A Proposed Hybrid Deep Learning and Ensemble Learning Model for Predicting Student Dropout in Education
A hybrid prediction framework that integrates deep neural representation learning with stacked ensemble architecture provides a robust and effective framework for early student dropout prediction, supporting data-driven educational interventions and informed decision-making in higher education.