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<p>In this paper, we first propose the basic technical support and service and management of university intelligent physical education composition, and on this basis, we propose a teaching quality assessment model based on multivariate statistical analysis and regression analysis. Among them, multivariate statistical analysis is used to calculate the factor loadings A of physical education teaching, and multiple linear regression is used to establish the teaching quality assessment model and test it. Finally, an empirical analysis of teaching quality assessment was conducted with smart physical education as an example. The results show that the comprehensive assessment value of smart physical education teaching quality based on a multiple linear regression model is very close to the actual value, and the error value ranges from [0.003 to 0.027]. The error values are smaller than [0.0135~0.0921] of the ANN model and [0.0635~0.1033] of the neural network model. The research in this paper can provide scientific quality assessment methods and theoretical support for smart physical education in colleges and universities to promote the development of smart physical education and improve teaching quality in colleges and universities.</p>
</abstract>

In this paper, we first propose the basic technical support and service and management of university intelligent physical education composition, and on this basis, we propose a teaching quality assessment model based on multivariate statistical analysis and regression analysis. Among them, multivariate statistical analysis is used to calculate the factor loadings A of physical education teaching, and multiple linear regression is used to establish the teaching quality assessment model and test it. Finally, an empirical analysis of teaching quality assessment was conducted with smart physical education as an example. The results show that the comprehensive assessment value of smart physical education teaching quality based on a multiple linear regression model is very close to the actual value, and the error value ranges from [0.003 to 0.027]. The error values are smaller than [0.0135~0.0921] of the ANN model and [0.0635~0.1033] of the neural network model. The research in this paper can provide scientific quality assessment methods and theoretical support for smart physical education in colleges and universities to promote the development of smart physical education and improve teaching quality in colleges and universities.

Quality assessment of intelligent physical education teaching in universities based on multivariate statistical analysis and regression analysis

Applied Mathematics and Nonlinear Sciences

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

In this paper, we first propose the basic technical support and service and management of university intelligent physical education composition, and on this...

Quality assessment of intelligent physical education teaching in universities based on multivariate statistical analysis and regression analysis

Published Online: Nov 01, 2023

Page range: -

Received: Feb 01, 2023

Accepted: May 12, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00914

Keywords
Smart sports, Multivariate statistical analysis, Factor loadings, Teaching quality assessment, Multiple linear regression

© 2023 Bo Li et al., published by Sciendo

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

Quality assessment of intelligent physical education teaching in universities based on multivariate statistical analysis and regression analysis

Published Online: Nov 01, 2023

Page range: -

Received: Feb 01, 2023

Accepted: May 12, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00914

KeywordsSmart sports, Multivariate statistical analysis, Factor loadings, Teaching quality assessment, Multiple linear regression

© 2023 Bo Li et al., published by Sciendo

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

Keywords
Smart sports, Multivariate statistical analysis, Factor loadings, Teaching quality assessment, Multiple linear regression