Chapter published in:
Beyond Concordance Lines: Corpora in language education
Edited by Pascual Pérez-Paredes and Geraldine Mark
[Studies in Corpus Linguistics 102] 2021
► pp. 9–34

Chapter 1
Research in data-driven learning

| ATILF, CNRS | University of Lorraine

Data-driven learning (DDL) typically involves language learners consulting corpus data, either directly or via prepared materials, to answer questions about language. The approach has been mooted since the beginning of the modern era of corpus linguistics and has come to be associated with work by Tim Johns who coined the term in print in 1990. Since then, hundreds of studies have attempted to evaluate some aspect of DDL, giving rise to several reviews and syntheses. This paper introduces DDL and discusses the syntheses to date, before analysing a rigorous collection of 351 studies published up to and including 2018. While previous syntheses have evaluated the field, the objective here is to provide an overview of how researchers see DDL across the board, to identify more clearly what DDL actually looks like today, how it has evolved from its early beginnings in the 1980s, and to suggest avenues for future research in underexplored areas.

Keywords: data-driven learning, concordancing, synthesis
Article outline
  • Data-driven learning
    • Criticisms of DDL: Theoretical and empirical support
    • Surveys of DDL
  • Methodology
  • Results and discussion
    • Publication sources
    • Language and geography
    • Demographics
    • Aims, corpora and tools
    • Design
  • Discussion and conclusion
  • Notes
  • References
Published online: 22 December 2021
https://doi.org/10.1075/scl.102.01bou
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