Article
Automated written corrective feedback: Error-correction performance and timing of delivery
Language Learning & Technology
(2022)
Abstract
To 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 a distraction-potential threshold
defined for the L2 student writers in our sample. Implications for the use of AWCF tools in L2 settings are
discussed.
Keywords
- Syntax/Grammar,
- Writing,
- Human-Computer Interaction
Disciplines
Publication Date
2022
Citation Information
Jim Ranalli and Taichi Yamashita. "Automated written corrective feedback: Error-correction performance and timing of delivery" Language Learning & Technology Vol. 26 Iss. 1 (2022) Available at: http://works.bepress.com/jim-ranalli/19/