Language corpus and data driven learning (DDL) in language classrooms: A systematic review

Abstract

This systematic review presents a comprehensive analysis of the literature on the use of corpora and data-driven learning (DDL) in language education. Corpus linguistics encompasses the use of electronic text collections for linguistic analysis, while DDL entails using corpora for pedagogical purposes in second/foreign language teaching. DDL allows language educators to move beyond traditional methods to enhance teaching practices and learning skills. An extensive database search identified 89 pertinent studies published between 1997 and 2022 that met the inclusion criteria. The selected studies focused on keywords such as "DDL," "corpus linguistics," and related phrases to identify relevant literature discussing DDL interventions in language classrooms. Only English, peer-reviewed texts with accessible PDFs were considered for inclusion. These studies described DDL implementation in classroom settings and the common pedagogical practices, difficulties, and limitations encountered. The findings suggest that DDL has significant potential as a pedagogical tool, but challenges exist that limit its positive impact on language learning. Tailored tasks, auxiliary guidance, supplemental support, and peer/group learning were identified as effective strategies for facilitating meaningful corpus engagement for lower-proficiency students.