Utilizing Data Mining Techniques for Enhancing Physical Education Teaching Quality: A Sports Science Perspective

Abstract

Quality physical education (PE) instruction plays a pivotal role in the development of talent and the overall success of educational institutions. Recent scholarly investigations have unveiled significant challenges within the realm of PE teaching. Consequently, it becomes imperative to establish a comprehensive PE teaching quality evaluation system that leverages data mining techniques. This study endeavors to design a robust model for evaluating PE teaching quality based on association rules mining. The process begins by defining evaluation indices specific to PE teaching quality. Subsequently, a meticulous analysis of the influencing factors affecting PE teaching quality is conducted. The results generated by the model presented herein closely align with established standard values, demonstrating superior evaluation quality. This model, rooted in data mining techniques, offers a promising reference point for future assessments and improvements in the domain of physical education teaching quality. In conclusion, the fusion of data mining methodologies within the sports science perspective provides a compelling framework for enhancing the quality of physical education teaching. This approach not only addresses existing challenges but also paves the way for more effective talent development and the advancement of educational institutions.