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基于协同教育环境的高校思想政治理论课网络资源整合分析。

Analysis of Network Resources Integration of Political Thought Courses in Institution of Higher Learning Based on Collaborative Education Environment.

机构信息

Jiangxi University of Finance and Economics,College of Marxism, Nanchang 330013, Jiangxi, China.

出版信息

J Environ Public Health. 2022 Aug 31;2022:7989779. doi: 10.1155/2022/7989779. eCollection 2022.

DOI:10.1155/2022/7989779
PMID:36089972
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9451983/
Abstract

The importance of political thought courses in higher education institutions is directly related to the caliber and extent of staff development. This essay carefully examines and discusses the integration of network resources for political thought courses in higher education institutions from the standpoint of collaborative education. This article analyses the current state of the integration of network resources into higher education institutions' political thought curricula. The general DM process and DM-related technologies are studied concurrently. The mining model based on the study of political thought course is chosen in conjunction with this course. Additionally, a teaching resource integration method based on the idea of collaborative learning is developed, including the development of an Arduino device identification program based on NN and an Arduino device learning resource base. In accordance with the design of the network resource integration method used in the political thought course, the article evaluates the model's performance in a number of different areas. The proposed algorithm's accuracy is 95.11 percent, according to experiments. The recall rate of this algorithm can also go as high as 93.94 percent. In order to further reform teaching and raise the standard of learning, this study can offer students learning direction.

摘要

高校思想政治理论课的重要性与师资队伍建设的水平和程度直接相关。本文从协同教育的角度,认真探讨和分析了高校思想政治理论课网络资源整合问题。本文分析了将网络资源整合到高校思想政治理论课中的现状。同时研究了一般的 DM 过程和 DM 相关技术。结合这门课程,选择了基于政治思想课程研究的挖掘模型。此外,还开发了一种基于协作学习理念的教学资源整合方法,包括基于 NN 的 Arduino 设备识别程序和 Arduino 设备学习资源库的开发。根据思想政治理论课网络资源整合方法的设计,本文从多个方面对模型的性能进行了评估。实验表明,该算法的准确率为 95.11%,召回率也高达 93.94%。为了进一步改革教学,提高学习水平,本研究可以为学生提供学习方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a76/9451983/63af4f77d7c2/JEPH2022-7989779.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a76/9451983/8c6603326ec0/JEPH2022-7989779.001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a76/9451983/63af4f77d7c2/JEPH2022-7989779.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a76/9451983/8c6603326ec0/JEPH2022-7989779.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a76/9451983/63a1457658e9/JEPH2022-7989779.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a76/9451983/cefa3a330f21/JEPH2022-7989779.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a76/9451983/afee59be6f62/JEPH2022-7989779.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a76/9451983/0e7168aec2ed/JEPH2022-7989779.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a76/9451983/eaaa2762666d/JEPH2022-7989779.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a76/9451983/63af4f77d7c2/JEPH2022-7989779.007.jpg

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