Machine Translation:
The Impacts of Machine Translation on Secondary-Aged English Language Learners’ Literacy

As a full time ELL support teacher in a BC secondary school, I am no stranger to the usage and abilities of translation tools. Having studied French from grade 5 through my bachelors, I have and still will use it to double check my syntax, vocabulary, and pronunciation. However, I have also been witness to this accelerated evolution of the accuracy, prevalence, and speed of translation. Not to sound like a curmudgeon, but back in my day, prior to Google Translate’s switch from Statistical Machine Translation (SMT) to Google Neural Machine Translation (GNMT), you had to understand how the translation worked (Johnson et al., 2016). For example, you need to change your input language to be more easily read and translated by the SMT to increase accuracy (I hope that I want → I hope to want). Also, you would need to be aware of idioms and context. Since SMTs translate word-for-word or sentence by sentence-by-sentence using a large database of bilingual texts, you need to develop a lot of metalinguistic skills in order to interact and use the tool appropriately. Alas, in the era of NMTs, artificial intelligence has almost eradicated the need for the metalinguistic awareness, especially as Large Language Models entered the market. But are these recent advancements in translation and generative language technology helping or hindering language learners’ literacy development?

Now professionally, I use it daily to work with students of various levels, languages, and cultures to help them understand and communicate their learning in their academic classes. As I’ve mentioned many times before in my blog, I am a huge AI advocate and believe that, if interacted with appropriately, it has the ability to increase the speed, retention, and depth of learning. Similarly, I hold this belief with the use of translation tools. Especially for my newcomers with little to no literacy in their first language let alone English, these translators are their life line – their only means to communicate during their most vulnerable, shocking, and confusing period of their entire educational experience. Yet, as we advocate for translators to be universal supports and accommodated in classroom environments, we’ve seen an over- and codependent relationship of reliance between students and translators. Since most non-literature academic classes, like Math and Science, are evaluating a student’s understanding of the content and skills and not their language ability, teachers are unbothered by students’ use of translation during class and assessments. However, my colleagues and I have been observing a plateau in student English level progress, and for some, even a regression. How can we find a balance where language learners are able to improve their academic literacy while also using just enough support to get them there with confidence?

Furthermore, my project will explore the historical and cultural significance of Machine Translation (MT) and the impact it has had on secondary-aged English language learning students’ literacy development. Though the effects of MTs on language is not exclusively prevalent in English, my personal and professional experiences have made me really want to research and focus on an area in particular. As we speak, I am researching new ways to redirect the use of MT for my learners with alternatives, like word-referencers and spaced repetition, to encourage growth without harming their participation and understanding of the materials. In the interactive presentation below, I have created anecdotes, an overview of research, studies, and findings regarding the impacts as well as comparison of risks and benefits. Lastly, I include my Guided decision flow chart to help educators decide if MT is necessary by using it as a tiered decision matrix.

Final Project – Machine Translation – The Impacts of MT on Secondary-Aged ELL Literacy  – ETEC 540

My goal for this final project was to really have a better understanding and expertise in how language literacy, more specifically written language literacy, is connected and affected by growing technologies in education. This project and topic are a huge passion of mine as it will and has literally changed my perspective, approach, and style of teaching in my day-to-day professional practice. Let me know what you think or if you have any other ideas or tools, I’m always ready to try something new in my practice 🙂

References:

Gnanadesikan, A. E. (2011). The first IT revolution. In The writing revolution: Cuneiform to the Internet (pp. 1–12). John Wiley and Sons. https://www.blackwellpublishing.com/content/bpl_images/content_store/sample_chapter/9781405154062/9781405154062_001.pdf

Johnson, M., Schuster, M., & Thorat, N. (2016). Zero-Shot Translation with Google’s Multilingual Neural Machine Translation System. Google Research Blog. https://research.google/blog/zero-shot-translation-with-googles-multilingual-neural-machine-translation-system/

Kirchhoff, P. (2024). Machine translation in English language teaching. ELT Journal, 78(4), 393–400. https://doi.org/10.1093/elt/ccae034

 

Lee, S. M., & Kang, N. (2024). Effects of machine translation on L2 writing proficiency: The complexity, accuracy, lexical diversity, and fluency. Language Learning & Technology, 28(1), 1–19. https://doi.org/10.64152/10125/73585

 

Murtisari, E. T., Kristianto, A. K., & Bonar, G. (2024). Self-directed use of machine translation among language learners: Does it lead to disruptive L2 avoidance? Foreign Language Annals, 57(4), 1094–1114. https://doi.org/10.1111/flan.12768

O’Neill, E. M. (2019). Training students to use online translators and dictionaries: The impact on second language writing scores. International Journal of Research Studies in Language Learning, 8(2), 47–65. https://doi.org/10.5861/ijrsll.2019.4002   

 

Stritar Kučuk, M. (2024). Investigating the usage of machine translation in L2 learning and its impact on writing proficiency. Lidil, 70. https://doi.org/10.4000/lidil.10737

Wang, H. (2022). Progress in machine translation. Engineering, 9(1), 25 to 35. https://doi.org/10.1016/j.eng.2021.08.015

Zheng, Y., Tan, J., Wang, Y., & Han, Y. (2024). Integration of artificial intelligence into language teaching: A literature review. Euro-Global Journal of Linguistics and Language Education, 11(1), 98–113.