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Analysis of classroom teaching evaluation methods based on student expression recognition from the perspective of computer vision

computational-biology
computer-education
computer-vision
data-mining-and-machine-learning
visual-analytics

Abstract

With the advancement of digital transformation in education, human-computer interaction technology, especially facial expression recognition technology, has attracted much attention in education. This study focuses on applying facial expression recognition in computer vision for classroom teaching evaluation, to provide new ideas for improving teaching quality. This method uses the YOLOv8 network model to detect classroom facial data in real-time and extract key features of students' facial expressions in class, accurately identifying expressions such as happiness, anger, neutral, and sadness. Subsequently, the head-up rate, facial expression score, and emotion category of students in the classroom were calculated separately to analyze their classroom focus, participation, and classroom emotions. This method can reflect students' learning status in real-time, help teachers detect emotions, grasp learning situations, optimize teaching content, enhance teacher-student interaction, and solve problems such as lagging evaluation, strong subjectivity, and neglect of students' emotional feedback in traditional classroom teaching. The research results indicate that this method can serve as an important reference for classroom teaching evaluation, promote high-quality development of classroom teaching, inject new vitality into the intelligent and precise development of education, and have broad application prospects.
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