Applied Mathematics and Nonlinear Sciences
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Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 8, Issue 2


Published
on

December 2, 2023


Pages


DOI

Article

Analysis of the Impact of Curriculum Civics on Students’ Quality Cultivation Based on Bayesian Networks

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Authors

Lili Zhang Affiliation:
School of Languages and Media, Anhui University of Finance & Economics, Bengbu, Anhui, 233000, China.


Abstract

This paper first analyzes the Bayesian network model based on probabilistic reasoning to study and explore the random phenomenon law of curriculum thinking. Secondly, probability theory and graph theory are utilized to deal with uncertain information and reduce the meta-multiplicity of the analysis of uncertain knowledge. Then, the Bayesian network structure learning problem is analyzed, and a constraint-based structure learning method is proposed to score students’ qualities by a given scoring function, and using a search algorithm, the best scoring network structure is searched. Finally, in order to analyze the influence of the Civics curriculum on the quality of students in college A, we should analyze the impact of the curriculum. The results show that from the innovative quality of students, the distribution density of innovative habit and innovative ability scores in the interval of 70~75 is (0.02, 0.035), and the distribution density of innovative consciousness scores in the interval of 70~86.25 is (0.005, 0.07). The data show that curriculum Civics can effectively promote the cultivation of innovative talents and the construction of an innovative country by internalizing the quality of innovation in the heart and externalizing it in the form of cultivating students’ innovative habits, improving their innovative consciousness and enhancing their innovative ability.


Keywords

Bayesian networks, Probability theory, Search algorithms, Curricular thinking, Quality development, 00A73


Citation

Zhang, L. (2023). Analysis of the impact of curriculum civics on students’ quality cultivation based on bayesian networks. Applied Mathematics and Nonlinear Sciences, 8(2). https://doi.org/10.2478/amns.2023.2.01332
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