The Korean Association for the Study of English Language and Linguistics
[ Article ]
Korea Journal of English Language and Linguistics - Vol. 23, No. 0, pp.1111-1135
ISSN: 1598-1398 (Print) 2586-7474 (Online)
Print publication date 30 Jan 2023
Received 07 Oct 2023 Revised 30 Oct 2023 Accepted 07 Dec 2023
DOI: https://doi.org/10.15738/kjell.23..202312.1111

A Case Study on Integrating Google Translate into College EFL Writing Instruction

Minji Kim ; Sumi Han
(First author) Administrative Assistant Ilsong College of Liberal Arts, Hallym University dazzlenglish@gmail.com
(Corresponding author) Associate Professor Dept. of English Language and Literature, Hallym University sumihan@hallym.ac.kr


© 2023 KASELL All rights reserved
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

This case study focused on integrating Google Translate (GT) and post-editing skills into English as a Foreign Language (EFL) writing instruction for Korean college students. Over a four-day writing class, twelve students completed four drafts, and the use of GT output, along with paraphrasing and error correction skills, was examined in detail. Surveys and interviews were utilized to gather student feedback. The findings revealed that initially, the students heavily relied on the output of GT. However, as they learned post-editing skills, they began independently changing expressions and correcting errors. In Draft 3, 54 out of 58 expressions were paraphrased, and 67 out of 80 errors were corrected based on the GT output. Yet, with the employment of post-editing skills in Draft 4, there was a noticeable decrease in reliance on the GT output, with students making more efforts to paraphrase and correct errors independently. They also expressed high satisfaction with the effectiveness of paraphrasing for writing. Nevertheless, they still faced challenges in grammar, vocabulary, and sentence structure, highlighting the need for teacher involvement in writing classrooms. The study concluded by addressing the implications, limitations, and providing suggestions for future research.

Keywords:

machine translation, Google Translate, post-editing skills, Korean college students, EFL writing instruction

Acknowledgments

This work is based on the first author’s MA thesis.

References

  • Ahn, S. J. and E. S.Chung. 2020. Students’ perceptions of the use of online machine translation in L2 writing. Multimedia-Assisted Language Learning 23(2), 10-35.
  • Alhaisoni, E. and M. Alhaysony. 2017. An investigation of Saudi EFL university students’ attitudes towards the use of Google Translate. International Journal of English Language Education 5(1), 72-82. [https://doi.org/10.5296/ijele.v5i1.10696]
  • Amin, E. A. 2019. Using awareness raising in syntactic and semantic errors to foster translation performance among Majmaah University EFL students. Arab World English Journal 10(2), 196-212. [https://doi.org/10.24093/awej/vol10no2.16]
  • Avramidis, E., V. Macketanz, U. Strohriegel, A. Burchardt, and S. Möller. 2020. Fine-grained linguistic evaluation for state-of-the-art machine translation. Proceedings of the 5th Conference on Machine Translation (WMT), 346-356.
  • Campbell, S. 1998. Translation into the Second Language. Harlow, New York: Addison Wesley Longman.
  • Chon, Y. V., D. Shin, and G. E. Kim. 2021. Comparing L2 learners’ writing against parallel machine-translated texts: Raters’ assessment, linguistic complexity and errors. System 96, 102408. [https://doi.org/10.1016/j.system.2020.102408]
  • Correa, M. 2014. Leaving the “peer” out of peer-editing: Online translators as a pedagogical tool in the Spanish as a second language classroom. Latin American Journal of Content and Language Integrated Learning 7, 1-20. [https://doi.org/10.5294/laclil.2014.7.1.1]
  • Ducar, C. and D. H. Schocket. 2018. Machine translation and the L2 classroom: Pedagogical solutions for making peace with Google translate. Foreign Language Annals 51(4), 779-795. [https://doi.org/10.1111/flan.12366]
  • Garcia, I. and M. Pena. 2011. Machine translation-assisted language learning: Writing beginners. Computer Assisted Language Learning 24, 471-487. [https://doi.org/10.1080/09588221.2011.582687]
  • Han, S. and J-A. Shin. 2017. Teaching Google search techniques in and L2 academic writing context. Language Learning & Technology 21(3), 172-194.
  • Jin, L. and E. Deifell. 2013. Foreign language learners' use and perception of online dictionaries: A survey study. MERLOT Journal of Online Learning and Teaching 9(4), 513-533.
  • Jo, I. H. 2021. Learning effect of using machine translation in EFL college writing classes. Studies in Linguistics 58, 385-416. [https://doi.org/10.17002/sil..58.202101.385]
  • Jolley, J. and L. Maimone. 2015. Free online machine translation: Use and perceptions by Spanish students and instructors. Learn Language, Explore Cultures, Transform Lives, 181-200.
  • Kazemzadeh, A. and A. Kashani. 2014. The effect of computer-assisted translation on L2 learners’ mastery of writing. International Journal of Research Studies in Language Learning 3, 29-44. [https://doi.org/10.5861/ijrsll.2013.396]
  • Kim, J. 2017. Machine translation in daily life. Korean Language 27(4), 63-79.
  • Kim, K. R. 2020. Translator-assisted L2 writing, necessary or not?: Beginner university learners’ perceptions of its validity. Journal of Digital Convergence 18(6), 99-108.
  • Kim, M. 2022. Integrating Machine Translation into EFL Writing Instruction: A Preliminary Investigation. Master’s thesis. Hallym University, Chuncheon, Korea.
  • Kol, S., M. Schcolnik. and E. Spector-Cohen. 2018. Google Translate in academic writing courses? The EuroCALL Review 26(2), 50-57. [https://doi.org/10.4995/eurocall.2018.10140]
  • Lee, J. 2019. A study on Korean university students’ English composition assisted by machine translators. Korean Association for Learner-Centered Curriculum and Instruction 20(19), 133-163. [https://doi.org/10.22251/jlcci.2020.20.19.133]
  • Lee, J. H. and K.W. Cha. 2019. A study on the effectiveness of machine translators for university freshmen in translating Korean writing into English. The Journal of Learner-Centered Curriculum and Instruction 19(8), 155-180. [https://doi.org/10.22251/jlcci.2019.19.8.155]
  • Lee, N. S., S. J. Lee, J. Y. Lee, and J. H. Lee. 2016. Future horizons of translation & interpretation: Artificial intelligence and interactive-converged translation & interpretation. The Journal of Translation Studies 17(2), 65-89. [https://doi.org/10.15749/jts.2016.17.2.003]
  • Lee, S. M. 2020. The impact of using machine translation on EFL students’ writing. Computer-Assisted Language Learning 33(3), 157-175. [https://doi.org/10.1080/09588221.2018.1553186]
  • Lee, S. M. and N. Briggs. 2021. Effects of using machine translation to mediate the revision process of Korean university students’ academic writing. ReCALL 33(1), 18-33. [https://doi.org/10.1017/S0958344020000191]
  • Macketanz, V., A. Burchardt. and H. Uszkoreit. 2018. TQ-Autotest: Novel Analytical Quality Measure Confirms that DeepL is Better than Google Translate. The Globalization and Localization Association.
  • Martínez, J., A. López-Díaz. and E. Pérez. 2020. Using process writing in the teaching of English as a foreign language. Revista Caribeña de Investigación Educativa 4(1), 49-61.
  • Marzec-Stawiarska, M. 2019. A search for paraphrasing and plagiarism avoidance strategies in the context of writing from sources in a foreign language. In B. Loranc-Paszylk (Ed.), Rethinking Directions in Language Learning and Teaching at University Level, 115-135. [https://doi.org/10.14705/rpnet.2019.31.894]
  • Mundt, K. and M. Groves. 2016. A double-edged sword: The merits and the policy implications of Google Translate in higher education. European Journal of Higher Education 6(4), 387-401. [https://doi.org/10.1080/21568235.2016.1172248]
  • Niño, A. 2009. Machine translation in foreign language learning: Language learners’ and tutors’ perceptions of its advantages and disadvantages. ReCALL 21(2), 241–258. [https://doi.org/10.1017/S0958344009000172]
  • Oh, H. S. 2017. The way of Kakao machine translator for obtaining high-quality large-scale learning data. Kakao AI report 8, 16-21.
  • Park, H. K. 2018. Teaching machine translation in master’s degree translation courses: A case study of post-editing activity in the Korean-Japanese language pair. The Journal of Translation Studies 19(3), 163-193. [https://doi.org/10.15749/jts.2018.19.3.007]
  • Park, M. H. and H. Lee. 2010. Exploring the effect of plagiarism-prevention training and the type of plagiarism in an L2 essay-writing test. Korea Journal of English Language and Linguistics 10(4), 785-805. [https://doi.org/10.15738/kjell.10.4.201012.785]
  • Poibeau, T. 2017. Machine Translation. Cambridge, MA: MIT Press. [https://doi.org/10.7551/mitpress/11043.001.0001]
  • Shi, L. 2012. Rewriting and paraphrasing source texts in second language writing. Journal of Second Language Writing 12(2), 134-148. [https://doi.org/10.1016/j.jslw.2012.03.003]
  • Stander, M. 2020. Strategies to help university students avoid plagiarism: A focus on translation as an intervention strategy. Journal of Further and Higher Education 44(2), 156-169. [https://doi.org/10.1080/0309877X.2018.1526260]
  • Sung, H. E. 2011. A Study on the Effects of Quotation and Paraphrase Practices for Avoidance of Plagiarism for Korean University Students. Master’s thesis, Ewha Womans University, Seoul, Korea.
  • Tsai, S. 2019. Using google translate in EFL drafts: A preliminary investigation. Computer Assisted Language Learning 32(12), 510-526. [https://doi.org/10.1080/09588221.2018.1527361]
  • Williams, L. 2006. Web-based machine translation as a tool for promoting electronic literacy and language awareness. Foreign Language Learning Annals 39(4), 565-578. [https://doi.org/10.1111/j.1944-9720.2006.tb02276.x]
  • Yang, Y. and X. Wang. 2019. Modeling the intention to use machine translation for student translators: An extension of technology acceptance mode. Computers & Education 133, 116-126. [https://doi.org/10.1016/j.compedu.2019.01.015]