Login | Register

Language learners’ perceptions of automatic speech recognition as a writing tool: A Technology Acceptance Model analysis


Language learners’ perceptions of automatic speech recognition as a writing tool: A Technology Acceptance Model analysis

Johnson, Carol (2021) Language learners’ perceptions of automatic speech recognition as a writing tool: A Technology Acceptance Model analysis. Masters thesis, Concordia University.

[thumbnail of Johnson_MA_F2021.pdf]
Text (application/pdf)
Johnson_MA_F2021.pdf - Accepted Version
Available under License Spectrum Terms of Access.


Automatic speech recognition (ASR) has the potential to mitigate the cognitive burden of L2 writing by facilitating the text input process (using a skill most humans possess: speaking) and offering assistance in terms of linguistic form, thus allowing writers to focus on other aspects of the task (e.g., cohesion, content). ASR is accessible, easy to use, and free; more importantly, it fulfills Chapelle’s (2001) criteria of an effective CALL tool (e.g., it promotes authenticity, has potential for language learning). Despite these affordances, there is a dearth of studies examining the affordances of ASR for writing, and none examining the use of ASR with adult ESL writers.
This mixed-methods one-shot study examines L2 writers’ perceptions of using ASR to write using the Technology Acceptance Model (TAM; Venkatesh & Davis, 2000), based on three criteria: usefulness, ease of use, and intention to use. The participants were ESL students at an English-medium university (N=17). They were provided with training on Google Voice Typing in Google Docs and, as part of the treatment, carried out two ASR-based writing tasks over a two-hour period. To measure their perceptions of the target criteria, participants filled in a TAM-informed survey after completing the treatment. To further explore the participants’ perceptions of using ASR to compose their texts, semi-structured interviews followed the writing tasks.
Findings indicate positive perceptions of ASR in terms of usefulness (language learning potential) and its ease of use (e.g., user-friendly voice commands). As observed in the literature (e.g., Hsu., 2016; Tsai, 2015), these positive perceptions will lead to an intention to continue to use ASR, suggesting that the technology has L2 pedagogical potential.

Divisions:Concordia University > Faculty of Arts and Science > Education
Item Type:Thesis (Masters)
Authors:Johnson, Carol
Institution:Concordia University
Degree Name:M.A.
Program:Applied Linguistics
Date:28 June 2021
Thesis Supervisor(s):Cardoso, Walcir
Keywords:automatic speech recognition L2 writing L2 pedagogy Technology Acceptance Model user perceptions
ID Code:988549
Deposited On:29 Nov 2021 16:54
Last Modified:29 Nov 2021 16:54


Alharbi, M. (2020). Exploring the potential of Google Doc in facilitating innovative teaching and learning practices in an EFL writing course. Innovation in Language Learning and Teaching, 14(3), 227–242.
Allen, H. (2018). Redefining writing in the foreign language curriculum: Toward a design approach. Foreign Language Annals, 51(3), 513–532.
American Academy of Arts and Sciences. (2017, February). America’s languages: Investing in language education for the 21st century. https://www.amacad.org/publication/americas-languages
Ashwell, T., & Elam, J. (2017). How accurately can the Google Web Speech API recognize and transcribe Japanese L2 English learners’ oral production? The JALT CALL Journal, 13(1), 59–76.
Aslan, E., & Ciftci, H. (2018). Synthesizing research on learner perceptions of CMC use in EFL/ESL writing. CALICO Journal, 36, 100–118.
Bañados-Santana, E. (2018). Combining theory, practice and technology in a CALL b-learning environment for EFL learners. European Journal of Applied Linguistics and TEFL, 7(2), 53–84.
Barkaoui, K. (2016). What and when second-language learners revise when responding to timed writing tasks on the computer: The roles of task type, second language proficiency, and keyboarding skills. The Modern Language Journal, 100(1), 320–340.
Bodnar, S., Cucchiarini, C., Penning de Vries, B., Strik, H., & van Hout, R. (2017). Learner affect in computerised L2 oral grammar practice with corrective feedback. Computer Assisted Language Learning, 30(3-4), 223–246.
Bodnar, S., Cucchiarini, C., & Strik, H. (2011). Computer-assisted grammar practice for oral communication. In A. Verbraeck, M. Helfert, J. Cordeiro, & B. Shishkov (Eds.), Proceedings of the 3rd International Conference on Computer Supported Education (CSEDU). (pp. 355-361). Noordwijkerhout.
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.
Cardoso, W. (in press). Technology for speaking development. In T. Derwing, M. Munro, & R. Thomson (Eds), Routledge handbook on second language acquisition and speaking. Routledge, Taylor & Francis Group.
Celce-Murcia, M., Brinton, D., & Goodwin, J. (2006). Teaching pronunciation: A reference for teachers of English to speakers of other languages. Cambridge University Press.
Chapelle, C. (2001). Computer applications in second language acquisition: Foundations for teaching, testing, and research. Cambridge University Press.
Chapelle, C. (2003). English language learning and technology. John Benjamins.
Chen, H. (2011). Developing and evaluating an oral skills training website supported by automatic speech recognition technology. ReCALL, 23(1), 59–78.
Chen Hsieh, J., Wu, W., & Marek, M. (2017). Using the flipped classroom to enhance EFL learning. Computer Assisted Language Learning, 30(1-2), 1–21.
Chiu, T., Liou, H., & Yeh, Y. (2007). A study of web-based oral activities enhanced by automatic speech recognition for EFL college learning. Computer Assisted Language Learning, 20(3), 209–233.
Clark, R. (1983). Reconsidering research on learning from media. Review of Educational Research, 53(4), 445–459.
Creswell, J. (2014). Research design: Qualitative, quantitative, and mixed method approaches. Sage Publications.
Cucchiarini, C., Nejjari, W., & Strik, H. (2012). My Pronunciation Coach: Improving English pronunciation with an automatic coach that listens. Language Learning in Higher Education, 1(2), 365–376.
Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Davis, F., Bagozzi, R., & Warshaw, P. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.
de Vries, B., Cucchiarini, C., Bodnar, S., Strik, H., & van Hout, R. (2015). Spoken grammar practice and feedback in an ASR-based CALL system. Computer Assisted Language Learning, 28(6), 550–576.
Derwing, T., Munro, M., & Carbonaro, M. (2000). Does popular speech recognition software work with ESL speech? TESOL Quarterly, 34(3), 592–603.
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.
Dizon, G. (2016). A comparative study of Facebook vs. paper-and-pencil writing to improve L2 writing skills. Computer Assisted Language Learning, 29(8), 1249–1258.
Dizon, G., & Thanyawatpokin, B. (2018). Web 2.0 tools in the EFL classroom: Comparing the effects of Facebook and blogs on L2 writing and interaction. The EuroCALL Review, 26(1), 29-42.
Elimat, A., & AbuSeileek, A. (2014). Automatic speech recognition technology as an effective means for teaching pronunciation. The JALT CALL Journal, 10(1), 21–47.
Eskenazi, M. (1999). Using a computer in foreign language pronunciation training: What advantages? CALICO Journal, 16(3), 447–469.
Field, A. (2018). Discovering statistics using IBM SPSS statistics. Sage Publications.
Filippidou, F., & Moussiades, L. (2020). Α benchmarking of IBM, Google and Wit automatic speech recognition systems. Artificial Intelligence Applications and Innovations, 583, 73–82.
Haghighi, H., Jafarigohar, M., Khoshsima, H., & Vahdany, F. (2019). Impact of flipped classroom on EFL learners’ appropriate use of refusal: Achievement, participation, perception. Computer Assisted Language Learning, 32(3), 261–293.
Hsu, L. (2016). An empirical examination of EFL learners’ perceptual learning styles and acceptance of ASR-based computer-assisted pronunciation training. Computer Assisted Language Learning, 29(5), 881–900.
Huang, Y., Huang, Y., Huang, S., & Lin, Y. (2012). A ubiquitous English vocabulary learning system: Evidence of active/passive attitudes vs. usefulness/ease-of-use. Computers & Education, 58(1), 273–282.
Hyland, K. (2011). Learning to write: Issues in theory, research, and pedagogy. In R. Manchón (Ed.), Learning to write and writing to learn in an additional language (pp. 17-35). John Benjamins.
Jenkins, J. (2000). The Phonology of English as an International Language. Oxford University Press.
Lee, L. (2017). Learners’ perceptions of the effectiveness of blogging for L2 writing in fully online language courses. International Journal of Computer-Assisted Language Learning and Teaching, 7(1), 19–33.
Levis, J., & Suvorov, R. (2012). Automatic speech recognition. In C. Chapelle (Ed.), The encyclopedia of applied linguistics. John Wiley & Sons.
Liakin, D., Cardoso, W., & Liakina, N. (2014). Learning L2 pronunciation with a mobile speech recognizer: French /y/. CALICO Journal, 32(1), 1–25.
Magnan, S., Murphy, D., Sahakyan, N., & Lafford, B. (2014). Goals of collegiate learners and the standards for foreign language learning. The Modern Language Journal, 98, i–xxiii.
McCrocklin, S. (2018). Learners’ feedback regarding ASR-based dictation practice for pronunciation learning. CALICO Journal, 36(2), 119–137.
McCrocklin, S., & Edalatishams, I. (2020). Revisiting popular speech recognition software for ESL speech. TESOL Quarterly, 54(4), 1–13.
Menke, M., & Anderson, A. (2019). Student and faculty perceptions of writing in a foreign language studies major. Foreign Language Annals, 52(2), 388–412.
Mirzaei, M., Meshgi, K., Akita, Y., & Kawahara, T. (2017). Partial and synchronized captioning: A new tool to assist learners in developing second language listening skill. ReCALL: The Journal of EUROCALL, 29(2), 178–199.
Mroz, A. (2018). Seeing how people hear you: French learners experiencing intelligibility through automatic speech recognition. Foreign Language Annals, 51(3), 617–637.
Moussalli, S., & Cardoso, W. (2020). Intelligent personal assistants: Can they understand and be understood by accented L2 learners? Computer Assisted Language Learning, 33(8), 865-890.
National Council of Teachers of English. (2018, November 14). Understanding and teaching writing: Guiding principles. https://ncte.org/statement/teachingcomposition/
Poulsen, R., Hastings, P., & Allbritton, D. (2007). Tutoring bilingual students with an automated reading tutor that listens. Journal of Educational Computing Research, 36(2), 191–221.
Quinlan, T. (2004). Speech recognition technology and students with writing difficulties: Improving fluency. Journal of Educational Psychology, 96(2), 337–346.
Saldaña, J. (2009). The coding manual for qualitative researchers. Sage Publications.
Scherer, R., & Teo, T. (2019). Editorial to the special section—Technology acceptance models: What we know and what we (still) do not know. British Journal of Educational Technology, 50(5), 2387–2393.
Schmidt, R. (1995). Consciousness and foreign language learning: A tutorial on the role of attention and awareness on learning. In R. Schmidt (Ed.). Attention and awareness in foreign language learning (pp. 1-63). University of Hawaii, Second Language Teaching & Curriculum Center.
Soleimani, E., Ismail, K., & Mustaffa, R. (2014). The acceptance of mobile assisted language learning (MALL) among post graduate ESL students in UKM. Procedia - Social and Behavioral Sciences, 118, 457–462.
Stevenson, M., Schoonen, R., & de Glopper, K. (2006). Revising in two languages: A multi-dimensional comparison of online writing revisions in L1 and FL. Journal of Second Language Writing, 15, 201–233.
Tan, P. (2019). An empirical study of how the learning attitudes of college students toward English e-tutoring websites affect site sustainability. Sustainability, 11(6), 1–19.
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: San Marcos, 32(1), 153–171.
Venkatesh, V., & Davis, F. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204.
Voicebot Research. (2019, July). Voice assistant SEO report for brands. Voicebot.Ai. https://voicebot.ai/wp-content/uploads/2019/07/voice_assistant_seo_report_for_brands_2019_voicebot.pdf
Walker, N., Cedergren, H., Trofimovich, P., & Gatbonton, E. (2011). Automatic speech recognition for CALL: A task-specific application for training nurses. Canadian Modern Language Review, 67(4), 459–479.
Weinberger, S. (2021). Speech accent archive. George Mason University. Retrieved from http://accent.gmu.edu
Wang, Y., & Young, S. (2014). Effectiveness of feedback for enhancing English pronunciation in an ASR-based CALL system. Journal of Computer Assisted Learning, 31, 493–504.
Young, S., & Wang, Y. (2014). The game embedded CALL system to facilitate English vocabulary acquisition and pronunciation. International Forum of Educational Technology & Society, 17(3), 239–251.
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Research related to the current document (at the CORE website)
- Research related to the current document (at the CORE website)
Back to top Back to top