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ERIC Number: EJ893467
Record Type: Journal
Publication Date: 2010-Jul
Pages: 18
Abstractor: As Provided
ISSN: ISSN-0265-5322
The Utility of Article and Preposition Error Correction Systems for English Language Learners: Feedback and Assessment
Chodorow, Martin; Gamon, Michael; Tetreault, Joel
Language Testing, v27 n3 p419-436 Jul 2010
In this paper, we describe and evaluate two state-of-the-art systems for identifying and correcting writing errors involving English articles and prepositions. Criterion[superscript SM], developed by Educational Testing Service, and "ESL Assistant", developed by Microsoft Research, both use machine learning techniques to build models of article and preposition usage which enable them to identify errors and suggest corrections to the writer. We evaluated the effects of these systems on users in two studies. In one, "Criterion" provided feedback about article errors to native and non-native speakers who were writing an essay for a college-level psychology course. The results showed a significant reduction in the number of article errors in the final essays of the non-native speakers. In the second study, "ESL Assistant" was used by non-native speakers who were composing email messages. The results indicated that users were selective in their choices among the system's suggested corrections and that, as a result, they were able to increase the proportion of valid corrections by making effective use of feedback. (Contains 6 figures and 4 tables.)
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Publication Type: Journal Articles; Reports - Evaluative
Education Level: Higher Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A