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<p>The evaluation of the quality of blended physical education teaching in colleges and universities, as a part of the evaluation of university education, is of great significance in guiding the construction and development of blended physical education teaching. This paper examines the evaluation system for hybrid physical education teaching quality in colleges and universities using the principle of evaluation system construction. There are 4 primary indicators, 11 secondary indicators, and 31 tertiary indicators in the system. By using the fuzzy comprehensive evaluation method to calculate the weights of each level 1 index and level 2 index, the weights of each index are judged to account for the proportion. The improved BP neural network algorithm was used to score the indicators. Through empirical analysis, it was found that the weight share of teaching effect, teaching implementation, teaching background, and teaching implementation were 0.33, 0.27, 0.21, and 0.19, respectively, and the teaching effect accounted for the largest share. The total evaluation score was 3.9. The scores of each level indicator in descending order are teaching background (4.13), teaching implementation (4.07), teaching effect (4.05), and teaching setup (3.37). The quality of blended physical education teaching in Colleges and Universities of S is better from the overall point of view.</p>
</abstract>

The evaluation of the quality of blended physical education teaching in colleges and universities, as a part of the evaluation of university education, is of great significance in guiding the construction and development of blended physical education teaching. This paper examines the evaluation system for hybrid physical education teaching quality in colleges and universities using the principle of evaluation system construction. There are 4 primary indicators, 11 secondary indicators, and 31 tertiary indicators in the system. By using the fuzzy comprehensive evaluation method to calculate the weights of each level 1 index and level 2 index, the weights of each index are judged to account for the proportion. The improved BP neural network algorithm was used to score the indicators. Through empirical analysis, it was found that the weight share of teaching effect, teaching implementation, teaching background, and teaching implementation were 0.33, 0.27, 0.21, and 0.19, respectively, and the teaching effect accounted for the largest share. The total evaluation score was 3.9. The scores of each level indicator in descending order are teaching background (4.13), teaching implementation (4.07), teaching effect (4.05), and teaching setup (3.37). The quality of blended physical education teaching in Colleges and Universities of S is better from the overall point of view.

Construction of Quality Evaluation System for Blended Physical Education Teaching in Colleges and Universities

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The evaluation of the quality of blended physical education teaching in colleges and universities, as a part of the evaluation of university education, is of...

Construction of Quality Evaluation System for Blended Physical Education Teaching in Colleges and Universities

Pubblicato online: 07 giu 2024

Pagine: -

Ricevuto: 01 mar 2024

Accettato: 12 mag 2024

DOI: https://doi.org/10.2478/amns-2024-1379

Parole chiave
Fuzzy comprehensive evaluation, BP neural network, Evaluation index, Hybrid physical education teaching

© 2024 Kai Tang, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Construction of Quality Evaluation System for Blended Physical Education Teaching in Colleges and Universities

Pubblicato online: 07 giu 2024

Pagine: -

Ricevuto: 01 mar 2024

Accettato: 12 mag 2024

DOI: https://doi.org/10.2478/amns-2024-1379

Parole chiaveFuzzy comprehensive evaluation, BP neural network, Evaluation index, Hybrid physical education teaching

© 2024 Kai Tang, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
Fuzzy comprehensive evaluation, BP neural network, Evaluation index, Hybrid physical education teaching