KFU. Card publication. Text complexity predictors: methods and approaches for assessment

KAZAN
FEDERAL UNIVERSITY

- RUS
- 中文
- ES

TEXT COMPLEXITY PREDICTORS: METHODS AND APPROACHES FOR ASSESSMENT

Form of presentation

Articles in Russian journals and collections

Year of publication

2023

Язык

русский

Authors, employees of KFU

Gatiyatullina Galiya Maratovna, author

Ziganshina Chulpan Rifovna, author

Kupriyanov Roman Vladimirovich, author

Solnyshkina Marina Ivanovna, author

Bibliographic description in the original language

Gatiyatullina G. M. Lexical density as a complexity predictor: the case of Science and Social Studies textbooks, Research Result / G. M. Gatiyatullina, M. I. Solnyshkina, R. V. Kupriyanov, C. R. Ziganshina // Theoretical and Applied Linguistics. - 2023. Is. 9 (1). - P. 11-26. DOI: 10.18413/2313-8912-2023-9-1-0-2

Annotation

Научный результат. Вопросы теоретической и прикладной лингвистики

Keywords

Lexical density; Readability; Text complexity; Textbooks; Science; Social studies

The name of the journal

Научный результат. Вопросы теоретической и прикладной лингвистики

On-line resource for training course

http://dspace.kpfu.ru/xmlui/bitstream/handle/net/175725/F_Lingvistika_9_1_2023_11_26.pdf?sequence=1&isAllowed=y

URL

http://rrlinguistics.ru/media/linguistics/2023/1/%D0%9B%D0%B8%D0%BD%D0%B3%D0%B2%D0%B8%D1%81%D1%82%D0%B8%D0%BA%D0%B0_9_1_2023-11-26.pdf

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

https://repository.kpfu.ru/eng/?p_id=279162&p_lang=2

Resource files

File name	Size (MB)	Format
F_Lingvistika_9_1_2023_11_26.pdf	0,71	pdf	show / download

Full metadata record

Field DC	Value	Language
dc.contributor.author	Gatiyatullina Galiya Maratovna	ru_RU
dc.contributor.author	Ziganshina Chulpan Rifovna	ru_RU
dc.contributor.author	Kupriyanov Roman Vladimirovich	ru_RU
dc.contributor.author	Solnyshkina Marina Ivanovna	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	Gatiyatullina G. M. Lexical density as a complexity predictor: the case of Science and Social Studies textbooks, Research Result / G. M. Gatiyatullina, M. I. Solnyshkina, R. V. Kupriyanov, C. R. Ziganshina // Theoretical and Applied Linguistics. - 2023. Is. 9 (1). - P. 11-26. DOI: 10.18413/2313-8912-2023-9-1-0-2	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=279162&p_lang=2	ru_RU
dc.description.abstract	Научный результат. Вопросы теоретической и прикладной лингвистики	ru_RU
dc.description.abstract	. An ever-increasing need for quality textbooks and objective linguistic expertise encourages more intensive research into complexity of academic discourse. The current research focuses on lexical density viewed as an effective complexity predictor and defined as the ratio of content words per number of words in a text. Being predominantly quantitative, the study also examines dynamics of Flesh Kincaid grade levels and ratios of parts of speech across 12 Science and Social Studies textbooks taught in Grades 7 – 12 of American schools. The analysis shows a consistent pattern of strong positive growth of nouns and adjectives across grade levels, while lexical verbal elements slightly decrease across the textbooks. The total adverb count changes slightly, and its movement vector depends on the discourse: it rises in Social Studies textbooks and is stable in Science textbooks. This multidirectional movement of components in Lexical density structure explains its marginal increase across the grades in Science and Social Studies discourse. The findings indicate discourse sophistication increase realized predominantly in text nominalization. We also discuss challenges which nominalization presents for comprehension of academic texts by readers and suggest that provided with reference values of text complexity features, educators receive a reliable tool to select reading texts and assess their suitability for target learner groups. The findings can be beneficial for textbooks authors, exam material developers and discourse researchers.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject		ru_RU
dc.title	Text complexity predictors: methods and approaches for assessment	ru_RU
dc.type	Articles in Russian journals and collections	ru_RU