<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper constructs a Bayesian network structure learning model using both the Bayesian network and the Notears algorithm. Combined with acyclic graph and conditional probability table, the structure and parameters of BN are described respectively. Focusing on the basic methods of Bayesian network structure, the hybrid algorithm based on Notears is chosen as the basic method of structure learning by analyzing the application of the evaluation search method, constraint method and hybrid search algorithm. The results show that the ideal education management method should focus on the integration with real life, so much so that it has a low rate of detachment from reality as well as a low rate of theory, accounting for 0.35 and 0.4, respectively. The traditional way of management of ideology and political education in colleges and universities is more theoretical and detached from the actual situation of education and management, with theoretical management accounting for 0.75, and management is inefficient, with a percentage of 0.6. The research in this paper promotes, to a certain extent, the innovation of the management of the ideological education in colleges and universities, for the management of ideological education in colleges and universities.</p>
</abstract>

This paper constructs a Bayesian network structure learning model using both the Bayesian network and the Notears algorithm. Combined with acyclic graph and conditional probability table, the structure and parameters of BN are described respectively. Focusing on the basic methods of Bayesian network structure, the hybrid algorithm based on Notears is chosen as the basic method of structure learning by analyzing the application of the evaluation search method, constraint method and hybrid search algorithm. The results show that the ideal education management method should focus on the integration with real life, so much so that it has a low rate of detachment from reality as well as a low rate of theory, accounting for 0.35 and 0.4, respectively. The traditional way of management of ideology and political education in colleges and universities is more theoretical and detached from the actual situation of education and management, with theoretical management accounting for 0.75, and management is inefficient, with a percentage of 0.6. The research in this paper promotes, to a certain extent, the innovation of the management of the ideological education in colleges and universities, for the management of ideological education in colleges and universities.

Innovative Path of Civic Education Management Mode in Colleges and Universities Based on Bayesian Networks

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper constructs a Bayesian network structure learning model using both the Bayesian network and the Notears algorithm. Combined with acyclic graph and...

Innovative Path of Civic Education Management Mode in Colleges and Universities Based on Bayesian Networks

Data publikacji: 08 lis 2023

Zakres stron: -

Otrzymano: 27 gru 2022

Przyjęty: 19 maj 2023

DOI: https://doi.org/10.2478/amns.2023.2.01057

Słowa kluczowe
Bayesian networks, Notears algorithm, Structural learning models, Educational management models, Evaluation search methods

© 2023 Jinping Xu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Innovative Path of Civic Education Management Mode in Colleges and Universities Based on Bayesian Networks

Data publikacji: 08 lis 2023

Zakres stron: -

Otrzymano: 27 gru 2022

Przyjęty: 19 maj 2023

DOI: https://doi.org/10.2478/amns.2023.2.01057

Słowa kluczoweBayesian networks, Notears algorithm, Structural learning models, Educational management models, Evaluation search methods

© 2023 Jinping Xu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Bayesian networks, Notears algorithm, Structural learning models, Educational management models, Evaluation search methods