Machine learning to identify key success indicators
Vladimir Nelyub, A. R. Glinscaya, В В Кукарцев, A.I. Borodulin, Dmitry Evsyukov
Abstract
This article explores the application of machine learning techniques in the context of identifying and analyzing key indicators of learner success. In particular, the paper focuses on the application of machine learning techniques such as decision trees, Kohonen maps and neural networks. Decision trees are a graphical model that helps to analyze and make decisions based on hierarchical data structure. They allow classification and regression analysis, which helps in highlighting optimal strategies and recommendations to improve learner success. Kohonen map are used to highlight key success indicators, find hidden patterns and group data. Neural networks are able to analyze complex relationships and predict outcomes based on input data. The selected machine learning methods allow to optimize the learning process, adapt teaching methods to individual needs and increase the effectiveness of education in general.