Optimizing teaching management in college physical education: a fuzzy neural network approach

Improving the quality of education through effective teaching evaluation is crucial. A scientific and rational assessment system for physical education is important. The assessment of teaching is a complex and dynamic process, but utilizing the cutting-edge technology of fuzzy neural networks can help navigate this complexity. This study aimed to improve the assessment of physical education classes. The first step towards creating a multi-index evaluation system for college physical education instructors’ performance is to use the analytical hierarchy technique. This technique evaluates the instructor’s teaching content, method, attitude, and influence. In this study, a system was developed for physical education using a fuzzy neural network model to evaluate faculty members in college-level physical education workshops. The input for the fuzzy neural network model is the assessment evaluation, and its output is a vector that indicates the quality of the college physical education received by the student, classified as great, good, average, or bad. Compared to other approaches for evaluating the quality of physical education courses in higher education, the fuzzy neural network model has shown higher accuracy, specificity, sensitivity, and F1 score. After implementing the proposed methods for imparting physical education, there was a significant improvement in accuracy (95%), specificity (94%), sensitivity (92%), and F1 score (93%). The proposed method is more efficient than the traditional approaches.