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Article published In:
Graded Resources for Second and Foreign Language Learning
Edited by David Alfter and Thomas François
[ITL - International Journal of Applied Linguistics 175:1] 2024
► pp. 103–126

Profiling English sentences based on CEFR levels

ORCID logo | Kyushu University
 | Osaka University
 | Ehime University
The study aims to demonstrate the procedure for constructing the CEFR-based Sentence Profile (CEFR-SP), a dataset with the CEFR levels assigned for sentences, and to identify the characteristics at each level. Basic statistics such as word length and sentence length are presented for each CEFR level for 7,511 carefully selected sentences, and statistical tests are conducted between adjacent levels to identify criterial features. The findings reveal significant differences in word length between adjacent levels, while word difficulty is not significant in discriminating levels at either end (A1–A2, C1–C2). Sentence length and depth are also not significant discriminators for higher levels (B2–C1, C1–C2). Notably, sentence-level data generally exhibit discriminative values compared to text-level statistics, indicating their direct capture of characteristics at each CEFR level.
Keywords: CEFR, sentence-based annotation, language resource, criterial features, English education
Article outline
  • Introduction
    • Aim and research questions
    • Related work
  • Building a sentence-based corpus
    • Annotation procedure
    • Pilot study for sentence level annotation
    • Annotator selection
    • Sentence selection
    • Annotation results
  • Methodology
  • Results
    • Basic statistics
    • Comparison between sentence-level and text-level datasets
    • Keywords
    • Typical and atypical sentences
  • Discussion
  • Conclusions
  • Acknowledgements
  • Notes
  • References
Available under the Creative Commons Attribution (CC BY) 4.0 license.

For any use beyond this license, please contact the publisher at [email protected].

Published online: 22 March 2024
DOI logo
https://doi.org/10.1075/itl.22018.uch
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