The Evaluation and Comparison of Translation Technologies on the Learning Outcomes of Legal Text Translation Studies

Main Article Content

Ann Margareth
Moch. Sukardjo
Robinson Situmorang

Abstract

This paper analyses the relationship between translational technology and learning outcomes and investigates the type of translational technology most beneficial towards students’ translations of legal texts. This study tests whether differences were found in the learning outcomes of legal text translation for students with high and low placement test scores using Machine Learning. Translations were conducted from Indonesian to English and vice versa, encompassing students from leading Indonesian universities enrolled in a professional legal translation certification programme. This research was designed with a treatment by level 3x2, quantitatively analysing the impacts of Machine Learning, Google Translate, and Online Dictionaries. The study revealed statistically significant interactive effects among the variables of translational technology in relation to the outcomes of legal translation learning. The data indicated variances in the students' learning with Machine Learning demonstrating the most substantial influence. We also found that low-scoring students on the placement test who used Machine Learning in translating legal texts revealed better learning outcomes than high-scoring students. This insight is useful for educators and scholars designing legal translation courses. This could also serve as a foundational element in the field of English for Specific Purposes particularly concerning the integration of technology in legal translation education.

Article Details

How to Cite
Margareth, A., Sukardjo, M., & Situmorang, R. (2025). The Evaluation and Comparison of Translation Technologies on the Learning Outcomes of Legal Text Translation Studies. LEARN Journal: Language Education and Acquisition Research Network, 18(1), 568–593. https://doi.org/10.70730/DFYU5629
Section
Research Articles
Author Biographies

Ann Margareth, Department of Postgraduate Program, Faculty of Educational Technology, Universitas Negeri Jakarta, Indonesia

A Ph.D. student at the Universitas Negeri Jakarta (UNJ) majoring in Educational Technology. She is also a legal translator professional and a founder of Juridique Law Firm. Her academic interests include English for Specific Purposes (ESP) and applied Artificial Intelligence (AI) for language education.

Moch. Sukardjo, Department of Postgraduate Program, Faculty of Educational Technology, Universitas Negeri Jakarta, Indonesia

An Educational Technology Professor at the Universitas Negeri Jakarta (UNJ). His research interests include experimental studies and the future of the applied educational technology for Indonesian society. He has received numerous awards from Indonesian Government for his contribution in the field of educational technology.

Robinson Situmorang, Department of Postgraduate Program, Faculty of Educational Technology, Universitas Negeri Jakarta, Indonesia

An Educational Technology Professor at the Universitas Negeri Jakarta (UNJ). His research interests include instructional system development and the impact of information & communication technologies (ICT) for the future of education. He is now the UNJ Program Director for the Educational Technology Post-Graduate Program.

References

Abidin, E. Z., Mustapha, N. F., Rahim, N. A., & Abdullah, S. N. (2020). Translation of idioms from Arabic into Malay via Google Translate: What needs to be done? GEMA Online Journal of Language Studies, 20(3), 156-180. https://doi.org/10.17576/gema-2020-2003-10

Abrams, Z. I. (2019). Collaborative writing and text quality in Google Docs. Language Learning & Technology, 23(2), 22-42. https://doi.org/10125/44681

Aghajani, M., & Amanzadeh, H. (2017). Computer-assisted language learning in reading comprehension by using visual mnemonics. International Journal of Applied Linguistics and English Literature, 7(1), 130-140. https://doi.org/10.7575/aiac.ijalel.v.7n.1p.130

Ahmed, A. M., & Lenchuk, I. (2024). The interaction between Morphosyntactic features and the performance of machine translation tools: The case of Google Translate, Systran, and Microsoft Bing in English-Arabic translation. Theory and Practice in Language Studies, 14(2), 614-625. https://doi.org/10.17507/tpls.1402.35

Akbari, O., & Sahibzada, J. (2020). Students’ self-confidence and its impacts on their learning process. American International Journal of Social Science Research, 5(1), 1-15. https://doi.org/10.46281/aijssr.v5i1.462

Aldawsari, H. A. (2023). Comparing the performance of Google Translate and SYSTRAN on Arabic lexical ambiguity. Arab World English Journal For Translation and Literary Studies, 7(3), 19-34. https://doi.org/10.24093/awejtls/vol7no3.2

Almarwaey, A. O., & Ahmad, U. K. (2021). Semantic change of hijab, halal and Islamist from Arabic to English. 3L: Language, Linguistics, Literature. The Southeast Asian Journal, 27(2), 161-176. https://doi.org/10.17576/3l-2021-2702-12

Ananda, R. P., Arsyad, S., & Dharmayana, I. W. (2018). Argumentative features of international English language testing system (IELTS) essays: A rhetorical analysis on successful exam essays. International Journal of Language Education, 2(1), 1-13. https://doi.org/10.26858/ijole.v2i1.4768

Barani, G. (2011). The relationship between computer assisted language learning (CALL) and listening skill of Iranian EFL learners. Procedia - Social and Behavioral Sciences, 15, 4059-4063. https://doi.org/10.1016/j.sbspro.2011.04.414

Bond, M., Buntins, K., Bedenlier, S., Zawacki-Richter, O., & Kerres, M. (2020). Mapping research in student engagement and educational technology in higher education: A systematic evidence map. International Journal of Educational Technology in Higher Education, 17. https://doi.org/10.1186/s41239-019-0176-8

Cahyaningrum, I. O. (2022). Google Translate for legal document. Proceedings of the 10th UNNES Virtual International Conference on English Language Teaching, Literature, and Translation. https://doi.org/10.4108/eai.14-8-2021.2317631

Cambridge Law Studio Limited. (2000). TOLES. https://toleslegal.com/

Chang, M. M., & Hung, H. T. (2019). Effects of technology-Enhanced language learning on second language acquisition: A Meta-Analysis. Educational Technology & Society, 22(4), 1-17. https://www.jstor.org/stable/26910181

Chun, D. M. (2019). Current and future directions in TELL. Educational Technology & Society, 22(2), 14-25. https://www.jstor.org/stable/26819614

Credé, M. (2018). What shall we do about grit? A critical review of what we know and what we don’t know. Educational Researcher, 47(9), 606-611. https://doi.org/10.3102/0013189x18801322

Creswell, J. (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research. Pearson.

Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92(6), 1087-1101. https://doi.org/10.1037/0022-3514.92.6.1087

Elliott, R. W., & Chen, X. (2019). Cross-cultural L2 learning exchange: A qualitative examination of strategies, tools, cognition and translation outcomes. International Journal of Evaluation and Research in Education (IJERE), 8(3), 519-530. https://doi.org/10.11591/ijere.v8i3.20247

Enayati, F., & Gilakjani, A. P. (2020). The impact of computer assisted language learning (CALL) on improving intermediate EFL learners' vocabulary learning. International Journal of Language Education, 4(1), 96-112. https://doi.org/10.26858/ijole.v4i2.10560

Fauzan, A., Basthomi, Y., & Ivone, F. M. (2022). Effects of using online corpus and online dictionary as data-driven learning on students' grammar mastery. LEARN Journal: Language Education and Acquisition Research Network, 15(2), 679-704.

Gayed, J. M., Carlon, M. K., Oriola, A. M., & Cross, J. S. (2022). Exploring an AI-based writing assistant's impact on English language learners. Computers and Education: Artificial Intelligence, 3. https://doi.org/10.1016/j.caeai.2022.100055

Godwin-Jones, R. (2019). Riding the digital wilds: Learner autonomy and informal language learning. Language Learning & Technology, 23(1), 8-25. https://doi.org/10125/44667

Haigh, R. (2015). Legal English (4th ed.). Routledge.

Hidalgo-Ternero, C. M. (2020). Google Translate vs. DeepL: Analysing neural machine translation performance under the challenge of phraseological variation. MonTI. Monografías de Traducción e Interpretación, 6, 154-177. https://doi.org/10.6035/monti.2020.ne6.5

Huong, L. P., & Hung, B. P. (2021). Mediation of digital tools in English learning. LEARN Journal: Language Education and Acquisition Research Network, 14(2), 512-528.

Ioannou, M., & Ioannou, A. (2020). Technology-enhanced embodied learning: Designing and evaluating a new classroom experience. Educational Technology & Society, 23(3), 81-94. https://www.jstor.org/stable/26926428

Jabu, B., Abduh, A., & Rosmaladewi. (2021). Motivation and challenges of trainee translators participating in translation training. International Journal of Language Education, 5(1), 490-500. https://doi.org/10.26858/ijole.v5i1.19625

Kamaluddin, M. I., Rasyid, M. W., Abqoriyyah, F., & Saehu, A. (2024). Accuracy analysis of DeepL: Breakthroughs in machine translation technology. Journal of English Education Forum (JEEF), 4(2), 122-126. https://doi.org/10.29303/jeef.v4i2.681

Khoiriah, U. F., Siha, A. S., Rahmani, P. H., Sutopo, A., & Haryanti, D. (2024). Law students' perception of AI in legal document translation: Opportunities and challenges. JPGENUS: Jurnal Pendidikan Generasi Nusantara, 2(2), 210-216. https://doi.org/10.61787/4rvf3x89

Kizil, A. S. (2019). Review of a guide to using corpora for English language learners. Language Learning & Technology, 23(3), 44-46. https://doi.org/10125/44694

Lerma-Noriega, C. A., Flores-Palacios, M. L., & Rebolledo-Méndez, G. (2020). InContext: A mobile application for the improvement of learning strategies at university. Comunicar, 28(64), 105-114. https://doi.org/10.3916/c64-2020-10

Loiacono, R. (2022). The translator as an expert witness in court. The International Journal for Translation and Interpreting Research, 14(1), 84-96. https://doi.org/10.12807/ti.114201.2022.a05

Müller, F. A., & Wulf, T. (2020). Technology-supported management education: A systematic review of antecedents of learning effectiveness. International Journal of Educational Technology in Higher Education, 17. https://doi.org/10.1186/s41239-020-00226-x

Reballiwar, L. V., Yergude, S. B., Urade, V. M., Birewar, S. R., & Karmarkar, B. (2023). Language translation using machine learning. International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), 3(1), 297-300. https://doi.org/10.48175/568

Reis, S. M., & Renzulli, J. S. (2018). The five dimensions of differentiation. International Journal for Talent Development and Creativity, 6, 87-94. https://eric.ed.gov/?id=EJ1296874

Salmani-Nodoushan, M. A. (2020). English for specific purposes: Traditions, trends, directions. Studies in English Language and Education, 7(1), 247-268. https://doi.org/10.24815/siele.v7i1.16342

Shadiev, R., & Huang, Y. M. (2022). Improving student academic emotions and learning satisfaction in lectures in a foreign language with speech-enabled language translation technology. Australasian Journal of Educational Technology, 38(3), 197-208. https://doi.org/10.14742/ajet.7428

Sittirak, N., & Ranong, S. N. (2023). Investigating novice translators’ instrumental competence in translating from and into a foreign language. LEARN Journal: Language Education and Acquisition Research Network, 16(2), 273-290.

Steenbergen-Hu, S., Makel, M. C., & Olszewski-Kubilius, P. (2016). What one hundred years of research says about the effects of ability grouping and acceleration on K–12 students’ academic achievement: Findings of two second-order meta-analyses. Review of Educational Research, 86(4), 849-899. https://doi.org/10.3102/0034654316675417

Sumakul, D. T., Hamied, F. A., & Sukyadi, D. (2022). Artificial intelligence in EFL classrooms: Friend or foe? LEARN Journal: Language Education and Acquisition Research Network, 15(1), 232-256.

Thomas, M. P. (2018). Personalized learning: A case study of supporting literature applied to practice and implementation in a high school [Unpublished doctoral dissertation]. University of Pittsburgh.

Ubhayawardhana, P. D., & Hansani, J. A. (2023). A study on the effectiveness of using google translate in legal translation: With special reference to selected legal documents of the registrar general’s department. Sri Lanka Journal of Humanities and Language Studies (LOGOS), 1(1), 168-190. http://logos.sab.ac.lk/

UC Davis Health. (2022). Interpreting and translation services. https://health.ucdavis.edu/interpreting-services/training.

Vázquez-Cano, E., Mengual-Andrés, S., & López-Meneses, E. (2021). Chatbot to improve learning punctuation in Spanish and to enhance open and flexible learning environments. International Journal of Educational Technology in Higher Education, 18. https://doi.org/10.1186/s41239-021-00269-8

Vázquez-Cano, E., Parra-González, M. E., Segura-Robles, A., & López-Meneses, E. (2022). The negative effects of technology on education: A bibliometric and topic modeling mapping analysis (2008-2019). International Journal of Instruction, 15(2), 37-60. https://doi.org/10.29333/iji.2022.1523a

Wang, Z., Hwang, G. J., Yin, Z., & Ma, Y. (2020). A contribution-oriented self-directed mobile learning ecology approach to improving EFL students’ vocabulary retention and second language motivation. Educational Technology & Society, 23(1), 16-29. https://www.jstor.org/stable/26915404

Yang, F. C. O., Lo, F. Y. R., Hsieh, J. C., & Wu, W. C. V. (2020). Facilitating communicative ability of EFL learners via high-immersion Virtual Reality. Educational Technology & Society, 23(1), 30-49. https://www.jstor.org/stable/26915405

Zhai, C., & Wibowo, S. (2023). A systematic review on artificial intelligence dialogue systems for enhancing English as foreign language students’ interactional competence in the university. Computers and Education: Artificial Intelligence, 4. https://doi.org/10.1016/j.caeai.2023.100134