Beyond Concordance Lines

Corpora in language education

Editors
| Universidad de Murcia
| Mary Immaculate College, University of Limerick
HardboundAvailable
ISBN 9789027209894 | EUR 99.00 | USD 149.00
 
e-Book
ISBN 9789027258496 | EUR 99.00 | USD 149.00
 
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.
[Studies in Corpus Linguistics, 102]  2021.  ix, 255 pp.
Publishing status: Available
Table of Contents
Acknowledgements
ix
Introduction
Pascual Pérez-Paredes and Geraldine Mark
1–8
Chapter 1. Research in data-driven learning
Alex Boulton
9–34
Chapter 2. Data-driven learning, theories of learning and second language acquisition: In search of intersections
Anne O'Keeffe
35–56
Chapter 3. Looking back on 25 years of TaLC: In conversation with Profs Mike McCarthy and Tony McEnery
Michael McCarthy, Tony McEnery, Geraldine Mark and Pascual Pérez-Paredes
57–74
Chapter 4. L2 development of -ing clauses: A longitudinal study of Norwegian learners
Hildegunn Dirdal
75–96
Chapter 5. Collocations in learner English: A true-longitudinal perspective
Rolf Kreyer
97–120
Chapter 6. Profiling learners through pragmatically and error annotated corpora
Martin Weisser
121–148
Chapter 7. Exploring the impact of data-driven learning in extensive reading
Gregory Hadley and Hiromi Hadley
149–176
Chapter 8. Data-driven learning: Using #LancsBox in academic collocation learning
Tanjun Liu
177–206
Chapter 9. Scoledit : A tool to analyse learner writing and better understand the challenges of language education
Claire Wolfarth, Claude Ponton and Catherine Brissaud
207–230
Chapter 10. CEFR-J × 28: Corpus-based multilingual pedagogical resources and e-learning systems for 28 languages
Yukio Tono
231–252
Index
253–255
Subjects & Metadata
BIC Subject: CFX – Computational linguistics
BISAC Subject: LAN009000 – LANGUAGE ARTS & DISCIPLINES / Linguistics / General
ONIX Metadata
ONIX 2.1
ONIX 3.0
U.S. Library of Congress Control Number:  2021037716 | Marc record