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

Laura K. Allen; Scott A. Crossley; Danielle S. McNamara

doi:10.1007/978-3-319-19773-9_54

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

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

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	Artificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings
Editors	Cristina Conati, Neil Heffernan, Antonija Mitrovic, M. Felisa Verdejo
Publisher	Springer Verlag
Pages	529-532
Number of pages	4
ISBN (Print)	9783319197722
DOIs	https://doi.org/10.1007/978-3-319-19773-9_54
State	Published - 2015
Externally published	Yes
Event	17th International Conference on Artificial Intelligence in Education, AIED 2015 - Madrid, Spain Duration: Jun 22 2015 → Jun 26 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9112
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Artificial Intelligence in Education, AIED 2015
Country/Territory	Spain
City	Madrid
Period	6/22/15 → 6/26/15

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

Keywords

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

Publisher link

10.1007/978-3-319-19773-9_54

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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 C. Conati, N. Heffernan, A. Mitrovic, & M. Felisa Verdejo (Eds.), Artificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings (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 S.
Artificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings. ed. / Cristina Conati; Neil Heffernan; Antonija Mitrovic; M. Felisa Verdejo. 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 proceeding › Conference contribution

Allen, LK, Crossley, SA & McNamara, DS 2015, Predicting misalignment between teachers’ and students’ essay scores using natural language processing tools. in C Conati, N Heffernan, A Mitrovic & M Felisa Verdejo (eds), Artificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings. 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 DS. Predicting misalignment between teachers’ and students’ essay scores using natural language processing tools. In Conati C, Heffernan N, Mitrovic A, Felisa Verdejo M, editors, Artificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings. Springer Verlag. 2015. p. 529-532. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-19773-9_54

Allen, Laura K. ; Crossley, Scott A. ; McNamara, Danielle S. / Predicting misalignment between teachers’ and students’ essay scores using natural language processing tools. Artificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings. editor / Cristina Conati ; Neil Heffernan ; Antonija Mitrovic ; M. Felisa Verdejo. 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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Predicting misalignment between teachers’ and students’ essay scores using natural language processing tools

Abstract

Publication series

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Bibliographical note

Keywords

Publisher link

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