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    <abstract>This study proposes an innovative method for evaluating cross-country scoring reliability (CCSR) in multilingual assessments, using hyperparameter optimization and a similarity-based weighted majority scoring within a single human scoring framework. Results show that this approach provides a cost-effective and comprehensive assessment of CCSR without the need for additional raters.</abstract>
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%0 Conference Proceedings
%T Optimizing Reliability Scoring for ILSAs
%A Jung, Ji Yoon
%A Bezirhan, Ummugul
%A von Davier, Matthias
%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 2025
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-218-84228-4
%F jung-etal-2025-optimizing
%X This study proposes an innovative method for evaluating cross-country scoring reliability (CCSR) in multilingual assessments, using hyperparameter optimization and a similarity-based weighted majority scoring within a single human scoring framework. Results show that this approach provides a cost-effective and comprehensive assessment of CCSR without the need for additional raters.
%U https://aclanthology.org/2025.aimecon-main.6/
%P 43-49

Markdown (Informal)

[Optimizing Reliability Scoring for ILSAs](https://aclanthology.org/2025.aimecon-main.6/) (Jung et al., AIME-Con 2025)

• Optimizing Reliability Scoring for ILSAs (Jung et al., AIME-Con 2025)

ACL

• Ji Yoon Jung, Ummugul Bezirhan, and Matthias von Davier. 2025. Optimizing Reliability Scoring for ILSAs. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 43–49, Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).