<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="jung-etal-2025-input">
    <titleInfo>
        <title>Input Optimization for Automated Scoring in Reading Assessment</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Ji</namePart>
        <namePart type="given">Yoon</namePart>
        <namePart type="family">Jung</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ummugul</namePart>
        <namePart type="family">Bezirhan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Matthias</namePart>
        <namePart type="family">von Davier</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2025-10</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Joshua</namePart>
            <namePart type="family">Wilson</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Christopher</namePart>
            <namePart type="family">Ormerod</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Magdalen</namePart>
            <namePart type="family">Beiting Parrish</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>National Council on Measurement in Education (NCME)</publisher>
            <place>
                <placeTerm type="text">Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-218-84228-4</identifier>
    </relatedItem>
    <abstract>This study examines input optimization for enhanced efficiency in automated scoring (AS) of reading assessments, which typically involve lengthy passages and complex scoring guides. We propose optimizing input size using question-specific summaries and simplified scoring guides. Findings indicate that input optimization via compression is achievable while maintaining AS performance.</abstract>
    <identifier type="citekey">jung-etal-2025-input</identifier>
    <location>
        <url>https://aclanthology.org/2025.aimecon-main.1/</url>
    </location>
    <part>
        <date>2025-10</date>
        <extent unit="page">
            <start>1</start>
            <end>8</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Input Optimization for Automated Scoring in Reading Assessment
%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-input
%X This study examines input optimization for enhanced efficiency in automated scoring (AS) of reading assessments, which typically involve lengthy passages and complex scoring guides. We propose optimizing input size using question-specific summaries and simplified scoring guides. Findings indicate that input optimization via compression is achievable while maintaining AS performance.
%U https://aclanthology.org/2025.aimecon-main.1/
%P 1-8

Markdown (Informal)

[Input Optimization for Automated Scoring in Reading Assessment](https://aclanthology.org/2025.aimecon-main.1/) (Jung et al., AIME-Con 2025)

• Input Optimization for Automated Scoring in Reading Assessment (Jung et al., AIME-Con 2025)

ACL

• Ji Yoon Jung, Ummugul Bezirhan, and Matthias von Davier. 2025. Input Optimization for Automated Scoring in Reading Assessment. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 1–8, Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).