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Neural Network Technologies for Predicting Student Learning Achievement within eLearning Environment of the HEI

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V.I. Toktarova, O.G. Kazantseva, O.V. Shashkov

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Abstract

The article actualizes the problem of modeling the learning process within eLearning environment based on the using neural networks technology to predict student learning progress. The relevance of using neural network technologies to improve the quality of the educational and pedagogical process is substantiated. The process of designing and developing a predictive model of student learning progress within eLearning environment of the HEI, implemented by an experimental study on the academic discipline "Fundamentals of Programming", is described.

General Information

Keywords: neural networks, predicting of learning achievement, educational results, eLearning environment, students, HEI

Article type: scientific article

For citation: Toktarova V.I., Kazantseva O.G., Shashkov O.V. Neural Network Technologies for Predicting Student Learning Achievement within eLearning Environment of the HEI. Digital Humanities and Technology in Education (DHTE 2022): Collection of Articles of the III All-Russian Scientific and Practical Conference with International Participation. November 17-18, 2022 / V.V. Rubtsov, M.G. Sorokova, N.P. Radchikova (Eds). Moscow: Publishing house MSUPE, 2022., pp. 388–398.

References

  1. Pasport federal'nogo proekta «Kadry dlya tsifrovoi ekonomiki» [Elektronnyi resurs] [Passport of the federal project "Personnel for the Digital Economy"]. Tsifrovaya ekonomika 2024 [Digital Economy 2024]. URL: https://digital.ac.gov.ru/poleznaya-informaciya/material/Паспорт-федерального-проекта-Кадры-для-цифровой-экономики.pdf (Accessed: 04.08.2022).
  2. Pozdneev B.M., Kabak I.S., Sukhanova N.V. Kontrol' znanij studentov na osnove nejronnyh setej [Control of students' knowledge based on neural networks]. Otkrytoe obrazovanie = Open Education, 2011, no. 6, pp.17-20. (In Russ., Abstr. in Engl.).
  3. Rusakov S.V., Rusakova O.L., Posokhina K.A. Neirosetevaya model' prognozirovaniya gruppy riska po uspevaemosti studentov pervogo kursa [A neural network model for predicting a risk group based on the progress of first-year students]. Sovremennye informatsionnye tekhnologii i IT-obrazovanie = Modern information technologies and IT education, 2018, no. 4, pp. 815-822. (In Russ., Abstr. in Engl.).
  4. Ukaz Prezidenta RF ot 09.05.2017 g. №203 «O Strategii razvitiya informatsionnogo obshchestva v Rossiiskoi Federatsii na 2017–2030 gody» [Elektronnyi resurs] [Decree of the President of the Russian Federation of May 9, 2017 No. 203 “On the Strategy for the Development of the Information Society in the Russian Federation for 2017–2030”]. Prezident Rossii [President of Russia]. URL: http://www.kremlin.ru/acts/bank/41919 (Accessed: 08.04.2022).
  5. Yasinskii I. F., Semenova M. B. Opyt prognozirovaniya uspevaemosti studentov pri pomoshchi neirosetevoi tekhnologii [The experience of students' progress forecasting using neuronet technology]. Vestnik IGEU = Vestnik of Ivanovo State Power Engineering University, 2007, no.4. pp. 29-31. (In Russ., Abstr. in Engl.).
  6. Okubo F. et al. A neural network approach for students' performance prediction. The Seventh International Learning Analytics & Knowledge Conference, 2017, pp. 598-599. doi: 10.1145/3027385.3029479
  7. Toktarova V.I. Pedagogical management of learning activities of students in the electronic educational environment of the university: a differentiated approach. International Education Studies, 2015, vol. 8, no. 5, pp. 205-212. doi:10.5539/ies.v8n5p205

Information About the Authors

Vera I. Toktarova, Doctor of Education, assistant professor, Professor of the Department of Applied Mathematics and Informatics, Rector's Advisor, Mari State University, Yoshkar-Ola, Russia, ORCID: https://orcid.org/0000-0002-3590-3053, e-mail: toktarova@yandex.ru

Olesya G. Kazantseva, graduate student, Mari State University, Yoshkar-Ola, Russia, ORCID: https://orcid.org/0000-0002-2666-1005, e-mail: olesya_popova10@mail.ru

Oleg V. Shashkov, director of the artificial intelligence center, Mari State University, Yoshkar-Ola, Russia, ORCID: https://orcid.org/0000-0002-0945-7096, e-mail: olegs@citymed12.ru

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