Digital-intelligence convergence and a three-pronged approach: innovation and practice in the big data talent development system
Journal: Region - Educational Research and Reviews DOI: 10.32629/rerr.v8i2.5110
Abstract
Facing challenges in big data talent development—including the separation of theory and practice, superficial industry-academia collaboration, and fragmented innovation training—this study proposes principles of "industry demand orientation, integrated practical competency cultivation, and the reconstruction of the innovation ecosystem". It establishes a three-pronged training system encompassing "curriculum resources, talent development pathways, and quality assurance mechanisms". Through university-industry collaboration, this study adopts a "classroom-competition-innovation-industry" chain-based pathway, and an "AI-enabled, data-driven" quality assurance system, aligning talent development with industry needs. Over three years, it served 244 students, achieving an 87%+ employment rate, 17 national competition awards, 60% industry case studies, and a 15% retake rate reduction, providing a replicable model for application-oriented undergraduate big data programs.
Keywords
big data talent development; industry-education integration; curriculum-competition-innovation-production
Funding
This work was supported in part by the Natural Science Foundation of Inner Mongolia under Grant 2024LHMS06001; in part by the "14th Five-Year Plan" Project for Educational Science of Inner Mongolia under Grant NGJGH2024472.
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Copyright © 2026 Yunfei ZHANG, Yingdong XIE, Zhicheng LIANG, Xin GUO, Li MA, Rui XIANG, Zeyuan LIU, Ning XU
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