MODELING INDIVIDUAL DIFFERENCES AMONG WRITERS USING READERBENCH

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

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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Abstract

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
Pages5269-5279
ISBN (Print)978-84-608-8860-4
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventEDULearn16: 8th International Conference on Education and New Learning Technologies - Barcelona, Spain
Duration: 4 Jul 20166 Jul 2016
https://library.iated.org/publications/EDULEARN16

Conference

ConferenceEDULearn16
Abbreviated titleEDULearn16
CountrySpain
CityBarcelona
Period4/07/166/07/16
Internet address

Fingerprint

writer
comprehension
vocabulary
student
text analysis
language
linguistics
Values

Keywords

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

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. https://doi.org/10.21125/edulearn.2016.2241
Allen, Laura ; Dascalu, Mihai ; McNamara, Danielle S. ; Crossly, Scott ; Trausan-Matu, Stefan. / MODELING INDIVIDUAL DIFFERENCES AMONG WRITERS USING READERBENCH. EDULEARN16 Proceedings: 8th International Conference on Education and New Learning Technologies. editor / L. Gómez Chova ; A. López Martínez ; I. Candel Torres. IATED Academy, 2016. pp. 5269-5279
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abstract = "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.",
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author = "Laura Allen and Mihai Dascalu and McNamara, {Danielle S.} and Scott Crossly and Stefan Trausan-Matu",
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Allen, L, Dascalu, M, McNamara, DS, 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. IATED Academy, pp. 5269-5279, EDULearn16, Barcelona, Spain, 4/07/16. https://doi.org/10.21125/edulearn.2016.2241

MODELING INDIVIDUAL DIFFERENCES AMONG WRITERS USING READERBENCH. / Allen, Laura; Dascalu, Mihai; McNamara, Danielle S.; Crossly, Scott; Trausan-Matu, Stefan.

EDULEARN16 Proceedings: 8th International Conference on Education and New Learning Technologies. ed. / L. Gómez Chova; A. López Martínez; I. Candel Torres. IATED Academy, 2016. p. 5269-5279.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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AB - 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.

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BT - EDULEARN16 Proceedings

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PB - IATED Academy

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Allen L, Dascalu M, McNamara DS, Crossly S, Trausan-Matu S. MODELING INDIVIDUAL DIFFERENCES AMONG WRITERS USING READERBENCH. In Gómez Chova L, López Martínez A, Candel Torres I, editors, EDULEARN16 Proceedings: 8th International Conference on Education and New Learning Technologies. IATED Academy. 2016. p. 5269-5279 https://doi.org/10.21125/edulearn.2016.2241