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<p>This paper focuses on improving the quality of talent cultivation for civil engineering students through reforming teaching quality and course selection management. Regarding teaching quality management, the article proposes to use improved Apriori algorithm to generate high interest rules and utilize SQL for effective data manipulation. Regarding course selection management, the content-based recommendation algorithm is used to optimize the course selection mechanism. After the implementation of the reform, the proportion of low scores in the self-quality evaluation of college students was significantly reduced, and the evaluation indexes of teachers also showed significant differences. In addition, employers were satisfied with the competence of civil engineering graduates. These results indicate that data-driven teaching management and personalized course selection recommendation can effectively improve education quality and student satisfaction.</p>
</abstract>

This paper focuses on improving the quality of talent cultivation for civil engineering students through reforming teaching quality and course selection management. Regarding teaching quality management, the article proposes to use improved Apriori algorithm to generate high interest rules and utilize SQL for effective data manipulation. Regarding course selection management, the content-based recommendation algorithm is used to optimize the course selection mechanism. After the implementation of the reform, the proportion of low scores in the self-quality evaluation of college students was significantly reduced, and the evaluation indexes of teachers also showed significant differences. In addition, employers were satisfied with the competence of civil engineering graduates. These results indicate that data-driven teaching management and personalized course selection recommendation can effectively improve education quality and student satisfaction.

Promoting Educational Reform to Enhance Talent Cultivation Quality of Civil Engineering Majors Based on Deep Learning Background

Applied Mathematics and Nonlinear Sciences

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

This paper focuses on improving the quality of talent cultivation for civil engineering students through reforming teaching quality and course selection...

Promoting Educational Reform to Enhance Talent Cultivation Quality of Civil Engineering Majors Based on Deep Learning Background

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 10. Jan. 2024

Akzeptiert: 16. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0444

Schlüsselwörter
Apriori, Content recommendation algorithm, Collaborative recommendation algorithm, Education reform

© 2024 Meng Zhu et al., published by Sciendo

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

Promoting Educational Reform to Enhance Talent Cultivation Quality of Civil Engineering Majors Based on Deep Learning Background

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 10. Jan. 2024

Akzeptiert: 16. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0444

SchlüsselwörterApriori, Content recommendation algorithm, Collaborative recommendation algorithm, Education reform

© 2024 Meng Zhu et al., published by Sciendo

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

Schlüsselwörter
Apriori, Content recommendation algorithm, Collaborative recommendation algorithm, Education reform