Student Data

Student data analysis leverages machine learning to improve educational outcomes, focusing on predicting student performance and employability. Current research emphasizes developing robust models, including cluster-based approaches and federated learning, to address challenges like data scarcity, privacy concerns, and the need for generalizability across diverse institutions. This work is crucial for enhancing personalized learning, improving educational resource allocation, and ensuring ethical data handling in educational settings. The quality of data, rather than sheer quantity, is increasingly recognized as a critical factor influencing model accuracy and reliability.