Predictive and Prescriptive Analytics in Education: A Deep Dive into Machine Learning and Deep Learning Applications

The increasing availability of educational data has paved the way for advanced data-driven approaches to enhance learning outcomes and decision-making. This paper explores the role of Predictive and Prescriptive Analytics in education by leveraging Machine Learning (ML) and Deep Learning (DL) techniques. Predictive analytics helps forecast student performance, dropout risks, and learning behaviors, while prescriptive analytics provides actionable recommendations to improve educational strategies and interventions. We analyze various ML and DL models used in Education Data Mining (EDM), such as decision trees, support vector machines, neural networks, and transformer-based architectures, highlighting their effectiveness in adaptive learning and personalized education. The paper also presents challenges, ethical concerns, and future research directions in applying advanced AI techniques to education. This study aims to provide valuable insights into how intelligent analytics can optimize teaching methodologies, student engagement, and institutional policies, ultimately driving innovation in education.