Cited by 95Second Language Acquisition and LearningEFL/ESL Teaching and LearningReading and Literacy Development

Lexical Input Processing and Vocabulary Learning

Barcroft, Joe·2015·Language Learning & Language Teaching

Abstract

This book focuses on theory, research, and practice related to lexical input processing (lex-IP), an exciting field exploring how learners allocate their limited processing resources when exposed to words and lexical phrases in the input. Unit 1 specifies parameters of lex-IP research among other levels of input processing as well as key components (form, meaning, mapping) and contexts (incidental/intentional) of vocabulary learning. Unit 2 highlights theoretical advances, such as the type of processing – resource allocation (TOPRA) model, consistent with research on tasks (sentence writing, word copying, word retrieval) that learners may perform during vocabulary learning. Unit 3 highlights patterns in partial word form learning and input-based effects, including the value of increased exposure, drawbacks of presenting vocabulary in semantic sets, and advantages of input enhancement, particularly with regard to increasing talker, speaking-style, and speaking-rate variability in spoken input. The book unifies a range of research pertinent to lex-IP, summarizes theoretical and instructional implications, and proposes intriguing new directions for future research.

Integrity signals

Registry

Crossref · 10.1075/lllt.43

Cites

147 of 147 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • There is a common notion of a correlation between exposure to new words and learning gains.

    “In addition, the existence of a correlation between exposure to new words and learning gains seems to be a common notion [10,11,21,22].”
  • Barcroft states that it is highly complicated to identify the number of times a learner encounters a new word, and that lack of focused attention and lack of controlled presentation pattern are commonly critical differences between incidental and intentional vocabulary learning.

    “However, as Barcroft states, it is highly complicated to identify the number of times a learner encounters a new word, claiming that, as a consequence, 'both lack of focused attention and lack of controlled presentation pattern are commonly critical differences that appear with incidental as compared to intentional vocabulary learning' [22].”

Cites

Flagged first, then by citations

10 of the 157 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Authors
Barcroft, J.
Affiliations
Washington University in St. Louis (US)
Corresponding author
Barcroft, Joe
Venue
Language Learning & Language Teaching
Published
2015-11-26
ISSN
1569-9471 (ISSN-L)
OpenAlex
W2297833162
Cited by
95
References
157
Citations by year
2026: 8 · 2025: 9 · 2024: 9 · 2023: 2 · 2022: 3 · 2021: 11 · 2020: 29 · 2019: 7 · 2018: 8 · 2017: 6 · 2016: 3
FWCI
5.67
Type
book
Language
en
Open access
closed
Locations
Language learning and language teaching (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 › Psychology › Developmental and Educational Psychology › Reading and Literacy Development
Keywords
vocabulary acquisition, resource allocation, incidental learning, intentional learning, input enhancement, word retrieval, sentence writing, form-meaning mapping

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 ›

Are you an author, or do you see a problem with this page? Request a review