Classroom Emotion Monitoring Based on Image Processing

Cèlia Llurba (), Gabriela Fretes and Ramon Palau
Additional contact information
Cèlia Llurba: Department of Pedagogy, Universitat Rovira i Virgili, Crta Valls s/n, 43007 Tarragona, Spain
Gabriela Fretes: Department of Pedagogy, Universitat Rovira i Virgili, Crta Valls s/n, 43007 Tarragona, Spain
Ramon Palau: Department of Pedagogy, Universitat Rovira i Virgili, Crta Valls s/n, 43007 Tarragona, Spain

Sustainability, 2024, vol. 16, issue 2, 1-14

Abstract: One challenge of teaching and learning the lack of information during these processes, including information about students’ emotions. Emotions play a role in learning and processing information, impacting accurate comprehension. Furthermore, emotions affect students’ academic engagement and performance. Consideration of students’ emotions, and therefore their well-being, contributes to building a more sustainable society. A new way of obtaining such information is by monitoring students’ facial emotions. Accordingly, the purpose of this study was to explore whether the use of such advanced technologies can assist the teaching–learning process while ensuring the emotional well-being of secondary school students. A model of Emotional Recognition (ER) was designed for use in a classroom. The model employs a custom code, recorded videos, and images to identify faces, follow action units (AUs), and classify the students’ emotions displayed on screen. We then analysed the classified emotions according to the academic year, subject, and moment in the lesson. The results revealed a range of emotions in the classroom, both pleasant and unpleasant. We observed significant variations in the presence of certain emotions based on the beginning or end of the class, subject, and academic year, although no clear patterns emerged. Our discussion focuses on the relationship between emotions, academic performance, and sustainability. We recommend that future research prioritise the study of how teachers can use ER-based tools to improve both the well-being and performance of students.

Keywords: image processing; emotion recognition; secondary school students; academic performance; students’ emotions; students’ well-being; Py-Feat (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/2/916/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/2/916/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:2:p:916-:d:1323603

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().