Intelligent Learning Analytics: Data-Driven Approaches for Personalized and Adaptive Learning in Higher Education

Abstract

The increasing use of data-driven methodologies in higher education has led to significant advancements in personalized and adaptive learning, particularly through the application of Intelligent Learning Analytics. This chapter explores the integration of predictive analytics into student retention strategies, focusing on early warning systems, data sources, and machine learning techniques that can identify at-risk students. By leveraging real-time and historical data, higher education institutions are better equipped to design personalized interventions aimed at improving student success and retention. Case studies of successful early warning systems are presented, illustrating the effectiveness of data-driven approaches in fostering proactive academic support. The role of peer mentorship and tutoring programs, informed by predictive insights, was highlighted as a key strategy for enhancing student engagement and performance. The chapter emphasizes the need for institutions to balance predictive accuracy, model interpretability, and fairness in the implementation of these advanced analytics. Key insights into the future of adaptive learning environments underscore the potential of predictive analytics to revolutionize higher education, ensuring students receive the tailored support need to succeed.