Multiple affordances of language corpora for data-driven learning / edited by Agnieszka Lenko-Szymanska, University of Warsaw ; Alex Boulton, ATILF-CNRS / University of Lorraine.

Includes bibliographical references and index.

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Table of contents; Multiple Affordances of Language Corpora for Data-driven Learning; Editorial page; Title page; LCC data; Table of contents; Editors' acknowledgements; Introduction; References; Data-driven learning and language learning theories; 1. Introduction; 2. Language learning theories and learning style; 3. The noticing hypothesis and DDL; 4. Constructivist learning and DDL; 5. Vygotskyan sociocultural theories and DDL; 6. Learning styles and DDL; 7. Conclusion; Acknowledgements; References; Teaching and language corpora; 1. Introduction; 2. Beginnings; 3. What's happened?

3.1 1975-1985: From manual to computer analysis3.2 1986-1990; 3.3 1991-2000; 3.4 2001-2014; 4. Corpus applications in language teaching: The current situation; 5. Who's using language corpora in 2012: Findings from a survey; 5.1 Respondents; 5.2 Who is using corpora in language teaching, and in what contexts?; 5.3 What tools and resources are they using?; 5.4 Favourite resources; 6. What are the benefits?; 7. Conclusion and future directions?; References; Part I. Corpora for language learning; Learning phraseology from speech corpora; 1. Why spoken phraseology matters.

2. Constructing a speech corpus for acquiring spoken phraseology3. Analysing a speech corpus: Some examples; 3.1 Starting from a list; 3.2 Starting from a listening experience; 3.3 One thing leads to another; 4. Implications: The role of the learner; References; Stealing a march on collocation; 1. Introduction and overview; 2. The Sketch Engine; 3. A constrained definition of collocation and its affordances; 4. Collocation Plus (C+); 5. Observing and using Topic Trails in full text; 6. Conclusion; References; Appendix 1: Text examples cited; Appendix 2: Corpora cited.

A corpus and grammatical browsing system for remedial EFL learners1. Appropriate level, needs-driven corpora for the EFL classroom; 2. Developing the Grammatical Pattern Profiling System (GPPS); 2.1 Using LWP-GRC as a model for the GPPS; 2.2 GPPS functionality; 2.3 Selection of grammatical categories; 2.4 Creation of search expressions and patterns; 3. Developing the Sentence Corpus of Remedial English (SCoRE); 3.1 Defining target population proficiency levels; 3.2 Sourcing potential corpus data; 3.3 Defining sentence length; 3.4 Defining the number of sentences.

3.5 Using the source corpus as a model for SCoRE3.6 Translation; 4. Pedagogical applications: Using SCoRE and the GPPS; 5. Limitations of SCoRE and the GPPS; 6. Conclusion; Acknowledgements; References; Part II. Corpora for skills development; Same task, different corpus; 1. Introduction; 2. Background to the course; 2.1 Course programme; 2.2 Course procedure; 3. Data; 3.1 Participants; 3.2 Corpus and worksheet data; 4. Corpus tools in the 'same task, different corpus' approach; 4.1 The Concordance tool; 4.2 The Word List tool; 4.3 The Collocates tool; 4.4 The Concordance Plot tool.

Data-driven learning typically involves the use of dedicated concordancers to explore linguistic corpora, which may require significant training if the technology is not to be an obstacle for teacher and learner alike. One possibility is to begin not with corpus or concordancer, but to find parallels with what 'ordinary' users already do. This paper compares the web to a corpus, regular search engines to concordancers, and the techniques used in web searches to data-driven learning. It also examines previous studies which exploit web searches in ways not incompatible with a DDL approach.

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