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        <title>Predicting Desirable Revisions of Evidence and Reasoning in Argumentative Writing</title>
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    <abstract>We develop models to classify desirable evidence and desirable reasoning revisions in student argumentative writing. We explore two ways to improve classifier performance – using the essay context of the revision, and using the feedback students received before the revision. We perform both intrinsic and extrinsic evaluation for each of our models and report a qualitative analysis. Our results show that while a model using feedback information improves over a baseline model, models utilizing context - either alone or with feedback - are the most successful in identifying desirable revisions.</abstract>
    <identifier type="citekey">afrin-litman-2023-predicting</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-eacl.193</identifier>
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        <date>2023-05</date>
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%0 Conference Proceedings
%T Predicting Desirable Revisions of Evidence and Reasoning in Argumentative Writing
%A Afrin, Tazin
%A Litman, Diane
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F afrin-litman-2023-predicting
%X We develop models to classify desirable evidence and desirable reasoning revisions in student argumentative writing. We explore two ways to improve classifier performance – using the essay context of the revision, and using the feedback students received before the revision. We perform both intrinsic and extrinsic evaluation for each of our models and report a qualitative analysis. Our results show that while a model using feedback information improves over a baseline model, models utilizing context - either alone or with feedback - are the most successful in identifying desirable revisions.
%R 10.18653/v1/2023.findings-eacl.193
%U https://aclanthology.org/2023.findings-eacl.193
%U https://doi.org/10.18653/v1/2023.findings-eacl.193
%P 2550-2561

Markdown (Informal)

[Predicting Desirable Revisions of Evidence and Reasoning in Argumentative Writing](https://aclanthology.org/2023.findings-eacl.193) (Afrin & Litman, Findings 2023)

• Predicting Desirable Revisions of Evidence and Reasoning in Argumentative Writing (Afrin & Litman, Findings 2023)

ACL

• Tazin Afrin and Diane Litman. 2023. Predicting Desirable Revisions of Evidence and Reasoning in Argumentative Writing. In Findings of the Association for Computational Linguistics: EACL 2023, pages 2550–2561, Dubrovnik, Croatia. Association for Computational Linguistics.