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An Empirical Study of Corpora Application in Data-Driven English Lexical Learning

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10108))

Abstract

Though there have been revolutions applying corpora in L2 teaching, empirical studies on the integrated use of corpora in English lexical learning are very limited. The paper takes an empirical study on a DDL approach applying corpora concordance to enhance lexical learning in the teaching of a college English course in mainland China. The corpora resources include a self-built English movie script corpus, a list of publicly available online corpora resources and the WebCorp. After asking students to post their DDL learning results of idiomatic words or expressions in English movies using the three kinds of resources on the course blog for a semester, observation of students’ blog posts shows that their use of corpora has achieved promising learning effects for decoding and/or encoding purposes. Questionnaire and interview surveys also indicate that students have not only recognized the advantages and limitations of each corpus, but also found suitable ways to use each kind of resources accordingly in their lexical learning. We conclude that the DDL approach of English lexical learning proposed by this study could be practical and acceptable if carefully planned and conducted.

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Acknowledgements

This paper is supported by the Science and Technology Project of Guangdong Province, China (2016A040403113), Guangdong Provincial Project for Higher Education Teaching Reform (GDJG20142204), Teaching and Research Project for Guangdong University of Foreign Studies (GWJYQN14002), and Innovative School Project in Higher Education of Guangdong, China (GWTP-LH-2015-10 and YQ2015062).

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Correspondence to Tianyong Hao .

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Wang, X., Hao, T. (2017). An Empirical Study of Corpora Application in Data-Driven English Lexical Learning. In: Wu, TT., Gennari, R., Huang, YM., Xie, H., Cao, Y. (eds) Emerging Technologies for Education. SETE 2016. Lecture Notes in Computer Science(), vol 10108. Springer, Cham. https://doi.org/10.1007/978-3-319-52836-6_39

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  • DOI: https://doi.org/10.1007/978-3-319-52836-6_39

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  • Publisher Name: Springer, Cham

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