Machine Translation-Based Learning as an Alternative Learning Strategy in the Translation Course for Fifth-Semester Students at Universitas Indraprasta PGRI
DOI:
https://doi.org/10.60012/cje.v4i2.237Keywords:
Machine Translation, Translation Learning, Education Technology, Post-Editing, Translation PedagogyAbstract
The rapid development of artificial intelligence (AI) has brought major changes to education, including translation learning. One of its most visible implementations is machine translation (MT) — tools such as Google Translate and DeepL — which are now able to produce contextual translations through Neural Machine Translation (NMT) systems. This study aims to describe the implementation of MT-based learning in the Translation course and to analyze students' perceptions of its effectiveness. A descriptive qualitative approach was employed, involving fifth-semester students and the lecturer of the Translation course at Universitas Indraprasta PGRI, Jakarta, as participants. Data were collected through observation, in-depth interviews, and documentation, and were analyzed using the interactive model of Miles, Huberman, and Saldaña. The finding shows that integrating machine translation into the learning process improved students' translation quality, critical thinking, linguistic awareness, and learning autonomy. Students used MT not as a substitute for their own translation ability but as an initial aid that was followed by analysis, discussion, and post-editing. The study concludes that MT-based learning can serve as an innovative and pedagogically sound alternative strategy for teaching Translation in higher education, provided it is guided by lecturers within a critical and ethical framework.
References
Adiputra, I. N., & Arase, Y. (2017). Statistical and neural machine translation for Japanese–Indonesian language pair. Conference proceedings.
Anderson, T. (2018). The theory and practice of online learning (2nd ed.). Athabasca University Press.
Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
Baker, M. (2018). In other words: A coursebook on translation (3rd ed.). Routledge.
Beatty, K. (2016). Teaching and researching computer-assisted language learning (2nd ed.). Pearson Education.
Benson, P. (2013). Teaching and researching autonomy (2nd ed.). Routledge.
Bhattacharyya, P. (2015). Machine translation. CRC Press.
Bowker, L. (2020). Machine translation literacy instruction for language learners and future translators: Empowerment from cognitive to critical. Translation and Translanguaging in Multilingual Contexts, 6(2), 202–222.
Briggs, N. (2018). Neural machine translation tools in the language learning classroom: Students' use, perceptions, and analyses. The Journal of Language Learning and Teaching, 8(2), 72–89.
Clifford, J., Merschel, L., & Reisinger, D. (2013). Surveying the landscape: What is the role of machine translation in language learning? Foreign Language Annals, 46(4), 511–529.
Colina, S. (2015). Fundamentals of translation. Cambridge University Press.
Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). SAGE Publications.
Garcia, I., & Pena, M. I. (2011). Machine translation-assisted language learning: Writing for beginners. Computer Assisted Language Learning, 24(5), 471–487.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
Groves, M., & Mundt, K. (2015). Friend or foe? Google Translate in language for academic purposes. English for Specific Purposes, 37, 112–121.
Hermanto, A., et al. (2015). Statistical machine translation for English–Indonesian bidirectional translation. Conference proceedings.
Hockly, N. (2018). Digital literacies (2nd ed.). Routledge.
Junczys-Dowmunt, M., Dwojak, T., & Hoang, H. (2016). Is neural machine translation ready for deployment? A case study on 30 translation directions. Proceedings of IWSLT.
Kiraly, D. (2014). A social constructivist approach to translator education: Empowerment from theory to practice. Routledge.
Koehn, P. (2020). Neural machine translation. Cambridge University Press.
Malmkjær, K. (2018). The Cambridge handbook of translation. Cambridge University Press.
Miles, M. B., Huberman, A. M., & Saldaña, J. (2014). Qualitative data analysis: A methods sourcebook (3rd ed.). SAGE Publications.
Niño, A. (2020). Machine translation in foreign language learning: Applications, uses, and perceptions. Language Learning & Technology, 24(1), 1–25.
O'Neill, E. M. (2019). Training students to evaluate machine translation output. The Language Educator, 14(2), 35–39.
ugiyono. (2019). Metode penelitian kuantitatif, kualitatif, dan R&D. Alfabeta.
Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., & Macherey, W. (2016). Google's neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144.
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