KFU. Card publication. QUANTITATIVE LARGE-SCALE STUDY OF SCHOOL STUDENT'S ACADEMIC PERFORMANCE PECULIARITIES DURING DISTANCE EDUCATION CAUSED BY COVID-19

- RUS
- 中文
- ES

QUANTITATIVE LARGE-SCALE STUDY OF SCHOOL STUDENT'S ACADEMIC PERFORMANCE PECULIARITIES DURING DISTANCE EDUCATION CAUSED BY COVID-19

Form of presentation

Articles in Russian journals and collections

Year of publication

2023

��

��

Authors, employees of KFU

Gafarov Fail Mubarakovich, author

Gilemzyanov Almaz Firdinantovich, author

Ustin Pavel Nikolaevich, author

Khalfieva Alisa Ramilevna, author

Yunusov Valentin Andreevich, author

Bibliographic description in the original language

Quantitative large-scale study of school student's academic performance peculiarities during distance education caused by COVID-19 / V. A. Yunusov, A. F. Gilemzyanov, F. M. Gafarov [et al.] // Proceedings of the Institute for Systems Analysis Russian Academy of Sciences. – 2023. – Vol. 73, No. 1. – P. 110-120. – DOI 10.14357/20790279230113

Annotation

PROCEEDINGS OF THE INSTITUTE FOR SYSTEMS ANALYSIS RUSSIAN ACADEMY OF SCIENCES

Keywords

DATA SCIENCE, BIG DATA, PYTHON, DASK, QUANTILE REGRESSION, CONDITIONAL QUANTILE FUNCTIONS, COVID-19

The name of the journal

PROCEEDINGS OF THE INSTITUTE FOR SYSTEMS ANALYSIS RUSSIAN ACADEMY OF SCIENCES

On-line resource for training course

http://dspace.kpfu.ru/xmlui/bitstream/handle/net/176368/F_elibrary_50740271_41699349.pdf?sequence=1&isAllowed=y

URL

https://elibrary.ru/item.asp?id=50740271

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=282895&p_lang=2

Resource files

File name	Size (MB)	Format
F_elibrary_50740271_41699349.pdf	0,52	pdf	show / download

Full metadata record

Field DC	Value	Language
dc.contributor.author	Gafarov Fail Mubarakovich	ru_RU
dc.contributor.author	Gilemzyanov Almaz Firdinantovich	ru_RU
dc.contributor.author	Ustin Pavel Nikolaevich	ru_RU
dc.contributor.author	Khalfieva Alisa Ramilevna	ru_RU
dc.contributor.author	Yunusov Valentin Andreevich	ru_RU
dc.date.accessioned	2023-01-01T00:00:00Z	ru_RU
dc.date.available	2023-01-01T00:00:00Z	ru_RU
dc.date.issued	2023	ru_RU
dc.identifier.citation	Quantitative large-scale study of school student's academic performance peculiarities during distance education caused by COVID-19 / V. A. Yunusov, A. F. Gilemzyanov, F. M. Gafarov [et al.] // Proceedings of the Institute for Systems Analysis Russian Academy of Sciences. – 2023. – Vol. 73, No. 1. – P. 110-120. – DOI 10.14357/20790279230113	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=282895&p_lang=2	ru_RU
dc.description.abstract	PROCEEDINGS OF THE INSTITUTE FOR SYSTEMS ANALYSIS RUSSIAN ACADEMY OF SCIENCES	ru_RU
dc.description.abstract	The paper presents the large-scale analysis results of the distance learning impact caused by COVID-19 and its influence on school student's academic performance. This multidisciplinary study is based on the large amount of the raw data containing school student's grades from 2015 till 2021 academic years taken from “Electronic education in Tatarstan Republic” system. The analysis is based on application of BigData and mathematical statistics methods, realized by using Python programming language. Dask framework for parallel cluster-based computation, Pandas library for data manipulation and large-scale analysis data is used. One of the main priorities of this paper is to identify the impact of different educational system's factors on school student's academic performance. For that purpose, the quantile regression method was used. This method is widely used for processing a large-scale data of various experiments in modern data science. Quantile regression models are designed to determine conditional quantile functions. Therefore, this method is especially suitable to exam conditional effects at various locations of the outcome distribution: e.g., lower and upper tails. The study-related conditional factors include such factors as student's marks from previous academic years, types of lessons in which grades were obtained, and various teacher's parameters such as age, gender and qualification category.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	DATA SCIENCE	ru_RU
dc.subject	BIG DATA	ru_RU
dc.subject	PYTHON	ru_RU
dc.subject	DASK	ru_RU
dc.subject	QUANTILE REGRESSION	ru_RU
dc.subject	CONDITIONAL QUANTILE FUNCTIONS	ru_RU
dc.subject	COVID-19	ru_RU
dc.title	QUANTITATIVE LARGE-SCALE STUDY OF SCHOOL STUDENT'S ACADEMIC PERFORMANCE PECULIARITIES DURING DISTANCE EDUCATION CAUSED BY COVID-19	ru_RU
dc.type	Articles in Russian journals and collections	ru_RU