Capstone Model for Retention Forecasting Using Business Intelligence Dashboards in Graduate Programs

Graduate student retention remains a critical challenge in higher education, impacting institutional reputation, funding, and student outcomes. This review explores the development and application of a capstone model for retention forecasting using business intelligence (BI) dashboards, aimed at enabling data-informed decisions by academic administrators. The paper evaluates how BI tools can integrate historical enrollment data, student engagement metrics, financial aid trends, and demographic information to identify at-risk students and forecast dropout probabilities. Emphasis is placed on interactive dashboards powered by predictive analytics and visual storytelling techniques that provide stakeholders with real-time insights into retention trends. By reviewing literature across educational data mining, decision-support systems, and dashboard design principles, this study offers a comprehensive framework for deploying BI-driven retention forecasting systems. The review also addresses implementation challenges, including data quality, privacy concerns, and faculty adoption, and proposes recommendations for designing scalable and adaptive retention models aligned with institutional goals. The overarching aim is to highlight how BI dashboards can transform student success strategies, personalize interventions, and improve institutional resilience in an increasingly data-centric educational environment.