Predicting human scores of essay quality using computational indices of linguistic and textual features

Scott A. Crossley, Rod Roscoe, Danielle McNamara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Citations (Scopus)

Abstract

This study assesses the potential for computational indices to predict human ratings of essay quality. The results demonstrate that linguistic indices related to type counts, given/new information, personal pronouns, word frequency, conclusion n-grams, and verb forms predict 43% of the variance in human scores of essay quality.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages438-440
Number of pages3
Volume6738 LNAI
DOIs
StatePublished - 2011
Externally publishedYes
Event15th International Conference on Artificial Intelligence in Education, AIED 2011 - Auckland, New Zealand
Duration: Jun 28 2011Jul 1 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6738 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other15th International Conference on Artificial Intelligence in Education, AIED 2011
CountryNew Zealand
CityAuckland
Period6/28/117/1/11

Fingerprint

Linguistics
Predict
N-gram
Count
Demonstrate
Human
Form

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Crossley, S. A., Roscoe, R., & McNamara, D. (2011). Predicting human scores of essay quality using computational indices of linguistic and textual features. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6738 LNAI, pp. 438-440). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6738 LNAI). https://doi.org/10.1007/978-3-642-21869-9_62

Predicting human scores of essay quality using computational indices of linguistic and textual features. / Crossley, Scott A.; Roscoe, Rod; McNamara, Danielle.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6738 LNAI 2011. p. 438-440 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6738 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Crossley, SA, Roscoe, R & McNamara, D 2011, Predicting human scores of essay quality using computational indices of linguistic and textual features. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 6738 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6738 LNAI, pp. 438-440, 15th International Conference on Artificial Intelligence in Education, AIED 2011, Auckland, New Zealand, 6/28/11. https://doi.org/10.1007/978-3-642-21869-9_62
Crossley SA, Roscoe R, McNamara D. Predicting human scores of essay quality using computational indices of linguistic and textual features. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6738 LNAI. 2011. p. 438-440. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-21869-9_62
Crossley, Scott A. ; Roscoe, Rod ; McNamara, Danielle. / Predicting human scores of essay quality using computational indices of linguistic and textual features. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6738 LNAI 2011. pp. 438-440 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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