Recurrence quantification analysis

A technique for the dynamical analysis of student writing

Laura K. Allen, Aaron D. Likens, Danielle McNamara

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

Abstract

The current study examined the degree to which the quality and characteristics of students' essays could be modeled through dynamic natural language processing analyses. Undergraduate students (n = 131) wrote timed, persuasive essays in response to an argumentative writing prompt. Recurrent patterns of the words in the essays were then analyzed using recurrence quantification analysis (RQA). Results of correlation and regression analyses revealed that the RQA indices were significantly related to the quality of students' essays, at both holistic and sub-scale levels (e.g., organization, cohesion). Additionally, these indices were able to account for between 11% and 43% of the variance in students' holistic and sub-scale essay scores. Overall, our results suggest that dynamic techniques can be used to improve natural language processing assessments of student essays.

Original languageEnglish (US)
Title of host publicationFLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference
PublisherAAAI Press
Pages240-245
Number of pages6
ISBN (Electronic)9781577357872
StatePublished - 2017
Event30th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2017 - Marco Island, United States
Duration: May 22 2017May 24 2017

Other

Other30th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2017
CountryUnited States
CityMarco Island
Period5/22/175/24/17

Fingerprint

Students
Processing

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

Cite this

Allen, L. K., Likens, A. D., & McNamara, D. (2017). Recurrence quantification analysis: A technique for the dynamical analysis of student writing. In FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference (pp. 240-245). AAAI Press.

Recurrence quantification analysis : A technique for the dynamical analysis of student writing. / Allen, Laura K.; Likens, Aaron D.; McNamara, Danielle.

FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference. AAAI Press, 2017. p. 240-245.

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

Allen, LK, Likens, AD & McNamara, D 2017, Recurrence quantification analysis: A technique for the dynamical analysis of student writing. in FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference. AAAI Press, pp. 240-245, 30th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2017, Marco Island, United States, 5/22/17.
Allen LK, Likens AD, McNamara D. Recurrence quantification analysis: A technique for the dynamical analysis of student writing. In FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference. AAAI Press. 2017. p. 240-245
Allen, Laura K. ; Likens, Aaron D. ; McNamara, Danielle. / Recurrence quantification analysis : A technique for the dynamical analysis of student writing. FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference. AAAI Press, 2017. pp. 240-245
@inproceedings{c43b9b811e5f45fc885decb78fc95c1f,
title = "Recurrence quantification analysis: A technique for the dynamical analysis of student writing",
abstract = "The current study examined the degree to which the quality and characteristics of students' essays could be modeled through dynamic natural language processing analyses. Undergraduate students (n = 131) wrote timed, persuasive essays in response to an argumentative writing prompt. Recurrent patterns of the words in the essays were then analyzed using recurrence quantification analysis (RQA). Results of correlation and regression analyses revealed that the RQA indices were significantly related to the quality of students' essays, at both holistic and sub-scale levels (e.g., organization, cohesion). Additionally, these indices were able to account for between 11{\%} and 43{\%} of the variance in students' holistic and sub-scale essay scores. Overall, our results suggest that dynamic techniques can be used to improve natural language processing assessments of student essays.",
author = "Allen, {Laura K.} and Likens, {Aaron D.} and Danielle McNamara",
year = "2017",
language = "English (US)",
pages = "240--245",
booktitle = "FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference",
publisher = "AAAI Press",

}

TY - GEN

T1 - Recurrence quantification analysis

T2 - A technique for the dynamical analysis of student writing

AU - Allen, Laura K.

AU - Likens, Aaron D.

AU - McNamara, Danielle

PY - 2017

Y1 - 2017

N2 - The current study examined the degree to which the quality and characteristics of students' essays could be modeled through dynamic natural language processing analyses. Undergraduate students (n = 131) wrote timed, persuasive essays in response to an argumentative writing prompt. Recurrent patterns of the words in the essays were then analyzed using recurrence quantification analysis (RQA). Results of correlation and regression analyses revealed that the RQA indices were significantly related to the quality of students' essays, at both holistic and sub-scale levels (e.g., organization, cohesion). Additionally, these indices were able to account for between 11% and 43% of the variance in students' holistic and sub-scale essay scores. Overall, our results suggest that dynamic techniques can be used to improve natural language processing assessments of student essays.

AB - The current study examined the degree to which the quality and characteristics of students' essays could be modeled through dynamic natural language processing analyses. Undergraduate students (n = 131) wrote timed, persuasive essays in response to an argumentative writing prompt. Recurrent patterns of the words in the essays were then analyzed using recurrence quantification analysis (RQA). Results of correlation and regression analyses revealed that the RQA indices were significantly related to the quality of students' essays, at both holistic and sub-scale levels (e.g., organization, cohesion). Additionally, these indices were able to account for between 11% and 43% of the variance in students' holistic and sub-scale essay scores. Overall, our results suggest that dynamic techniques can be used to improve natural language processing assessments of student essays.

UR - http://www.scopus.com/inward/record.url?scp=85029483467&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85029483467&partnerID=8YFLogxK

M3 - Conference contribution

SP - 240

EP - 245

BT - FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference

PB - AAAI Press

ER -