Teaching quality monitoring and evaluation of physical education teaching in ordinary college based on edge computing optimization model

This paper explores the potential of edge computing and artificial intelligence to improve the quality evaluation of physical education (PE) teaching in ordinary colleges. Unlike other discipline-based teaching, PE lacks fixed classrooms, materials, and tasks, making it difficult to uniformly evaluate teaching quality. To address this issue, an edge computing optimization model is proposed to optimize the PE quality evaluation model. The evaluation of physical education curriculum teaching is a critical component of physical education teaching work, directly affecting various aspects of teaching activities. The proposed teaching evaluation model focuses on the physical fitness compliance rate of students' evaluation and reweights the proportion of final examination scores. In addition, a PE teaching health system is designed to monitor teaching quality in real-time, allowing students to evaluate the entire learning process. The system is optimized using edge computing technology, reducing network transmission costs by 20% and increasing transmission efficiency by 15%. Experimental results demonstrate that the proposed system effectively collects student evaluations and improves the scientific nature of PE teaching goals. The integration of edge computing and artificial intelligence technologies has the potential to enhance the capabilities of the system and improve teaching quality in ordinary colleges.