Determination of students' academic performance using machine learning methods

The research paper examines important aspects of using approaches to learning analytics (LA) and educational data mining (EDM), which can be used to develop educational programs and track progress. It has been shown that the integration of learning analytics into a learning management system (LMS) can improve the effectiveness of the educational process by predicting and preventing problems faced by students with regard to academic performance. The process of analyzing and predicting academic performance data using machine learning methods, including linear regression, with the determination of the ratio of academic performance in a discipline and GPA points is considered. The result of the study shows the importance of choosing the right functions to improve the accuracy of forecasting using machine learning models, and also provides input data to improve the educational program. The process of creating predictive models for assessing student academic performance by collecting and preparing data using machine learning algorithms is considered. A clear description of the use of models to predict the future academic performance of students is given, and special attention is paid to the interpretation of the results obtained to identify the main factors affecting academic performance. This work contributes to the development of the field of learning analytics and the fields of educational data production, offering practical recommendations for curriculum management and improving the quality of data-based education.