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Toward Revision-Sensitive Feedback in Automated Writing Evaluation

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Bibliographic Details
Title:	Toward Revision-Sensitive Feedback in Automated Writing Evaluation
Language:	English
Authors:	Allen, Laura K., Jacovina, Matthew E., Johnson, Adam C., McNamara, Danielle S., Roscoe, Rod D.
Source:	Grantee Submission. 2016Paper presented at the International Conference on Educational Data Mining (9th, 2016).
Peer Reviewed:	Y
Page Count:	3
Publication Date:	2016
Sponsoring Agency:	Institute of Education Sciences (ED)
Contract Number:	R305A120707
Document Type:	Speeches/Meeting Papers Reports - Research
Education Level:	High Schools
Descriptors:	Revision (Written Composition), Automation, Writing Evaluation, Feedback (Response), High School Students, Intelligent Tutoring Systems, Documentation, Writing (Composition), Identification
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. [This paper was published in the Proceedings of the 9th International Conference on Educational Data Mining 2016, p628-629.]
Abstractor:	As Provided
Number of References:	9
IES Funded:	Yes
Entry Date:	2018
Accession Number:	ED586437
Database:	ERIC

Description
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. [This paper was published in the Proceedings of the 9th International Conference on Educational Data Mining 2016, p628-629.]