Machine Translation Technology in Language Pedagogy: A Linguistic and Engineering Perspective on Computational Analysis

Ayesha Rashid; Shumaila Noreen; Aamir Ali Asad; Muhammad Waleed Butt; Mahnoor Ghani Sheikh; Engr Yaseen; Mudasar Jahan

Authors

Ayesha Rashid Information Technology University of Gujrat, Pakistan
Shumaila Noreen Lecturer, Department of English, University of Wah, Pakistan
Aamir Ali Asad Lecturer, Lawrence College, Murree, Pakistan
Muhammad Waleed Butt Grand Asian University, Sialkot, Pakistan
Mahnoor Ghani Sheikh Department of English, University of Gujrat. Pakistan
Engr Yaseen Production Manager, Shaukat Soap and Ghee industries, Pakistan
Mudasar Jahan Associate Lecturer in English, University of Gujrat, Pakistan

Keywords:

Machine Translation, Language Pedagogy, UTAUT Framework, Computational Analysis, Language Learning Outcomes.

Abstract

This mixed-methods study explores the impact of Machine Translation (MT) technology on language pedagogy, aiming to investigate its benefits, challenges, and future directions, guided by three research questions focusing on performance expectancy, effort expectancy, and social influence. The problem statement underlying this study is the limited understanding of MT technology's impact on language pedagogy, despite its increasing popularity, and the context is the growing demand for language instruction and the need for effective language learning tools. Relevant research has shown MT technology's potential to improve language learning outcomes, enhance motivation, and support personalized learning (Chapelle, 2003; García, 2015). The study selected a population of language instructors and learners from various educational institutions, emphasizing MT technology's importance in facilitating communication and language learning. Employing a quantitative survey and qualitative semi-structured interviews, the study utilized the Unified Theory of Acceptance and Use of Technology (UTAUT) framework, which provides a comprehensive understanding of the factors influencing MT technology adoption. The scope of this study is limited to exploring MT technology's impact on language pedagogy, focusing on performance expectancy, effort expectancy, social influence on pedagogical implications. The significance of this study lies in its potential to contribute to the understanding of MT technology's impact on language pedagogy, informing the development of effective language learning tools and instructional strategies. Data analysis procedures included descriptive statistics and frequency analysis for quantitative data by using SPSS software, and thematic analysis using NVivo for qualitative data, with results revealing several themes, providing insights into MT technology's impact on language pedagogy, highlighting its benefits, challenges, and future directions.

References

Alharbi, A., Alotaibi, A., & Almutairi, A. (2022). The impact of machine translation on language learners' reading comprehension skills. Journal of Language and Linguistics, 21(3), 1-15.

Benson, P. (2020). Autonomy and technology in language learning. In M. Thomas & H. Reinders (Eds.), Task-based language learning and teaching with technology (pp. 227-244). Continuum.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101.

Chapelle, C. A. (2003). English language learning and technology. John Benjamins Publishing.

Chen, X., Zhang, Y., & Li, X. (2022). Using machine translation to improve language learners' pronunciation. Journal of Educational Technology Development and Exchange, 14(1), 1-12.

Chiu, T., Kao, C., & Reynolds, B. (2020). The effects of machine translation on language learners' writing skills. Journal of Language and Linguistics, 19(2), 1-18.

Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). Sage Publications.

Crystal, D. (2003). English as a global language (2nd ed.). Cambridge University Press.

García, I. (2019). Machine translation in language teaching: A review of the literature. ReCALL, 31(1), 1-18. doi: 10.1017/S0958344018000063

Graddol, D. (2006). English next: Why global English may mean the end of 'English as a foreign language'. British Council.

Higgins, J., & Johns, T. (1984). Computers in language learning. Collins.

Hutchins, J. (2015). Machine translation: A concise history. Journal of Translation Studies, 18(1), 1-21.

Koehn, P. (2010). Statistical machine translation. Cambridge University Press.

Lai, C., & Zhang, J. (2020). Exploring the use of machine translation in language learning. Journal of Language and Linguistics, 19(1), 1-15.

Lee, S., & Lee, J. (2020). The impact of machine translation on language learners' speaking skills. Journal of Language and Linguistics, 19(3), 1-18.

Levy, M. (1997). Computer-assisted language learning: Context and conceptualization. Oxford University Press.

Levy, M. (2009). Technologies in language teaching. In M. H. Long & C. J. Doughty (Eds.), The handbook of language teaching (pp. 698-718). Wiley-Blackwell.

Lopez, A. (2008). Statistical machine translation for automatic subtitling. Journal of Artificial Intelligence Research, 32, 341-363.

Patton, M. Q. (2015). Qualitative research and evaluation methods (4th ed.). Sage Publications.

Tashakkori, A., & Teddlie, C. (2010). Sage handbook of mixed methods in social & behavioral research (2nd ed.). Sage Publications.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.

Wang, X., Li, X., & Zhang, Y. (2022). Using machine translation to improve language learners' vocabulary acquisition. Journal of Educational Technology Development and Exchange, 14(2), 1-12.