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

8 Scopus citations

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
EditorsVasile Rus, Zdravko Markov
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

Publication series

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

Other

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

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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