Proficiency and the Use of Machine Translation: A Case Study of Four Japanese Learners

Abstract

While the use of machine translation (MT) in the classroom has been explored from various perspectives, the relationship between language proficiency and MT use regarding learners' behaviors and beliefs remains unclear in the research literature. This study focused on four Japanese learners with various language proficiencies from a fourth-year Japanese language class (two advanced-level, one intermediatehigh, and one novice-high level) and investigated how they edited self-written text with MT by examining the scope and types of revisions they made as well as their perceptions about using MT for editing. The data included four types of drafts of a writing assignment: (1) D1 (self-written drafts in Japanese without the help of MT); (2) D2 (revised corresponding drafts in L1 provided by MT); (3) D3 (drafts in Japanese provided by MT based on D2); (4) D4 (revised drafts based on comparison of D1 and D3) and their reflection papers. The results show that the four participants adopted various ways of editing self-written text. While all the participants' revisions are at local levels, the two advanced level learners primarily focused on vocabulary revision while the other two learners' revisions extended to the sentence level. The findings also show that the advanced-level and intermediate-high-level learners have various degrees of positive attitudes toward using MT. In contrast, while the positive effects of MT use are acknowledged, the novice-high level learner also feels ashamed and dishonest when using MT. This article concludes with insights that can assist instructors in facilitating MT as a pedagogical tool for language learning and teaching with diverse students.

Exploring the Effectiveness of Machine Translation for Improving English Proficiency: A Case Study of A Japanese University's Large-scale Implementation

“…Numerous machine translation functions, including but not limited to Google Translate (https://translate.google.co.jp/) and DeepL (https://www.deepl.com/translator), are freely available to users. However, despite the popularity of these technologies among students, some teachers have outrightly rejected them, as demonstrated by the Google Irreverent Classroom (Urlaub and Dessein, 2022;Ducar and Schocket, 2018;Henshaw, 2020), and there are ongoing concerns about the negative impact on language learning (Clifford et al, 2013;Correa, 2011;Faber & Turrero-Garcia, 2020;Jolley & Maimone, 2015;Tian, 2018;Xu, 2022). Nevertheless, foreign language learners continue to use machine translation, regardless of how teachers respond (Clifford et al, 2013;Correa, 2011;Faber & Turrero-Garcia, 2020;Jolley & Maimone, 2015;Tian, 2018;Xu, 2022).…”

“…However, despite the popularity of these technologies among students, some teachers have outrightly rejected them, as demonstrated by the Google Irreverent Classroom (Urlaub and Dessein, 2022;Ducar and Schocket, 2018;Henshaw, 2020), and there are ongoing concerns about the negative impact on language learning (Clifford et al, 2013;Correa, 2011;Faber & Turrero-Garcia, 2020;Jolley & Maimone, 2015;Tian, 2018;Xu, 2022). Nevertheless, foreign language learners continue to use machine translation, regardless of how teachers respond (Clifford et al, 2013;Correa, 2011;Faber & Turrero-Garcia, 2020;Jolley & Maimone, 2015;Tian, 2018;Xu, 2022). While we may be able to regulate the use of machine translation in some classes, we cannot socially stop this trend as long as our society values freedom.…”