Predicting misalignment between teachers’ and students’ essay scores using natural language processing tools

Laura K. Allen, Scott A. Crossley, Danielle McNamara

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

3 Scopus citations

Abstract

We investigated linguistic factors that relate to misalignment between students’ and teachers’ ratings of essay quality. Students (n = 126) wrote essays and rated the quality of their work. Teachers then provided their own ratings of the essays. Results revealed that students who were less accurate in their self-assessments produced essays that were more causal, contained less meaningful words, and had less argument overlap between sentences.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings
EditorsCristina Conati, Neil Heffernan, Antonija Mitrovic, M. Felisa Verdejo
PublisherSpringer Verlag
Pages529-532
Number of pages4
ISBN (Print)9783319197722
DOIs
StatePublished - 2015
Event17th International Conference on Artificial Intelligence in Education, AIED 2015 - Madrid, Spain
Duration: Jun 22 2015Jun 26 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9112
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other17th International Conference on Artificial Intelligence in Education, AIED 2015
Country/TerritorySpain
CityMadrid
Period6/22/156/26/15

Keywords

  • Cohesion
  • Computational linguistics
  • Corpus linguistics
  • Intelligent tutoring systems
  • Natural language processing
  • Writing pedagogy

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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