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

Scott A. Crossley, Rod Roscoe, Danielle S. McNamara

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

23 Scopus citations

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 publicationArtificial Intelligence in Education - 15th International Conference, AIED 2011
Pages438-440
Number of pages3
DOIs
StatePublished - Jun 23 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)0302-9743
ISSN (Electronic)1611-3349

Other

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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