Design and implementation of an educational data warehouse with advanced mining techniques for data-driven decision making: A Comprehensive ReviewECISION MAKING: A COMPREHENSIVE REVIEW
DOI:
https://doi.org/10.22399/ijcesen.5190Keywords:
Design educational data , implementation educational data , advanced mining techniques, data-driven decision makingAbstract
In the contemporary educational landscape, the exponential growth of data from Learning Management Systems (LMS), student information systems, and assessment portals presents both a challenge and an opportunity. This review paper critically examines the integration of Educational Data Warehousing (EDW) with Educational Data Mining (EDM) to foster data-driven decision-making in higher education. We explore the architectural requirements for a scalable EDW, focusing on hybrid cloud infrastructures that ensure security and accessibility. Furthermore, the review analyzes the efficacy of advanced data mining techniques—specifically clustering, classification, and predictive modeling using algorithms like Decision Trees, Neural Networks, and Naïve Bayes—in identifying at-risk students and optimizing curriculum effectiveness. By synthesizing findings from recent high-impact literature (2021–2025), we identify a critical gap in the real-time processing capabilities of current systems and propose a unified framework that couples robust warehousing with intelligent AI-driven dashboards. This research contributes to the strategic alignment of institutional goals with student outcomes, offering a roadmap for administrators to implement precision education.
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