Data Engineering Pathways: Transforming Education Through Personalized Learning Analytics

Abstract

This article explores the transformative role of data engineering in educational contexts, focusing on its capacity to enable personalized learning environments. It examines the paradigm shift from traditional instructional models toward data-driven approaches that respond dynamically to individual student needs. The article details the technical foundations supporting educational data systems, including infrastructure requirements, collection methodologies, and ethical frameworks. It analyzes adaptive learning systems through the lens of computational models for student performance prediction, real-time feedback mechanisms, and successful implementation case studies. The article further shows analytics-driven personalization techniques, including pattern recognition, curriculum customization, and efficacy measurement. Finally, it discusses emerging trends in educational data engineering, highlighting integration with immersive technologies and AI tutoring systems, challenges in scaling personalized learning across diverse contexts, and critical research opportunities that will shape future developments in this rapidly evolving field.