Research on Collaborative Education of Labor Education and Database System Courses

Authors

  • Wenjiao Yu Yantai Institute of Science and Technology, Yantai, China Author
  • Yupeng Li Yantai Institute of Science and Technology, Yantai, China Author

DOI:

https://doi.org/10.70088/fn6sz771

Keywords:

labor education, database systems, collaborative education, applied universities, curriculum integration

Abstract

Background/Objectives: In the current context of educational development in the new era, the core mission of applied universities is to cultivate applied talents with both theoretical knowledge and practical competencies. Promoting the construction of a collaborative education system between labor education and curriculum will inevitably become a key task for applied universities and an objective demand for the development of the times. Methods: Colleges and universities need to closely adhere to the fundamental goal of cultivating people with moral integrity and deeply integrate the two educational dimensions. This article focuses on the study of labor education practice and the construction of collaborative education in database system courses. In course teaching, knowledge transfer, value guidance, and ideological and political education are organically integrated to educate students throughout the entire teaching process. Through the construction of an intelligent agriculture project, students can experience the advantages of digital labor and develop a deeper appreciation for technology-driven practical work. Results: The proposed collaborative education model is conducive to serving the local community, transforming the management and operation model of course delivery, and stimulating students' identification with core socialist values. Students demonstrate enhanced practical skills, stronger teamwork abilities, and a more comprehensive understanding of the societal relevance of their professional training. Conclusions: This research can not only promote the advancement and development of professional courses and help students develop in an all-round way, but also effectively cultivate high-quality industrial talents, which has important exemplary value for the cultivation of new engineering talents in applied universities.

References

P. Wei and L. Wei, "Research on 'Matrix Structure Design Competition' Teaching Mode," in *2019 3rd International Seminar on Education, Management and Social Sciences (ISEMSS 2019)*, 2019, pp. 687–692, Atlantis Press.

J. Xiang, "Research on the Design of Ideological and Political Online Open Course of Swimming Course," World Scientific Research Journal, vol. 9, no. 2, pp. 74–86, 2023.

Y. Zeng and S. Zheng, "Research and Exploration on the Effective Integration of Labor Education and Chemical Engineering Education," Adult and Higher Education, vol. 4, 2022.

Q. Wang, Y. Liu, Z. Jiang, and S. Y. Lee, "Collaborative leadership in industry–higher education partnerships: A review of theories and practices in workforce reskilling," Int. J. Learn., Teach. Educ. Res., vol. 24, no. 10, pp. 869–891, 2025.

W. Min, "Research on the Path Innovation and Evaluation System Construction of Integrating Labor Education into Student Management in Colleges and Universities in the New Era," J. Sociol. Educ., vol. 1, no. 4, 2025.

M. Murray and M. Guimaraes, "Expanding the database curriculum," J. Comput. Sci. Coll., vol. 23, no. 3, p. 69, 2008.

K. Hein, "Linked Labor Market Data: Towards a novel data housing strategy," in 2024 19th Conference on Computer Science and Intelligence Systems (FedCSIS), 2024, pp. 355–362, IEEE.

Z. Fu, "Development and application of artificial intelligence-based training system for labor education innovation ability," J. Comput. Methods Sci. Eng., 2024, doi: 10.3233/JCM-251366776.

X. Zhu, "KG-LECR-IMS: a knowledge graph-driven intelligent management system for labor education curriculum resources in universities," in *International Conference on Algorithms, Image Processing, and Deep Learning (AIPDL 2025)*, vol. 13794, 2025, pp. 477–481, SPIE.

Z. Chen, "Intelligent evaluation system for labor education quality based on data mining," in 2024 IEEE 7th Eurasian Conference on Educational Innovation (ECEI), 2024, pp. 254–257, IEEE.

A. Fischer and J. Dörpinghaus, "Web mining of online resources for german labor market research and education: Finding the ground truth?," Knowledge, vol. 4, no. 1, pp. 51–67, 2024.

S. Nahhas, O. Bamasag, M. Khemakhem, and N. Bajnaid, "Bridging education and labor skills by a novel competency-based course linked-data model," IEEE Access, vol. 7, pp. 119087–119098, 2019.

S. Davis, L. Jacobson, and S. Wandner, "US Department of Labor Employment and Training Administration Using Workforce Data Quality Initiative Databases to Develop and Improve Consumer Report Card Systems," U.S. Department of Labor, Employment and Training Administration, May 2014.

Downloads

Published

10 August 2026

Issue

Section

Article

How to Cite

Yu, W., & Li, Y. (2026). Research on Collaborative Education of Labor Education and Database System Courses. Education Insights, 3(8), 128-135. https://doi.org/10.70088/fn6sz771