Machine Learning-Based Strategies for Enhancing Student Achievement in Higher Education through Engagement and Support

This paper explores the application of machine learning (ML) methodologies in improving teacher support and enhancing student learning engagement within higher education settings. By harnessing advanced analytical techniques, educational institutions can analyze vast amounts of data to uncover patterns and predictive trends related to student performance and engagement. The study focuses on various ML models, such as decision trees, neural networks, and ensemble methods, which have been shown to effectively identify at-risk students, streamline administrative tasks, and personalize learning experiences. Additionally, this research highlights the integration of ML tools in developing intelligent tutoring systems and adaptive learning platforms that provide tailored feedback, guide instructional strategies, and foster collaborative learning environments. ​Empirical evidence indicates that ML-driven interventions lead to improved retention rates and academic success by facilitating timely support for students and empowering educators to address individual learning needs more effectively.​ Ultimately, this research advocates for a comprehensive strategy that leverages machine learning to transform educational practices and drive student engagement in the increasingly digital landscape of higher education.