Big-data-driven approaches to enhancing the precision of student management in higher education
Dan Lu
Baoji University of Arts and Sciences
DOI: https://doi.org/10.59429/pmcs.v7i4.12800
Keywords: big data; student management; higher education; data-driven decision-making; precision governance
Abstract
This study explores how big-data technologies can enhance the precision, efffciency, and scientiffc rigor of student management in higher education. Traditional management approaches often rely on fragmented information and static processes, leading to limited predictive capability and insufffcient personalized support. By introducing big-data-driven frameworks, universities can integrate multisource data, build dynamic student proffles, improve early-warning systems, and optimize decision-making processes. This paper adopts a conceptual and analytical research method, drawing on existing big-data practices in education, and constructs a multi-dimensional precision-management model. The results indicate that big-data-enhanced student management can reffne service delivery, strengthen risk control, and boost institutional governance capacity. The study concludes with recommendations for data infrastructure construction, management workffow redesign, privacy-protection mechanisms, and future development directions in higher education.
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