The DOI matches its Crossref record.
A Theory‐Based Approach to the Measurement of Foreign Language Learning Ability: The Canal‐F Theory and Test
Abstract
This article presents a rationale, description, and partial construct validation of a new theory of foreign language aptitude: CANAL‐F—Cognitive Ability for Novelty in Acquisition of Language (Foreign). The theory was applied and implemented in a test of foreign language aptitude (CANAL‐FT). Three unique features differentiate the new test from many existing tests of FL aptitude. The CANAL‐FT is grounded in cognitive theory, dynamic rather than static, and simulation‐based. This article outlines the CANAL‐F theory and details of its instrumentation through the test (CANAL‐FT), discusses the psychometric properties of the CANAL‐FT, and presents the first construct‐validation data on the theory and test.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Individual Differences in Second Language Learning
Dörnyei, Z., & Skehan, P. (2003) · The Handbook of Second Language Acquisition · doi:10.1002/9780470756492.ch18 · Cited by 563
The robustness of aptitude effects in near-native second language acquisition
Abrahamsson, N., & Hyltenstam, K. (2008) · Studies in Second Language Acquisition · doi:10.1017/s027226310808073x · Cited by 446
Aspects of working memory in L2 learning
Juffs, A., & Harrington, M. (2011) · Language Teaching · doi:10.1017/s0261444810000509 · Cited by 249
Foreign language aptitude theory: Yesterday, today and tomorrow
Wen, Z. (., Biedroń, A., & Skehan, P. (2017) · Language Teaching · doi:10.1017/s0261444816000276 · Cited by 248
Individual Differences and the Ergodicity Problem
Lowie, W. M., & Verspoor, M. H. (2019) · Language Learning · doi:10.1111/lang.12324 · Cited by 244
Metadata
- Authors
- Grigornko, E. L., Sternberg, R. J., & Ehrman, M. E.
- Venue
- The Modern Language Journal
- Publisher
- Wiley
- Published
- 2000-09
- Volume
- 84
- Issue
- 3
- Pages
- 390-405
- ISSN
- 0026-7902 (ISSN-L)
- 1540-4781
- OpenAlex
- W2097942542
- Cited by
- 241
- References
- 0
- FWCI
- 14.82
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A Theory‐Based Approach to the Measurement of Foreign Language Learning Ability: The Canal‐F Theory and Test. https://app.cochat.ai/papers/10.1111/0026-7902.00076
- The paper
- Grigornko, E. L., Sternberg, R. J., & Ehrman, M. E. (2000). A Theory‐Based Approach to the Measurement of Foreign Language Learning Ability: The Canal‐F Theory and Test. The Modern Language Journal. https://doi.org/10.1111/0026-7902.00076
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
