Hybrid machine learning techniques for improving student management and academic performance

Student management is a crucial aspect of educational institutions, encompassing activities such as admissions, academic tracking, attendance, and behavioural monitoring. Traditional management systems often lack the ability to generate actionable insights, which affects performance evaluation and decision-making. This study proposes a hybrid machine learning model that integrates supervised and unsupervised learning techniques to enhance student management. Using historical academic records, attendance logs, and behavioural data, the model predicts academic outcomes, identifies at-risk students, and suggests interventions. It employs clustering for student segmentation, predictive modelling for learning outcomes, and adaptive learning support for dynamic decision-making. Real-world data evaluations show improved accuracy and reliability over conventional approaches. The proposed model offers a scalable, intelligent solution for modern student management challenges.