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The role of guided induction in paper-based data-driven learning

Published online by Cambridge University Press:  19 February 2014

Jonathan Smart
Jonathan Smart*
Affiliation:
University of Pittsburgh, USA (email:jsmart@pitt.edu)
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Abstract

This study examines the role of guided induction as an instructional approach in paper-based data-driven learning (DDL) in the context of an ESL grammar course during an intensive English program at an American public university. Specifically, it examines whether corpus-informed grammar instruction is more effective through inductive, data-driven learning or through traditional deductive instruction. In the study, 49 participants completed two weeks of ESL grammar instruction on the passive voice in English. The learners participated in one of three instructional treatments: a data-driven learning treatment, a deductive instructional treatment using corpus-informed teaching materials, and a deductive instructional treatment using traditional (i.e., non-corpus-informed) materials. Results from pre-test, post-test, and delayed post-test indicated that the DDL group significantly improved their grammar ability with the passive voice, while the other two treatment groups did not show significant gains. The findings from this study suggest that in this learning context there are measurable benefits to teaching ESL grammar inductively using paper-based DDL.

Keywords

data-driven learningguided inductionESL grammar

Information

Type
Research Article
Information
ReCALL , Volume 26 , Issue 2: Researching uses of corpora for language teaching and learning , May 2014 , pp. 184 - 201
Copyright
Copyright © European Association for Computer Assisted Language Learning 2014 

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