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

Find It

Find It at U of M Libraries

Cite this

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)).

@inproceedings{adfae255491d479da028a896c521fa9d,

title = "Predicting misalignment between teachers{\textquoteright} and students{\textquoteright} essay scores using natural language processing tools",

abstract = "We investigated linguistic factors that relate to misalignment between students{\textquoteright} and teachers{\textquoteright} 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.",

keywords = "Cohesion, Computational linguistics, Corpus linguistics, Intelligent tutoring systems, Natural language processing, Writing pedagogy",

author = "Allen, {Laura K.} and Crossley, {Scott A.} and McNamara, {Danielle S.}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 17th International Conference on Artificial Intelligence in Education, AIED 2015 ; Conference date: 22-06-2015 Through 26-06-2015",

year = "2015",

doi = "10.1007/978-3-319-19773-9_54",

language = "English (US)",

isbn = "9783319197722",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "529--532",

editor = "Cristina Conati and Neil Heffernan and Antonija Mitrovic and {Felisa Verdejo}, M.",

booktitle = "Artificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings",

}

TY - GEN

T1 - Predicting misalignment between teachers’ and students’ essay scores using natural language processing tools

AU - Allen, Laura K.

AU - Crossley, Scott A.

AU - McNamara, Danielle S.

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

KW - Cohesion

KW - Computational linguistics

KW - Corpus linguistics

KW - Intelligent tutoring systems

KW - Natural language processing

KW - Writing pedagogy

UR - http://www.scopus.com/inward/record.url?scp=84949009876&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84949009876&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-19773-9_54

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

M3 - Conference contribution

AN - SCOPUS:84949009876

SN - 9783319197722

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 529

EP - 532

BT - Artificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings

A2 - Conati, Cristina

A2 - Heffernan, Neil

A2 - Mitrovic, Antonija

A2 - Felisa Verdejo, M.

PB - Springer Verlag

T2 - 17th International Conference on Artificial Intelligence in Education, AIED 2015

Y2 - 22 June 2015 through 26 June 2015

ER -

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

Publisher link

Find It

Other files and links

Fingerprint

Cite this