In over 30 years of data-driven learning (DDL) research, there has been a growing sophistication in the ways we collect, analyse, and put corpus data to use. This volume takes a three-fold perspective on DDL. It first looks at DDL and its role in informing language learning theory and how it might shed light on the language development process; secondly it addresses how DDL can help us characterise learner language and inform teaching accordingly, and thirdly it showcases practical applications for the use of DDL in classrooms. The contributors to this volume examine a variety of instructional settings and languages across the world. They reflect on theoretical, methodological and classroom implications using both novel and established language learning theories, natural language processing (NLP), longitudinal research designs, and a variety of language learning targets. The present volume is an invitation from some of the leading researchers in DDL to reflect on the research avenues that will define the field in the coming years.