noortgate
Additional contact information
Pieter Vanneste: KU Leuven, Faculty of Psychology and Educational Sciences, 3000 Leuven, Belgium
José Oramas: University of Antwerp, Department of Computer Science, Internet Data Lab (IDLab), 2000 Antwerpen, Belgium
Thomas Verelst: KU Leuven, Department of Electrical Engineering, research group on Processing Speech and Images (PSI), 3000 Leuven, Belgium
Tinne Tuytelaars: KU Leuven, Department of Electrical Engineering, research group on Processing Speech and Images (PSI), 3000 Leuven, Belgium
Annelies Raes: KU Leuven, Faculty of Psychology and Educational Sciences, 3000 Leuven, Belgium
Fien Depaepe: KU Leuven, Faculty of Psychology and Educational Sciences, 3000 Leuven, Belgium
Wim Van den Noortgate: KU Leuven, Faculty of Psychology and Educational Sciences, 3000 Leuven, Belgium

Mathematics, 2021, vol. 9, issue 3, 1-20

Abstract: 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.

Keywords: student engagement; synchronous hybrid learning; computer vision (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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