Data-driven learning for languages other than English: Charting the territory

Data-driven learning (DDL) has seen a rapid development in the last couple of decades, and a number of studies have explored the different ways in which it can be operationalised, and the effects it has on second/foreign language learning (Leńko-Szymańska & Boulton, 2015). This has determined the need to sketch a coherent picture in relation to DDL effects in relation to a wide number of variables, which has been done by several critical reviews and meta-analyses (Boulton & Cobb, 2017; Chen & Flowerdew, 2018; Lee et al., 2018; Pérez-Paredes, 2019). What emerges, however, is that the large majority of studies considered focus primarily on DDL for English language learning purposes, leaving us with little evidence concerning DDL for languages other than English. In this poster, we seek to provide an overview of how DDL for languages other than English is currently developing. We combine data from the following sources: - published meta-analyses and critical reviews; - literature review; - CorpusCALL Facebook group entry questionnaire data. The overview will look at the kind of DDL approaches that have been developed for the different languages, as well as the state-of-the-art related to evaluating DDL effects for learning those languages, both in terms of the influence on overall language competence, and in terms of how it affects student attitudes towards language learning. The aim of the poster is to provide a broader, and hopefully more comprehensive, view of DDL for languages other than English so as to complement the theme for this year’s proposed Eurocall CorpusCALL Symposium SIG (Data-driven learning for languages other than English) which will focus on four languages only: German, French, Spanish and Italian. The poster seeks to start charting the broader territory upon which DDL is currently developing.