LEADER 06001nam 2200721 450 001 9910493208603321 005 20170821162045.0 010 $a90-272-6871-1 035 $a(CKB)3710000000409637 035 $a(EBL)2040186 035 $a(SSID)ssj0001482422 035 $a(PQKBManifestationID)12613766 035 $a(PQKBTitleCode)TC0001482422 035 $a(PQKBWorkID)11412144 035 $a(PQKB)11076477 035 $a(PQKBManifestationID)16038232 035 $a(PQKB)22353391 035 $a(MiAaPQ)EBC2040186 035 $a(DLC) 2015006473 035 $a(EXLCZ)993710000000409637 100 $a20150520h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aMultiple affordances of language corpora for data-driven learning /$fedited by Agnieszka Lenko-Szymanska, University of Warsaw, Alex Boulton, ATILF-CNRS / University of Lorraine 210 1$aAmsterdam, Netherlands ;$aPhiladelphia, Pennsylvania :$cJohn Benjamins Publishing Company,$d2015. 210 4$dİ2015 215 $a1 online resource (320 p.) 225 1 $aStudies in Corpus Linguistics (SCL),$x1388-0373 ;$vVolume 69 300 $aDescription based upon print version of record. 311 $a90-272-0377-6 320 $aIncludes bibliographical references and indexes. 327 $aMultiple Affordances of Language Corpora for Data-driven Learning; Editorial page; Title page; LCC data; Table of contents; 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? 327 $a3.1 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 327 $a2. 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 327 $aA 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 327 $a3.5 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 327 $a5. Evaluation of the course 330 $aData-driven learning typically involves the use of dedicated concordancers to explore linguistic corpora, which may require significant training if the technology is not to be an obstacle for teacher and learner alike. One possibility is to begin not with corpus or concordancer, but to find parallels with what 'ordinary' users already do. This paper compares the web to a corpus, regular search engines to concordancers, and the techniques used in web searches to data-driven learning. It also examines previous studies which exploit web searches in ways not incompatible with a DDL approach. 410 0$aStudies in corpus linguistics ;$vVolume 69. 606 $aEnglish language$xStudy and teaching$xData processing 606 $aEnglish language$xDiscourse analysis$xData processing 606 $aComputational linguistics 606 $aEnglish language$xStudy and teaching$xForeign speakers$xResearch 606 $aCorpora (Linguistics) 608 $aElectronic books. 615 0$aEnglish language$xStudy and teaching$xData processing. 615 0$aEnglish language$xDiscourse analysis$xData processing. 615 0$aComputational linguistics. 615 0$aEnglish language$xStudy and teaching$xForeign speakers$xResearch. 615 0$aCorpora (Linguistics) 676 $a420/.285 702 $aLenko-Szymanska$b Agnieszka 702 $aBoulton$b Alex 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910493208603321 996 $aMultiple affordances of language corpora for data-driven learning$92449541 997 $aUNINA