Incorporating Learning Characteristics into Automatic Essay Scoring Models: What Individual Differences and Linguistic Features Tell Us about Writing Quality

Autor/inn/en	Crossley, Scott A.; Allen, Laura K.; Snow, Erica L.; McNamara, Danielle S.
Titel	Incorporating Learning Characteristics into Automatic Essay Scoring Models: What Individual Differences and Linguistic Features Tell Us about Writing Quality
Quelle	In: Journal of Educational Data Mining, 8 (2016) 2, S.1-19 (19 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2157-2100
Schlagwörter	Essays; Scoring; Writing Evaluation; Natural Language Processing; Standardized Tests; Scores; Surveys; Individual Differences; Accuracy; Writing Instruction; Syntax; Vocabulary Development; Data Analysis; Researchers; Computational Linguistics; Writing Attitudes; Intelligent Tutoring Systems; Reading Comprehension; Writing Apprehension; Statistical Analysis; Correlation; High School Students; Student Attitudes; Multiple Regression Analysis; Reading Tests; Arizona; Gates MacGinitie Reading Tests + Suchen Sie Ihr Suchwort? Essay; Aufsatzunterricht; Bewertung; Natürliche Sprache; Standadised tests; Standardisierter Test; Survey; Umfrage; Befragung; Individueller Unterschied; Schreibunterricht; Wortschatzarbeit; Auswertung; Researcher; Forscher; Linguistics; Computerlinguistik; Intelligentes Tutorsystem; Leseverstehen; Statistische Analyse; Korrelation; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Schülerverhalten; Lesetest
Abstract	This study investigates a novel approach to automatically assessing essay quality that combines natural language processing approaches that assess text features with approaches that assess individual differences in writers such as demographic information, standardized test scores, and survey results. The results demonstrate that combining text features and individual differences increases the accuracy of automatically assigned essay scores over using either individual differences or text features alone. The findings presented here have important implications for writing educators because they reveal that essay scoring methods can benefit from the incorporation of features taken not only from the essay itself (e.g., features related to lexical and syntactic complexity), but also from the writer (e.g., vocabulary knowledge and writing attitudes). The findings have implications for educational data mining researchers because they demonstrate new natural language processing approaches that afford the automatic assessment of performance outcomes. (As Provided).
Anmerkungen	International Working Group on Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: http://www.educationaldatamining.org/JEDM/index.php/JEDM/index
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2022/4/11