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Evaluation Method of Physical Education Teaching and Training Quality Based on Deep Learning

To solve the problems of great difficulty and low accuracy in the evaluation of physical education teaching results, an evaluation method of physical education teaching and training quality based on deep learning is proposed. The evaluation index system is constructed based on the teaching content,...

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Detalles Bibliográficos
Autores principales: Wang, Hong, Yang, Fei, Xing, Xingyang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9246637/
https://www.ncbi.nlm.nih.gov/pubmed/35785060
http://dx.doi.org/10.1155/2022/1680888
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author Wang, Hong
Yang, Fei
Xing, Xingyang
author_facet Wang, Hong
Yang, Fei
Xing, Xingyang
author_sort Wang, Hong
collection PubMed
description To solve the problems of great difficulty and low accuracy in the evaluation of physical education teaching results, an evaluation method of physical education teaching and training quality based on deep learning is proposed. The evaluation index system is constructed based on the teaching content, teaching attitude, teaching content, teaching method, and teaching effects that affect the teaching quality. After the influence factors of each index are quantified, the resolution coefficient of the index is dynamically taken, the index correlation relationship based on weight is established, and the score distribution and score progress are taken as the evaluation results. The test results show that the correlation coefficient between the evaluation result of the design method and the actual results is 0.9430, and the evaluation accuracy is 94.73%.
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spelling pubmed-92466372022-07-01 Evaluation Method of Physical Education Teaching and Training Quality Based on Deep Learning Wang, Hong Yang, Fei Xing, Xingyang Comput Intell Neurosci Research Article To solve the problems of great difficulty and low accuracy in the evaluation of physical education teaching results, an evaluation method of physical education teaching and training quality based on deep learning is proposed. The evaluation index system is constructed based on the teaching content, teaching attitude, teaching content, teaching method, and teaching effects that affect the teaching quality. After the influence factors of each index are quantified, the resolution coefficient of the index is dynamically taken, the index correlation relationship based on weight is established, and the score distribution and score progress are taken as the evaluation results. The test results show that the correlation coefficient between the evaluation result of the design method and the actual results is 0.9430, and the evaluation accuracy is 94.73%. Hindawi 2022-06-23 /pmc/articles/PMC9246637/ /pubmed/35785060 http://dx.doi.org/10.1155/2022/1680888 Text en Copyright © 2022 Hong Wang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wang, Hong
Yang, Fei
Xing, Xingyang
Evaluation Method of Physical Education Teaching and Training Quality Based on Deep Learning
title Evaluation Method of Physical Education Teaching and Training Quality Based on Deep Learning
title_full Evaluation Method of Physical Education Teaching and Training Quality Based on Deep Learning
title_fullStr Evaluation Method of Physical Education Teaching and Training Quality Based on Deep Learning
title_full_unstemmed Evaluation Method of Physical Education Teaching and Training Quality Based on Deep Learning
title_short Evaluation Method of Physical Education Teaching and Training Quality Based on Deep Learning
title_sort evaluation method of physical education teaching and training quality based on deep learning
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9246637/
https://www.ncbi.nlm.nih.gov/pubmed/35785060
http://dx.doi.org/10.1155/2022/1680888
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