Research on the Path of Improving the Quality of School Physical Education Teaching Based on Data Mining Technology

Abstract

Abstract The explosion of digital technology and the Internet has elevated big data as a critical driver of progress in various fields, including sports education in higher education institutions. This article explores the application of structured data mining to refine sports education, beginning with a decision tree algorithm for student sports data analysis. It then employs the Apriori algorithm to explore gender-based sports information correlations with teaching levels and the K-means algorithm to measure the enhancement in sports teaching quality pre and post-technology adoption. Findings reveal a strong association between improved teaching quality and student physical well-being, highlighted by the College of Physical Education’s top teaching quality score of 10.0. Initially, most teaching quality evaluations were in the “poor” to “good” range (81.2%), shifting significantly to “excellent” and “good” (78.4%) after the intervention. This study evidences the importance of data mining in revolutionizing physical education, significantly boosting educational quality.