Analysis of Physical Education Teaching Quality Based on Hierarchical Fuzzy Set Theory

Abstract The quality of physical education often faces challenges due to inadequate evaluation methods that fail to provide accurate, real-time teaching evaluation. These challenges influence student performance and overall teaching quality. This article introduces a quality assessment method using fuzzy set theory (QAM-FST) to evaluate physical education teaching. The proposed method extracts and classifies all the available teaching data to compute the students' performance over sessions through fuzzy rough set differentiations over partial and complete derivatives. The complete derivatives identify the factors contributing the maximum to the teaching quality, while the partial derivatives acquire the minimal influence factors. These derivatives are clubbed together through the hierarchical process to identify the precise quality impacting factors and least impacting factors to replace or recommend alternate suggestions. The QAM-FST framework offers a comprehensive, data-driven assessment ensuring the enhancement of PE teaching quality. The QAM-FST outperforms three current models in terms of suggestion accuracy (96.8%), assessment time reduction (22.66%), and total performance evaluation efficiency (16.78%). This data-driven platform guarantees improved physical education instruction quality through actionable insights obtained from real-time feedback.