Toward revision-sensitive feedback in automated writing evaluation

Rod D. Roscoe; Matthew E. Jacovina; Laura K. Allen; Adam C. Johnson; Danielle S. McNamara

Toward revision-sensitive feedback in automated writing evaluation

Rod D. Roscoe, Matthew E. Jacovina, Laura K. Allen, Adam C. Johnson, Danielle S. McNamara

Research output: Contribution to conference › Paper › peer-review

Abstract

Revising is an essential writing process yet automated writing evaluation systems tend to give feedback on discrete essay drafts rather than changes across drafts. We explore the feasibility of automated revision detection and its potential to guide feedback. Relationships between revising behaviors and linguistic features of students’ essays are discussed.

Original language	English (US)
Pages	628-629
Number of pages	2
State	Published - 2016
Externally published	Yes
Event	9th International Conference on Educational Data Mining, EDM 2016 - Raleigh, United States Duration: Jun 29 2016 → Jul 2 2016

Conference

Conference	9th International Conference on Educational Data Mining, EDM 2016
Country/Territory	United States
City	Raleigh
Period	6/29/16 → 7/2/16

Bibliographical note

Funding Information:
This research was supported in part by the Institute for Educational Sciences (IES R305A120707). Opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views of the IES.

Publisher Copyright:
© 2016 International Educational Data Mining Society. All rights reserved.

Keywords

Automated writing evaluation
Feedback
Intelligent tutoring systems
Natural language processing
Revising
Writing

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keywords = "Automated writing evaluation, Feedback, Intelligent tutoring systems, Natural language processing, Revising, Writing",

author = "Roscoe, {Rod D.} and Jacovina, {Matthew E.} and Allen, {Laura K.} and Johnson, {Adam C.} and McNamara, {Danielle S.}",

note = "Funding Information: This research was supported in part by the Institute for Educational Sciences (IES R305A120707). Opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views of the IES. Publisher Copyright: {\textcopyright} 2016 International Educational Data Mining Society. All rights reserved.; 9th International Conference on Educational Data Mining, EDM 2016 ; Conference date: 29-06-2016 Through 02-07-2016",

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language = "English (US)",

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TY - CONF

T1 - Toward revision-sensitive feedback in automated writing evaluation

AU - Roscoe, Rod D.

AU - Jacovina, Matthew E.

AU - Allen, Laura K.

AU - Johnson, Adam C.

AU - McNamara, Danielle S.

N1 - Funding Information: This research was supported in part by the Institute for Educational Sciences (IES R305A120707). Opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views of the IES. Publisher Copyright: © 2016 International Educational Data Mining Society. All rights reserved.

PY - 2016

Y1 - 2016

N2 - Revising is an essential writing process yet automated writing evaluation systems tend to give feedback on discrete essay drafts rather than changes across drafts. We explore the feasibility of automated revision detection and its potential to guide feedback. Relationships between revising behaviors and linguistic features of students’ essays are discussed.

AB - Revising is an essential writing process yet automated writing evaluation systems tend to give feedback on discrete essay drafts rather than changes across drafts. We explore the feasibility of automated revision detection and its potential to guide feedback. Relationships between revising behaviors and linguistic features of students’ essays are discussed.

KW - Automated writing evaluation

KW - Feedback

KW - Intelligent tutoring systems

KW - Natural language processing

KW - Revising

KW - Writing

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M3 - Paper

AN - SCOPUS:85052012212

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T2 - 9th International Conference on Educational Data Mining, EDM 2016

Y2 - 29 June 2016 through 2 July 2016

ER -

Toward revision-sensitive feedback in automated writing evaluation

Abstract

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