The DOI matches its Crossref record.
Dynamic testing: the nature and measurement of learning potential
Abstract
Part I. Theoretical Framework: 1. Abilities as forms of developing expertise Part II. The Nature of Dynamic Testing: 2. An introduction to dynamic testing 3. A capsule history of dynamic testing Part III. Leading Modern Approaches to Dynamic Testing: 4. The approach of Feuerstein 5. The approach of Budoff 6. Diverse approaches to dynamic testing 7. A three-prong approach to dynamic testing Part IV. Two Case Studies using Dynamic Testing: 8. Using dynamic testing to reveal hidden potential 9. Combining instruction with assessment: measuring foreign-language learning ability 10. Measurement aspects of dynamic testing: quantifying change Epilogue.
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
The power of testing memory: Basic research and implications for educational practice
Roediger, H. L., & Karpicke, J. D. (2006) · Perspectives on Psychological Science · doi:10.1111/j.1745-6916.2006.00012.x · Cited by 2,037
Alternative Approaches to Second Language Acquisition
(2011) · Routledge eBooks · doi:10.4324/9780203830932 · Cited by 1,079
The Zone of Proximal Development in Vygotsky's Analysis of Learning and Instruction
Chaiklin, S. (2003) · Vygotsky's Educational Theory in Cultural Context · doi:10.1017/cbo9780511840975.004 · Cited by 1,055
Universal intelligence: a definition of machine intelligence
Legg, S., & Hutter, M. (2007) · Minds and Machines · doi:10.1007/s11023-007-9079-x · Cited by 803
Addressing the assessment challenge with an online system that tutors as it assesses
Feng, M., Heffernan, N., & Koedinger, K. (2009) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-009-9063-7 · Cited by 502
Metadata
- Venue
- Choice Reviews Online
- Publisher
- American Library Association
- Published
- 2003-03-01
- Volume
- 40
- Issue
- 07
- Pages
- 40-4136-40-4136
- ISSN
- 0009-4978 (ISSN-L)
- 1523-8253
- OpenAlex
- W572143073
- Cited by
- 529
- References
- 0
- Type
- book review
- 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: Dynamic testing: the nature and measurement of learning potential. https://app.cochat.ai/papers/10.5860/choice.40-4136
- The paper
- Dynamic testing: the nature and measurement of learning potential. (2003). Choice Reviews Online. https://doi.org/10.5860/choice.40-4136
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.