<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Good sports interest helps college students to actively participate in physical exercise, excellently complete sports tasks, form good sports habits and have a strong body. Given this, this paper establishes an innovative model of college physical education based on the 4R Theory, fully integrates physical education services with classroom teaching, and designs teaching experiments related to physical education services and sports education in colleges and universities. The subjects were Chengdu University of Technology students. The cluster analysis algorithm was used to cluster students’ sports interests, and the parameter estimation of the multiple linear regression model was carried out by the least squares method to deeply analyze the relevant factors affecting students’ sports interests in colleges and universities. The study found that: students’ sports interest clustering is divided into five categories, the number of students in cluster 1 sports awareness reached 542, and students’ sports interest increased by 0.627 percentage points for every 1 percentage point increase in sports skills, and students’ sports participation interest in the experimental class increased by 46.79% compared to that of the control class after the experiment. Adopting diversified physical education teaching methods and contents can effectively enhance college students’ interest in sports, thus providing guarantee for improving students’ physical fitness.</p>
</abstract>

Good sports interest helps college students to actively participate in physical exercise, excellently complete sports tasks, form good sports habits and have a strong body. Given this, this paper establishes an innovative model of college physical education based on the 4R Theory, fully integrates physical education services with classroom teaching, and designs teaching experiments related to physical education services and sports education in colleges and universities. The subjects were Chengdu University of Technology students. The cluster analysis algorithm was used to cluster students’ sports interests, and the parameter estimation of the multiple linear regression model was carried out by the least squares method to deeply analyze the relevant factors affecting students’ sports interests in colleges and universities. The study found that: students’ sports interest clustering is divided into five categories, the number of students in cluster 1 sports awareness reached 542, and students’ sports interest increased by 0.627 percentage points for every 1 percentage point increase in sports skills, and students’ sports participation interest in the experimental class increased by 46.79% compared to that of the control class after the experiment. Adopting diversified physical education teaching methods and contents can effectively enhance college students’ interest in sports, thus providing guarantee for improving students’ physical fitness.

Exploring the development of college students’ sports interests in physical education based on cluster analysis

Sports Institute of Chengdu University of Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Good sports interest helps college students to actively participate in physical exercise, excellently complete sports tasks, form good sports habits and have a...

Exploring the development of college students’ sports interests in physical education based on cluster analysis

Publié en ligne: 26 févr. 2024

Pages: -

Reçu: 12 janv. 2024

Accepté: 19 janv. 2024

DOI: https://doi.org/10.2478/amns-2024-0532

Mots clés
4R theory, Cluster analysis, Least squares method, Multiple linear regression, Sports interest

© 2023 Yegang Zheng, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Exploring the development of college students’ sports interests in physical education based on cluster analysis

Publié en ligne: 26 févr. 2024

Pages: -

Reçu: 12 janv. 2024

Accepté: 19 janv. 2024

DOI: https://doi.org/10.2478/amns-2024-0532

Mots clés4R theory, Cluster analysis, Least squares method, Multiple linear regression, Sports interest

© 2023 Yegang Zheng, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
4R theory, Cluster analysis, Least squares method, Multiple linear regression, Sports interest