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• W4297073022 abstract "Despite the fact that big data technology has been applied in education, there are no studies and cases that combine big data with ideological and political (IP) teaching quality. At the same time, the existing methods of IP teaching quality evaluation lack the consideration of multiple values, and the system is not complete and systematic. The use of big data analysis technology can improve the rigor of teaching quality assessment and make the data analysis more scientific, so as to improve the management system of universities and enhance the education quality. Therefore, this paper fully considers the background conditions of large data at this stage, on the basis of studying the methods of evaluating the quality of IP teaching in colleges. The big data about teaching quality is obtained by distributed algorithm, and multiple value indicators are drawn into the quality evaluation system as a main driver to emphasize the multiple value theory. Hierarchical analysis (AHP) method and fuzzy comprehensive evaluation (FCE) method are selected as the data analysis methods to provide evaluation basis for the proposed model. This model can further test the evaluation index system of education and further verify the rationality of the distribution of the weight of indicators at all levels. The evaluation results based on the large educational data and research data of a university show that the IP teaching quality of the university is excellent. The comprehensive evaluation model overcomes the limitations of traditional evaluation methods and provides a more comprehensive analysis about the teaching quality of IP teaching in colleges. Meanwhile, the conclusions obtained by the proposed evaluation model can be used for both the overall comprehensive evaluation of teachers’ teaching quality and a single comprehensive evaluation of the single factor affecting teaching quality. Using the evaluation results obtained by the model, we can set up advanced models and encourage backward students to have evidence. With the single-index evaluation, we can know what advantages the IP teaching or a certain teacher has and what aspects need to be strengthened. Therefore, we can put forward reasonable suggestions to progress instructing strategies and educating quality." @default.
• W4297073022 created "2022-09-26" @default.
• W4297073022 creator A5042157861 @default.
• W4297073022 date "2022-09-16" @default.
• W4297073022 modified "2023-10-01" @default.
• W4297073022 title "Evaluation of Teaching Quality on IP Environment Driven by Multiple Values Theory Based on Big Data" @default.
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• W4297073022 doi "https://doi.org/10.1155/2022/4857155" @default.
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