The Use of Linear Regression Equation in Physical Education Teaching in Higher Education Institutions

• Published in: Journal of Electrical Systems Vol. 20; no. 3s; pp. 1843 - 1852
• Main Author: Cui, Jinxi
• Format: Journal Article
• Language: English
• Published: Paris Engineering and Scientific Research Groups 31.03.2024
• Subjects: Accuracy; COVID-19; Curricula; Distance learning; Exercise; Higher education institutions; Machine learning; Performance assessment; Performance prediction; Physical fitness; Regression; Statistical analysis; Statistical models; Teaching methods
• ISSN: 1112-5209
• DOI: 10.52783/jes.1723

This study investigates the application of linear regression equations in physical education teaching within higher education institutions, utilizing the Integrated Linear Logistic Regression AdaBoost Classifier (ILLR-ABC). The research aims to enhance teaching methodologies and performance assessment in physical education through the integration of statistical modelling techniques. Through simulated experiments and empirical validations, the effectiveness of the ILLR-ABC model is evaluated in predicting student performance and guiding instructional interventions. Results demonstrate significant improvements in accuracy and precision compared to traditional teaching methods. For instance, the ILLR-ABC model achieved an average accuracy rate of 90% in predicting student achievement levels, enabling educators to tailor teaching strategies to individual student needs effectively. Additionally, the model provides valuable insights into the factors influencing student performance, facilitating targeted interventions and curriculum adjustments. These findings highlight the potential of integrating statistical modelling techniques like ILLR-ABC to optimize physical education teaching practices and enhance student learning outcomes.