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

<p>Based on the conventional genetic algorithm, this work suggests an adaptive variation genetic algorithm (AGA), which increases population diversity and speeds up convergence by increasing variation probability. The entropy approach is used to first assess the caliber of physical education and to produce a priori assessment samples. It is combined with the AGA-BP model based on adaptive variation probability. The BP neural network is then utilized for assessment learning in the field of evaluating the quality of physical education. It is optimized by an adaptive variation-based genetic algorithm. Finally, student physical activity levels were assessed both before and after the physical education reform using a more thorough and scientifically based EM-AGA-BP teaching quality evaluation model. The findings revealed that, at 41.2% and 53.4%, respectively, the percentage of students’ static activity time after the reform was much lower than that before the reform. By using independent samples t-tests, each revealed significant differences (P 0.05).</p>
</abstract>

Based on the conventional genetic algorithm, this work suggests an adaptive variation genetic algorithm (AGA), which increases population diversity and speeds up convergence by increasing variation probability. The entropy approach is used to first assess the caliber of physical education and to produce a priori assessment samples. It is combined with the AGA-BP model based on adaptive variation probability. The BP neural network is then utilized for assessment learning in the field of evaluating the quality of physical education. It is optimized by an adaptive variation-based genetic algorithm. Finally, student physical activity levels were assessed both before and after the physical education reform using a more thorough and scientifically based EM-AGA-BP teaching quality evaluation model. The findings revealed that, at 41.2% and 53.4%, respectively, the percentage of students’ static activity time after the reform was much lower than that before the reform. By using independent samples t-tests, each revealed significant differences (P 0.05).

Current situation and reform trends of physical education teaching evaluation in the context of deep learning

Applied Mathematics and Nonlinear Sciences

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

Based on the conventional genetic algorithm, this work suggests an adaptive variation genetic algorithm (AGA), which increases population diversity and speeds...

Current situation and reform trends of physical education teaching evaluation in the context of deep learning

Publicado en línea: 11 oct 2023

Páginas: -

Recibido: 22 dic 2022

Aceptado: 21 abr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00594

Palabras clave
Entropy value method, EM-AGA-BP model, AGA algorithm, AGA-BP model, Physical education evaluation

© 2023 Lianjin Miao, published by Sciendo

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

Current situation and reform trends of physical education teaching evaluation in the context of deep learning

Publicado en línea: 11 oct 2023

Páginas: -

Recibido: 22 dic 2022

Aceptado: 21 abr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00594

Palabras claveEntropy value method, EM-AGA-BP model, AGA algorithm, AGA-BP model, Physical education evaluation

© 2023 Lianjin Miao, published by Sciendo

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

Palabras clave
Entropy value method, EM-AGA-BP model, AGA algorithm, AGA-BP model, Physical education evaluation