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

2 Citations (Scopus)

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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages529-532
Number of pages4
Volume9112
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)03029743
ISSN (Electronic)16113349

Other

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

Fingerprint

Misalignment
Natural Language
Students
Self-assessment
Processing
Overlap
Linguistics

Keywords

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

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Allen, L. K., Crossley, S. A., & McNamara, D. (2015). Predicting misalignment between teachers’ and students’ essay scores using natural language processing tools. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9112, pp. 529-532). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9112). Springer Verlag. https://doi.org/10.1007/978-3-319-19773-9_54

Predicting misalignment between teachers’ and students’ essay scores using natural language processing tools. / Allen, Laura K.; Crossley, Scott A.; McNamara, Danielle.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9112 Springer Verlag, 2015. p. 529-532 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9112).

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

Allen, LK, Crossley, SA & McNamara, D 2015, Predicting misalignment between teachers’ and students’ essay scores using natural language processing tools. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 9112, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9112, Springer Verlag, pp. 529-532, 17th International Conference on Artificial Intelligence in Education, AIED 2015, Madrid, Spain, 6/22/15. https://doi.org/10.1007/978-3-319-19773-9_54
Allen LK, Crossley SA, McNamara D. Predicting misalignment between teachers’ and students’ essay scores using natural language processing tools. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9112. Springer Verlag. 2015. p. 529-532. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-19773-9_54
Allen, Laura K. ; Crossley, Scott A. ; McNamara, Danielle. / Predicting misalignment between teachers’ and students’ essay scores using natural language processing tools. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9112 Springer Verlag, 2015. pp. 529-532 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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