Proceedings of the International Scientific Conference “Digitalization of Education: History, Trends and Prospects” (DETP 2020)

Application of Big Data in Determining and Regulating Trends in Education

Authors
A.F. Galimyanov, F.M. Gafarov, A.I. Muzafarova
Corresponding Author
A.F. Galimyanov
Available Online 13 May 2020.
DOI
https://doi.org/10.2991/assehr.k.200509.121How to use a DOI?
Keywords
ABC-abilities, competencies, big data, neural networks
Abstract
The relevance of the study is determined by the need to build new methods for the study of educational activity, which is aimed at developing competencies. Competencies are considered as functions of ABC-abilities. As an example, the expression of mathematical culture is given as a vector function of ABC-abilities. It is necessary to process a large amount of data obtained as a result of such formalization to determine and regulate the trends of the educational activity. These data occupy a very large memory amount, are constantly updated both in volume and in nomenclature. These possibly unstructured data are called big data and special methods for working with them are created. This data can be useful in the further optimal design of the educational activity with proper processing. In this regard, this article is aimed at revealing the features of big data application in the educational process, substantiating the need for the use of neural networks to study and apply the hidden patterns of the educational process. It is proposed to use an interpolation polynomial to smooth the initial data.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

TY  - CONF
AU  - A.F. Galimyanov
AU  - F.M. Gafarov
AU  - A.I. Muzafarova
PY  - 2020
DA  - 2020/05/13
TI  - Application of Big Data in Determining and Regulating Trends in Education
BT  - International Scientific Conference “Digitalization of Education: History, Trends and Prospects” (DETP 2020)
PB  - Atlantis Press
SP  - 677
EP  - 680
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.200509.121
DO  - https://doi.org/10.2991/assehr.k.200509.121
ID  - Galimyanov2020
ER  -