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Unlearning overgenerated be through data-driven learning in the secondary EFL classroom

Published online by Cambridge University Press:  20 September 2017

Soyeon Moon  and
Sun-Young Oh
Soyeon Moon
Affiliation:
Seoul National University, Republic of Korea (email: soyeonmoon@snu.ac.kr)
Sun-Young Oh
Affiliation:
Seoul National University, Republic of Korea (email: sunoh@snu.ac.kr)
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Abstract

This paper reports on the cognitive and affective benefits of data-driven learning (DDL), in which Korean EFL learners at the secondary level notice and unlearn their “overgenerated be” by comparing native English-speaker and learner corpora with guided induction. To select the target language item and compile learner-corpus-based materials, writing samples of 285 learners were collected. The participants were randomly divided into traditional grammar learning and DDL groups. After providing instruction for each group, one immediate and one delayed writing sessions were implemented. Revealing a lower ratio of overgenerated be after the instruction than the control group, the DDL group showed statistically significant retention as well as immediate effects in terms of the raw counts of the target item. Based on this improvement in grammar learning and retention, DDL is considered helpful for these learners as it facilitated their efforts to discover and apply rules. In addition, their positive attitudes toward DDL including both native speaker and learner data provide useful pedagogical implications. Learning from the negative evidence produced in their own classroom helped learners, especially at lower levels, to raise their grammar consciousness and boost their motivation to learn.

Keywords

data-driven learninglearner corpusovergenerated besecondary educationguided induction
Type
Regular papers
Information
ReCALL , Volume 30 , Issue 1 , January 2018 , pp. 48 - 67
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Copyright
Copyright © European Association for Computer Assisted Language Learning 2017 

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