Teaching Quality Analysis of University Physical Education Classes Based on Big Data Decision Tree Algorithm

Abstract

The Teaching Quality Analysis of University Physical Education Classes Based on Big Data Decision Tree Method investigates the use of big data analytics and decision tree techniques to evaluate teaching quality in university physical education classes. With the proliferation of digital technologies and data-driven approaches, there is growing interest in leveraging big data analytics to enhance teaching effectiveness and student learning outcomes. This study focuses on utilizing decision tree algorithms to analyze large datasets comprising various teaching metrics, such as student attendance, engagement levels, performance assessments, and instructor feedback. By applying the decision tree algorithm to these datasets, patterns and relationships between teaching practices and student outcomes are identified, enabling instructors and administrators to make data-driven decisions to improve teaching quality. Through a comprehensive analysis of teaching practices and student performance indicators, this research aims to provide valuable insights into optimizing physical education classes at the university level, ultimately fostering a more effective and engaging learning environment for students.