Innovation in Language Learning

Effective multilingual communication is crucial in the globalized healthcare sector. This study examines the use of health sciences corpora for LSP learning and research. By implementing corpus tools such as Sketch Engine [1] and AntConc [2], Applied Linguistics students become acquainted with specialized tools.
Our research implements a data-driven learning (DDL) methodology [3] for corpus building and analysis at postgraduate level, focusing on health sciences terminology, discourse, and translation. By presenting students with research queries related to medical communication, we aim to create a dynamic learning environment that allows for the exploration of language usage across various healthcare contexts and enables students to develop transversal skills and competencies.
Students learn to distinguish different textual genres relevant to health sciences, comprising diverse communication levels, exploring language complexity by studying the occurrence of different linguistic patterns across languages. They gain hands-on experience in compiling and exploring corpora, including data collection, cleaning, and analysis.
The approach facilitates cross-linguistic analysis, allowing students to compare and contrast medical language use in different languages. This study not only enhances students' understanding of medical language across different communication levels but also equips them with valuable skills in corpus linguistics and data analysis.