The period 2002–2008 solidified a practical and theoretical foundation for applying data mining techniques to higher education, emphasizing predictive models, descriptive dashboards, and data-driven interventions to improve student outcomes, retention, and policy decisions. Methodologically, researchers employed classification, clustering, and regression-like analytics on institutional data, often pairing results with dashboards to support decision-making and resource planning. Cross-domain exploration with K-12 settings demonstrated the transferability of predictive analytics to school-level planning and performance forecasting.