The Relationship between Sports Measurement and Evaluation in Physical Education through Intelligent Analysis and Data Mining
Yazhou Sheng
Abstract
This study presents a novel framework for assessing physical education (PE) performance by integrating principal component analysis (PCA) and neural networks (NN). A comprehensive sports measurement system was developed, incorporating diverse indicators such as physical endurance, skill proficiency, and teamwork. PCA was applied to extract key performance indicators, reducing data dimensionality while retaining critical information. These refined indicators were used as inputs for an NN model, which provided detailed and objective evaluations of student performance. The proposed framework demonstrated superior accuracy, F1-scores, and recall compared to traditional methods and advanced machine learning models, such as SVM, RF, and GBM. Furthermore, the insights generated by the model enabled the design of personalized training plans tailored to individual student needs. This data-driven approach offers a significant advancement in PE assessment, ensuring objective evaluations and fostering effective, individualized teaching strategies.