@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",
note = "Publisher Copyright: Copyright {\textcopyright} 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 30th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2017 ; Conference date: 22-05-2017 Through 24-05-2017",
year = "2017",
language = "English (US)",
series = "FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference",
publisher = "AAAI press",
pages = "240--245",
editor = "Vasile Rus and Zdravko Markov",
booktitle = "FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference",
}