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The Korean Association for the Study of English Language and Linguistics
[ Article ]
Korea Journal of English Language and Linguistics - Vol. 26, No. 0, pp.525-545
ISSN: 1598-1398 (Print) 2586-7474 (Online)
Print publication date 31 Mar 2026
Received 26 Jan 2026 Revised 27 Feb 2026 Accepted 16 Mar 2026
DOI: https://doi.org/10.15738/kjell.26..202603.525

Comparing the Effectiveness of Two Machine Translation-Based Instructional Models for English Writing

Young-Ju Lee
Professor, Department of English Language & Literature, Hanbat National University 125 Dongseo-daero, Yuseong-gu Daejeon, Korea, Tel: +82-42-821-1319 yjulee@hanbat.ac.kr


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This is an open-access article distributed under the terms of the Creative Commons License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The present study employed a mixed-methods research design to compare the effectiveness of two machine translation (MT)-integrated instructional models for English writing and to examine students’ perceptions of each model. A total of 47 university students enrolled in two sections of an English for Academic Purposes (EAP) writing course participated in the study. The two models differed in the instructional role of Google Translate. Model 1 utilized back-translation (English → Korean) to help students identify discrepancies between their intended meaning and translated output, thereby supporting error detection and revision. Model 2 allowed students to selectively use Google Translate (Korean → English) at the word, phrase, and sentence levels to assist with linguistic production during the revision process. Three research questions were investigated and the results can be summarized as follows. First, a t-test showed no statistically significant difference in effectiveness between the two models. Second, a Kruskal–Wallis test showed no significant differences in perceived usefulness across English proficiency levels, indicating that instructional designs tailored to English proficiency may not be necessary in this context. Third, students also perceived back-translation as particularly useful for identifying vocabulary errors, and views on its usefulness for grammar correction were divergent. This study provides pedagogical implications for designing MT-integrated writing instruction in similar EAP courses.

Keywords:

machine translation, Google Translate, English writing instruction, instructional models

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