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Predicting Misalignment Between Teachers’ and Students’ Essay Scores Using Natural Language Processing Tools

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Artificial Intelligence in Education (AIED 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9112))

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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.

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References

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Author information

Authors and Affiliations

  1. Learning Sciences Institute, Arizona State University, Tempe, AZ, 85287, USA

    Laura K. Allen & Danielle S. McNamara

  2. Department of Applied Linguistics/ESL, Georgia State University, 34 Peachtree St. Suite 1200, One Park Tower Building, Atlanta, GA, 30303, USA

    Scott A. Crossley

Authors
  1. Laura K. Allen
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  2. Scott A. Crossley
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  3. Danielle S. McNamara
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Corresponding author

Correspondence to Laura K. Allen .

Editor information

Editors and Affiliations

  1. University of British Columbia, Vancouver, British Columbia, Canada

    Cristina Conati

  2. Computer Science Department, Worcester Polytechnic Institute, Worcester, Massachusetts, USA

    Neil Heffernan

  3. Department of Computer Science and Software Engineering, University of Canterbury, Christchurch, New Zealand

    Antonija Mitrovic

  4. E.T.S.I. Informática, Universidad National de Educacion a Distancia, Madrid, Spain

    M. Felisa Verdejo

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© 2015 Springer International Publishing Switzerland

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Allen, L.K., Crossley, S.A., McNamara, D.S. (2015). Predicting Misalignment Between Teachers’ and Students’ Essay Scores Using Natural Language Processing Tools. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M. (eds) Artificial Intelligence in Education. AIED 2015. Lecture Notes in Computer Science(), vol 9112. Springer, Cham. https://doi.org/10.1007/978-3-319-19773-9_54

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  • DOI: https://doi.org/10.1007/978-3-319-19773-9_54

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19772-2

  • Online ISBN: 978-3-319-19773-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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