Using Grammar Checkers in an ESL Context: An Investigation of Automatic Corrective Feedback.

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Title: Using Grammar Checkers in an ESL Context: An Investigation of Automatic Corrective Feedback.
Authors: John, Paul1 paul.john@uqtr.ca, Woll, Nina2 nina.woll@uqtr.ca
Source: CALICO Journal. 2020, Vol. 37 Issue 2, p169-192. 24p.
Subject Terms: *Psychological feedback, *Classroom activities, *Second language acquisition, Checkers, Grammar, False alarms
Abstract: Our study examines written corrective feedback generated by two online grammar checkers (GCs), Grammarly and Virtual Writing Tutor, and by the grammar checking function of Microsoft Word. We tested the technology on a wide range of grammatical error types from two sources: a set of authentic ESL compositions and a series of simple sentences we generated ourselves. The GCs were evaluated in terms of (1) coverage (number of errors flagged), (2) appropriacy of proposed replacement forms, and (3) rates of "false alarms" (forms mistakenly flagged as incorrect). Although Grammarly and Virtual Writing Tutor outperformed Microsoft Word, neither of the online GCs had high rates of overall coverage (<50%). Consequently, they cannot be relied on to supply comprehensive feedback on student compositions. The finding of higher identification rates for errors from simple rather than authentic sentences reinforces this conclusion. Nonetheless, since few inaccurate replacement forms and false alarms were observed, only rarely is the feedback actively misleading. In addition, the GCs were better at handling some error types than others. Ultimately, we suggest that teachers use GCs with specially designed classroom activities that target selected error types before learners apply the technology to their own writing. [ABSTRACT FROM AUTHOR]
Copyright of CALICO Journal is the property of University of Toronto Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Our study examines written corrective feedback generated by two online grammar checkers (GCs), Grammarly and Virtual Writing Tutor, and by the grammar checking function of Microsoft Word. We tested the technology on a wide range of grammatical error types from two sources: a set of authentic ESL compositions and a series of simple sentences we generated ourselves. The GCs were evaluated in terms of (1) coverage (number of errors flagged), (2) appropriacy of proposed replacement forms, and (3) rates of &quot;false alarms&quot; (forms mistakenly flagged as incorrect). Although Grammarly and Virtual Writing Tutor outperformed Microsoft Word, neither of the online GCs had high rates of overall coverage (&lt;50%). Consequently, they cannot be relied on to supply comprehensive feedback on student compositions. The finding of higher identification rates for errors from simple rather than authentic sentences reinforces this conclusion. Nonetheless, since few inaccurate replacement forms and false alarms were observed, only rarely is the feedback actively misleading. In addition, the GCs were better at handling some error types than others. Ultimately, we suggest that teachers use GCs with specially designed classroom activities that target selected error types before learners apply the technology to their own writing. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;Copyright of CALICO Journal is the property of University of Toronto Press and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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        Value: 10.1558/cj.36523
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      – SubjectFull: Second language acquisition
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      – SubjectFull: Grammar
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      – SubjectFull: False alarms
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      – TitleFull: Using Grammar Checkers in an ESL Context: An Investigation of Automatic Corrective Feedback.
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              Text: 2020
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