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Data-driven learning for beginners: The case of German verb-preposition collocations

Published online by Cambridge University Press:  12 January 2016

Nina Vyatkina
Nina Vyatkina*
Affiliation:
University of Kansas, United States (email: vyatkina@ku.edu)
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Abstract

Research on data-driven learning (DDL), or teaching and learning languages with the help of electronic corpora, has shown that it is both effective and efficient. Nevertheless, DDL is still far from common pedagogical practice, not least because the empirical research on it is still limited and narrowly focused. This study addresses some gaps in that research by exploring the effectiveness of DDL for teaching low-proficiency learners lexico-grammatical constructions (verb-preposition collocations) in German, a morphologically rich language. The study employed a pretest-posttest design with intact third- and fourth-semester classes for German as a foreign language at a US university. The same collocations were taught to each group during one class period, with one group at each course level taking a paper-based DDL lesson with concordance lines from a native-speaker corpus and the other one taking a traditional rule-based lesson with textbook exercises. These constructions were new to third-semester students, whereas fourth-semester students had been exposed to them in the previous semester. The results show that, whereas the DDL method and the traditional method were both effective and resulted in lexical and grammatical gains, DDL was more effective for teaching new collocations. The study thus argues in favor of using paper-based DDL in the classroom at lower proficiency levels and for languages other than English.

Keywords

data-driven learningpaper-basedguided inductionmorphologyGerman
Type
Regular papers
Information
ReCALL , Volume 28 , Issue 2 , May 2016 , pp. 207 - 226
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Copyright
Copyright © European Association for Computer Assisted Language Learning 2016 

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