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Dr. Thomas George is a Professor in the Department of Electronics and Communication Engineering at Mount Zion College of Engineering, Kerala, India. He has extensive teaching and research experience in electronics, communication systems, AI applications, and intelligent predictive models.
Precise forecasting of student performance is crucial for enhancing learning outcomes, facilitating early interventions, and underpinning data-driven educational decision-making. This chapter examines AI-driven student performance prediction through hybrid machine learning models that integrate various techniques to improve forecast accuracy and robustness. Hybrid models can elucidate intricate linkages within varied educational datasets by incorporating approaches such as decision trees, support vector machines, neural networks, ensemble learning, and deep learning. The chapter delineates a complete framework that integrates data acquisition from learning management systems, academic records, attendance, and behavioural analytics, succeeded by preprocessing, feature engineering, and model training.