Hey Google, Let’s Write

Examining L2 Learners’ Acceptance of Automatic Speech Recognition as a Writing Tool

Authors

  • Carol Johnson Concordia University
  • Walcir Cardoso Concordia University

DOI:

https://doi.org/10.1558/cj.22431

Keywords:

automatic speech recognition, L2 writing, technology acceptance model

Abstract

Writing involves more than attention to form (e.g., orthography, grammar), since it requires attention to text type, content, and genre. However, most students of English as a second language (L2) tend to prioritize linguistic accuracy in their writing, to the detriment of the content of their texts. Automatic speech recognition (ASR) has the potential to mitigate this, as it reduces the cognitive burden of writing by facilitating the text input process (using a skill most humans possess—speaking), offering assistance in spelling, and allowing a focus on other aspects of the task (e.g., cohesion, content). Automatic speech recognition is not only accessible and free, but it also fulfills Chapelle’s (2001) criteria of an effective computer-assisted language learning tool (e.g., authenticity, learner fit). Despite these affordances, there is a dearth of studies examining the possible affordances of ASR for writing. This mixed methods, one-shot study examines L2 writers’ perceptions of using ASR to write using the technology acceptance model (TAM). Seventeen (N = 17) undergraduate students at a Canadian university were provided with training on Google Voice Typing (Google Docs) and carried out a series of ASR-based writing tasks over a two-hour period. In order to measure their perceptions of the target criteria, participants filled in a TAM-informed survey consisting of statements about their experience with ASR scored on a 7-point Likert scale. To further explore the participants’ perceptions, semi-structured interviews followed. Findings indicate positive perceptions of ASR’s usefulness in terms of language learning and its ease of use due to the user-friendly voice commands. This suggests that ASR has pedagogical potential, thus requiring further examination to determine its optimal use for L2 writing.

Author Biographies

  • Carol Johnson, Concordia University

    Carol Johnson is a PhD candidate in Education (specialization in Applied Linguistics) at Concordia University. Her research interests include L2 pronunciation, particularly the use of automatic speech recognition for both pedagogical and assessment purposes. She teaches university-level ESL courses and ESL teacher education courses.

  • Walcir Cardoso, Concordia University

    Walcir Cardoso is a Professor of Applied Linguistics at Concordia University. He conducts research in second language acquisition, exploring phonology, morphosyntax and vocabulary, and the effects of computer technology (e.g., clickers, text-to-speech synthesizers, automatic speech recognition) on language learning.

References

Alves, R., Castro, S., Sousa, L., & Stromqvist, S. (2007). Influence of typing skill on pause-execution cycles in written composition. In M. Torrance, L. van Waes, & D. Galbraith (Eds.), Writing and cognition: Research and applications (pp. 55–65). Amsterdam: Elsevier. https://doi.org/10.1163/9781849508223_005

Arcon, N., Klein, P., & Dombroski, J. (2017). Effects of dictation, speech to text, and handwriting on the written composition of elementary school English language learners. Reading and Writing Quarterly, 33(6), 533–548. https://doi.org/10.1080/10573569.2016.1253513

Aslan, E., & Ciftci, H. (2018). Synthesizing research on learner perceptions of CMC use in EFL/ESL writing. CALICO Journal, 36(2), 100–118. https://doi.org/10.1558/cj.34818

Barkaoui, K. (2014). Examining the impact of L2 proficiency and keyboarding skills on scores on TOEFL-iBT writing tasks. Language Testing, 31(2) 241–259. https://doi.org/10.1177/0265532213509810

Brissaud, C., & Chevrot, J.-P. (2011). The late acquisition of a major difficulty of French inflectional orthography: The homophonic /E/ verbal endings. Writing Systems Research, 3(2), 129–144. https://doi.org/10.1093/wsr/wsr003

Cardoso, W. (2022). Technology for speaking development. In T. Derwing, M. Munro & R. Thomson (Eds), The Routledge handbook on second language acquisition and speaking (pp. 299–313). New York, London: Routledge, Taylor & Francis Group. https://doi.org/10.4324/9781003022497-26

Chapelle, C. (2001). Computer applications in second language acquisition: Foundations for teaching, testing, and research. Cambridge: Cambridge University Press.

Choi, I., & Deane, P. (2021). Evaluating writing process features in an adult EFL writing assessment context: A keystroke logging study. Language Assessment Quarterly, 18(2), 107–132. https://doi.org/10.1080/15434303.2020.1804913

Creswell, J. (2014). Research design: Qualitative, quantitative, and mixed method approaches. Los Angeles: Sage Publications.

Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Derwing, T., Munro, M., & Carbonaro, M. (2000). Does popular speech recognition software work with ESL speech TESOL Quarterly, 34(3), 592–603. https://doi.org/10.2307/3587748

Ding, Y., & Zhao, T. (2019). Chinese university EFL teachers’ and students’ beliefs about EFL writing: Differences, influences, and pedagogical implications. Chinese Journal of Applied Linguistics, 42(2), 163–181. https://doi.org/10.1515/CJAL-2019-0010

Ertmer, P., Ottenbreit-Leftwich, A., Sadik, O., Sendurur, E., & Sendurur, P. (2012). Teacher beliefs and technology integration practices: A critical relationship. Computers & Education, 59(2), 423–435. https://doi.org/10.1016/j.compedu.2012.02.001

Field, A. (2018). Discovering statistics using IBM SPSS statistics. Los Angeles: Sage Publications.

Flower, L., & Hayes, J. (1981). A cognitive process theory of writing. College Composition and Communication, 32(4), 365–387. https://doi.org/10.2307/356600

Hyland, K. (2013). Writing in the university: Education, knowledge and reputation. Language Teaching, 46(1), 53–70. https://doi.org/10.1017/S0261444811000036

Knoch, U., Rouhshad, A., Oon, S., & Storch, N. (2015). What happens to ESL students’ writing after three years of study at an English medium university? Journal of Second Language Writing, 28, 39–52. https://doi.org/10.1016/j.jslw.2015.02.005

Liakin, D., Cardoso, W., & Liakina, N. (2014). Learning L2 pronunciation with a mobile speech recognizer: French /y/. CALICO Journal, 32(1), 1–25. https://doi.org/10.1558/cj.v32i1.25962

Liu, Q., & Chao, C. (2017). CALL from an ecological perspective: How a teacher perceives affordance and fosters learner agency in a technology-mediated language classroom. ReCALL, 30(1), 68–87. https://doi.org/10.1017/S0958344017000222

Magnan, S., Murphy, D., Sahakyan, N., & Lafford, B. (2014). Goals of collegiate learners and the standards for foreign language learning. Modern Language Journal, 98(S1), 1–11. https://doi.org/10.1111/j.1540-4781.2013.12056_3.x

McCrocklin, S. (2019). Learners’ feedback regarding ASR-based dictation practice for pronunciation learning. CALICO Journal, 36(2), 119–137. https://doi.org/10.1558/cj.34738

McCrocklin, S., & Edalatishams, I. (2020). Revisiting popular speech recognition software for ESL speech. TESOL Quarterly, 54(4), 1–13. https://doi.org/10.1002/tesq.3006

Mohsen, M. (2021). L1 versus L2 writing processes: What insight can we obtain from a keystroke logging program? Language Teaching Research, 1–25. https://doi.org/10.1177/13621688211041292

Mroz, A. (2018). Seeing how people hear you: French learners experiencing intelligibility through automatic speech recognition. Foreign Language Annals, 51(3), 617–637. https://doi.org/10.1111/flan.12348

Quinlan, T. (2004). Speech recognition technology and students with writing difficulties: Improving fluency. Journal of Educational Psychology, 96(2), 337–346. https://doi.org/10.1037/0022-0663.96.2.337

Saldaña, J. (2009). The coding manual for qualitative researchers. London: Sage Publications.

Sundberg, R., & Cardoso, W. (2022). The feasibility of using Bande à Part to aid French language learners. CALICO Journal, 39(2), 196–218. https://doi.org/10.1558/cj.41796

Torrance, M., & Galbraith, D. (2006). The processing demands of writing. In C. MacAuthur, S. Graham, & J. Fitzgerald (Eds.), Handbook of writing research (pp. 67–80). New York: Guilford.

Tsai, Y. (2015). Applying the technology acceptance model (TAM) to explore the effects of a course management system (CMS)-assisted EFL writing instruction. CALICO Journal, 32(1), 153–171. https://doi.org/10.1558/calico.v32i1.25961

Venkatesh, V., & Davis, F. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

Voicebot Research (2020). Smartphone voice assistant consumer adoption report: Executive summary. Voicebot.Ai, November. https://research.voicebot.ai/report-list/smartphone-voice-assistant-consumer-adoption-report-2020

Zhang, Z. (2020). Engaging with automated writing evaluation (AWE) feedback on L2 writing: Student perceptions and revisions. Assessing Writing, 43, 100439. https://doi.org/10.1016/j.asw.2019.100439

Published

2024-06-25

Issue

Section

Articles

How to Cite

Johnson, C., & Cardoso, W. (2024). Hey Google, Let’s Write: Examining L2 Learners’ Acceptance of Automatic Speech Recognition as a Writing Tool. CALICO Journal, 41(2), 122-145. https://doi.org/10.1558/cj.22431