Ana Frankenberg-Garcia1, Robert Lew2, Geraint Paul Rees1, Jonathan C. Roberts3 & Nirwan Sharma3
1 University of Surrey, UK,
2 Adam Mickiewicz University in Poznań, Poland
3 Bangor University, UK
Discovery or data-driven learning (DDL) has taken two main directions since Johns (1991) put forward an approach to language learning whereby learners build their knowledge of word meanings and uses on the basis of concentrated exposure to corpus data. Learners can (1) engage with corpora and corpus software directly, in a ‘hard’ or ‘hands-on’ approach to DDL; or (2) they can be presented with corpus data that has been pre-selected, in a ‘soft’ or ‘hands-off’ approach (Gabrielatos 2005; Boulton 2010). The advantages and disadvantages of direct and indirect corpus consultation have been widely discussed in the literature, and the two are not incompatible (Frankenberg-Garcia 2012, 2016). In this paper, we would like to present a third way of engaging with DDL, where learners are neither given free corpus access, nor presented with a fixed set of concordances. What we propose is that learners engage with corpus data dynamically, as and when required, but that this data is curated in such a way that learners do not get lost or distracted in the process. This dynamic approach to DDL is being implemented in the development of ColloCaid, an intuitive writing assistant to help writers with academic English collocations in real time (Frankenberg-Garcia et al. forthcoming). We will demonstrate how ColloCaid integrates user needs, curated corpus data and human-computer interaction to make DDL maximally relevant and minimally distracting to writers.
This research is funded by the Arts and Humanities Research Council (AHRC), UK, ref. AH/P003508/1
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ColloCaid (n.d.) www.collocaid.uk
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