Learner corpora in foreign language education: examples from the multilingual SWIKO corpus
DOI :
https://doi.org/10.55393/babylonia.v2i.388Mots-clés :
learner corpus, data-driven learning, task effects, foreign languages, compulsory educationRésumé
This contribution introduces the Swiss learner corpus SWIKO and provides examples on how this rich and near-authentic collection can be utilized in foreign language education, while also addressing some critical issues that corpus linguistic applications face in pedagogical contexts. SWIKO is a multilingual corpus currently being developed at the Institute of Multilingualism in Fribourg. The corpus contains written and spoken productions by Swiss lower secondary school students, both in their language of schooling and foreign languages learnt at school (English, French, and German). Participating students completed eight communicative tasks which systematically vary by rhetorical type (descriptive or argumentative), topic (personal or academic), and structure (more or less restrictive input). The resulting productions were analysed with a focus on linguistic features (e.g., Karges et al., 2022) and in relation to human ratings according to the levels of the CEFR (e.g., Studer & Hicks, 2022). Based on our findings, we present two scenarios on how SWIKO can be used in educational settings, i.e., teacher training and material development. First, the productions can serve as an illustration of learners’ abilities at the end of mandatory schooling. Our findings show how the length, complexity, and accuracy of learner texts heavily depend on the task, which can be addressed in teacher training. Second, an analysis of frequent errors in foreign language productions can shed light on challenging structures, while the language of schooling sub-corpus can serve as a peer-reference in the development of corresponding material. We exemplify this process focusing on negation in German, offering differentiated teaching material suitable for the secondary school classroom.
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(c) Tous droits réservés Nina Hicks, Thomas Studer 2024
Ce travail est disponible sous la licence Creative Commons Attribution 4.0 International .