The recent advent of neural-network-based machine translation (MT) tools has significant implications for world language teaching and learning. That said, little is known about actual student use of MT tools: most research on student use has focused on reported data (e.g., from surveys) (e.g., Clifford et al., 2013; Briggs, 2018). How students use MT tools and what influences this usage are important components of the larger ecology surrounding MT and language teaching/learning: from an ecological theoretical perspective, multiple factors (e.g., experience, beliefs, platform design and functionality, policy) interact across scale levels (e.g., individual, classroom, institution, society) to impact how digital technologies are understood and used in language learning contexts.

In this talk, we draw on our recent computer tracking study to examine student uses of MT and what influences these uses. Our study engaged 74 learners of French, Spanish, and Mandarin in a short written task in the target language while we observed and recorded their screen. Thereafter, we conducted a stimulated recall, asking students to narrate key moments in their writing task. Finally, we conducted post-interviews to dive deeper into student actions and motivations. We documented a myriad of machine translation use strategies in terms of input (intentional input, changing input) and output (changing course, rephasing, seeking examples, rechecking/triangualing output). We present these strategies with video and audio data to showcase the complexity of the actions. We also identified additional factors that influence these strategies, including: knowledge of language, specific beliefs about online tools, learners’ perceptions of their own role and the roles ascribed to online tools and classroom policies.

We conclude the talk with a discussion of how instructors and researchers might draw on this research to think differently about integrating MT into classroom practices.

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Abstract Summary
The recent advent of more powerful, neural-network-based machine translation (MT) tools has significant implications for world language teaching and learning. That said, little is known about actual student use of MT tools. In this talk, we draw on our recent computer tracking study to examine how learners of French, Spanish, and Mandarin use machine translation and what influences these uses. Our findings revealed a myriad of complex machine translation use strategies in terms of input and output, which we present with video and audio data. We also identified additional factors that influence these strategies, including: knowledge of language, specific beliefs about online tools, learners’ perceptions of their own role and the roles ascribed to online tools and classroom policies. We conclude the talk with a discussion of how instructors and researchers might draw on this research to think differently about integrating MT into classroom practices.

Authors:
Kimberly Vinall, University of California, Berkeley, United States
Emily Hellmich, University of California, Berkeley, United States

About the Presenter(s)
Dr. Kimberly Vinall (PhD, UC Berkeley) is the Executive Director of the UC Berkeley Language Center. Her research explores how learners engage with cultural / linguistic differences through critical pedagogy, literary texts, and digital tools.

Connect on Linkedin
www.linkedin.com/in/kimberly-vinall-172528ab

Additional website of interest
https://blc.berkeley.edu/2021/12/10/kimberlyvinall/

See this presentation on the full schedule – Saturday Schedule

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