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Effects of using machine translation to mediate the revision process of Korean university students’ academic writing

Published online by Cambridge University Press:  11 September 2020

Sangmin-Michelle Lee Open the ORCID record for Sangmin-Michelle Lee [Opens in a new window]  and
Neil Briggs Open the ORCID record for Neil Briggs [Opens in a new window]
Sangmin-Michelle Lee
Affiliation:
Kyung Hee University, Korea (sangminlee@khu.ac.kr)
Neil Briggs
Affiliation:
Independent researcher, Canada (nrbteach@gmail.com)
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Abstract

In recent years, marked gains in the accuracy of machine translation (MT) outputs have greatly increased its viability as a tool to support the efforts of English as a foreign language (EFL) students to write in English. This study examines error corrections made by 58 Korean university students by comparing their original L2 texts to that of MT outputs. Based on the results of the error analysis, the error types were categorized into 12 categories. Students were divided into three distinctive groups to determine differences among them according to the frequency of errors in their writing. The t-test results reveal that the numbers of errors significantly decreased in the revised versions for most of the error types among all groups. The results of the regression analysis also reveal a positive correlation relationship between the number of changes and the reduction of errors. However, the results also indicate that although all groups made error corrections at similar rates, students who less frequently committed errors in their L2 texts (higher language proficiency groups) generally tended to correct a higher proportion of errors. Based on the findings, pedagogical implications are discussed regarding how EFL teachers can effectively incorporate MT into the classroom.

Keywords

machine translationL2 writingrevision processerror analysis
Type
Regular papers
Information
ReCALL , Volume 33 , Issue 1 , January 2021 , pp. 18 - 33
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Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of European Association for Computer Assisted Language Learning

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