Modeling the academic performance of students based on intelligent analysis of educational data

Abstract

Subject. The article considers the application of machine learning methods to analyze students' academic performance. Objectives. The aims are to identify factors influencing the academic performance of students, detect hidden patterns, useful and interpretable knowledge about the results of educational process and its participants, using the intellectual analysis of educational data. Methods. The study rests on methods of econometric modeling, multidimensional classification, and big data clustering. Results. The developed models of intellectual analysis of educational data enable to perform a comparative analysis of students and forecast the level of student’s mastering an educational program, depending on factors like the total score of entrance tests, average score of academic performance, basis and form of education, course, the level of training, student’s gender and age. Conclusions. The results of the application of machine learning methods to analyze academic performance will help differentiate effective teaching methods and technologies for groups of students with different levels of academic results, timely take corrective actions regarding students from risk group. Eventually, this will contribute to retention of students and improvement of the educational process quality.