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            <namePart type="given">Joshua</namePart>
            <namePart type="family">Wilson</namePart>
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        <name type="personal">
            <namePart type="given">Magdalen</namePart>
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    <abstract>An XGBoost prediction model was fitted to online skill practice data for a collection of 1800+ skills to construct a vertically aligned skill difficulty scale spanning kindergarten through 8th grade math. The results were validated through association with an independently derived Rasch vertical scale.</abstract>
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%0 Conference Proceedings
%T Using XGBoost to Construct a Vertically Skill Difficulty Scale
%A Bielinski, John
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting-Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers
%D 2026
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-9983004-0-0
%F bielinski-2026-using
%X An XGBoost prediction model was fitted to online skill practice data for a collection of 1800+ skills to construct a vertically aligned skill difficulty scale spanning kindergarten through 8th grade math. The results were validated through association with an independently derived Rasch vertical scale.
%U https://aclanthology.org/2026.aimecon-main.26/
%P 239-244

Markdown (Informal)

[Using XGBoost to Construct a Vertically Skill Difficulty Scale](https://aclanthology.org/2026.aimecon-main.26/) (Bielinski, AIME-Con 2026)

• Using XGBoost to Construct a Vertically Skill Difficulty Scale (Bielinski, AIME-Con 2026)

ACL

• John Bielinski. 2026. Using XGBoost to Construct a Vertically Skill Difficulty Scale. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 239–244, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).