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Investigation on Effect Evaluation of Undergraduates’ Education in Ideology and Politics Based on Small Sample Multivariate Data Analysis

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Cyber Security Intelligence and Analytics (CSIA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1147))

Abstract

As the main force of China’s future improvement, higher occupational undergraduates students play an important role in the future social construction. In order to improve the healthy growth and improvement of undergraduates in higher occupational undergraduates, education in ideological and political must be carried out to help them shape correct values. With the fast improvement of society, multi-value culture has emerged. Therefore, it is of profound significance to continuously help undergraduates students to establish firm political beliefs and improve the timeliness of education in ideology and politics for undergraduates students. However, due to the extensive data information involved in the effectiveness assessment, it is hard to collect and analyze, so the multivariate data analysis of small samples effectively solves this problem. The purpose of this paper is to find the methods and strategies to improve the effectiveness of education in ideological and political by evaluating the effectiveness of education in ideological and political. This paper first summarizes the concept of multivariate data analysis of small samples, then evaluates the effectiveness of education in ideological and political of higher occupational undergraduates students with the help of cluster analysis algorithm, and proposes strategies to improve the effectiveness of education on this basis. The experiment of this paper shows that the actual effect of education in ideological and political of undergraduates students in China is relatively low, and there are significant problems in education and teaching.

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Correspondence to Shengli Wang .

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Wang, S. (2020). Investigation on Effect Evaluation of Undergraduates’ Education in Ideology and Politics Based on Small Sample Multivariate Data Analysis. In: Xu, Z., Parizi, R., Hammoudeh, M., Loyola-González, O. (eds) Cyber Security Intelligence and Analytics. CSIA 2020. Advances in Intelligent Systems and Computing, vol 1147. Springer, Cham. https://doi.org/10.1007/978-3-030-43309-3_55

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