Exploring the innovative path of the management mechanism of applied talent training in computer science in colleges and universities based on the integration of industry and education
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https://doi.org/10.14419/15dr4033
Received date: July 30, 2025
Accepted date: October 4, 2025
Published date: October 12, 2025
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Industry-Education Integration; Applied Talent Training; Management Mechanisms; Computer Science; University-Enterprise Cooperation -
Abstract
This research explores the innovative pathways for the management mechanisms of applied talent training in computer science in universities, based on the integration of industry and education. By employing a combination of literature review, questionnaires, interviews, case studies, data analysis, field observations, policy analysis, and expert consultation, this study provides a comprehensive analysis of the status and challenges of applied talent training in computer science. The findings highlight the importance of aligning university curriculum with industry needs, strengthening practical teaching components, and enhancing university-enterprise cooperation through effective management mechanisms. The research proposes specific strategies to optimize the organizational management of university-enterprise cooperation, improve collaborative teaching models, and establish robust interest coordination and incentive mechanisms. These strategies aim to foster a more effective and sustainable model for applied talent training in computer science, ensuring that graduates are better prepared to meet the demands of the modern technology industry. The study concludes that a well-integrated industry-education model can significantly enhance the quality of applied talent training and contribute to the development of the computer science field.
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References
- Zhang, J., Zhou, Y., & Luo, X. (2022). Reconstructing the competency framework for applied computer-science talent in the era of cloud-native computing. Journal of Higher Engineering Education, 44(3), 15-28.
- Su, L., Chen, H., & Wang, M. (2023). Upstream contribution volume as a predictor of graduate employability: Evidence from CNCF projects. Computers & Education, 198, 104-118.
- Li,W., & Wu, Q. (2021). Micro-modularity for curriculum agility: A GitOps-inspired approach to computer-science education. Chinese Journal of Distance Education, 39(6), 27-35.
- Dong, Y., Liu, S., & He, J. (2025). Integrating LLM APIs into programming courses: Effects on creative problem-solving. ACM Transactions on Computing Education, 25(2), 1-20.
- Xu, R., Pan, Z., & Lin, T. (2025). Embedding SLA literacy and chaos engineering into national programme standards. Software Engineering Re-view, 42(1), 55-68.
- Huawei Cloud Education Lab. (2023). GitOps-driven syllabus pipeline: A case study on university-industry co-governance. Internal White Paper.
- Liu, C., & Xu, J. (2023). Telemetry-triggered remediation workshops: An observability approach to talent flywheels. Proceedings of the 7th Inter-national Conference on Computer Science Education Innovation, 112-119.
- National Computer Basic Education Summit. (2025). Generative-AI talent flywheel: Synthesis report. Beijing: Higher Education Press.
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How to Cite
Fan, H. ., & Abrea , D. R. R. . (2025). Exploring the innovative path of the management mechanism of applied talent training in computer science in colleges and universities based on the integration of industry and education. International Journal of Basic and Applied Sciences, 14(6), 237-241. https://doi.org/10.14419/15dr4033
