Chunking in the Second Language: Implications for Language Learning and Teaching

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Title: Chunking in the Second Language: Implications for Language Learning and Teaching
Language: English
Authors: Serene Y. Wang (ORCID 0000-0001-9354-0206), Morten H. Christiansen (ORCID 0000-0002-3850-0655)
Source: Language Teaching Research Quarterly. 2024 44:84-106.
Availability: European Knowledge Development (EUROKD). e-mail: editorial@eurokd.com; Web site: https://www.eurokd.com/journal/jd/1
Peer Reviewed: Y
Page Count: 23
Publication Date: 2024
Document Type: Journal Articles
Reports - Evaluative
Descriptors: Phrase Structure, Second Language Learning, Second Language Instruction, Native Language, Language Proficiency, Language Processing, Linguistic Input, Error Analysis (Language), Error Patterns, Learning Processes, Transfer of Training, Grammar, Teaching Methods
ISSN: 2667-6753
Abstract: Among the various challenges that adult and other late language learners face on their journey to achieving nativelike proficiency, chunking has been identified as one of the most difficult tasks to master. Language users are able to derive and utilize chunks during language processing -- both in the first (L1) and the second language (L2) -- yet the extent to which the L2 learners utilize and benefit from chunking is not on a par with L1 speakers. L2 learners are generally less sensitive to the statistical regularities in the linguistic input and possess a smaller repertoire of multiword expressions, leaving them susceptible to slower real-time language processing, hampered comprehension during conversation, and distinct production errors. Drawing on insights from Brian MacWhinney's Unified Competition Model of L1 and L2 acquisition, this review examines these unique challenges in L2 chunking as a function of differences between L1 and L2 learning. According to the Unified Competition Model, the existence of deeply entrenched L1 linguistic representations may hinder effective L2 chunking by encouraging over-segmentation in favor of L1 transference at the lexical level and diverting the necessary attention away from the grammatical elements in prefabricated multiword units. Based on these observations, we offer practical suggestions for educators to facilitate chunking in L2 learners and bring them closer to nativelike fluency.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1457275
Database: ERIC
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  Data: Among the various challenges that adult and other late language learners face on their journey to achieving nativelike proficiency, chunking has been identified as one of the most difficult tasks to master. Language users are able to derive and utilize chunks during language processing -- both in the first (L1) and the second language (L2) -- yet the extent to which the L2 learners utilize and benefit from chunking is not on a par with L1 speakers. L2 learners are generally less sensitive to the statistical regularities in the linguistic input and possess a smaller repertoire of multiword expressions, leaving them susceptible to slower real-time language processing, hampered comprehension during conversation, and distinct production errors. Drawing on insights from Brian MacWhinney's Unified Competition Model of L1 and L2 acquisition, this review examines these unique challenges in L2 chunking as a function of differences between L1 and L2 learning. According to the Unified Competition Model, the existence of deeply entrenched L1 linguistic representations may hinder effective L2 chunking by encouraging over-segmentation in favor of L1 transference at the lexical level and diverting the necessary attention away from the grammatical elements in prefabricated multiword units. Based on these observations, we offer practical suggestions for educators to facilitate chunking in L2 learners and bring them closer to nativelike fluency.
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        StartPage: 84
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      – SubjectFull: Phrase Structure
        Type: general
      – SubjectFull: Second Language Learning
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      – SubjectFull: Second Language Instruction
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      – SubjectFull: Language Proficiency
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      – SubjectFull: Language Processing
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      – SubjectFull: Error Analysis (Language)
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      – SubjectFull: Error Patterns
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      – SubjectFull: Learning Processes
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      – SubjectFull: Transfer of Training
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      – SubjectFull: Grammar
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