Improving the quality of the university students’ academic performance prediction model
Predicting the educational success of students is one of the actual tasks of the intellectual analysis of educational data. In this article, two research issues are considered: improving the quality of the university students’ academic performance prediction model and implementation the developed model into the real university educational process. The models predicting academic performance are based on XGBoost algorithm and the linear regression algorithm. According to the results of the study, it was revealed that data on the use of electronic and university libraries make it possible to improve the quality of predicting the students’ academic performance, and also confirm the fact that monitoring the students’ academic performance in dynamics is more informative in making managerial decisions in the educational process than the absolute values of the academic performance results. The models for predicting the students’ academic performance studied in this work can be used in educational institutions of higher education for the timely identification of at-risk students, providing feedback to students and teachers regarding the educational success of students and managing the educational process.