Are You Reading My Mind? Modeling Students' Reading Comprehension Skills with Natural Language Processing Techniques

Autor/inn/en	Allen, Laura K.; Snow, Erica L.; McNamara, Danielle S.
Titel	Are You Reading My Mind? Modeling Students' Reading Comprehension Skills with Natural Language Processing Techniques
Quelle	(2015), (9 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
DOI	10.1145/2723576.2723617
Schlagwörter	Reading Comprehension; Reading Skills; Natural Language Processing; Intelligent Tutoring Systems; Predictor Variables; Student Evaluation; Computer Assisted Instruction; Computer Uses in Education; Discourse Analysis; Linguistics; Models; High School Students; Reading Tests; Gates MacGinitie Reading Tests + Suchen Sie Ihr Suchwort? Leseverstehen; Reading skill; Lesefertigkeit; Natürliche Sprache; Intelligentes Tutorsystem; Prädiktor; Schulnote; Studentische Bewertung; Computer based training; Computerunterstützter Unterricht; Computernutzung; Diskursanalyse; Linguistik; Analogiemodell; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Lesetest
Abstract	This study builds upon previous work aimed at developing a student model of reading comprehension ability within the intelligent tutoring system, iSTART. Currently, the system evaluates students' self-explanation performance using a local, sentence-level algorithm and does not adapt content based on reading ability. The current study leverages natural language processing tools to build models of students' comprehension ability from the linguistic properties of their self-explanations. Students (n=126) interacted with iSTART across eight training sessions where they self-explained target sentences from complex science texts. Coh-Metrix was then used to calculate the linguistic properties of their aggregated self-explanations. The results of this study indicated that the linguistic indices were predictive of students' reading comprehension ability, over and above the current system algorithms. These results suggest that natural language processing techniques can inform stealth assessments and ultimately improve student models within intelligent tutoring systems. [This paper was published in "LAK '15," ISBN 978-1-4503-3417-4, March 16-20, 2015.] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01