SDL trados live cloud vs traditional subtitling workflows: An empirical evaluation of performance
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Abstract
This study investigates the pedagogical effectiveness of SDL Live Cloud training technologies in developing subtitling skills among master’s students in audiovisual translation. The study compares the performance of students who utilize these technologies with their counterparts who rely on traditional methods of subtitling. Sixty students enrolled in an audiovisual translation program during the academic year 2024–2025 participated in the study. They were divided evenly into an experimental group, which received hands-on training with SDL Live Cloud, and a control group, which relied on conventional subtitling methods. Both groups were taught by the same instructor to ensure consistency. The research employed a pre-test/post-test quasi-experimental design across an 8-week intervention. Students were assessed along six constructs central to subtitling performance: (1) accuracy of translation, (2) use of specialized terminology, (3) readability and naturalness, (4) style and flow, (5) conciseness and respect for space constraints, and (6) cohesion and consistency. The results demonstrated statistically significant improvement in both groups; however, the experimental group exhibited markedly greater gains. Post-test comparisons confirmed that students trained with SDL Live Cloud outperformed their peers across all six dimensions of subtitling competence. The most substantial improvements were observed in style and flow, reflecting enhanced ability to preserve tone, rhythm, and audiovisual synchronization, and in specialized terminology use, indicating more accurate and context-sensitive lexical choices. The study concludes that training with SDL Live Cloud has had a transformative impact on students' proficiency in audiovisual translation and that cloud translation environments should be integrated into translation education programs.
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