Russian Federation
The article provides a cluster analysis of student academic performance using Kohonen neural networks. For clustering, data from the dean's office of one of the Voronezh universities were taken based on the results of the examination session of 4 groups of 82 students in 9 subjects. Using a multilayer perceptron, based on the performance of passing 3 exams, a forecast of the assessment of passing the 4th exam was implemented for 27 students.
student academic achievement, cluster analysis, Kohonen neural networks
1. Bezusova, T.A. Ispol'zovanie metodov matematicheskoy statistiki v pedagogicheskom issledovanii. / T.A. Bezusova // Gumanizaciya obrazovaniya. – 2018. – № 6. – S. 143 – 149. EDN: https://elibrary.ru/VUGESU
2. Vengerova, N.N. Ball'no-reytingovaya sistema ocenki uspevaemosti studentov vysshey shkoly / N.N. Vengerova, L.T. Kudashova // Zdorov'e – osnova chelovecheskogo potenciala: problemy i puti ih resheniya. – 2017. – T.12, №1. – S. 359 – 363. EDN: https://elibrary.ru/NQTVNR
3. Grachev, Yu.P. Matematicheskie metody planirovaniya eksperimenta/ Yu.P. Grachev, Yu.M. Plaksin. – M.: DeLi print, 2005. – 296 s. EDN: https://elibrary.ru/QJPJNR
4. Drozdov, I.N. Puti povyshenie effektivnosti podgotovki k professional'noy deyatel'nosti studentov s razlichnym urovnem uspevaemosti / I.N. Drozdov// Novaya nauka: Problemy i perspektivy. – 2015. – №1,(1). – S. 21 – 24. EDN: https://elibrary.ru/TXZLND
5. Portnova, A.G. Ispol'zovanie matematicheskih metodov dlya monitoringa kachestva uspevaemosti studentov / A.G. Portnova, S.L. Lesnikova, N.A. Rusakova // Vestnik Kemerovskogo gosudarstvennogo universiteta. Seriya: Gumanitarnye i obschestvennye nauki. – 2020. – T 4. – № 3,(15). – S. 218 – 226. DOI: https://doi.org/10.21603/2542-1840-2020-4-3-218-226; EDN: https://elibrary.ru/YTCGUZ
6. Hartman, K. Planirovanie eksperimenta v issledovanii tehnologicheskih processov/ K. Hartman [i dr.]. – M.: Mir, 1977. – 552 s.
7. Arapov, D. V. Avtomatizirovannaya mobil'naya sistema prinyatiya resheniy dlya ispol'zovaniya v special'noy medicine / D. V. Arapov, V. A. Kuricyn, S. A. Skorobogatov // Modelirovanie sistem i processov. – 2024. – T. 17, № 3. – S. 7-16. – DOIhttps://doi.org/10.12737/2219-0767-2024-5-13. – EDN CMPAPT.
8. Burakov, M.V. Neyronnye seti i neyrokontrollery: uchebnoe posobie. – S-Peterburg: GUAP, 2013. – 284 s. EDN: https://elibrary.ru/TTPLAF
9. Kireeva, K.A. Razrabotka iskusstvennoy neyronnoy seti dlya klassifikacii EKG/ K.A. Kireeva, L.A. Korobova, D.V. Arapov// Modelirovanie sistem i processov. – 2023. – T.16, №3. – S. 42 – 54. – DOI:https://doi.org/10.12737/2219-0767-2023-16-3-42-54. EDN PERZZQ.
10. Asadov, H. G. Modelirovanie temperaturnogo polya pri lesnyh pozharah / H. G. Asadov, G. Z. Bayramov // Modelirovanie sistem i processov. – 2024. – T. 17, № 4. – S. 7-15. – DOIhttps://doi.org/10.12737/2219-0767-2024-17-4-7-15. – EDN ECSUWP.
