Laura Allen, Mihai Dascalu, Danielle S. McNamara, Scott Crossly, Stefan Trausan-Matu

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The current study builds upon a previous study, which examined the degree to which the lexicalproperties of students’ essays could predict their vocabulary scores. We expand on this previousresearch by incorporating new natural language processing indices related to both the surface- anddiscourse-levels of students’ essays. Additionally, we investigate the degree to which these NLPindices can be used to account for variance in students’ reading comprehension skills. We calculatedlinguistic essay features using our framework, ReaderBench, which is an automated text analysis toolsthat calculates indices related to linguistic and rhetorical features of text. University students (n = 108)produced timed (25 minutes), argumentative essays, which were then analyzed by ReaderBench.Additionally, they completed the Gates-MacGinitie Vocabulary and Reading Comprehension tests.The results of this study indicated that two indices were able to account for 32.4% of the variance invocabulary scores and 31.6% of the variance in reading comprehension scores. Follow-up analysesrevealed that these models further improved when only considering essays that contained multipleparagraph (R2 values = .61 and .49, respectively). Overall, the results of the current study suggest thatnatural language processing techniques can help to inform models of individual differences amongstudent writers.
Original languageEnglish
Title of host publicationEDULEARN16 Proceedings
Subtitle of host publication8th International Conference on Education and New Learning Technologies
EditorsL. Gómez Chova, A. López Martínez, I. Candel Torres
PublisherIATED Academy
ISBN (Print)978-84-608-8860-4
Publication statusPublished - 2016
Externally publishedYes
EventEDULearn16: 8th International Conference on Education and New Learning Technologies - Barcelona, Spain
Duration: 4 Jul 20166 Jul 2016


Abbreviated titleEDULearn16
Internet address


  • writing skills
  • automated writing evaluation
  • comprehension prediction
  • vocabulary measures
  • natural language processing

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    Cite this

    Allen, L., Dascalu, M., McNamara, D. S., Crossly, S., & Trausan-Matu, S. (2016). MODELING INDIVIDUAL DIFFERENCES AMONG WRITERS USING READERBENCH. In L. Gómez Chova, A. López Martínez, & I. Candel Torres (Eds.), EDULEARN16 Proceedings: 8th International Conference on Education and New Learning Technologies (pp. 5269-5279). IATED Academy.