Classroom Emotion Monitoring Based on Image Processing

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.