Predicting Text Comprehension, Processing, and Familiarity in Adult Readers: New Approaches to Readability Formulas

Autor/inn/en	Crossley, Scott A.; Skalicky, Stephen; Dascalu, Mihai; McNamara, Danielle S.; Kyle, Kristopher
Titel	Predicting Text Comprehension, Processing, and Familiarity in Adult Readers: New Approaches to Readability Formulas
Quelle	In: Discourse Processes: A multidisciplinary journal, 54 (2017) 5-6, S.340-359 (20 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0163-853X
DOI	10.1080/0163853X.2017.1296264
Schlagwörter	Reading Processes; Reading Comprehension; Readability; Adults; Familiarity; Models; Natural Language Processing; Identification; Prediction; Comparative Analysis; Readability Formulas; Criticism; Periodicals; Computational Linguistics; Online Surveys; Reading Habits; Television Viewing; News Reporting; Statistical Analysis; Computer Software; Connected Discourse; Correlation; Multiple Regression Analysis; Text Structure; Flesch Reading Ease Formula; Dale Chall Readability Formula; Flesch Kincaid Grade Level Formula + Suchen Sie Ihr Suchwort? Leseprozess; Leseverstehen; Lesbarkeit; Analogiemodell; Natürliche Sprache; Identifikation; Identifizierung; Vorhersage; Kritik; Periodical; Journal; Zeitschrift; Fachzeitschrift; Periodikum; Linguistics; Computerlinguistik; Reading habit; Lesegewohnheit; Fernsehkonsum; News report; Reportage; Statistische Analyse; Korrelation; Textstruktur
Abstract	Research has identified a number of linguistic features that influence the reading comprehension of young readers; yet, less is known about whether and how these findings extend to adult readers. This study examines text comprehension, processing, and familiarity judgment provided by adult readers using a number of different approaches (i.e., natural language processing, crowd-sourced ratings, and machine learning). The primary focus is on the identification of the linguistic features that predict adult text readability judgments, and how these features perform when compared to traditional text readability formulas such as the Flesch-Kincaid grade level formula. The results indicate the traditional readability formulas are less predictive than models of text comprehension, processing, and familiarity derived from advanced natural language processing tools. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01