Loading...
Dr. Rajesh R is an Associate Professor at the Institute of Management, Kristu Jayanti (Deemed to be University), Bengaluru, Karnataka, India. His academic interests include Artificial Intelligence, educational analytics, business intelligence, data-driven decision-making, and higher education management. He actively contributes to research on intelligent educational technologies and innovative learning systems, while mentoring students in management and emerging digital technologies.
The swift integration of Artificial Intelligence (AI), Internet of Things (IoT), Learning Management Systems (LMS), Machine Learning (ML), and Big Data Analytics has revolutionised higher education by facilitating data-informed instruction, learning, and academic choices. Educational establishments consistently provide substantial amounts of data concerning student attendance, evaluations, learning patterns, online engagement, classroom involvement, and academic performance. Artificial Intelligence has arisen as a formidable technology for anticipating student achievement by recognising learning trends, projecting academic results, identifying pupils at risk, and facilitating tailored educational interventions. AI-powered prediction frameworks aid educators, administrators, and policymakers in augmenting academic achievement, diminishing dropout rates, refining curriculum development, and maximising institutional efficacy. This chapter delineates the principles, framework, machine learning methodologies, applications, obstacles, and prospective research avenues for AI-driven student performance forecasting in tertiary education.