Research on the Application of Big Data Analytics in University Education Management

DOI: https://doi.org/10.62517/jhve.202616412

Author(s)

Yuanji Wu

Affiliation(s)

Chongqing Polytechnic University of Engineering, Chongqing, China

Abstract

Universities now generate large volumes of administrative and behavioural data, and education management is expected to make use of it rather than leave it in separate systems. This study examines how big data analytics can be applied to university education management and what conditions make such applications work. Drawing on recent empirical studies and classic analytics literature, the paper first describes the data foundations of education management, covering student behaviour records, teaching process data, and administrative transaction data. It then analyses three application scenarios in detail: student profiling and academic early warning, teaching quality assessment, and resource allocation for administrative services. Evidence from published case studies shows measurable effects, such as improved freshman education scores after a big data based management information system was introduced, and association rule mining results that support enrolment and teaching evaluation decisions. The paper further identifies four implementation conditions, including a unified data platform, explicit data governance rules, methods matched to management questions, and staff who can translate analysis results into action. It also discusses difficulties related to data silos, privacy protection, and analytical capability, and proposes corresponding countermeasures. The study concludes that big data analytics changes education management from experience-based judgement into evidence-based decision making, and that the change depends on technology, institutions, and people advancing together rather than on any single element.

Keywords

Big Data Analytics; Higher Education Management; Learning Analytics; Data-Driven Decision Making; Education Administration

References

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