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 language | English (US) |
|---|---|
| Title of host publication | FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference |
| Publisher | AAAI Press |
| Pages | 240-245 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781577357872 |
| State | Published - 2017 |
| Event | 30th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2017 - Marco Island, United States Duration: May 22 2017 → May 24 2017 |
Other
| Other | 30th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2017 |
|---|---|
| Country | United States |
| City | Marco Island |
| Period | 5/22/17 → 5/24/17 |
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ASJC Scopus subject areas
- Artificial Intelligence
- Software
Cite this
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 proceeding › Conference contribution
}
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.
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M3 - Conference contribution
AN - SCOPUS:85029483467
SP - 240
EP - 245
BT - FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference
PB - AAAI Press
ER -