Optimizing Enrollment and Administrative Decision-making through Data Analysis for Local Universities and Colleges - Diaz, Rex Valiña; Jain, Archieval M. (International Journal of Computing Sciences Research; Vol 9 (2025): Volume 9, 2025)
Purpose  – Local universities and colleges (LUCs) often face recurring challenges in managing enrollment and generating data-driven administrative decisions due to limited analytical tools. These inefficiencies affect institutional performance and student satisfaction. This study aimed to develop a data-driven enrollment and administrative decision-making system for Balian Community College, intended to serve as a model for other LUCs. Method  – The study employed a system development approach using the Agile methodology. Institutional data were processed using predictive and prescriptive analytics and integrated into a customized decision support system. System quality was assessed following ISO/IEC 25010 standards, while user acceptance was evaluated through the Technology Acceptance Model (TAM). Results  – System evaluation generated high ratings in software quality, with mean scores of 4.68 for functionality, 4.55 for usability, and 4.60 for efficiency. TAM results showed strong user acceptance, with perceived usefulness rated at 4.70 and perceived ease of use at 4.66. The system streamlined enrollment workflows, reduced administrative workload by around 35%, and provided actionable insights that improved decision-making and resource allocation. Conclusion  – The developed system effectively addressed key administrative challenges by integrating data analytics into institutional operations. However, its implementation was limited to a single LUC, suggesting a need for broader validation. Recommendations  – Future research should refine the system’s predictive models, integrate advanced analytics features, and test scalability across multiple LUCs to enhance robustness and interoperability. Research   Implications  – Findings demonstrate the potential of analytics-driven systems to strengthen institutional planning, resource management, and evidence-based decision-making in higher education. Practical   Implications  – Implementing data-driven platforms can substantially improve enrollment efficiency, reduce manual workload, and enhance transparency in administrative processes, thereby supporting better service delivery in LUCs. Social   Implications  – Improved institutional efficiency may lead to better student experiences and more equitable access to quality education within local communities.