NEURAL NETWORK MODEL OF PREDICTING THE RISK GROUP FOR THE ACCESSION OF STUDENTS OF THE FIRST COURSE
Journal Title: Современные информационные технологии и ИТ-образование - Year 2018, Vol 14, Issue 4
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.
Authors and Affiliations
Sergey Rusakov, Olga Rusakova, Kristina Posokhina
Keywords
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- EP ID EP518753
- DOI 10.25559/SITITO.14.201804.815-822
- Views 107
- Downloads 0
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