KFU. Card publication. Analysis of Students' Academic Performance by Using Machine Learning Tools

KAZAN
FEDERAL UNIVERSITY

- RUS
- 中文
- ES

ANALYSIS OF STUDENTS' ACADEMIC PERFORMANCE BY USING MACHINE LEARNING TOOLS

Form of presentation

Articles in Russian journals and collections

Year of publication

2020

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Authors, employees of KFU

Gafarov Fail Mubarakovich, author

Rudneva Yana Borisovna, author

Authors, students KFU

Sharifov Umar Yusufovich, author

Bibliographic description in the original language

Gafarov F.M., Rudneva Ya.B, Sharifov U.Yu., Trofimova A.V., Bormotov P.M. Analysis of Students' Academic Performance by Using Machine Learning Tools // Advances in Social Science, Education and Humanities Research. Volume 437. International Sci-entific Conference «Digital-ization of Education: Histo-ry, Trends and Prospects DETP 2020. Atlantis Press. S. 574-579.

Annotation

International Scientific Conference «Digitalization of Education: History, Trends and Prospects DETP 2020»

Keywords

academic (educational) analytics, data mining, Python, predictors, academic success, forecasting, neural networks

The name of the journal

International Scientific Conference «Digitalization of Education: History, Trends and Prospects DETP 2020»

URL

https://doi.org/10.2991/assehr.k.200509.104

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

https://repository.kpfu.ru/eng/?p_id=302345&p_lang=2

Resource files

File name	Size (MB)	Format
2020_Analiz_uspevaemosti.pdf	0,35	pdf	show / download

Full metadata record

Field DC	Value	Language
dc.contributor.author	Gafarov Fail Mubarakovich	ru_RU
dc.contributor.author	Rudneva Yana Borisovna	ru_RU
dc.contributor.author	Sharifov Umar Yusufovich	ru_RU
dc.date.accessioned	2020-01-01T00:00:00Z	ru_RU
dc.date.available	2020-01-01T00:00:00Z	ru_RU
dc.date.issued	2020	ru_RU
dc.identifier.citation	Gafarov F.M., Rudneva Ya.B, Sharifov U.Yu., Trofimova A.V., Bormotov P.M. Analysis of Students' Academic Performance by Using Machine Learning Tools // Advances in Social Science, Education and Humanities Research. Volume 437. International Sci-entific Conference «Digital-ization of Education: Histo-ry, Trends and Prospects DETP 2020. Atlantis Press. S. 574-579.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=302345&p_lang=2	ru_RU
dc.description.abstract	International Scientific Conference «Digitalization of Education: History, Trends and Prospects DETP 2020»	ru_RU
dc.description.abstract	In higher education, considerable experience has been gained in applying analytics using multidimensional databases (including retrospective ones). One of the promising areas in this area is data mining. Data mining as an interdisciplinary field of research allows creating predictive models of students' academic success. However, questions remain in the scientific community about the types and sources of data relevant for building prognostic models, about the methods of processing this data, and about the variables that determine students' academic success. The purpose of the study is to analyze, using machine learning methods and artificial neural networks, which variables affect the academic success of students. SPSS Statistics and data mining methods using the Python programming language were used to process and analyze data. The study analyzed data on student performance at Kazan Federal University from 2012 to 2019. Preliminary results showed that data mining methods have good potential for creating information-analytical systems that allow not only modeling or visualizing data, but also predicting stable trends.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	academic (educational) analytics	ru_RU
dc.subject	data mining	ru_RU
dc.subject	Python	ru_RU
dc.subject	predictors	ru_RU
dc.subject	academic success	ru_RU
dc.subject	forecasting	ru_RU
dc.subject	neural networks	ru_RU
dc.title	Analysis of Students' Academic Performance by Using Machine Learning Tools	ru_RU
dc.type	Articles in Russian journals and collections	ru_RU