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Journal Nonlinear World №1 for 2025 г.
Article in number:
Numerical analysis of multicomponent dynamic models of stage-by-stage knowledge assimilation
Type of article: scientific article
DOI: https://doi.org/10.18127/j20700970-202501-04
UDC: 519.6, 51-7, 52-17, 378.01
Authors:

D.D. Matorin1, A.Yu. Cherepkov2

1, 2 I.A. Bunin Yelets State University (Yelets, Russia)
1dmitr.matorin@yandex.ru 2cherepkov.andrey@mai.ru

Abstract:

The development of models that make it possible to study the dynamics of knowledge assimilation in an educational environment and evaluate the effectiveness of knowledge retention is one of the topical research tasks. In educational systems it is not always possible to optimize the learning process in such a way that knowledge is reliably retained and not forgotten in the short term. The construction, as well as analytical and numerical study of mathematical models to describe the processes of knowledge accumulation and assimilation is aimed at creating effective tools for improving the elements of learning systems and educational technologies. The aim of the study is to perform a computerized investigation of multicomponent models of step-by-step knowledge assimilation described by finite-dimensional systems of ordinary differential equations using the Python programming language. We study multicomponent models of knowledge assimilation describing transitions between different levels of knowledge strength, from quickly forgotten to the most stable. The models are specified using finite-dimensional systems of ordinary differential equations, whose parameters allow us to control the process of knowledge distribution by levels and analyze the dynamics characteristic of the knowledge assimilation process. Cases with different number of knowledge strength levels are considered, phase trajectories are visualized and solutions are analyzed for given sets of parameters. The obtained results can be used in the tasks of computer research of multicomponent dynamic models, mathematical modeling of pedagogical processes, optimization of the learning process.

Pages: 27-34
For citation

Matorin D.D., Cherepkov A.Yu. Numerical analysis of multicomponent dynamic models of stage-by-stage knowledge assimilation. Nonlinear World. 2025. V. 23. № 1. P. 27–34. DOI: https://doi.org/10.18127/ j20700970-202501-04 (In Russian)

References
  1. Petrov A.A., Druzhinina O.V., Masina O.N. Modelirovanie sistem ocenivaniya znanij v ramkah gibridnoj intellektual'noj obu­chayushchej sredy. Sovremennye informacionnye tekhnologii i IT-obrazovanie. 2021. T. 17. № 1. S. 1–14 (In Russian).
  2. Druzhinina O.V., Masina O.N., Petrov A.A. Postroenie differencial'nyh matematicheskih modelej, ispol'zuemyh pri razrabotke gibridnoj intellektual'noj obuchayushchej sredy, s uchetom zapazdyvaniya i upravlyayushchih vozdejstvij. Continuum. Matematika. Informatika. Obrazovanie. 2021. № 1(21). S. 69–80 (In Russian).
  3. Druzhinina O.V., Masina O.N., Petrov A.A. Razrabotka instrumental'nogo obespecheniya modulej gibridnoj intellektual'noj obuchayushchej sredy na osnove postroeniya nejrosetevyh i nechetkih modelej. Continuum. Matematika. Informatika. Obrazovanie. 2023. № 1(29). S. 57–69 (In Russian).
  4. Majer R.V. Zakonomernosti usvoeniya, zabyvaniya i imitacionnoe modelirovanie obucheniya. Innovacii v obrazovanii. 2017. № 5. S. 145–152 (In Russian).
  5. Majer R.V. Mnogokomponentnaya model' obucheniya i ee ispol'zovanie dlya issledovaniya didakticheskih sistem. Fundamental'nye issledovaniya: Pedagogicheskie nauki. 2013. № 10. S. 2524–2528 (In Russian).
  6. Majer R.V. Imitacionnoe modelirovanie usvoeniya i zabyvaniya osmyslennoj informacii. Uspekhi sovremennoj nauki. 2017. T. 1. № 1.S. 42–44 (In Russian).
  7. Majer R.V. Issledovanie matematicheskih modelej didakticheskih sistem na komp'yutere. Glazov: Glazov. gos. ped. in-t. 2018 (In Russian).
  8. Cherepkov A.Yu., Matorin D.D., Zajcev D.S. Instrumental'noe obespechenie interaktivnogo testirovaniya i prognosticheskogo modelirovaniya urovnya znanij studentov s primeneniem nejronnyh setej. Nelinejnyj mir. 2023. T. 21. № 4. S. 39–45 (In Russian).
  9. Cherepkov A.Yu., Druzhinina O.V. Instrumental'no-metodicheskoe obespechenie ocenivaniya i prognozirovaniya znanij v pedagogicheskom processe. Nelinejnyj mir. 2024. T. 22. № 1. S. 15–21 (In Russian).
  10. Matorin D.D., Cherepkov A.Yu., Zajcev D.S. Nejrosetevye arhitektury dlya modelirovaniya obrazovatel'nyh processov. Nejrokomp'yutery: razrabotka, primenenie. 2023. T. 25. № 1. S. 63–71 (In Russian).
  11. Oliphant T.E. Python for scientific computing. Computing in Science and Engineering. 2007. V. 9. № 3. P. 10–20.
Date of receipt: 21.01.2025
Approved after review: 04.02.2025
Accepted for publication: 26.02.2025