Abstract
The proliferation of educational technologies has created unprecedented opportunities for data-driven insights in learning environments, yet the heterogeneous nature of educational data sources presents significant harmonization challenges. This study investigates the application of supervised learning techniques for dynamic data harmonization across diverse technology-rich educational platforms. Through a mixed-methods approach involving 847 students across three institutional settings, we developed and evaluated a novel framework combining ensemble learning algorithms with adaptive feature engineering to reconcile disparate data formats, temporal inconsistencies, and semantic variations inherent in modern educational ecosystems. Our findings demonstrate that supervised learning approaches achieve 87.3% accuracy in automated data harmonization tasks, reducing manual preprocessing time by 74% while maintaining data integrity across multiple educational platforms. The research contributes to educational data mining literature by providing empirical evidence for scalable harmonization solutions and offers practical implications for institutions seeking to implement comprehensive learning analytics systems. This study addresses a significant gap in literature on educational data mining by examining how supervised learning techniques could systematically be applied to help achieve dynamic data harmonization in technology-rich educational environments. It reflects a growing need for institutions to utilize their many data assets while saving on the complexity and expense inherent in traditional harmonization means.