NEURAL NETWORK MODEL OF PREDICTING THE RISK GROUP FOR THE ACCESSION OF STUDENTS OF THE FIRST COURSE

Abstract

Many Russian universities face the problem when applicants who successfully passed a single state examination in core disciplines become outsiders as a result of the first academic period. Especially it concerns the specialties connected with the exact sciences, including those focused on the training of IT specialists. The relationship between the amount of funding the university and the number of students, as well as the opportunity to conduct the educational process itself, is determined by the accreditation of the university, the successful passage of which is related to a number of indicators. These indicators include the minimum passing grade for the unified state exam (USE) and the percentage of students in relation to the admission control numbers. Thus, academic achievement is currently one of the most pressing problems of higher education. Identifying the most significant factors that affect the process and quality of training is an important task of many studies. It is important to be able to make a forecast about the success of the training already based on the information that people entering the first year report in their personal files. This will allow from the first days of the student's stay in the university to pay more attention from tutors and teachers to those of them who fall into the risk group. In this article, we propose a neural network model for predicting the risk group for the progress of students of the Perm State National Research University in the field of Applied Mathematics and Informatics based on the results of the introductory tests, the results of the first academic period (trimester), and a number of other factors.

Author Biographies

Сергей Владимирович Русаков, Perm State National Research University

Doctor of Physical and Mathematical Sciences, Professor, Head of the Department of Applied Mathematics and Informatics

Ольга Леонидовна Русакова, Perm State National Research University

Сandidate of Physical and Mathematical Sciences, Associate Professor of the Department of Applied Mathematics and Informatics

Кристина Андреевна Посохина, Perm State National Research University

master student

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Published
2018-12-10
How to Cite
РУСАКОВ, Сергей Владимирович; РУСАКОВА, Ольга Леонидовна; ПОСОХИНА, Кристина Андреевна. NEURAL NETWORK MODEL OF PREDICTING THE RISK GROUP FOR THE ACCESSION OF STUDENTS OF THE FIRST COURSE. Modern Information Technologies and IT-Education, [S.l.], v. 14, n. 4, p. 815-822, dec. 2018. ISSN 2411-1473. Available at: <http://sitito.cs.msu.ru/index.php/SITITO/article/view/479>. Date accessed: 10 july 2025. doi: https://doi.org/10.25559/SITITO.14.201804.815-822.
Section
IT education: methodology, methodological support