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决策树在大数据背景下的体育教学分析与管理中的应用。

Application of Decision Tree in PE Teaching Analysis and Management under the Background of Big Data.

机构信息

Department of Public Education, Anyang Preschool Teachers College, Anyang, Henan 456150, China.

Exercise Science School, Beijing Sport University, Beijing 100084, China.

出版信息

Comput Intell Neurosci. 2022 Jul 8;2022:8091838. doi: 10.1155/2022/8091838. eCollection 2022.

DOI:10.1155/2022/8091838
PMID:35845912
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9286987/
Abstract

With the continuous development of physical education reform, the defects and deficiencies of physical education teaching in colleges and universities are increasingly exposed. The reform of the original physical education teaching thought, education system, teaching mode, and method has achieved little. At present, the research on the prediction of physical education teaching achievements is mainly aimed at the prediction of athletes and physical education teaching achievements or the prediction of the past data of college students. This paper studies the physical education under the decision tree under the background of big data and constructs the physical education management system. When the number of tests reaches 40, the qualified rate of long-distance running is 65.2%, that of basketball is 68.1%, and that of volleyball is 68.2%. The quality of physical education teaching determines the lifeline of the development of school physical education teaching. In the process of collecting and selecting teaching materials, this paper enriches teaching materials and teaching reflection, cultivates one's own understanding, and improves the artistic appeal and creativity of teaching.

摘要

随着体育教学改革的不断发展,高校体育教学的缺陷和不足日益暴露。对原有体育教学思想、教育体系、教学模式和方法的改革收效甚微。目前,对体育教学成绩的预测研究主要集中在对运动员和体育教学成绩的预测,或者是对大学生过去数据的预测。本文以大数据为背景,研究决策树下的体育教育,构建体育教育管理系统。当测试次数达到 40 次时,长跑的合格率为 65.2%,篮球为 68.1%,排球为 68.2%。体育教学质量决定着学校体育教学发展的生命线。在收集和选择教材的过程中,本文丰富了教学材料和教学反思,培养了自己的理解,提高了教学的艺术感染力和创造性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/137f/9286987/e4b02f3dfaf9/CIN2022-8091838.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/137f/9286987/8e20020f6a69/CIN2022-8091838.001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/137f/9286987/46b744514c43/CIN2022-8091838.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/137f/9286987/e4b02f3dfaf9/CIN2022-8091838.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/137f/9286987/8e20020f6a69/CIN2022-8091838.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/137f/9286987/c8446cd9b5a8/CIN2022-8091838.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/137f/9286987/0fafad91b149/CIN2022-8091838.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/137f/9286987/f01136187eb2/CIN2022-8091838.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/137f/9286987/5dd178947817/CIN2022-8091838.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/137f/9286987/46b744514c43/CIN2022-8091838.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/137f/9286987/e4b02f3dfaf9/CIN2022-8091838.007.jpg

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