@inproceedings{nam-2026-design,
title = "Design Choices in Encoder-Based {IRT}-3{PL} Parameter Prediction for Passage-Based Multiple-Choice Questions",
author = "Nam, Sungjin",
editor = "Wilson, Joshua and
Ormerod, Christopher and
Beiting-Parrish, Magdalen",
booktitle = "Proceedings of the Artificial Intelligence in Measurement and Education Conference ({AIME}-Con): Full Papers",
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-main.17/",
pages = "161--169",
ISBN = "979-8-9983004-0-0",
abstract = "We examine model design choices for encoder-based prediction of IRT-3PL item parameters in reading comprehension tasks with passage-based multiple-choice questions (MCQs). We compare three aspects: (1) encoding strategies for passage and question texts, including optional cross-attention, (2) per-parameter vs. joint-parameter prediction with MSE and CRPS losses, and (3) random vs. passage-grouped batching. Results show that separate-then-fuse encoding is substantially more efficient than concatenated-input encoding while maintaining comparable parameter recovery. Cross-attention improves recovery for discrimination and guessing but weakens difficulty recovery. Joint-parameter prediction achieves performance close to per-parameter prediction in the strongest configurations while reducing training time. CRPS is most useful for joint prediction, and passage-grouped batching consistently improves recovery of the guessing parameter. Overall, joint prediction with CRPS and passage-grouped batching provides the most balanced performance while maintaining the efficiency of separate-then-fuse encoding without cross-attention, showing that the proposed model design can support efficient IRT parameter prediction for passage-based MCQs."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="nam-2026-design">
<titleInfo>
<title>Design Choices in Encoder-Based IRT-3PL Parameter Prediction for Passage-Based Multiple-Choice Questions</title>
</titleInfo>
<name type="personal">
<namePart type="given">Sungjin</namePart>
<namePart type="family">Nam</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): 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">979-8-9983004-0-0</identifier>
</relatedItem>
<abstract>We examine model design choices for encoder-based prediction of IRT-3PL item parameters in reading comprehension tasks with passage-based multiple-choice questions (MCQs). We compare three aspects: (1) encoding strategies for passage and question texts, including optional cross-attention, (2) per-parameter vs. joint-parameter prediction with MSE and CRPS losses, and (3) random vs. passage-grouped batching. Results show that separate-then-fuse encoding is substantially more efficient than concatenated-input encoding while maintaining comparable parameter recovery. Cross-attention improves recovery for discrimination and guessing but weakens difficulty recovery. Joint-parameter prediction achieves performance close to per-parameter prediction in the strongest configurations while reducing training time. CRPS is most useful for joint prediction, and passage-grouped batching consistently improves recovery of the guessing parameter. Overall, joint prediction with CRPS and passage-grouped batching provides the most balanced performance while maintaining the efficiency of separate-then-fuse encoding without cross-attention, showing that the proposed model design can support efficient IRT parameter prediction for passage-based MCQs.</abstract>
<identifier type="citekey">nam-2026-design</identifier>
<location>
<url>https://aclanthology.org/2026.aimecon-main.17/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>161</start>
<end>169</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Design Choices in Encoder-Based IRT-3PL Parameter Prediction for Passage-Based Multiple-Choice Questions
%A Nam, Sungjin
%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 nam-2026-design
%X We examine model design choices for encoder-based prediction of IRT-3PL item parameters in reading comprehension tasks with passage-based multiple-choice questions (MCQs). We compare three aspects: (1) encoding strategies for passage and question texts, including optional cross-attention, (2) per-parameter vs. joint-parameter prediction with MSE and CRPS losses, and (3) random vs. passage-grouped batching. Results show that separate-then-fuse encoding is substantially more efficient than concatenated-input encoding while maintaining comparable parameter recovery. Cross-attention improves recovery for discrimination and guessing but weakens difficulty recovery. Joint-parameter prediction achieves performance close to per-parameter prediction in the strongest configurations while reducing training time. CRPS is most useful for joint prediction, and passage-grouped batching consistently improves recovery of the guessing parameter. Overall, joint prediction with CRPS and passage-grouped batching provides the most balanced performance while maintaining the efficiency of separate-then-fuse encoding without cross-attention, showing that the proposed model design can support efficient IRT parameter prediction for passage-based MCQs.
%U https://aclanthology.org/2026.aimecon-main.17/
%P 161-169
Markdown (Informal)
[Design Choices in Encoder-Based IRT-3PL Parameter Prediction for Passage-Based Multiple-Choice Questions](https://aclanthology.org/2026.aimecon-main.17/) (Nam, AIME-Con 2026)
ACL