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Economic Analysis: Theory and Practice
 

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

Vol. 22, Iss. 2, FEBRUARY 2023

Received: 13 December 2022

Received in revised form: 24 December 2022

Accepted: 9 February 2023

Available online: 28 February 2023

Subject Heading: ANALYSIS OF INTELLECTUAL CAPITAL

JEL Classification: C01, C38, C51, C53, C55

Pages: 235253

https://doi.org/10.24891/ea.22.2.235

Viktoriya V. BOBROVA Orenburg State University, Orenburg, Russian Federation
bobrova1971@mail.ru

https://orcid.org/0000-0003-3558-5662

Ol'ga I. BANTIKOVA Russian Biotechnological University (BIOTECH University), Moscow, Russian Federation
bantikova777@mail.ru

https://orcid.org/0000-0002-0577-6276

Vlada A. NOVIKOVA MIREA Russian Technological University, Moscow, Russian Federation
vlanovickova@gmail.com

ORCID id: not available

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 students 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, students 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.

Keywords: educational data, data mining, academic performance, modeling, machine learning

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