Assessing Students' Use of Evidence and Organization in Response-to-Text Writing: Using Natural Language Processing for Rubric-Based Automated Scoring

Autor/inn/en	Rahimi, Zahra; Litman, Diane; Correnti, Richard; Wang, Elaine; Matsumura, Lindsay Clare
Titel	Assessing Students' Use of Evidence and Organization in Response-to-Text Writing: Using Natural Language Processing for Rubric-Based Automated Scoring
Quelle	In: International Journal of Artificial Intelligence in Education, 27 (2017) 4, S.694-728 (35 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1560-4292
DOI	10.1007/s40593-017-0143-2
Schlagwörter	Scoring; Automation; Scoring Rubrics; Natural Language Processing; Evidence; Models; Essays; Grade 5; Grade 6; Grade 7; Grade 8; Predictive Validity; Writing Evaluation; Organization; Writing (Composition) + Suchen Sie Ihr Suchwort? Bewertung; Scoring formulas; Auswertungsbogen; Natürliche Sprache; Evidenz; Analogiemodell; Essay; Aufsatzunterricht; School year 05; 5. Schuljahr; Schuljahr 05; School year 06; 6. Schuljahr; Schuljahr 06; School year 07; 7. Schuljahr; Schuljahr 07; School year 08; 8. Schuljahr; Schuljahr 08; Organisation; Organisationsstruktur; Schreibübung
Abstract	This paper presents an investigation of score prediction based on natural language processing for two targeted constructs within analytic text-based writing: 1) students' effective use of evidence and, 2) their organization of ideas and evidence in support of their claim. With the long-term goal of producing feedback for students and teachers, we designed a task-dependent model, for each dimension, that aligns with the scoring rubric and makes use of the source material. We believe the model will be meaningful and easy to interpret given the writing task. We used two datasets of essays written by students in grades 5-6 and 6-8. Our experimental results show that our task-dependent model (consistent with the rubric) performs as well as if not outperforms competitive baselines. We also show the potential generalizability of the rubric-based model by performing cross-corpus experiments. Finally, we show that the predictive utility of different feature groups in our rubric-based modeling approach is related to how much each feature group covers a rubric's criteria. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01