Research on the Construction of a Precision Guidance Model for Collaborative Planning of College Students’ Academic and Career Driven by Big Data
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Abstract
Addressing the separation between academic management and career planning in higher education, which causes data disconnection and delayed intervention, this paper develops a big data-driven precision guidance model for collaborative academic-career planning. First, multisource data from academic affairs systems, campus behavior records, student affairs systems, and job-market platforms are integrated to construct comprehensive student profiles. Second, deep neural networks are used to mine implicit associations between academic trajectories and career competency requirements. Third, a hybrid recommendation engine combining collaborative filtering and knowledge graphs is developed to dynamically match academic warnings, course-selection suggestions, skill-development resources, internships, and career guidance. Finally, a visualization platform is designed to support tiered intervention by counselors and students. Experimental results show that the model reduces the response time of high-risk academic warnings from 12.5 days to 4.8 days, achieves a Top-5 recommendation accuracy of 81%, increases resource adoption by 72.6%, and improves student GPA by 0.31. The findings verify the model’s effectiveness in proactive intervention, academic-career alignment, and precision education support.
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