Automated written corrective feedback: Error-correction performance and timing of delivery

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dc.contributor.authorRanalli, Jim
dc.contributor.authorYamashita, Taichi
dc.date.accessioned2022-03-04T22:39:58Z
dc.date.available2022-03-04T22:39:58Z
dc.date.issued2022-03-07
dc.description.abstractTo the extent automated written corrective feedback (AWCF) tools such as Grammarly are based on sophisticated error-correction technologies, such as machine-learning techniques, they have the potential to find and correct more common L2 error types than simpler spelling and grammar checkers such as the one included in Microsoft Word (technically known as MS-NLP). Moreover, AWCF tools can deliver feedback synchronously, although not instantaneously, as often appears to be the case with MS-NLP. Cognitive theory and recent L2 research suggest that synchronous corrective feedback may aid L2 development, but also that error-flagging at suboptimal times could cause disfluencies in L2 students’ writing processes. To contribute to the knowledge needed for appropriate application of this new genre of writing-support technology, we evaluated Grammarly’s capacity to address common L2 problem areas, as well as issues with its feedback-delivery timing, using MS-NLP as a benchmark. Grammarly was found to flag 10 times as many common L2 error types as MS-NLP in the same corpus of student texts while also displaying an average 17.5-second delay in feedback delivery, exceeding the distraction-potential threshold defined for the L2 student writers in our sample. Implications for the use of AWCF tools in L2 settings are discussed.
dc.identifier.citationRanalli, J., & Yamashita, T. (2022). Automated written corrective feedback: Error-correction performance and timing of delivery. Language Learning & Technology, 26(1), 1–25. http://hdl.handle.net/10125/73465
dc.identifier.issn1094-3501
dc.identifier.urihttp://hdl.handle.net/10125/73465
dc.language.isoen-US
dc.publisherUniversity of Hawaii National Foreign Language Resource Center
dc.publisherCenter for Language & Technology
dc.publisher(co-sponsored by Center for Open Educational Resources and Language Learning, University of Texas at Austin)
dc.rights.licenseCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectSyntax/Grammar
dc.subjectWriting
dc.subjectHuman-Computer Interaction
dc.titleAutomated written corrective feedback: Error-correction performance and timing of delivery
dc.typeArticle
dc.type.dcmiText
prism.endingpage25
prism.number1
prism.publicationnameLanguage Learning & Technology
prism.startingpage1
prism.volume26

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Volume 26 Number 1, 2022