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

<p>This paper first proposes an integral function algorithm of the support vector machine model for path optimization of physical education and health courses in colleges and universities according to the core literacy integration teaching situation. Secondly, the integral function algorithm is used to map the initial data from the original space to a higher dimensional space so that the samples exhibit linear differentiability in the higher dimensional space and the significance and correlation of the factors influencing physical education and health in colleges and universities are analyzed using spatiotemporal sequences. Then a physical education and health test management system was constructed according to the needs of physical education teachers and students, while an integral function algorithm was used to analyze the behavior of physical education and health knowledge in colleges and universities. The results showed that there was a significant difference (p>0.05) between the knowledge of physical education and health ( <italic>X</italic> ± <italic>SD</italic> ) for 10.6 ± 4.5 and physical education and health behavior ( <italic>X</italic> ± <italic>SD</italic>) for 7.2 ± 3.5 showing that there was no significant difference between the knowledge and behavior of physical education and health, which can be considered to have a high degree of consistency in knowledge and behavior.</p>
</abstract>

This paper first proposes an integral function algorithm of the support vector machine model for path optimization of physical education and health courses in colleges and universities according to the core literacy integration teaching situation. Secondly, the integral function algorithm is used to map the initial data from the original space to a higher dimensional space so that the samples exhibit linear differentiability in the higher dimensional space and the significance and correlation of the factors influencing physical education and health in colleges and universities are analyzed using spatiotemporal sequences. Then a physical education and health test management system was constructed according to the needs of physical education teachers and students, while an integral function algorithm was used to analyze the behavior of physical education and health knowledge in colleges and universities. The results showed that there was a significant difference (p>0.05) between the knowledge of physical education and health ( X ± SD ) for 10.6 ± 4.5 and physical education and health behavior ( X ± SD) for 7.2 ± 3.5 showing that there was no significant difference between the knowledge and behavior of physical education and health, which can be considered to have a high degree of consistency in knowledge and behavior.

A study on the optimization of teaching paths of integration of core literacy in physical education and health courses in colleges and universities based on function integral algorithm

Applied Mathematics and Nonlinear Sciences

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

This paper first proposes an integral function algorithm of the support vector machine model for path optimization of physical education and health courses in...

A study on the optimization of teaching paths of integration of core literacy in physical education and health courses in colleges and universities based on function integral algorithm

Online veröffentlicht: 15. Okt. 2023

Seitenbereich: -

Eingereicht: 19. Nov. 2022

Akzeptiert: 25. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00648

Schlüsselwörter
Support vector machine model, Functional integral algorithm, Spatio-temporal sequence, Sports and health

© 2023 Yan He et al., published by Sciendo

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

A study on the optimization of teaching paths of integration of core literacy in physical education and health courses in colleges and universities based on function integral algorithm

Online veröffentlicht: 15. Okt. 2023

Seitenbereich: -

Eingereicht: 19. Nov. 2022

Akzeptiert: 25. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00648

SchlüsselwörterSupport vector machine model, Functional integral algorithm, Spatio-temporal sequence, Sports and health

© 2023 Yan He et al., published by Sciendo

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

Schlüsselwörter
Support vector machine model, Functional integral algorithm, Spatio-temporal sequence, Sports and health