Home → Releases → № 2 (76) →

THE USE OF A NEURAL NETWORK FOR ANALYZING AND PREDICTING ADMISSION CONTROL FIGURES IN AN EDUCATIONAL ORGANIZATION ON THE EXAMPLE OF THE USPU BRANCH IN NIZHNY TAGIL

Management of Educational Institutions in the Information Era , UDC: 378.1 DOI: 10.24412/2072-9014-2026-276-121-129

Authors

  • Kolganov Yefim Alexandrovich
  • Bolshakov Nikita I.
  • Buzhinskaya Nadezhda V. Candidate of Pedagogical Sciences, Associate Professor
  • Koksharova Elena A. Candidate of Pedagogical Sciences, Associate Professor

Annotation

The article considers an approach to the development and application of neural network models to predict the number of applicants who are interested in full-time education at the Nizhny Tagil State Socio-Pedagogical Institute (a branch of USPU in Nizhny Tagil). The article proposes a model of neural network technologies for strategic planning of admission campaigns in the context of demographic decline. The model is integrated into the workflow of the Department of Information Technology at NGSPI and is scalable for other regional universities.
Tags:
admission control figures; neural network; forecasting; admission campaign; Pandas library

How to link insert

Kolganov, Y. A., Bolshakov, N. I., Buzhinskaya, N. V. & Koksharova, E. A. (2026). THE USE OF A NEURAL NETWORK FOR ANALYZING AND PREDICTING ADMISSION CONTROL FIGURES IN AN EDUCATIONAL ORGANIZATION ON THE EXAMPLE OF THE USPU BRANCH IN NIZHNY TAGIL Bulletin of the Moscow City Pedagogical University. Series "Pedagogy and Psychology", № 2 (76), 121. https://doi.org/10.24412/2072-9014-2026-276-121-129
References
1. 1. Semenova E. V. Integration of New Educational Technologies into the System of Strategic Management of the Competitiveness of a Higher Education Institution / E. V. Semenova // Modern Problems of Pedagogy and Education: Collection of Articles of the IV International Scientific and Practical Conference. Petrozavodsk: Novaya nauka, 2025. Р. 109–115.
2. 2. Voronov A. A. University Systems for Managing the Quality of Education / A. A. Voronov, I. Ya. Lvovich, A. A. Plotnikov // Bulletin of the Voronezh Institute of High Technologies. 2020. No. 2 (33). P. 69–73.
3. 3. Grachev M. I. A Model for Improving the Efficiency of Educational Organization Management Processes Based on Solving an Inverse Problem / M. I. Grachev, V. G. Burlov // T-Comm: Telecommunications and Transport. 2025. Vol. 19. No. 9. P. 43–49. DOI: 10.36724/2072-8735-2025-19-9-43-49
4. 4. Bodunov A. Yu. Using Neural Networks to Implement a Forecasting Algorithm / A. Yu. Bodunov // Youth. Intelligence. Initiative: Proceedings of the XII International Scientific and Practical Conference of Students and Master’s Students (Vitebsk, April 26, 2024). V. 1. Vitebsk: Masherov Vitebsk State University, 2024. P. 18–19.
5. 5. Malyshev I. O. Review of Modern Generative Neural Networks: Russian and International Practice / I. O. Malyshev, A. A. Smirnov // International Journal of Humanities and Natural Sciences. 2024. No. 1-2 (88). P. 168–171.
6. 6. Barshchevsky E. G. Use of Artificial Intelligence / E. G. Barshchevsky // Eastern European Scientific Journal. 2023. No. 3-2 (88). P. 56–58.
7. 7. Udilov N. S. Neural Networks: A Historical Review of Development, Formation, and Features of Neural Networks in Comparison with Biological Networks / N. S. Udilov // Trends in the Development of Science and Education, 2024. No. 106-11. P. 119–125.
Download file .pdf 554.5 kb