Evaluating Multiple Models for Predicting Item Difficulty in a Principled Assessment Design

Alexandra Lane Perez; Christina Schneider; Sangdon Lim; Garron Gianopulos; Kang Xue

Evaluating Multiple Models for Predicting Item Difficulty in a Principled Assessment Design

Alexandra Lane Perez, Christina Schneider, Sangdon Lim, Garron Gianopulos, Kang Xue

Correct Metadata for

Use this form to create a GitHub issue with structured data describing the correction. You will need a GitHub account. Once you create that issue, the correction will be reviewed by a staff member.

⚠️ Mobile Users: Submitting this form to create a new issue will only work with github.com, not the GitHub Mobile app.

Important: The Anthology treat PDFs as authoritative. Please use this form only to correct data that is out of line with the PDF. See our corrections guidelines if you need to change the PDF.

Title Adjust the title. Retain tags such as <fixed-case>.

Authors Adjust author names and order to match the PDF.

Abstract Correct abstract if needed. Retain XML formatting tags such as <tex-math>. You may use ... for bold, ... for italic, ... for underline, <sc>...</sc> for small-caps, <tt>...<tt> for typewriter text, <url>...</url> for URLs, <a href=...> for hyperlinks, and <par/> for paragraph breaks.

Verification against PDF Ensure that the new title/authors match the snapshot below. (If there is no snapshot or it is too small, consult the PDF.)

Authors concatenated from the text boxes above:

ALL author names match the snapshot above—including middle initials, hyphens, and accents.

Abstract

Item difficulty should, in theory, be predictable from features derived from RPLDs, task characteristics, and linguistic complexity. This study evaluates the performance of multiple statistical and machine learning models in estimating item difficulty. We found similar results across all four models, the item features selected explain 53%–56% of the variance across all grades.

Anthology ID:: 2026.aimecon-wip.20
Volume:: Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress
Month:: October
Year:: 2026
Address:: Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States
Editors:: Joshua Wilson, Christopher Ormerod, Magdalen Beiting-Parrish
Venue:: AIME-Con
SIG:
Publisher:: National Council on Measurement in Education (NCME)
Note:
Pages:: 152–158
Language:
URL:: https://aclanthology.org/2026.aimecon-wip.20/
DOI:
Bibkey:
Cite (ACL):: Alexandra Lane Perez, Christina Schneider, Sangdon Lim, Garron Gianopulos, and Kang Xue. 2026. Evaluating Multiple Models for Predicting Item Difficulty in a Principled Assessment Design. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 152–158, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).
Cite (Informal):: Evaluating Multiple Models for Predicting Item Difficulty in a Principled Assessment Design (Perez et al., AIME-Con 2026)
Copy Citation:
PDF:: https://aclanthology.org/2026.aimecon-wip.20.pdf

PDF Cite Search Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{perez-etal-2026-evaluating,
    title = "Evaluating Multiple Models for Predicting Item Difficulty in a Principled Assessment Design",
    author = "Perez, Alexandra Lane  and
      Schneider, Christina  and
      Lim, Sangdon  and
      Gianopulos, Garron  and
      Xue, Kang",
    editor = "Wilson, Joshua  and
      Ormerod, Christopher  and
      Beiting-Parrish, Magdalen",
    booktitle = "Proceedings of the Artificial Intelligence in Measurement and Education Conference ({AIME}-Con): Works in Progress",
    month = oct,
    year = "2026",
    address = "Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States",
    publisher = "National Council on Measurement in Education (NCME)",
    url = "https://aclanthology.org/2026.aimecon-wip.20/",
    pages = "152--158",
    ISBN = "979-8-9983004-1-7",
    abstract = "Item difficulty should, in theory, be predictable from features derived from RPLDs, task characteristics, and linguistic complexity. This study evaluates the performance of multiple statistical and machine learning models in estimating item difficulty. We found similar results across all four models, the item features selected explain 53{\%}{--}56{\%} of the variance across all grades."
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="perez-etal-2026-evaluating">
    <titleInfo>
        <title>Evaluating Multiple Models for Predicting Item Difficulty in a Principled Assessment Design</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Alexandra</namePart>
        <namePart type="given">Lane</namePart>
        <namePart type="family">Perez</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Christina</namePart>
        <namePart type="family">Schneider</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sangdon</namePart>
        <namePart type="family">Lim</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Garron</namePart>
        <namePart type="family">Gianopulos</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Kang</namePart>
        <namePart type="family">Xue</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-10</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress</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">979-8-9983004-1-7</identifier>
    </relatedItem>
    <abstract>Item difficulty should, in theory, be predictable from features derived from RPLDs, task characteristics, and linguistic complexity. This study evaluates the performance of multiple statistical and machine learning models in estimating item difficulty. We found similar results across all four models, the item features selected explain 53%–56% of the variance across all grades.</abstract>
    <identifier type="citekey">perez-etal-2026-evaluating</identifier>
    <location>
        <url>https://aclanthology.org/2026.aimecon-wip.20/</url>
    </location>
    <part>
        <date>2026-10</date>
        <extent unit="page">
            <start>152</start>
            <end>158</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T Evaluating Multiple Models for Predicting Item Difficulty in a Principled Assessment Design
%A Perez, Alexandra Lane
%A Schneider, Christina
%A Lim, Sangdon
%A Gianopulos, Garron
%A Xue, Kang
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting-Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress
%D 2026
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-9983004-1-7
%F perez-etal-2026-evaluating
%X Item difficulty should, in theory, be predictable from features derived from RPLDs, task characteristics, and linguistic complexity. This study evaluates the performance of multiple statistical and machine learning models in estimating item difficulty. We found similar results across all four models, the item features selected explain 53%–56% of the variance across all grades.
%U https://aclanthology.org/2026.aimecon-wip.20/
%P 152-158

Download as File

Markdown (Informal)

[Evaluating Multiple Models for Predicting Item Difficulty in a Principled Assessment Design](https://aclanthology.org/2026.aimecon-wip.20/) (Perez et al., AIME-Con 2026)

Evaluating Multiple Models for Predicting Item Difficulty in a Principled Assessment Design (Perez et al., AIME-Con 2026)

ACL

Alexandra Lane Perez, Christina Schneider, Sangdon Lim, Garron Gianopulos, and Kang Xue. 2026. Evaluating Multiple Models for Predicting Item Difficulty in a Principled Assessment Design. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 152–158, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).