Corpus Applications in Language Teaching and Research

Corpus Applications in Language Teaching and Research: The Case of Data-Driven Learning of German provides a historical overview of corpus applications in language teaching with a focus on German. The book identifies challenges in using corpus applications and data-driven learning (DDL) research for Languages Other Than English (LOTEs) and addresses these challenges through various approaches. Overall, this book:

• surveys corpus applications for teaching and learning German, highlighting the growth of the L2 German DDL field and identifying trends in integrating DDL into pedagogical practice;








• presents empirical research on the effectiveness of DDL applications for teaching and learning German in comparison with research on English and other LOTEs, emphasizing the need for expanding the scope of DDL research to include more languages, skills, and study types;








• compares teaching interventions for L2 collocations in the fields of Instructed Second Language Acquisition (ISLA) and DDL, highlighting methodological differences between the two paradigms and proposing a combined ISLA/DDL framework to bridge the disconnect;








• showcases a successful DDL intervention that resulted in significant learning gains in German collocation knowledge, filling a gap in DDL research;








• proposes an Open Educational Resource (OER) for teaching and learning German, incorporating open access corpora, learner-fit criteria, new tools and technology, and usage-based learning principles;








• examines the current difficulties encountered by the DDL field and highlights potential directions for future research and pedagogical approaches.

This book offers insights and resources for researchers, language teaching practitioners, and students interested in corpus-based learning and teaching methods. While the focus is on teaching German to English-speaking students, the book's findings have broader applicability to language teaching and learning in different contexts.

Nina Vyatkina draws on her wealth of expertise in the area to provide a lively and engaging insight into the potential of language corpora for teaching and learning German, with her usual enthusiasm, original thinking and scientific rigour. With its solid theoretical foundation combined with a meticulous attention to empirical research, this book is an essential companion for novice and experienced scholars interested in DDL, especially for languages other than English ? German and beyond. Students and teachers will also appreciate its easy readability and the judicious use of illustrative tables and figures, always with an eye for practical implications (not just chapter 6). The section linking DDL and ISLA is hugely insightful and should inspire other fruitful comparisons of this type.

Alex Boulton, ATILF, University of Lorraine & CNRS, France

Through the lens of data-driven learning for German, Nina Vyatkina masterfully addresses fundamental issues in data-driven learning as a whole: the under-representation of languages other than English, the hiatus between data-driven learning and second language acquisition research, and the gap between research findings and current pedagogical practices. Her innovative approach in dealing with such issues makes her book an essential reference resource that significantly moves the field forward, a must-read for researchers, teachers, and students.

Luciana Forti, University for Foreigners of Perugia, Italy

In her outstanding book on data-driven learning, Nina Vyatkina exemplifies exceptional expertise in the field. She adeptly blends in-depth reflexions on the domain with scaffolded practical insights. She offers educators, learners and researchers alike an invaluable, accessible and comprehensive resource built around German and easily transferable to any language.

Fanny Meunier, UCLouvain, Belgium