Predicting Human Scores of Essay Quality Using Computational Indices of Linguistic and Textual Features

  • Scott A. Crossley
  • Rod Roscoe
  • Danielle S. McNamara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6738)


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.


Textual Feature Word Frequency Personal Pronoun Linguistic Feature Intelligent Tutoring System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Scott A. Crossley
    • 1
  • Rod Roscoe
    • 2
  • Danielle S. McNamara
    • 2
  1. 1.Department of Applied LinguisticsGeorgia State UniversityAtlantaUSA
  2. 2.Institute for Intelligent SystemsThe University of Memphis, FedEx Institute of TechnologyMemphisUSA

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