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

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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., Popova 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.

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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. Popova, Master’s student at the Institute of Digital Technologies, 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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