KFU. Card publication. Usage of social media text topic analysis for student's academic success prediction

KAZAN
FEDERAL UNIVERSITY

- RUS
- 中文
- ES

USAGE OF SOCIAL MEDIA TEXT TOPIC ANALYSIS FOR STUDENT'S ACADEMIC SUCCESS PREDICTION

Form of presentation

Articles in Russian journals and collections

Year of publication

2020

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Authors, employees of KFU

Vakhitov Galim Zaribzyanovich, author

Prokopev Nikolay Arkadevich, author

Ustin Pavel Nikolaevich, author

Authors, students KFU

Mamadzhanova Sevara Murodovna, author

Bibliographic description in the original language

N. Prokopyev, G. Vakhitov, P. Ustin, S. Mamadjanova (2020) Usage of social media text topic analysis for student's academic success prediction, ICERI2020 Proceedings, pp. 5466-5470.

Annotation

ICERI2020 Proceedings

Keywords

natural language processing, text indexing, data analysis, topic extraction, school psychometry

The name of the journal

ICERI2020 Proceedings

URL

https://library.iated.org/view/PROKOPYEV2020USA

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

https://repository.kpfu.ru/eng/?p_id=242583&p_lang=2

Full metadata record

Field DC	Value	Language
dc.contributor.author	Vakhitov Galim Zaribzyanovich	ru_RU
dc.contributor.author	Prokopev Nikolay Arkadevich	ru_RU
dc.contributor.author	Ustin Pavel Nikolaevich	ru_RU
dc.contributor.author	Mamadzhanova Sevara Murodovna	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	N. Prokopyev, G. Vakhitov, P. Ustin, S. Mamadjanova (2020) Usage of social media text topic analysis for student's academic success prediction, ICERI2020 Proceedings, pp. 5466-5470.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=242583&p_lang=2	ru_RU
dc.description.abstract	ICERI2020 Proceedings	ru_RU
dc.description.abstract	Rapid development of information technology, mathematical methods and the possibilities of big data processing makes it possible to build and verify formal psychometric models for use in creating of software systems that can predict personal activity success. This paper was prepared within the problem framework of a project to develop a psychometric model of success, which is based on a selected set of cognitive behavioral predictors of personal activity. One task of this project is to develop an automated system for predicting the academic success of students based on data from the information-analytical system of the University and from their profiles on social networks. Some of the important sources of personalized data that can be converted into psychometric characteristics of a person are posts and reposts texts extracted from personal webpages. The paper presents the description of a software module in which classical methods of information retrieval are used to process these texts, namely: text indexing and word frequency characteristics analysis. After processing, topic extraction is applied, that is, the extraction of main topics that the student raises in his texts on social network. According to results of the study, the most typical text topics for groups of relatively successful, average and unsuccessful students were presented and identified as one of the cognitive behavioral predictors of academic success.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	natural language processing	ru_RU
dc.subject	text indexing	ru_RU
dc.subject	data analysis	ru_RU
dc.subject	topic extraction	ru_RU
dc.subject	school psychometry	ru_RU
dc.title	Usage of social media text topic analysis for student's academic success prediction	ru_RU
dc.type	Articles in Russian journals and collections	ru_RU