Automated Claim Identification Using NLP Features in Student Argumentative Essays

Qian Wan, Scott Crossley, Michelle Banawan, Renu Balyan, Yu Tian, Danielle McNamara, Laura Allen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The current study explores the ability to predict argumentative claims in structurally-annotated student essays to gain insights into the role of argumentation structure in the quality of persuasive writing. Our annotation scheme specified six types of argumentative components based on the well-established Toulmin’s model of argumentation. We developed feature sets consisting of word count, frequency data of key n-grams, positionality data, and other lexical, syntactic, semantic features based on both sentential and suprasentential levels. The suprasentential Random Forest model based on frequency and positionality features yielded the best results, reporting an accuracy of 0.87 and kappa of 0.73. This model will be included in an online writing assessment tool to generate feedback for student writers.

Original languageEnglish (US)
Title of host publicationProceedings of the 14th International Conference on Educational Data Mining, EDM 2021
EditorsI-Han Hsiao, Shaghayegh Sahebi, Francois Bouchet, Jill-Jenn Vie
PublisherInternational Educational Data Mining Society
Pages375-383
Number of pages9
ISBN (Electronic)9781733673624
StatePublished - 2021
Externally publishedYes
Event14th International Conference on Educational Data Mining, EDM 2023 - Paris, France
Duration: Jun 29 2021Jul 2 2021

Publication series

NameProceedings of the 14th International Conference on Educational Data Mining, EDM 2021

Conference

Conference14th International Conference on Educational Data Mining, EDM 2023
Country/TerritoryFrance
CityParis
Period6/29/217/2/21

Bibliographical note

Publisher Copyright:
© EDM 2021.All rights reserved.

Keywords

  • Argumentation
  • Argumentative writing
  • Claim identification

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