| Форма представления | Статьи в российских журналах и сборниках |
| Год публикации | 2020 |
| Язык | английский |
|
Гафаров Фаиль Мубаракович, автор
Руднева Яна Борисовна, автор
|
|
Шарифов Умар Юсуфович, автор
|
| Библиографическое описание на языке оригинала |
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. |
| Аннотация |
International Scientific Conference «Digitalization of Education: History, Trends and Prospects DETP 2020» |
| Ключевые слова |
academic (educational) analytics, data mining, Python, predictors, academic success, forecasting,
neural networks |
| Название журнала |
International Scientific Conference «Digitalization of Education: History, Trends and Prospects DETP 2020»
|
| URL |
https://doi.org/10.2991/assehr.k.200509.104 |
| Пожалуйста, используйте этот идентификатор, чтобы цитировать или ссылаться на эту карточку |
https://repository.kpfu.ru/?p_id=302345 |
| Файлы ресурса | |
|
|
Полная запись метаданных  |
| Поле DC |
Значение |
Язык |
| dc.contributor.author |
Гафаров Фаиль Мубаракович |
ru_RU |
| dc.contributor.author |
Руднева Яна Борисовна |
ru_RU |
| dc.contributor.author |
Шарифов Умар Юсуфович |
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/?p_id=302345 |
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 |
Статьи в российских журналах и сборниках |
ru_RU |
|
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