Literaturnachweis - Detailanzeige
| Autor/inn/en | Wan, Qian; Crossley, Scott; Banawan, Michelle; Balyan, Renu; Tian, Yu; McNamara, Danielle; Allen, Laura |
|---|---|
| Titel | Automated Claim Identification Using NLP Features in Student Argumentative Essays [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (14th, Online, Jun 29-Jul 2, 2021). |
| Quelle | (2021), (9 Seiten)
PDF als Volltext kostenfreie Datei |
| Zusatzinformation | Weitere Informationen |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Essays; Persuasive Discourse; Automation; Identification; Natural Language Processing; Documentation; Accuracy; Undergraduate Students; Computer Uses in Education |
| 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. [For the full proceedings, see ED615472.] (As Provided). |
| Anmerkungen | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/ |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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