Lexical complexity in exemplar EFL texts: towards text adaptation for 12 grades of basic English curriculum in China.
Published In: IRAL: International Review of Applied Linguistics in Language Teaching, 2024, v. 62, n. 1. P. 137 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Su, Yanfang; Liu, Kanglong; Liu, Fengkai; Lee, John; Jin, Tan 3 of 3
Abstract
Lexical complexity has been a key consideration of teaching preparation in determining grade appropriateness of teaching materials. However, the lack of quantified and defined standards for benchmarking lexical complexity has made it difficult for teachers when adapting source texts to target learners. This study has assessed quantitative differences in lexical complexity of exemplar texts at different points of schooling using a range of lexical diversity and sophistication features. The data consists of 2,372 texts from popular curriculum packages adopted from 1 to 12 grades of the English curriculum in China. One-way ANOVAs revealed significant differences in 16 out of 17 lexical complexity indices among different grades. Subsequent post hoc tests identified three lexical diversity features and four sophistication features that helped to differentiate exemplar texts across these 12 grades. These findings on the nature and role of lexical complexity have yielded new insights into the establishment of grade-level benchmarks for material preparation. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:IRAL: International Review of Applied Linguistics in Language Teaching. 2024/03, Vol. 62, Issue 1, p137
- Document Type:Article
- Subject Area:Language and Linguistics
- Publication Date:2024
- ISSN:0019-042X
- DOI:10.1515/iral-2022-0236
- Accession Number:175872617
- Copyright Statement:Copyright of IRAL: International Review of Applied Linguistics in Language Teaching is the property of De Gruyter and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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