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

<p>Big data analysis of education management in colleges and universities plays an important role in the improvement of education levels. In this paper, a triangular fuzzy algorithm based on association rule mining is proposed to realize a better data mining effect. Firstly, the relative similarity in WBARM algorithm is used to find out the similarity difference index of attributes from decision-making data, and then the association rule mining algorithm is used to find out the influence factor of attributes, and finally, the weight of attributes is obtained by combining the above results, and the generated association factor is evaluated, and the research is carried out in terms of enrollment and admission, learning behavior and teaching evaluation. The results show that: in terms of college enrollment and admission, the three colleges and universities in M can increase the promotion of candidates with scores between 451-580 on the college entrance examination, and expand the number of enrollment in the majors of visual communication design, software engineering, and mechanics. In terms of teachers’ teaching quality, the overall teaching level of teachers in the 3 colleges and universities is at a medium level (3.06), among which G1 has the best teaching level, with an average score of 4.13. By communicating more with teachers from G1, teachers from the other two schools can enhance their teaching level. Developing the educational management of colleges and universities towards precision can be accomplished through the results of big data analysis.</p>
</abstract>

Big data analysis of education management in colleges and universities plays an important role in the improvement of education levels. In this paper, a triangular fuzzy algorithm based on association rule mining is proposed to realize a better data mining effect. Firstly, the relative similarity in WBARM algorithm is used to find out the similarity difference index of attributes from decision-making data, and then the association rule mining algorithm is used to find out the influence factor of attributes, and finally, the weight of attributes is obtained by combining the above results, and the generated association factor is evaluated, and the research is carried out in terms of enrollment and admission, learning behavior and teaching evaluation. The results show that: in terms of college enrollment and admission, the three colleges and universities in M can increase the promotion of candidates with scores between 451-580 on the college entrance examination, and expand the number of enrollment in the majors of visual communication design, software engineering, and mechanics. In terms of teachers’ teaching quality, the overall teaching level of teachers in the 3 colleges and universities is at a medium level (3.06), among which G1 has the best teaching level, with an average score of 4.13. By communicating more with teachers from G1, teachers from the other two schools can enhance their teaching level. Developing the educational management of colleges and universities towards precision can be accomplished through the results of big data analysis.

Big Data Enables Intelligent Management of College Education

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Big Data Enables Intelligent Management of College Education"}

Big data analysis of education management in colleges and universities plays an important role in the improvement of education levels. In this paper, a...

Big Data Enables Intelligent Management of College Education

Data publikacji: 31 sty 2024

Zakres stron: -

Otrzymano: 20 gru 2023

Przyjęty: 27 gru 2023

DOI: https://doi.org/10.2478/amns-2024-0207

Słowa kluczowe
WBARM algorithm, Association rule algorithm, Data mining, Intelligent management

© 2024 Shiyu Yang et al., published by Sciendo

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

Big Data Enables Intelligent Management of College Education

Data publikacji: 31 sty 2024

Zakres stron: -

Otrzymano: 20 gru 2023

Przyjęty: 27 gru 2023

DOI: https://doi.org/10.2478/amns-2024-0207

Słowa kluczoweWBARM algorithm, Association rule algorithm, Data mining, Intelligent management

© 2024 Shiyu Yang et al., published by Sciendo

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

Słowa kluczowe
WBARM algorithm, Association rule algorithm, Data mining, Intelligent management