Effective corpus use in second language learning: A meta-analytic approach

Abstract

Data-driven learning (DDL) refers to the use of corpora by second and foreign language (L2) learners to explore and inductively discover patterns of their target language use from authentic language data without interventions from others. Although previous meta-analyses have demonstrated the positive effects of DDL on L2 learning (Boulton and Cobb, 2017), the number of empirical studies has been increasing since then. Therefore, this study included more recent studies and used meta-analyses to examine the extent to which: (1) DDL exerts an effect on L2 learning; and (2) moderator variables affect DDL's influence on L2 learning. The results demonstrated small to medium effect sizes for experimental/control group comparisons and pre/post and pre/delayed designs. Moreover, the moderator analyses found that moderator variables, such as publication types, learners’ factors, and research designs, influence the magnitude of DDL effectiveness in L2 learning.