Speaker
Description
Collocational competence is essential for producing natural and accurate translations, yet it remains a persistent difficulty for translation students. Although corpus tools such as English-Corpora provide access to authentic language data, their unguided use may overwhelm learners. This study examines the effectiveness of a guided data-driven learning (DDL) approach in improving students’ use of collocations in translation tasks. 40 students from 2 classes majoring in in Translation and Interpreting were randomly chosen and placed into 2 groups. The experimental group received corpus-informed instruction through pre-selected concordance lines and structured noticing activities, while the control group followed traditional methods. Data were collected through pre- and post-translation tests and analyzed in terms of collocational accuracy and naturalness. The results show that the experimental group outperformed the control group, demonstrating significant improvement in both accuracy and appropriateness of collocation use. The findings suggest that guided corpus-based instruction effectively supports learners in noticing and applying collocational patterns in translation. This study proposes a practical model for integrating guided DDL into translation pedagogy.
Key words: translation, collocations, Corpora, explicit instructions
