ANALYSIS AND FORECASTING OF STUDENT PERFORMANCE BASED ON DIGITAL TECHNOLOGIES

Abstract

The article examines methods for analyzing student performance and forecasting academic outcomes using digital technologies. In modern education, efficiently analyzing large volumes of collected data and utilizing them to predict academic achievements is of significant importance. The study explores techniques for assessing student activity based on artificial intelligence, data visualization, statistical analysis, and machine learning algorithms. Results indicate that digital tools enable precise analysis and forecasting of both individual and group-level learning processes. Furthermore, these technologies enhance educational effectiveness, personalize the learning process, and help identify students with learning difficulties. The article highlights practical applications of integrating digital technologies into pedagogical processes.