The Impact of Using DeepL Artificial Intelligence on Students’ Writing Skills in an Indonesian Senior Highschool Context
(1) English Language Education, Universitas Pendidikan Ganesha Singaraja
(2) English Language Education, Universitas Pendidikan Ganesha Singaraja
(3) English Language Education, Universitas Pendidikan Ganesha Singaraja
(*) Corresponding Author
DOI: https://doi.org/10.26858/eltww.v11i2.67029
Abstract
This study looks at how eleventh-grade students at SMA Negeri 2 Singaraja's writing abilities are affected by the use of DeepL artificial intelligence. The study uses mixed methods with an explanatory sequential design; for quantitative analysis, a quasi-experimental non-equivalent control group design is used, and for qualitative insights, in-depth interviews utilizing the RASE model are conducted. Purposive random sampling was used to choose the samples, and two classes participated in the investigation. While the control group received traditional instruction, the experimental group received training via DeepL-based AI. Descriptive and inferential data analysis showed that the experimental group's mean score (81.36) was higher than the control group's (76.26). Furthermore, the inferential analysis demonstrated a significant difference in writing competency between students taught with DeepL AI and those without (Sig. (2-tailed) = 0.000, p < 0.05), supporting the alternative hypothesis that DeepL AI-enhanced instruction leads to superior outcomes. Interview findings further underscored DeepL's efficacy in enhancing writing skills by facilitating vocabulary expansion, offering grammar corrections, and suggesting alternative word choices for language learners. Additionally, inferential analysis showed a significant difference in writing competency between students who received DeepL-based AI instruction and those who did not (Sig.(2-tailed) = 0.000, p < 0.05), confirming the alternative hypothesis that better results are obtained from DeepL AI-enhanced instruction. The results of the interviews provided more evidence of DeepL's effectiveness in improving writing abilities by helping language learners expand their vocabulary, suggest other words, and provide grammatical corrections.
Keywords
Full Text:
PDFReferences
Apsari, Yanuarti, and Aseptiana Parmawati. 2022. Improving students’ writing skill through blended learning during covid-19 pandemic. JPE (Jurnal Pendidikan Edutama 9(1): 93–98.
Arta, Gede Juni, Ni Made Ratminingsih, and Made Hery Santosa. 2019. The effectiveness of blended learning strategy on students’ writing competency of the tenth grade students. JPI (Jurnal Pendidikan Indonesia) 8(1): 29. doi:10.23887/jpi-undiksha.v8i1.13501.
Birdsell, Brian. 2022. Student writings with DeepL: teacher evaluations and implications for teaching. JALT Postconference Publication 2021(1): 117. doi:10.37546/jaltpcp2021-14.
Churchill, D., King, M., Webster, B., & Fox, B. (2013). Integrating learning design, interactivity, and technology. 30th Annual Conference on Australian Society for Computers in Learning in Tertiary Education, ASCILITE 2013, 139–143.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publication, Inc.
Fitria, Tira Nur. 2021. The use technology based on artificial intelligence in English teaching and learning. ELT Echo : The Journal of English Language Teaching in Foreign Language Context 6(2). doi:10.24235/eltecho.v6i2.9299.
Harmer, Jeremy. 2001. How to teach English? an introduction to the practice of English language teaching. England: Pearson Education Limited.
Jacobs, H. L., Wormouth, D. R., Zinkgraf, S. A., Hearfiel, V. F., & Hughey, J. B. (1981). Testing ESL composition: A practical approach. Newbury House.
Miles, M. B., Huberman, A. M., & Saldana, J. (2014). Qualitative data analysis: A methods sourcebook. In SAGE Publications, Inc. (3rd ed., Vol. 6).
Polakova, Petra, and Blanka Klimova. 2023. Using DeepL Translator in learning English as an applied foreign language – an empirical pilot study. Heliyon 9(8): e18595. doi:10.1016/j.heliyon.2023.e18595.
Santosa, Made Hery, I Putu Surya Pratama, and I Nyoman Adi Jaya Putra. 2020. Developing android-based English vocabulary learning materials for primary school students. JEELS (Journal of English Education and Linguistics Studies) 7(1): 161–85.
Toba, Rostanti, Widya Noviana Noor, and La Ode Sanu. 2019. The current issues of Indonesian EFL students’ writing skills: ability, problem, and reason in writing comparison and contrast essay. Dinamika Ilmu: 57–73. doi:10.21093/di.v19i1.1506.
Wang, Rui. 2019. Research on artificial intelligence promoting english learning change. doi:http://dx.doi.org/10.2991/emehss-19.2019.79.
Wiraningsih, Putu, and Made Hery Santosa. 2020. EFL teachers’ challenges in promoting learner autonomy in the 21st-century learning. Journal on English as a Foreign Language 10(2): 290–314. doi:10.23971/jefl.v10i2.1881.
Yamada, Masaru. 2019. The impact of Google neural machine translation on post-editing by student translators. The Journal of Specialised Translation Issue.
Yulianto, Ahmad, and Rina Supriatnaningsih. 2021. Google Translate vs. DeepL: a quantitative evaluation of close-language pair translation. AJELP: Asian Journal of English Language and Pedagogy 9(2): 109–27. doi:10.37134/ajelp.vol9.2.9.2021.
Ziyad Mohammed. 2019. Artificial intelligence definition, ethics, and standards.
Zhang, J., Wang, Y., Zhao, Y., & Cai, X. (2018). Applications of inferential statistical methods in library and information science. Data and Information Management, 2(2), 103–120. https://doi.org/10.2478/dim-2018-0007
Article Metrics
Abstract view : 955 times | PDF view : 225 timesRefbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Published by:
Pascasarjana, Universitas Negeri Makassar
Jalan Bonto Langkasa, Banta-Bantaeng, Rappocini, Banta-Bantaeng, Kec. Rappocini, Kota Makassar, Sulawesi Selatan 90222
elt.worldwide@unm.ac.id
E-ISSN: 2503-2291
P-ISSN: 2303-3037
ELT Worldwide: Journal of English Language Teaching is licensed under a Creative Commons Attribution-NonCommercial- 4.0 International License.