Corpus-Based Data-Driven Learning to Augment L2 Students’ Vocabulary Repertoire

Abstract

Corpus-based data-driven learning (DDL) is an inductive instructional approach using computer-generated concordances. It provides students with the opportunity to analyze different language forms across contexts found in the concordance output. The idea of engaging students to discover the language rules and patterns from authentic learning materials is central to the theory of inquiry-based learning. Despite the robust research support, however, DDL has not been widely adopted, in part because of a dearth of practical and specific recommendations for teachers. More studies are needed to corroborate the claim that the approach can promote the development of different language learning areas effectively. This article synthesizes relevant theories and research findings on the use of DDL for second language instruction and illuminates the understanding of how corpus-based vocabulary instructional strategies may work in English for Academic Purposes (EAP) courses in non-English speaking countries. The study recommendations include a corpus-based DDL framework to expand students’ vocabulary and suggestions for future research.