A Quantitative Inquiry Into Translation App Usage and Post-Editing Strategies Among Taiwanese EFL Tertiary Students

Authors

  • Ya-Ting Zhan Chaoyang University of Technology
  • Hsiao-Tung Hsu Chaoyang University of Technology

DOI:

https://doi.org/10.17507/tpls.1606.21

Keywords:

translation apps, English learning, tertiary EFL learners, post-editing strategies

Abstract

With enhanced technology, numerous students increasingly use varied applications to facilitate their English learning, and some have chosen translation apps to improve their comprehension. However, due to the imperfect and inconsistent quality of translation, the content produced by these apps may still require verification and correction. This study examined how Taiwanese tertiary students utilized translation apps to help them learn English, what post-editing strategies they adopted to check the final product, and their perceptions of using these apps. A total of 519 participants were recruited, and data were analyzed using descriptive statistics, independent t-tests, and one-way ANOVA through SPSS Statistics 23. The results revealed that students primarily used translation apps for reading comprehension, vocabulary acquisition, and pronunciation practice. They frequently applied light post-editing strategies to correct grammar and word choices and full post-editing strategies to add context or rectify inaccurate translations. It also showed that English proficiency influenced students’ use of post-editing strategies; intermediate and proficient students employed post-editing strategies more frequently than those at the preliminary level. Most students expressed positive attitudes toward using translation apps to learn English. Finally, this study highlights the potential of integrating translation apps and post-editing strategies to improve students’ learning efficiency, reinforce their linguistic abilities, and foster metacognitive development.

Author Biographies

Ya-Ting Zhan, Chaoyang University of Technology

Department of Applied English

Hsiao-Tung Hsu, Chaoyang University of Technology

Department of Applied Englis

References

Alaboud, A. (2022). The positive effect of translation on improving reading comprehension among female Arabic learners of English as foreign language. Arab World English Journal, 13(2), 424-436. https://doi.org/10.24093/awej/vol13no2.29

Alhaisoni, E., & Alhaysony, M. (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.

Alotaibi, H., & Salamah, D. (2023). The impact of translation apps on translation students’ performance. Education and Information Technologies, 28(8), 10709-10729. https://doi.org/10.1007/s10639-023 11578-y

Araghian, R., Ghonsooly, B., & Ghanizadeh, A. (2018). Investigating problem solving strategies of translation trainees with high and low levels of self-efficacy. Translation, Cognition & Behavior, 1(1), 74-97. https://doi.org/10.1075/tcb.00004.ara

Bahri, H., & Mahadi, T. S. T. (2016). Google Translate as a supplementary tool for learning Malay: A case study at University Sains Malaysia. Advances in Language and Literary Studies, 7(3), 161-167.

Bin Dahmash, N. (2020). 'I can’t live without Google Translate': A close look at the use of Google Translate App by second language learners in Saudi Arabia. Arab World English Journal, 11(3), 226-240. https://doi.org/10.24093/awej/vol11no3.14

Cárdenas, A. M. (2018). Tackling intermediate students’ fossilized grammatical errors in speech through self-evaluation and self-monitoring strategies. Profile: Issues in Teachers’ Professional Development, 20(2), 195-209. https://doi.org/10.15446/profile.v20n2.67996

Caswell, I., & Liang, B. (2020). Recent advances in Google Translate. Retrieved March 12, 2023, from https://ai.googleblog.com/2020/06/recent-advances-in google-translate.html

Chang, L. C. (2022). Chinese language learners evaluating machine translation accuracy. The JALT CALL Journal, 18(1), 110-136. https://doi.org/10.29140/jaltcall.v18n1.592

Chang, Y. T. (2020). Differences in machine translation post-editing (MTPE) strategies between technical and tourism texts: Analysis from the perspective of register theory [Unpublished master’s thesis]. National Kaohsiung University of Science and Technology.

Chung, E. S. (2020). The effect of L2 proficiency on post-editing machine translated texts. The Journal of Asia TEFL, 17(1), 182-193. http://dx.doi.org/10.18823/asiatefl.2020.17.1.11.182

Cui, Y., Liu, X., & Cheng, Y. (2023). A Comparative study on the effort of human translation and post-editing in relation to text types: An eye tracking and key-logging experiment. SAGE Open, 13(1), 1-15. https://doi.org/10.1177/21582440231155849

do Carmo, F. E. M. (2017). Post-editing: A theoretical and practical challenge for translation studies and machine learning [Unpublished doctoral dissertation]. University of Porto.

Edelia, A. D., & Maharsi, I. (2022). A survey on translation as a learning strategy by EFL higher education students in English learning. Pedagogy: Journal of English Language Teaching, 10(2), 111-122. https://doi.org/10.32332/joelt.v10i2.4809

Forman, S. (2023). DeepL. Retrieved November 8, 2023, from https://research.contrary.com/reports/deepl

Girletti, S. (2022). Working with pre-translated texts: Preliminary findings from a survey on post-editing and revision practices in Swiss corporate in house language services. In Proceedings of the 23rd Annual Conference of the European Association for Machine Translation (pp. 271–280). Switzerland: European Association for Machine Translation.

Harto, S., Hamied, F. A., Musthafa, B., & Setyarini, S. (2022). Exploring undergraduate students’ experiences in dealing with post-editing of machine translation. Indonesian Journal of Applied Linguistics, 11(3), 696-707. https://doi.org/10.17509/ijal.v11i3.42825

Hoi, V. N. (2020). Understanding higher education learners’ acceptance and use of mobile devices for language learning: A Rasch-based path modeling approach. Computers & Education, 146, 1-15. https://doi.org/10.1016/j.compedu.2019.103761

Hong, J., & Lee, I. J. (2021). A satisfaction survey on the human translation outcomes and machine translation post-editing outcomes. International Journal of Advanced Smart Convergence, 10(2), 86-96. https://doi.org/10.7236/IJASC.2021.10.2.86

Indarti, D. (2024). Investigating the metacognitive strategies during post-editing translation process: An application of think-aloud protocols (TAP). JOLLT Journal of Languages and Language Teaching, 12(2), 765-778. https://doi.org/10.33394/jollt.v12i2.10297

Jeong, K. O. (2022). Facilitating sustainable self-directed learning experience with the use of mobile-assisted language learning. Sustainability, 14(5), 1-13. https://doi.org/10.3390/su14052894

Jia, Y., & Lai, S. (2022). Post-editing metaphorical expressions: Productivity, quality, and strategies. Journal of Foreign Languages and Cultures, 6(2), 28-43. https://doi.org/10.53397/hunnu.jflc.202202004

Jia, Y., & Sun, S. (2022). Man or machine? Comparing the difficulty of human translation versus neural machine translation post-editing. Perspectives, 31(5), 950-968. https://doi.org/10.1080/0907676X.2022.2129028

Kacetl, J., & Klímová, B. (2019). Use of smartphone applications in English language learning—A challenge for foreign language education. Education Sciences, 9(3), 1-9. https://doi.org/10.3390/educsci9030179

Khan, R. M. I., Kumar, T., Supriyatno, T., & Nukapangu, V. (2021). The phenomenon of Arabic-English translation of foreign language classes during the pandemic. Ijaz Arabi Journal of Arabic Learning, 4(3), 570-582. https://doi.org/10.18860/ijazarabi.v4i3.13597

Kim, H. S., & Cha, Y. (2023). The role of AI translators on reading comprehension. Korean Journal of English Language and Linguistics, 23, 38-58. https://doi.org/10.15738/kjell.23.202301.38

Koponen, M. (2016). Is machine translation post-editing worth the effort? A survey of research into post-editing and effort. The Journal of Specialised Translation, (25), 131-148.

Kornacki, M. (2018). Computer-assisted translation (CAT) tools in the translator training process (Vol. 58; B. Lewandowska-Tomaszczyk & Ł. Bogucki, Eds.). Berlin, Germany: Peter Lang.

Krings, H. P. (2001). Repairing texts: Empirical investigations of machine translation post-editing processes (G. S. Koby, Ed.). Kent, Ohio, United States: The Kent State University Press.

Lake, V. E., & Beisly, A. H. (2019). Translation apps: Increasing communication with dual language learners. Early Childhood Education Journal, 47(4), 489-496. https://doi.org/10.1007/s10643-019-00935-7

Liao, P. (2006). EFL learners’ beliefs about and strategy use of translation in English learning. RELC Journal, 37(2), 191-215. https://doi.org/10.1177/0033688206067428

Lin, C. L., Liao, P. S., Chang, C. C., Shih, C. L., Chen, B. J., Chen, P. W., & Chin, C. C. (2021). Fanyi jiaoyu ruhe mianlin AI de tiaozhan ji ruhe yunyong AI [How Translation Education Faces the Challenges of AI and Uses AI]. Compilation and Translation Review, 14(1), 157-181. https://doi.org/10.29912/CTR.202103_14(1).0006

Liu, K., Kwok, H. L., Liu, J., & Cheung, A. K. F. (2022). Sustainability and influence of machine translation: Perceptions and attitudes of translation instructors and learners in Hong Kong. Sustainability, 14(11), 1-29. https://doi.org/10.3390/su14116399

Lu, Y., & Xiong, T. (2023). The attitudes of high school students and teachers toward mobile apps for learning English: A Q methodology study. Social Sciences & Humanities Open, 8(1), 1-11. https://doi.org/10.1016/j.ssaho.2023.100555

Massardo, I., van der Meer, J., O’Brien, S., Hollowood, F., Aranberri, N., & Drescher, K. (2016). MT post-editing guidelines. Amsterdam, The Netherlands: TAUS Signature Editions.

Metruk, R. (2021). The use of smartphone English language learning apps in the process of learning English: Slovak EFL students’ perspectives. Sustainability, 13(15), 1-17. https://doi.org/10.3390/su13158205

Michael, C., Moritz, S., & Srinivas, B. (2016). New directions in empirical translation process research: Exploring the CRITT TPR-DB (1st ed.). Cham, Switzerland: Springer.

Mohan, K. D., & Skotdal, J. (2021). Microsoft Translator: Now translating 100 languages and counting! Retrieved June 12, 2023, from https://www.microsoft.com/en-us/research/blog/microsoft-translator-now translating-100-languages-and-counting/

Mujiono. (2023). Mobile-assisted language learning intervention and its effect on English language proficiency of EFL learners: A meta-analysis. Theory and Practice in Language Studies, 13(5), 1124-1135. https://doi.org/10.17507/tpls.1305.05

Niño, A. (2008). Evaluating the use of machine translation post-editing in the foreign language class. Computer Assisted Language Learning, 21(1), 29–49. https://doi.org/10.1080/09588220701865482

O'Malley, J. M., & Chamot, A. U. (1990). Learning strategies in second language acquisition. Cambridge, England: Cambridge University Press.

OpenAI. (2023). ChatGPT — Release notes. Retrieved October 5, 2023, from https://help.openai.com/en/articles/6825453-chatgpt-release-notes

Pitman, J. (2021). Google Translate: One billion installs, one billion stories. Retrieved January 08, 2023, from https://blog.google/products/translate/one billion-installs/

Polakova, P., & Klimova, B. (2023). Using DeepL translator in learning English as an applied foreign language–An empirical pilot study. Heliyon, 9(8), 1–7. https://doi.org/10.1016/j.heliyon.2023.e18595

Potapova, R., Potapov, V., & Kuzmin, O. (2022). Logistics translator. Concept vision on future interlanguage computer assisted translation. In S. R. M. Prasanna, A. Karpov, K. Samudravijaya, & S. S. Agrawal (Eds.), Speech and Computer (pp. 579–589). Cham, Switzerland: Springer.

Qassem, M. (2021). Translation strategy and procedure analysis: A cultural perspective. Asia Pacific Translation and Intercultural Studies, 8(3), 300–319. https://doi.org/10.1080/23306343.2021.2003511

Ross, R. K., Lake, V. E., & Beisly, A. H. (2021). Preservice teachers’ use of a translation app with dual language learners. Journal of Digital Learning in Teacher Education, 37(2), 86–98. https://doi.org/10.1080/21532974.2020.1800536

Samardali, M. F. S., & Ismael, A. M. H. (2017). Translation as a tool for teaching English as a second language. Journal of Literature, Languages and Linguistics, 40, 64–69.

Shih, C.-L. (2021). Re-looking into machine translation errors and post-editing strategies in a Changing high-tech context. Compilation and Translation Review, 14(2), 125–166. https://doi.org/10.29912/CTR.202109_14(2).0004

Shin, D., & Chon, Y. V. (2023). Second language learners’ post-editing strategies for machine translation errors. Language Learning & Technology, 27(1), 1–25. https://hdl.handle.net/10125/73523

Shreve, G. M. (2009). Recipient-orientation and metacognition in the translation process. In E. A. Nida, R. Dimitriu, & M. Shlesinger (Eds.), Translators and their readers (pp. 255–270). Brussels, Belgium: Les Editions du Hazard.

Stapleton, P., & Leung, B. K. K. (2019). Assessing the accuracy and teachers' impressions of Google Translate: A study of primary L2 writers in Hong Kong. English for Specific Purposes, 56, 18–34. https://doi.org/10.1016/j.esp.2019.07.001

Tavares, C., Tallone, L., Oliveira, L., & Ribeiro, S. (2023). The challenges of teaching and assessing technical translation in an era of neural machine translation. Education Sciences, 13(6), 1–19. https://doi.org/10.3390/educsci13060541

Thani, A. S., & Ageli, N. R. (2020). The use of translation as a learning strategy: A case study of students of the University of Bahrain. International Journal of Linguistics, Literature and Translation, 3(12), 87–101. https://doi.org/10.32996/ijllt.2020.3.12.12

Timothy, M. (2023). How to use ChatGPT as a language translation tool. Retrieved December 2, 2023, from https://www.makeuseof.com/how-to translate-with-chatgpt/

Varela Salinas, M.-J., & Burbat, R. (2023). Google Translate and DeepL: Breaking taboos in translator training. Observational study and analysis. Iberica, (45), 243–266. https://doi.org/10.17398/2340-2784.45.243

Whyatt, B., & Naranowicz, M. (2020). A robust design of the translator’s skill set: Evidence for transfer of metacognitive skills to intralingual paraphrasing. The Interpreter and Translator Trainer, 14(1), 1–18. https://doi.org/10.1080/1750399X.2019.1617028

Wongsuriya, P. (2020). Improving the Thai students’ ability in English pronunciation through mobile application. Educational Research Review, 15(4), 175–185.

Yang, D. H. (2018). A comparative study of strategic differences in Chinese English bidirectional post-MT editing: Use of 3C user manual as samples [Unpublished master’s thesis]. National Kaohsiung University of Science and Technology.

Yang, Y., & Wang, X. (2023). Predicting student translators’ performance in machine translation post-editing: Interplay of self-regulation, critical thinking, and motivation. Interactive Learning Environments, 31(1), 340–354. https://doi.org/10.1080/10494820.2020.1786407

Yang, Z., & Mustafa, H. R. (2022). On postediting of machine translation and workflow for undergraduate translation program in China. Human Behavior and Emerging Technologies, 2022, 1–11. https://doi.org/10.1155/2022/5793054

Ying, B. T., Hoon, A. L., Halim, H. A., & Majtanova, M. (2018). Students’ beliefs on translation strategy in learning German language. Gema Online Journal of Language Studies, 18(1), 69–86. https://doi.org/10.17576/gema-2018-1801-05

Yu, Z., & Lu, Y. (2023). Post-editing performance of English-major undergraduates in China: A case study of C-E translation with pedagogical reflections. In W. Hong & Y. Weng (Eds.), 17th International Conference on Computer Science and Education (ICCSE 2022) (pp. 408–420). Singapore: Springer.

Yulita, D. (2021). The correlation of English proficiency level and translation strategies used by Indonesian EFL learners. LLT Journal: A Journal on Language and Language Teaching, 24(1), 240–248. https://doi.org/10.24071/llt.v24i1.2812

Downloads

Published

2026-06-01

Issue

Section

Articles