Assessing Student Translator Competence in Digital Tourism Texts: A Rubric-Based Comparison with Machine Translation
DOI:
https://doi.org/10.59992/IJESA.2026.v5n9p2الكلمات المفتاحية:
Digital Tourism، Skopos Theory، Translation Pedagogy، Student Translators، Rubric-Based Assessment، Saudi Vision 2030، Machine Translationالملخص
This mixed-methods study examines Arabic translations of a digital tourism text as a skopos-driven task and compares senior students' performance with neural machine translation (NMT). Thirty students (15 male and 15 female) translated the approximately 300-word promotional text "AlUla Calling." Two trained raters assessed the translations with a five-dimension analytic rubric covering idiomaticity, cultural and stylistic transfer, digital discourse adaptation, semantic accuracy, and fluency/readability. Students achieved a mean total of 16.60/ 20 (SD = 3.22, 95% CI [15.39, 17.80]). Their strongest dimensions were idiomaticity (M = 3.53) and digital discourse adaptation (M = 3.47); fluency/readability was weakest and most variable (M = 2.87, SD = 1.00). Google Translate and DeepL outputs, used as task-specific benchmarks, scored 18.00/20. NMT was stronger in semantic accuracy, fluency, and digital discourse adaptation, whereas students scored higher in idiomaticity and cultural-stylistic transfer. Thematic analysis identified literal phrasing, register drift, inconsistent treatment of digital features, and minor semantic errors, alongside effective idiomatic substitution, localization, creative adaptation, and compensation. The findings support instruction that combines fluency and meaning-control practice with critical NMT post-editing while preserving the culturally resonant voice that human translators contribute.
المراجع
• Agorni, M. (2012). Questions of mediation in the translation of tourist texts. Altre Modernità, (20), 1–11. https://doi.org/10.13130/2035-7680/1963.
• Dann, G. M. S. (1996). The language of tourism: A sociolinguistic perspective. CABI.
• Francesconi, S. (2014). Reading tourism texts: A multimodal analysis. Channel View Publications. https://doi.org/10.21832/9781845414283.
• González-Davies, M. (2014). Towards a plurilingual development paradigm: From spontaneous to informed use of translation in additional language learning. The Interpreter and Translator Trainer, 8(1), 8–31. https://doi.org/10.1080/1750399X.2014.908555.
• House, J. (1997). Translation quality assessment: A model revisited. Gunter Narr Verlag.
• Hurtado Albir, A. (2015). The acquisition of translation competence. Meta, 60(2), 256–280. https://doi.org/10.7202/1032857ar.
• Hurtado Albir, A. (Ed.). (2017). Researching translation competence by PACTE Group. John Benjamins. https://doi.org/10.1075/btl.127.
• Kelly, D. (2005). A handbook for translator trainers: A guide to reflective practice. St. Jerome.
• Läubli, S., Sennrich, R., & Volk, M. (2018). Has machine translation achieved human parity? A case for document-level evaluation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 4791–4796). Association for Computational Linguistics. https://doi.org/10.18653/v1/D18-1512.
• Manca, E. (2016). Persuasion in tourism discourse: Methodologies and models. Cambridge Scholars Publishing.
• Nord, C. (1997). Translating as a purposeful activity: Functionalist approaches explained. St. Jerome.
• Padilla, X. A. (2023). Emoji code: From frequency-function interface to digital discursive identity. Círculo de Lingüística Aplicada a la Comunicación, 93, 243–257. https://doi.org/10.5209/clac.83394.
• PACTE Group. (2003). Building a translation competence model. In F. Alves (Ed.), Triangulating translation: Perspectives in process-oriented research (pp. 43–66). John Benjamins.
• PACTE Group. (2009). Results of the validation of the PACTE translation competence model: Acceptability and decision making. Across Languages and Cultures, 10(2), 207–230. https://doi.org/10.1556/Acr.10.2009.2.3.
• PACTE Group. (2015). Results of PACTE’s experimental research on the acquisition of translation competence: The acquisition of declarative and procedural knowledge in translation: The dynamic translation index. Translation Spaces, 4(1), 29–53. https://doi.org/10.1075/ts.4.1.02bee.
• Patton, M. Q. (2015). Qualitative research & evaluation methods (4th ed.). Sage.
• Reiss, K., & Vermeer, H. J. (2014). Towards a general theory of translational action: Skopos theory explained (C. Nord, Trans.). Routledge. (Original work published 1984). https://doi.org/10.4324/9781315759715.
• Saudi Vision 2030. (2024). Vision 2030 annual report 2024. https://www.vision2030.gov.sa/media/r3ij40wu/en-annual-report-vision2030-2024.pdf.
• Taivalkoski-Shilov, K., & Koponen, M. (2023). Literary post-editing and the question of copyright. HERMES - Journal of Language and Communication in Business, (63), 195–207. https://doi.org/10.7146/hjlcb.vi63.137012.
• Thurlow, C., & Jaworski, A. (2010). Tourism discourse: Language and global mobility. Palgrave Macmillan.
• Toral, A., & Way, A. (2018). What level of quality can neural machine translation attain on literary text? From principles to practice. In J. Moorkens, S. Castilho, F. Gaspari, & S. Doherty (Eds.), Translation quality assessment (pp. 263–287). Springer. https://doi.org/10.1007/978-3-319-91241-7_12.
• Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems 30 (pp. 5998–6008). Curran Associates, Inc.
• Vermeer, H. J. (2012). Skopos and commission in translational action (A. Chesterman, Trans.). In L. Venuti (Ed.), the translation studies reader (3rd ed., pp. 191–202). Routledge. (Original work published 1989).
• Waddington, C. (2001). Different methods of evaluating student translations: The question of validity. Meta, 46(2), 311–325. https://doi.org/10.7202/004583ar.
• Zappavigna, M. (2018). Searchable talk: Hashtags and social media metadiscourse. Bloomsbury.
