Lecturer's Perception of Google Translate as an Academic Tool: Appraisal Analysis
Keywords:
Appraisal, Higher Education, Google Translate, Lecturer, Perception
Abstract
Although research on Google Translate (GT) has been widely documented, there is a dearth of research examining Arabic lecturers' perspectives on GT from an appraisal point of view. This empirical research discusses how Arabic lecturers perceive the usage of GT as a tool for academics. The participants come from ten different Arabic language lecturers. Surveys and in-depth interviews were undertaken to gather data. The appraisal theory proposed by Martin and White (2005) was used for data analysis. The findings demonstrate how lecturers' perceptions of the usage of GT can be divided into four categories happiness and unhappiness, satisfied and unsatisfied. GT is popular among lecturers since it is simple to use, accessible, affordable, provides voice recording functions, and requires little language proficiency. Meanwhile, their unhappiness with GT arises because they find it challenging to grasp due to its literal translation, ambiguity, and poor source language input. Satisfaction with the lecturers arises from GT's assistance with their tasks, whereas dissatisfaction results from GT's inability to translate cultural terms. To this end, discussion and implications are discussed at the end of the study.
References
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Tsai, S. C. (2017). Effectiveness of ESL students’ performance by computational assessment and role of reading strategies in courseware-implemented business translation tasks. Computer Assisted Language Learning, 30(6), 474–487. https://doi.org/10.1080/09588221.2017.1313744
Widodo, H. P. (2014). Methodological considerations in interview data transcription. International Journal of Innovation in English Language Teaching and Research, 3(1), 101–107.
Xia, Y. (2020). Research on statistical machine translation model based on deep neural network. Computing, 102(3), 643–661. https://doi.org/10.1007/s00607-019-00752-1
Zhang, J. J., & Zong, C. Q. (2020). Neural machine translation: Challenges, progress and future. Science China Technological Sciences, 1–23. https://doi.org/10.1007/s11431-020-1632-x
Agustin, R., & Wulandari, S. (2022). TheAnalysis Of Grammatical Errors On Students’ Essay Writing By Using Grammarly. Jurnal Pendidikan Bahasa Inggris Proficiency, 4(1), 39–46.
Aiken, M., & Balan, S. (2011). An Analysis of Google Translate Accuracy. Translation Journal, 16(2), 1–3.
Alhaisoni, E., & Alhaysony, M. (2017). An Investigation of Saudi EFL University Students’ Attitudes towards the Use of Google Translate. International Journal of English Language Education, 5(1), 72–82. https://doi.org/10.5296/ijele.v5i1.10696
Attia, M. A. (2008). Handling Arabic Morphological and Syntactic Ambiguity within the LFG Framework with a View to Machine Translation. University of Manchester for the degree of Doctor of Philosophy.
Bahri, & Mahadi. (2016). Google Translate as a Supplementary Tool for Learning Malay: A Case Study at Universiti Sains Malaysia. Advances in Language and Literary Studies, 7(3), 161–167. https://doi.org/10.7575/aiac.alls.v.7n.3p.161
Benkova, L., Munkova, D., Benko, Ľ., & Munk, M. (2021). Evaluation of English–Slovak neural and statistical machine translation. Applied Sciences (Switzerland), 11(7), 2948. https://doi.org/10.3390/app11072948
Briggs, N. (2018). Neural machine translation tools in the language learning classroom: Students’ use, perceptions, and analyses. JALT CALL Journal, 14(1), 2–24. https://doi.org/10.29140/jaltcall.v14n1.221
Brinkmann, S. (2020). Unstructured and semistructured interviewing. In The Oxford Handbook of Qualitative Research (pp. 277–299). https://doi.org/10.1093/oxfordhb/9780190847388.013.22
Cancino, M., & Panes, J. (2021). The impact of Google Translate on L2 writing quality measures: Evidence from Chilean EFL high school learners. System, 98, 102464. https://doi.org/10.1016/J.SYSTEM.2021.102464
Dajun, Z., & Yun, W. (2015). Corpus-based Machine Translation : Its Current Development and Perspectives. International Forum of Teaching and Studies, 11(1/2), 90.
Geluso, J. (2013). Phraseology and frequency of occurrence on the web: Native speakers’ perceptions of Google-informed second language writing. Computer Assisted Language Learning, 26(2), 1441–1457. https://doi.org/10.1080/09588221.2011.639786
Glynn, S. (1985). Science and perception as design. Design Studies, 6(3), 122–126. https://doi.org/10.1016/0142-694X(85)90001-8
Han, S., & Shin, J. A. (2017). Teaching Google search techniques in an L2 academic writing context. Language Learning and Technology, 21(3), 172–194.
Irhamni. (2011). Hambatan Penerjemahan Teks Bahasa Arab Ke Dalam Bahasa Indonesia : Pengalaman Mahasiswa Sastra Arab. Bahasa Dan Seni, 39(2), 23–36.
Lee, S. M. (2020). The impact of using machine translation on EFL students’ writing. Computer Assisted Language Learning, 33(3), 1571–1575. https://doi.org/10.1080/09588221.2018.1553186
Maamuujav, U., Olson, C. B., & Chung, H. (2021). Syntactic and lexical features of adolescent L2 students’ academic writing. Journal of Second Language Writing, 53, 100822. https://doi.org/10.1016/j.jslw.2021.100822
Madkour, M. (2016). Linguistic levels of translation: A generic exploration of translation difficulties in literary textual corpus. International Journal of Applied Linguistics and English Literature, 5(6), 99–118. https://doi.org/10.7575/aiac.ijalel.v.5n.6p.99
Martin, J. R., & White, P. R. R. (2005). The Language of Evaluation: Appraisal in English. In The Language of Evaluation: Appraisal in English. Palgrave Macmillan. https://doi.org/10.1057/9780230511910
Mundt, K., & Groves, M. (2016). A double-edged sword: the merits and the policy implications of Google Translate in higher education. European Journal of Higher Education, 6(4), 387–401. https://doi.org/10.1080/21568235.2016.1172248
Newmark, P. (1998). A Textbook of Translation. Prentice Hall.
Samokhin, I., & Sokolova, N. (2018). Some Weaknesses of Modern Machine Translation (by Example of Google Translate Web Service). Nauchnyy Dialog, 10, 148–157. https://doi.org/10.24224/2227-1295-2018-10-148-157
Slocum, J. (1982). The LRC machine translation system. ACM SIGART Bulletin, 79, 158. https://doi.org/10.1145/1056663.1056707
Stahlberg, F. (2020). Neural machine translation: A review. Journal of Artificial Intelligence Research, 69, 343–418. https://doi.org/10.1613/JAIR.1.12007
Stapleton, P., & Leung Ka Kin, B. (2019). Assessing the accuracy and teachers’ impressions of Google Translate: A study of primary L2 writers in Hong Kong. English for Specific Purposes, 56, 18–34. https://doi.org/10.1016/J.ESP.2019.07.001
Tongpoon-Patanasorn, A., & Griffith, K. (2020). Google Translate and translation quality: A case of translating academic abstracts from Thai to English. PASAA, 60, 134–163.
Tsai, S. C. (2017). Effectiveness of ESL students’ performance by computational assessment and role of reading strategies in courseware-implemented business translation tasks. Computer Assisted Language Learning, 30(6), 474–487. https://doi.org/10.1080/09588221.2017.1313744
Widodo, H. P. (2014). Methodological considerations in interview data transcription. International Journal of Innovation in English Language Teaching and Research, 3(1), 101–107.
Xia, Y. (2020). Research on statistical machine translation model based on deep neural network. Computing, 102(3), 643–661. https://doi.org/10.1007/s00607-019-00752-1
Zhang, J. J., & Zong, C. Q. (2020). Neural machine translation: Challenges, progress and future. Science China Technological Sciences, 1–23. https://doi.org/10.1007/s11431-020-1632-x
Published
2023-05-01
How to Cite
Gunawan, F., & Khairunnisa, A. (2023). Lecturer’s Perception of Google Translate as an Academic Tool: Appraisal Analysis. Indonesian Journal of EFL and Linguistics, 8(1), 137-149. https://doi.org/10.21462/ijefl.v8i1.606
Section
Articles
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