<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="tran-etal-2025-using">
    <titleInfo>
        <title>Using Large Language Models to Analyze Students’ Collaborative Argumentation in Classroom Discussions</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Nhat</namePart>
        <namePart type="family">Tran</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Diane</namePart>
        <namePart type="family">Litman</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Amanda</namePart>
        <namePart type="family">Godley</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>Collaborative argumentation enables students to build disciplinary knowledge and to think in disciplinary ways. We use Large Language Models (LLMs) to improve existing methods for collaboration classification and argument identification. Results suggest that LLMs are effective for both tasks and should be considered as a strong baseline for future research.</abstract>
    <identifier type="citekey">tran-etal-2025-using</identifier>
    <location>
        <url>https://aclanthology.org/2025.aimecon-main.13/</url>
    </location>
    <part>
        <date>2025-10</date>
        <extent unit="page">
            <start>111</start>
            <end>125</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Using Large Language Models to Analyze Students’ Collaborative Argumentation in Classroom Discussions
%A Tran, Nhat
%A Litman, Diane
%A Godley, Amanda
%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 tran-etal-2025-using
%X Collaborative argumentation enables students to build disciplinary knowledge and to think in disciplinary ways. We use Large Language Models (LLMs) to improve existing methods for collaboration classification and argument identification. Results suggest that LLMs are effective for both tasks and should be considered as a strong baseline for future research.
%U https://aclanthology.org/2025.aimecon-main.13/
%P 111-125

Markdown (Informal)

[Using Large Language Models to Analyze Students’ Collaborative Argumentation in Classroom Discussions](https://aclanthology.org/2025.aimecon-main.13/) (Tran et al., AIME-Con 2025)

• Using Large Language Models to Analyze Students’ Collaborative Argumentation in Classroom Discussions (Tran et al., AIME-Con 2025)

ACL

• Nhat Tran, Diane Litman, and Amanda Godley. 2025. Using Large Language Models to Analyze Students’ Collaborative Argumentation in Classroom Discussions. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 111–125, Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).