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Aligning linguistic complexity with the difficulty of English texts for L2 learners based on CEFR levels

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The model achieved 62.6% exact-level accuracy across six CEFR levels, improving text classification for L2 learners.
Using 24 linguistic complexity features, the study trained a model outperforming baseline readability formulas in text classification.
Expert educators classified 1,181 texts from the CommonLit corpus into CEFR levels to enhance L2 learners' reading experiences.
The findings emphasize the need for tailored text classification models reflecting diverse linguistic profiles among learners.

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This study employs linguistic complexity to classify texts for L2 learners, indicating improved text selection.

Zhang et al. (2025) studied this question.

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