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Article type: Research Article
Authors: Wang, Chena; b
Affiliations: [a] College of Education and Science, Northwest Normal University, Lanzhou, Gansu, China | [b] Gansu Police Vocational College, Lanzhou, Gansu, China | E-mail: yuanfangdechenai@126.com
Correspondence: [*] Corresponding author: Gansu Police Vocational College, Lanzhou, Gansu, China. E-mail: yuanfangdechenai@126.com.
Abstract: Constructing the evaluation system of ideological and political education of new media in colleges is both beneficial to evaluate the established ideological and political education work and an important guide to improve the corresponding work. At present, promoting ideological and political education work with high integration of information technology has become an important way of ideological and political education work in colleges. However, the theoretical circles are still not focused enough on how to evaluate the ideological and political education work in colleges. Based on the characteristics of the information age, this paper establishes the teaching quality evaluation system of ideological and political education courses in colleges, introduces BP neural network evaluation method, and obtains strong empirical support through simulation experiments, so as to build a feasible teaching quality evaluation model of ideological and political courses in colleges. At the same time, the corresponding optimization suggestions are put forward, including improving the relevance of ideological and political education work, dynamically grasping students’ ideological and political information and doing a good job of data processing, and improving the professional information literacy of the ideological and political work team, in order to provide some reference for the efficient development of ideological and political education work in colleges.
Keywords: Ideological and political education evaluation, BP neural network, teaching quality
DOI: 10.3233/JCM-226935
Journal: Journal of Computational Methods in Sciences and Engineering, vol. 23, no. 6, pp. 3093-3102, 2023
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