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  • Pooya Razavi, Sonya J. Powers
    |
    Apr 9th, 2025
    |
    preprint
    Pooya Razavi, Sonya J. Powers
    Apr 9th, 2025

    Estimating item difficulty through field-testing is often resource-intensive and time-consuming. As such, there is strong motivation to develop methods that can predict item difficulty at scale using only the item content. Large Language Models (LLMs) represent a new frontier for this goal. The present research examines the feasibility of using an LLM to predict item difficulty for K-5 mathematics and reading assessment items (N = 5170). Two estimation approaches were implemented: (a) a...

Last update from database: 01/06/2025, 05:15 (UTC)
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