Cited by 13

The DOI matches its Crossref record.

Optimizing post-critical-period language learning

Doughty, Catherine J.·2013·Language Learning & Language Teaching

Abstract

This chapter reports on a new language aptitude test, the High-level Language Aptitude Battery (Hi-LAB), whose development was motivated by the need for an aptitude measure for more advanced L2 speakers. Since many language learners begin as adults, critical-period constraints work against the desired outcome. All may not be lost, however, given that some individuals attain high-level, if not native, proficiency, despite a late start. We hypothesize that they possess language aptitude comprising inherent cognitive and perceptual abilities that compensate, at least in part, for the typical post-critical-period degradation in language-learning capacity. While tests currently in use were designed to predict early rate of learning in instructed settings, Hi-LAB is conceptualized to predict successful ultimate attainment. Aptitude is a measurable ceiling on language learning, holding equal all other factors. We discuss constructs and measures, reliability and validity evidence, and uses of Hi-LAB for selecting learners for language training and in aptitude-by-treatment interaction studies.

Overview

Integrity signals

Registry

Crossref · 10.1075/lllt.35.06dou

Cites

Statistics

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

Verification

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?

Citations

Cited by

Sorted by citations

See all 13

Metadata

Authors
Doughty, C. J.
Affiliations
University of Maryland Center for Advanced Study of Language
Venue
Language Learning & Language Teaching
Published
2013-03-29
Pages
153-176
ISSN
1569-9471
Cited by
13
References
0
Language
en

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