Effectiveness of data-driven learning (DDL) approaches

Abstract

This study examines the effectiveness of Data-Driven Learning (DDL) in enhancing grammar instruction for Thai English as a Foreign Language (EFL) learners, addressing the limitations of traditional methods such as the Grammar Translation Method (GTM). DDL, rooted in corpus linguistics, engages learners in analysing authentic language patterns through concordancing tools and corpus-based exercises, promoting grammatical accuracy, lexical proficiency, and learner autonomy. Using a quasi-experimental design, 60 undergraduate students were divided into an experimental group, taught using DDL, and a control group, adhering to conventional methods. The six-week intervention included pre- and post-tests to measure language proficiency and Likert-scale questionnaires to assess learner engagement. Results indicated significant improvements in the experimental group’s grammatical competence and satisfaction with the learning process, highlighting DDL’s capacity to bridge the gap between knowledge and application. Despite challenges such as low-proficiency learners navigating authentic data and increased teacher preparation, scaffolding and resource development were effective solutions. The findings underscore DDL’s potential to transform EFL classrooms, particularly in contexts with structural language differences and systemic challenges, fostering active and meaningful learning. This study contributes to the growing body of research advocating innovative, learner-centred approaches, offering valuable insights for educators and policymakers seeking to modernise grammar instruction.