Corpus-Driven Approaches for Advancing L2 Grammar: An Empirical Investigation

Second language (L2) acquisition remains a complex and multifaceted process. Learners grapple with various linguistic aspects, including vocabulary, syntax, and pragmatics. Traditional language teaching methods often rely on textbooks, exercises, and classroom instruction. However, in recent years, corpus linguistics has emerged as a powerful tool for understanding language patterns and informing pedagogy. Corpus-based approaches allow researchers and educators to explore authentic language use, uncover hidden structures, and tailor instructional materials to learners’ needs. Despite the potential benefits of corpus-driven approaches, questions persist regarding their practical effectiveness in enhancing L2 learners’ grammatical competence. This study explores the effectiveness of corpus-based, data-driven learning approaches in enhancing second language (L2) grammar acquisition for non-native learners. Unlike traditional grammar instruction, corpus-driven methods use authentic language data from corpora to guide learners in uncovering patterns and rules of language use. Through a mixed-methods approach, this investigation draws on both quantitative and qualitative data to examine the impact of corpus-driven activities on L2 learners’ grammatical accuracy and awareness. A group of intermediatelevel English learners participated in a series of targeted grammar tasks based on authentic corpus data. Pre- and post-tests were conducted to measure improvements in grammatical performance, while learner reflections and interviews provided insight into their experiences with the approach. The results indicate a significant improvement in grammatical accuracy and a deeper understanding of language structure, supporting the efficacy of corpus-driven learning for L2 grammar.These findings contribute to ongoing discussions about the role of data-driven learning in second language pedagogy, highlighting the potential of corpus tools as a dynamic resource for L2 instruction.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-12-05
DOI
https://doi.org/10.5281/zenodo.14284069
Primary Topic
Second Language Acquisition and Learning
Type
article
Field-Weighted Citation Impact
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article

Corpus-Driven Approaches for Advancing L2 Grammar: An Empirical Investigation

Zenodo (CERN European Organization for Nuclear Research)
Second Language Acquisition and Learning
article

Corpus-Driven Approaches for Advancing L2 Grammar: An Empirical Investigation

article en

Abstract

Second language (L2) acquisition remains a complex and multifaceted process. Learners grapple with various linguistic aspects, including vocabulary, syntax, and pragmatics. Traditional language teaching methods often rely on textbooks, exercises, and classroom instruction. However, in recent years, corpus linguistics has emerged as a powerful tool for understanding language patterns and informing pedagogy. Corpus-based approaches allow researchers and educators to explore authentic language use, uncover hidden structures, and tailor instructional materials to learners’ needs. Despite the potential benefits of corpus-driven approaches, questions persist regarding their practical effectiveness in enhancing L2 learners’ grammatical competence. This study explores the effectiveness of corpus-based, data-driven learning approaches in enhancing second language (L2) grammar acquisition for non-native learners. Unlike traditional grammar instruction, corpus-driven methods use authentic language data from corpora to guide learners in uncovering patterns and rules of language use. Through a mixed-methods approach, this investigation draws on both quantitative and qualitative data to examine the impact of corpus-driven activities on L2 learners’ grammatical accuracy and awareness. A group of intermediatelevel English learners participated in a series of targeted grammar tasks based on authentic corpus data. Pre- and post-tests were conducted to measure improvements in grammatical performance, while learner reflections and interviews provided insight into their experiences with the approach. The results indicate a significant improvement in grammatical accuracy and a deeper understanding of language structure, supporting the efficacy of corpus-driven learning for L2 grammar.These findings contribute to ongoing discussions about the role of data-driven learning in second language pedagogy, highlighting the potential of corpus tools as a dynamic resource for L2 instruction.

Zenodo (CERN European Organization for Nuclear Research)
Northern University of Malaysia (MY)
Quality Education
Openalex Percentile: Top 49%
Second Language Acquisition and Learning
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