Using automatic scoring models to detect changes in student writing in an intelligent tutoring system

Scott A. Crossley, Rod Roscoe, Danielle McNamara

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

22 Citations (Scopus)

Abstract

This study compares automated scoring increases and linguistic changes for student writers in two groups: a group that used an intelligent tutoring system embedded with an automated writing evaluation component (Writing Pal) and a group that used only the automated writing evaluation component. The primary goal is to examine automated scoring differences in both groups from pretest to posttest essays to investigate score gains and linguistic development. The study finds that both groups show significant increases in automated writing scores and significant development in lexical, syntactic, cohesion, and rhetorical features. However, the Writing-Pal group shows greater raw frequency gains (i.e., negative v. positive gains).

Original languageEnglish (US)
Title of host publicationFLAIRS 2013 - Proceedings of the 26th International Florida Artificial Intelligence Research Society Conference
Pages208-213
Number of pages6
StatePublished - 2013
Event26th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2013 - St. Pete Beach, FL, United States
Duration: May 22 2013May 24 2013

Other

Other26th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2013
CountryUnited States
CitySt. Pete Beach, FL
Period5/22/135/24/13

Fingerprint

Intelligent systems
Linguistics
Students
Syntactics

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Crossley, S. A., Roscoe, R., & McNamara, D. (2013). Using automatic scoring models to detect changes in student writing in an intelligent tutoring system. In FLAIRS 2013 - Proceedings of the 26th International Florida Artificial Intelligence Research Society Conference (pp. 208-213)

Using automatic scoring models to detect changes in student writing in an intelligent tutoring system. / Crossley, Scott A.; Roscoe, Rod; McNamara, Danielle.

FLAIRS 2013 - Proceedings of the 26th International Florida Artificial Intelligence Research Society Conference. 2013. p. 208-213.

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

Crossley, SA, Roscoe, R & McNamara, D 2013, Using automatic scoring models to detect changes in student writing in an intelligent tutoring system. in FLAIRS 2013 - Proceedings of the 26th International Florida Artificial Intelligence Research Society Conference. pp. 208-213, 26th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2013, St. Pete Beach, FL, United States, 5/22/13.
Crossley SA, Roscoe R, McNamara D. Using automatic scoring models to detect changes in student writing in an intelligent tutoring system. In FLAIRS 2013 - Proceedings of the 26th International Florida Artificial Intelligence Research Society Conference. 2013. p. 208-213
Crossley, Scott A. ; Roscoe, Rod ; McNamara, Danielle. / Using automatic scoring models to detect changes in student writing in an intelligent tutoring system. FLAIRS 2013 - Proceedings of the 26th International Florida Artificial Intelligence Research Society Conference. 2013. pp. 208-213
@inproceedings{93c9834c261d4d479fa8f4a6f4d1d77d,
title = "Using automatic scoring models to detect changes in student writing in an intelligent tutoring system",
abstract = "This study compares automated scoring increases and linguistic changes for student writers in two groups: a group that used an intelligent tutoring system embedded with an automated writing evaluation component (Writing Pal) and a group that used only the automated writing evaluation component. The primary goal is to examine automated scoring differences in both groups from pretest to posttest essays to investigate score gains and linguistic development. The study finds that both groups show significant increases in automated writing scores and significant development in lexical, syntactic, cohesion, and rhetorical features. However, the Writing-Pal group shows greater raw frequency gains (i.e., negative v. positive gains).",
author = "Crossley, {Scott A.} and Rod Roscoe and Danielle McNamara",
year = "2013",
language = "English (US)",
isbn = "9781577356059",
pages = "208--213",
booktitle = "FLAIRS 2013 - Proceedings of the 26th International Florida Artificial Intelligence Research Society Conference",

}

TY - GEN

T1 - Using automatic scoring models to detect changes in student writing in an intelligent tutoring system

AU - Crossley, Scott A.

AU - Roscoe, Rod

AU - McNamara, Danielle

PY - 2013

Y1 - 2013

N2 - This study compares automated scoring increases and linguistic changes for student writers in two groups: a group that used an intelligent tutoring system embedded with an automated writing evaluation component (Writing Pal) and a group that used only the automated writing evaluation component. The primary goal is to examine automated scoring differences in both groups from pretest to posttest essays to investigate score gains and linguistic development. The study finds that both groups show significant increases in automated writing scores and significant development in lexical, syntactic, cohesion, and rhetorical features. However, the Writing-Pal group shows greater raw frequency gains (i.e., negative v. positive gains).

AB - This study compares automated scoring increases and linguistic changes for student writers in two groups: a group that used an intelligent tutoring system embedded with an automated writing evaluation component (Writing Pal) and a group that used only the automated writing evaluation component. The primary goal is to examine automated scoring differences in both groups from pretest to posttest essays to investigate score gains and linguistic development. The study finds that both groups show significant increases in automated writing scores and significant development in lexical, syntactic, cohesion, and rhetorical features. However, the Writing-Pal group shows greater raw frequency gains (i.e., negative v. positive gains).

UR - http://www.scopus.com/inward/record.url?scp=84889850660&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84889850660&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84889850660

SN - 9781577356059

SP - 208

EP - 213

BT - FLAIRS 2013 - Proceedings of the 26th International Florida Artificial Intelligence Research Society Conference

ER -