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Cited by 276

The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.

Modelling Learning Difficulty and Second Language Proficiency: The Differential Contributions of Implicit and Explicit Knowledge

Ellis, Rod·2006·Applied Linguistics

Abstract

This article re-examines the question of what makes some grammatical structures more difficult to learn than others, arguing that this question can only be properly understood and investigated with reference to the distinction between implicit and explicit knowledge of a second language. Using a battery of tests that were designed to measure implicit and explicit L2 grammatical knowledge of seventeen grammatical structures (Ellis 2005), learning difficulty in relation to these two types of knowledge was investigated. The results showed that structures that were easy in terms of implicit knowledge were often difficult in terms of explicit knowledge and sometimes vice versa and that, overall, there was no correlation between the rank orders of difficulty of seventeen grammatical structures for the two types of knowledge. A correlational analysis showed that the structures varied as to whether it was implicit or explicit knowledge of them that was related to a measure of general language proficiency. A regression analysis demonstrated that both types of knowledge predict general language proficiency.

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Metadata

Authors
Ellis, R.
Venue
Applied Linguistics
Published
2006-09-01
Volume
27
Issue
3
Pages
431-463
ISSN
  • 0142-6001 (ISSN-L)
  • 1477-450X
OpenAlex
W1974817072
Cited by
276
References
50
FWCI
40.77
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Modelling Learning Difficulty and Second Language Proficiency: The Differential Contributions of Implicit and Explicit Knowledge. https://app.cochat.ai/papers/10.1093/applin/aml022
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Ellis, R. (2006). Modelling Learning Difficulty and Second Language Proficiency: The Differential Contributions of Implicit and Explicit Knowledge. Applied Linguistics. https://doi.org/10.1093/applin/aml022

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