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  • Hotaka Maeda, Yikai Lu
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    Feb 10th, 2025
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    preprint
    Hotaka Maeda, Yikai Lu
    Feb 10th, 2025

    We fine-tuned and compared several encoder-based Transformer large language models (LLM) to predict differential item functioning (DIF) from the item text. We then applied explainable artificial intelligence (XAI) methods to these models to identify specific words associated with DIF. The data included 42,180 items designed for English language arts and mathematics summative state assessments among students in grades 3 to 11. Prediction $R^2$ ranged from .04 to .32 among eight focal and...

Last update from database: 28/10/2025, 11:15 (UTC)
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