Annotation and Classification of Argumentative Writing Revisions [Konferenzbericht] Paper Presented at the Workshop on Innovative Use of NLP for Building Educational Applications (10th, NAACL Workshop, Denver, CO, Jun 4, 2015).

Autor/inn/en	Zhang, Fan; Litman, Diane
Titel	Annotation and Classification of Argumentative Writing Revisions [Konferenzbericht] Paper Presented at the Workshop on Innovative Use of NLP for Building Educational Applications (10th, NAACL Workshop, Denver, CO, Jun 4, 2015).
Quelle	(2015), (11 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Notetaking; Classification; Persuasive Discourse; Revision (Written Composition); Sentences; Essays; Reliability; Writing Strategies; Writing Evaluation; Predictor Variables; Writing Improvement; High School Students; Writing Assignments; Scores; Regression (Statistics) + Suchen Sie Ihr Suchwort? Classification system; Klassifikation; Klassifikationssystem; Persuasion; Persuasive Kommunikation; Korrektur; Sentence analysis; Satzanalyse; Essay; Aufsatzunterricht; Reliabilität; Schreibtechnik; Prädiktor; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Regression; Regressionsanalyse
Abstract	This paper explores the annotation and classification of students' revision behaviors in argumentative writing. A sentence-level revision schema is proposed to capture why and how students make revisions. Based on the proposed schema, a small corpus of student essays and revisions was annotated. Studies show that manual annotation is reliable with the schema and the annotated information helpful for revision analysis. Further-more, features and methods are explored for the automatic classification of revisions. Intrinsic evaluations demonstrate promising performance in high-level revision classification (surface vs. text-based). Extrinsic evaluations demonstrate that our method for automatic revision classification can be used to predict a writer's improvement. (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01