Abstract
The article presents the results of a study to evaluate the effectiveness of applying the Gradient Boosting Machine (GBM) algorithm integrated into the Learning Management System (LMS) to enhance student performance and identify residual knowledge. The experiment was conducted among 98 third-year students, divided into control and experimental groups. The results of the final testing showed a significant improvement in performance in the experimental group compared to the control, as well as increased activity and engagement of students in the educational process. The analysis of data collected using GBM provided accurate identification of knowledge gaps, which allowed for the adaptation of educational materials to eliminate residual knowledge. The study confirms the potential of using machine learning to optimise the educational process. It emphasises the importance of further developing and integrating such technologies into the educational environment.