Enhancing Pedagogical Management with Machine Learning for Smarter Education Systems

Abstract

This research examines the transformative role of machine learning (ML) in pedagogical management, emphasizing its potential to enhance educational practices and outcomes. By leveraging advanced algorithms and data analytics, ML fosters personalized learning experiences, enabling educators to tailor instruction based on individual student needs and learning styles. The integration of ML into pedagogical management facilitates the analysis of vast datasets, providing actionable insights that inform decision-making processes related to curriculum design, student engagement, and resource allocation. Moreover, ML applications can predict student outcomes, identify at-risk learners, and optimize administrative tasks, allowing educators to focus more on direct interaction with students. This study highlights various use cases of ML in educational contexts, showcasing its ability to streamline operations and improve educational equity. ​Ultimately, the research posits that the adoption of ML technologies can lead to smarter education systems, better preparing institutions to meet the diverse needs of learners in an increasingly digital landscape.​ Through a comprehensive understanding of these dynamics, this research aims to contribute to the ongoing conversation on the future of education and the integration of technology within pedagogical frameworks.