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

<p>Based on Apriori algorithm and BP neural network technology, this paper deeply integrates association rules and data mining technology in education and informatization management, which provides a new method and a new way of thinking for reforming education management work in the context of intelligent technology. In real-life settings, education management is being managed using this new method. Taking the university district of DN201 as the research object, the allocation level of basic education resources in the university district is measured and optimized in a balanced way through indicator analysis. The parameter settings described allowed for the search of strong correlation rules for the mastery level of students in each course cluster. The results show that SN201 has the highest level of educational resource allocation at 8.32. SN215 is at the lowest level at 2.62. This shows that there is an imbalance in its resource allocation. The coefficient of variation of educational resource allocation of SN217 is 0.83 before optimization, and after optimization, the coefficient of variation is 0.65, and the variation becomes lower. The courses have a higher level of enhanced strong association than 4.</p>
</abstract>

Based on Apriori algorithm and BP neural network technology, this paper deeply integrates association rules and data mining technology in education and informatization management, which provides a new method and a new way of thinking for reforming education management work in the context of intelligent technology. In real-life settings, education management is being managed using this new method. Taking the university district of DN201 as the research object, the allocation level of basic education resources in the university district is measured and optimized in a balanced way through indicator analysis. The parameter settings described allowed for the search of strong correlation rules for the mastery level of students in each course cluster. The results show that SN201 has the highest level of educational resource allocation at 8.32. SN215 is at the lowest level at 2.62. This shows that there is an imbalance in its resource allocation. The coefficient of variation of educational resource allocation of SN217 is 0.83 before optimization, and after optimization, the coefficient of variation is 0.65, and the variation becomes lower. The courses have a higher level of enhanced strong association than 4.

Reform of educational management work in higher education based on departmental sociology in the context of intelligent technology

Applied Mathematics and Nonlinear Sciences

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

Based on Apriori algorithm and BP neural network technology, this paper deeply integrates association rules and data mining technology in education and...

Reform of educational management work in higher education based on departmental sociology in the context of intelligent technology

Publié en ligne: 31 janv. 2024

Pages: -

Reçu: 22 déc. 2023

Accepté: 30 déc. 2023

DOI: https://doi.org/10.2478/amns-2024-0281

Mots clés
Association rules, Data mining, Educational management, BP neural network

© 2024 Xiaoyang Zhu, published by Sciendo

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

Reform of educational management work in higher education based on departmental sociology in the context of intelligent technology

Publié en ligne: 31 janv. 2024

Pages: -

Reçu: 22 déc. 2023

Accepté: 30 déc. 2023

DOI: https://doi.org/10.2478/amns-2024-0281

Mots clésAssociation rules, Data mining, Educational management, BP neural network

© 2024 Xiaoyang Zhu, published by Sciendo

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

Mots clés
Association rules, Data mining, Educational management, BP neural network