Cited by 132Second Language Acquisition and LearningEFL/ESL Teaching and LearningDiscourse Analysis in Language Studies

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Enhancing writing pedagogy with learner corpus data

Cotos, Elena·2014·ReCALL

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Abstract

Learner corpora have become prominent in language teaching and learning, enhancing data-driven learning (DDL) pedagogy by promoting ‘learning driven data’ in the classroom. This study explores the potential of a local learner corpus by investigating the effects of two types of DDL activities, one relying on a native-speaker corpus (NSC) and the second combining native-speaker and learner corpora. Both types of activities aimed at improving second language writers’ knowledge of linking adverbials and were based on a preliminary analysis of adverbial use in the local learner corpus produced by 31 study participants. Quantitative and qualitative data, obtained from writing samples, pre/post-tests, and questionnaires, were converged through concurrent triangulation. The results showed an increase in frequency, diversity and accuracy in all participants’ use of adverbials, but more significant improvement was made by the students who were exposed to the corpus containing their own writing. The findings of this study are thus interpreted as suggestive that combining learner and native-speaker data is a feasible and effective practice, which can be readily integrated in DDL-based instruction with positive impact.

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Authors: Cotos, E.
Affiliations: Iowa State University (US)
Corresponding author: Cotos, Elena
ORCID: Cotos, Elena
Venue: ReCALL
Publisher: Cambridge University Press (CUP)
Published: 2014-02-21
Volume: 26
Issue: 2
Pages: 202-224
ISSN: 0958-3440 (ISSN-L)
1474-0109
DOI: 10.1017/s0958344014000019
OpenAlex: W2134547826
Cited by: 132
References: 76
Citations by year: 2026: 3 · 2025: 26 · 2024: 9 · 2023: 12 · 2022: 7 · 2021: 7 · 2020: 10 · 2019: 26 · 2018: 6 · 2017: 12 · 2016: 6 · 2015: 8
FWCI: 20.32
Type: journal article
Language: en
Open access: closed
Licence: www.cambridge.org/core/terms (unspecified)
Locations: ReCALL (publishedVersion)
Topics: Social Sciences › Psychology › Developmental and Educational Psychology › Second Language Acquisition and Learning
Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
Social Sciences › Linguistics and Language › Discourse Analysis in Language Studies
Keywords: learner corpus, data-driven learning, writing instruction, second language writing, linking adverbials, native speaker corpus, corpus-based instruction, adverbial use

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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Enhancing writing pedagogy with learner corpus data

Abstract

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Integrity signals

Verification

Claims the paper makes

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