Applied Mathematics and Nonlinear Sciences
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Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 9, Issue 1


Published
on

February 26, 2024


Pages


DOI

Article

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

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Authors

Meng Zhu Affiliation:
School of Civil Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Seberang Perai Selatan, Pulau Pinang, Malaysia.
, Sharifah Akmam Syed Zakaria Affiliation:
School of Civil Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Seberang Perai Selatan, Pulau Pinang, Malaysia.
and Chenyu Wang Affiliation:
School of Architecture and Engineering, Jiaxing Nanhu University, Jiaxing, Zhejiang, 314000, China.


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.


Keywords

Apriori, Content recommendation algorithm, Collaborative recommendation algorithm, Education reform, 62N01


Citation

Zhu, M., Zakaria, S. A. S., & Wang, C. (2024). Promoting educational reform to enhance talent cultivation quality of civil engineering majors based on deep learning background. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns-2024-0444
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Published by: Engineering Journals

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