Cloud-Based Big Data Analytics for Academic Decision Support Systems: A Case Study of University Learning Management Systems
Year: 2025
Type: article
Abstract: This study addresses the computational limitations of legacy on-premises servers in higher education by proposing an integrated, cloud-based Big Data Analytics framework tailored for Learning Management Systems (LMS). Employing a literature-driven case study methodology, recent peer-reviewed studies from prominent academic databases were systematically extracted, evaluated, and synthesized to identify infrastructural gaps and formulate a novel conceptual model. Findings indicate that while existing analytical models achieve high predictive accuracy, they frequently falter at institutional scales due to latency bottlenecks and constrained pedagogical usability. To surmount these limitations, this study formulates a three-tiered architecture comprising a Data Ingestion Layer, a Cloud Analytics Engine Layer, and a Decision Support System (DSS) Presentation Layer. This framework efficiently offloads intensive computational workloads to scalable cloud environments while translating complex algorithmic outputs into actionable insights through an intuitive academic dashboard. Representative implementation scenarios, such as student early warning systems and curriculum difficulty evaluations, demonstrate the framework's practical utility relative to conventional paradigms. Ultimately, the proposed architecture effectively transforms static LMS platforms into proactive DSS environments. Future research should prioritize empirical prototyping with real-time institutional data alongside the integration of advanced cryptographic protocols to ensure compliance with educational data privacy regulations.
Author: Agis Abhi Rafdhi
Institution: Universitas Komputer Indonesia
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W7223129484