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<p>This paper first takes the deep learning theory as the basis of the thesis of sports and aesthetic education, constructs the flipped classroom teaching model of sports and aesthetic education based on the theory, and introduces the Bayesian estimation theory and the structural equation model to apply it to sports and aesthetic education. Secondly, the research object and method are selected for the flipped classroom teaching model of physical education and aesthetic education, and the teaching process of the flipped classroom experiment and the assumptions related to students’ learning satisfaction are given. Finally, the application value of physical education and aesthetics in colleges and universities is analyzed through the convergence of path coefficients in Bayesian structural equation modeling and hypothesis testing. When the number of iterations reaches 100*10<sup>3</sup> times, the H6-H11 path coefficient convergence value interval is [-22.5, 14.2]. The convergence interval fluctuates a lot and shows a more unstable trend than the H1-H5 path coefficient. Students learning satisfaction will have a direct effect on their aesthetic ability and improve their aesthetic awareness, and the path coefficients are 0.629 and 0.524, respectively. Deep learning theory based on sports and aesthetics in colleges and universities under the background of big data can improve students’ learning satisfaction and then promote the enhancement of students’ aesthetic ability and aesthetic awareness.</p>
</abstract>

This paper first takes the deep learning theory as the basis of the thesis of sports and aesthetic education, constructs the flipped classroom teaching model of sports and aesthetic education based on the theory, and introduces the Bayesian estimation theory and the structural equation model to apply it to sports and aesthetic education. Secondly, the research object and method are selected for the flipped classroom teaching model of physical education and aesthetic education, and the teaching process of the flipped classroom experiment and the assumptions related to students’ learning satisfaction are given. Finally, the application value of physical education and aesthetics in colleges and universities is analyzed through the convergence of path coefficients in Bayesian structural equation modeling and hypothesis testing. When the number of iterations reaches 100*103 times, the H6-H11 path coefficient convergence value interval is [-22.5, 14.2]. The convergence interval fluctuates a lot and shows a more unstable trend than the H1-H5 path coefficient. Students learning satisfaction will have a direct effect on their aesthetic ability and improve their aesthetic awareness, and the path coefficients are 0.629 and 0.524, respectively. Deep learning theory based on sports and aesthetics in colleges and universities under the background of big data can improve students’ learning satisfaction and then promote the enhancement of students’ aesthetic ability and aesthetic awareness.

The Rationale of Physical Education and Aesthetics in the Context of Big Data and the Value and Significance of its Implementation in Colleges and Universities

Department of Sports and Military Teaching

Applied Mathematics and Nonlinear Sciences

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

This paper first takes the deep learning theory as the basis of the thesis of sports and aesthetic education, constructs the flipped classroom teaching model...

The Rationale of Physical Education and Aesthetics in the Context of Big Data and the Value and Significance of its Implementation in Colleges and Universities

Publicado en línea: 16 dic 2023

Páginas: -

Recibido: 15 feb 2023

Aceptado: 26 jun 2023

DOI: https://doi.org/10.2478/amns.2023.2.01491

Palabras clave
Deep learning, Bayesian estimation, Structural equation modeling, Flipped classroom, Sports aesthetic education

© 2023 Bing Chen et al., published by Sciendo

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

The Rationale of Physical Education and Aesthetics in the Context of Big Data and the Value and Significance of its Implementation in Colleges and Universities

Publicado en línea: 16 dic 2023

Páginas: -

Recibido: 15 feb 2023

Aceptado: 26 jun 2023

DOI: https://doi.org/10.2478/amns.2023.2.01491

Palabras claveDeep learning, Bayesian estimation, Structural equation modeling, Flipped classroom, Sports aesthetic education

© 2023 Bing Chen et al., published by Sciendo

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

Palabras clave
Deep learning, Bayesian estimation, Structural equation modeling, Flipped classroom, Sports aesthetic education