Impact of stacking ensemble depth on generalization ability of academic performance prediction models

Abstract

The obtained results allow justifying the choice of optimal stacking ensemble depth for academic performance prediction tasks, ensuring high prediction accuracy with minimal model complexity. The developed recommendations can be applied in educational process management systems, early detection systems for at-risk students, and adaptive educational platforms. Determining optimal stacking depth allows optimizing the use of computational resources and ensuring high prediction reliability in practical applications.