Correlative Analysis and Prediction of Physical Education Data Via Machine Learning: A Case Study on Grade Evaluation Method in University

Abstract

Physical education is an important way to strengthen the physical quality of students in the university. Sport performance is an important criterion for judging the physical fitness of college students. But during the process of calculating and collecting the comprehensive score for each student, teachers will use different equipment or various grading formulas to evaluate the grade for many years. Thus, referenced score will lose their values if the measurement data for each student in different years are not unified and sometimes with the subjective factor because of the manual calculations. This article analyzes the details of physical fitness test results and the relationship with the comprehensive score. It is discussed that the comprehensive score can provide serious help for teachers to know students’ physical quality level and make reasonable teaching program to each student according to their personal radar chat. Therefore, the artificial influence is excluded, and the prediction model is designed by using the principal component analysis method and the back propagation (BP) neural network technology. The predictive model allows students to evaluate their own physical test score in advance, get a preliminary understanding of their physical fitness. Comparisons are made between the applications of this model in different years and errors are discussed to verify the accuracy. The results indicate that the comprehensive score prediction model supplies one effective approach to unify the scoring standards and improve the computation efficiency in physical education.