Learner Corpus Research in EAP: Some key issues and future pathways
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Learner corpus research is now almost twenty-five years old. While in the 1990s learner corpus research was mostly directed towards English for general academic purposes (EGAP), i.e. argumentative essay writing, more emphasis is now being given to English for specific academic purposes (ESAP) writing. This article discusses three core issues, namely the optimum size of the corpus, whether the study is corpus-based or corpus-driven and the interpretation of the corpus data, with respect to key learner corpus studies of EGAP and ESAP writing. These three issues, which have been much discussed in the literature on corpus linguistics in general, have been accorded less attention in learner corpus research and therefore merit further reflection and discussion. The final section of the paper maps out some ongoing and future avenues for learner corpus research. These include the compilation and analysis of more longitudinal data, the use of more sophisticated statistical procedures, and the collection of more individual metadata.
Abstract
Learner corpus research is now almost twenty-five years old. While in the 1990s learner corpus research was mostly directed towards English for general academic purposes (EGAP), i.e. argumentative essay writing, more emphasis is now being given to English for specific academic purposes (ESAP) writing. This article discusses three core issues, namely the optimum size of the corpus, whether the study is corpus-based or corpus-driven and the interpretation of the corpus data, with respect to key learner corpus studies of EGAP and ESAP writing. These three issues, which have been much discussed in the literature on corpus linguistics in general, have been accorded less attention in learner corpus research and therefore merit further reflection and discussion. The final section of the paper maps out some ongoing and future avenues for learner corpus research. These include the compilation and analysis of more longitudinal data, the use of more sophisticated statistical procedures, and the collection of more individual metadata.
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