Research on Physical Education Teaching Improvement Strategies and Algorithms Based on Big Data Analysis

Abstract

The study "Physical Education Teaching Improvement based on Deep Learning (PETDEL) which Combines Decision Tree with Fuzzy Level Algorithm" presents a novel method for improving teaching methods in physical education (PE) by involving advanced machine learning techniques. The novel framework PETDEL, which this study creates, combines fuzzy logic and decision trees to produce a strong model that can handle the difficult structures present in PE data. By defining exact pathways based on measurable data from physical education contexts, such as student attendance, performance indicators and exam outcomes, the decision tree algorithm helps structured decision-making. Similarly, the fuzzy logic feature provides variation and flexibility to the model by taking into consideration highly individualized and subjective variables, which may be difficult to accurately quantify, such effort and student involvement levels. With this combination, PETDEL is able to process and analyze large amounts of data collected in educational environments in an efficient manner, which results in more exact forecasts of student outcomes and more customized teaching approaches. Because of its ability to combine and analyze both clear and fuzzy data, the system is very good at giving useful information that can guide changes to programs and instructional strategies. The findings of the experiments show that PETDEL delivers notable increases in the variation of teaching approaches and greatly improves the prediction accuracy of student performance, both of which have a favorable impact on the overall quality of physical education. This research proves that physical education may be more responsive to the varied requirements of students by paving the path for more advanced instructional tools and improving teaching methods through data-driven insights.