Design of Intelligent Teaching Evaluation System Based on Emotion Recognition Algorithm

Recently, with the rapid development of artificial intelligence technology, emotion recognition algorithms provide new possibilities for innovative intelligent teaching evaluation methods, which is both an important direction for the development of modern educational technology and a research hotspot in the field of educational evaluation. Taking the emotion algorithm as the theoretical basis and using advanced computer vision technology, this paper designed an intelligent teaching evaluation system based on facial expressions and postural movements to accurately assess the classroom status of students in real time. The system uses residual network model to classify and recognize facial expressions and postural movements, and designs an event statistical model to calculate student engagement and status scores as one of the reference indicators for quality evaluation in real-time classrooms. The experimental results show that the system can correctly record the real-time situation of the classroom, provide educators with intelligent teaching evaluation, innovate the teaching evaluation system, and provide a strong guarantee for improving the quality of teaching and the learning effect of students.