KFU. Card publication. Prediction of reading difficulty in Russian academic texts

KAZAN
FEDERAL UNIVERSITY

- RUS
- 中文
- ES

PREDICTION OF READING DIFFICULTY IN RUSSIAN ACADEMIC TEXTS

Form of presentation

Articles in international journals and collections

Year of publication

2019

Язык

английский

Authors, employees of KFU

Solnyshkina Marina Ivanovna, author

Solovev Valeriy Dmitrievich, author

Bibliographic description in the original language

Solovyev Valery, Solnyshkina Marina, Ivanov Vladimir, Prediction of reading difficulty in Russian academic texts//Journal of Intelligent & Fuzzy Systems. - 2019. - Vol.36, Is.5. - P.4553-4563.

Annotation

Education policy makers view measuring academic texts readability and profiling classroom textbooks as a primary task of education management aimed at sustaining quality of reading programs. As Russian readability metrics, i.e. “objective” features of texts determining its complexity for readers, are still a research niche, we undertook a comparative analysis of academic texts features exemplified in textbooks on Social Science and examination texts of Russian as a foreign language. Experiments for 7 classifiers and 4 methods of linear regression on Russian Readability corpus demonstrated that ranking textbooks for native speakers is a much more difficult task than ranking examination texts written (or designed) for foreign students. The authors see a possible reason for this in differences between two processes: acquiring a native language on the one hand and learning a foreign language on the other. The results of the current study are extremely relevant in modern Russia which is joining the Bologna Process and needs to provide profiled texts for all types of learners and testees. Based on a qualitative and quantitative analysis of a text, the research offers a guide for education managers to help build consensus on selecting a reading material when educators have differing views.

Keywords

Text readability, machine learning, Russian academic text, text complexity, examination tests

The name of the journal

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS

URL

https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs179007

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

https://repository.kpfu.ru/eng/?p_id=203642&p_lang=2

Full metadata record

Field DC	Value	Language
dc.contributor.author	Solnyshkina Marina Ivanovna	ru_RU
dc.contributor.author	Solovev Valeriy Dmitrievich	ru_RU
dc.date.accessioned	2019-01-01T00:00:00Z	ru_RU
dc.date.available	2019-01-01T00:00:00Z	ru_RU
dc.date.issued	2019	ru_RU
dc.identifier.citation	Solovyev Valery, Solnyshkina Marina, Ivanov Vladimir, Prediction of reading difficulty in Russian academic texts//Journal of Intelligent & Fuzzy Systems. - 2019. - Vol.36, Is.5. - P.4553-4563.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=203642&p_lang=2	ru_RU
dc.description.abstract	JOURNAL OF INTELLIGENT & FUZZY SYSTEMS	ru_RU
dc.description.abstract	Education policy makers view measuring academic texts readability and profiling classroom textbooks as a primary task of education management aimed at sustaining quality of reading programs. As Russian readability metrics, i.e. “objective” features of texts determining its complexity for readers, are still a research niche, we undertook a comparative analysis of academic texts features exemplified in textbooks on Social Science and examination texts of Russian as a foreign language. Experiments for 7 classifiers and 4 methods of linear regression on Russian Readability corpus demonstrated that ranking textbooks for native speakers is a much more difficult task than ranking examination texts written (or designed) for foreign students. The authors see a possible reason for this in differences between two processes: acquiring a native language on the one hand and learning a foreign language on the other. The results of the current study are extremely relevant in modern Russia which is joining the Bologna Process and needs to provide profiled texts for all types of learners and testees. Based on a qualitative and quantitative analysis of a text, the research offers a guide for education managers to help build consensus on selecting a reading material when educators have differing views.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	Text readability	ru_RU
dc.subject	machine learning	ru_RU
dc.subject	Russian academic text	ru_RU
dc.subject	text complexity	ru_RU
dc.subject	examination tests	ru_RU
dc.title	Prediction of reading difficulty in Russian academic texts	ru_RU
dc.type	Articles in international journals and collections	ru_RU