MATHEMATICAL MODELING OF LEARNING PROCESSES AND PREDICTION OF STUDENTS SUCCESS

Abstract

This review explores key modeling approaches, including regression analysis, cluster analysis, neural networks, Bayesian networks, and machine learning methods. These techniques help educators assess student performance, identify academic risks, and implement tailored interventions. Predictive models use factors such as prior grades, study time, and engagement in digital learning systems. Machine learning algorithms, including decision trees and neural networks, enhance forecast accuracy by adapting to new data. While mathematical models provide valuable insights, challenges such as incomplete data, emotional factors, and overfitting can affect reliability.