ArticleComputational intelligence and neuroscience2022
Evaluation Method of Physical Education Teaching and Training Quality Based on Deep Learning.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Deep learning based multi attribute evaluation for holistic student assessment in physical education.Scientific reports · 2025Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.