The study of the role of artificial intelligence in the personalization of educational trajectories of students of technical universities based on the analysis of big data

Abstract

The article is devoted to a comprehensive study of the use of artificial intelligence technologies to personalize the educational trajectories of technical university students based on big data analysis. The conducted research demonstrates the significant potential of integrating data mining, machine learning and deep learning methods into the educational process to form individualized educational paths. The empirical base of the study included data from the digital footprint of 1,854 engineering students from 8 Russian technical universities in the period 2020-2023. A multicomponent educational trajectory personalization model has been developed using machine learning algorithms (Random Forest, Gradient Boosting, neural networks) to identify individual educational patterns and predict academic performance. Statistical analysis has shown that the introduction of adaptive learning systems with elements of artificial intelligence increases the average student score by 0.73 points (on a 5-point scale) and improves student retention rates in the educational program by 27% compared to traditional methods. The results of a comparative analysis of the effectiveness of various machine learning algorithms for predicting educational achievement are presented, where models based on gradient boosting demonstrated the highest accuracy (87,4%). The study also identified key predictors of academic success in technical disciplines based on digital footprint data. The pedagogical, ethical and technological aspects of the introduction of personalization systems in higher technical education, as well as the prospects for the development of adaptive educational technologies are discussed.