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Academic Paper


Title: The role of guided induction in paper-based data-driven learning
Author: Jonathan Smart
Institution: University of Pittsburgh
Linguistic Field: Applied Linguistics; Syntax; Text/Corpus Linguistics
Subject Language: English
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.

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This article appears IN ReCALL Vol. 26, Issue 2, which you can READ on Cambridge's site or on LINGUIST .



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