Intelligent Analysis of College Physical Education Teaching Effect Based on Data Mining Technology

Abstract

This study enhances the college PE by using data-driven strategies with data mining techniques applied to large datasets, including attendance records, demographic information, and student performance metrics. It is within these methods that methods of k-fold cross-validation, linear regression, decision trees, and k-means clustering are used to draw out the hidden key patterns and trends into clear-cut performance metrics. The results very strongly point out the critical attributes of student attendance and participation in PE with strong correspondence to the student fitness scores. Through decision tree analysis, observation of attendance was established as an important factor in deciding student outcomes, and clustering enables specific intervention toward different groups of students for optimizing PE curricula and teaching methods.