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

Scott A. Crossley, Stephen Skalicky, Mihai Dascalu, Danielle S. McNamara, Kristopher Kyle

Research output: Contribution to journalArticle

31 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)340-359
Number of pages20
JournalDiscourse Processes
Volume54
Issue number5-6
DOIs
Publication statusPublished - 2017 Jul 4

All Science Journal Classification (ASJC) codes

  • Communication
  • Language and Linguistics
  • Linguistics and Language

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