The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Learning progressions: Aligning curriculum, instruction, and assessment
Open accessAbstract
Scholarship on learning progressions (LPs) in science has emerged over the past 5 years, with the first comprehensive descriptions of LPs, on the nature of matter and evolution, published as commissioned reports (Catley, Lehrer, & Reiser, 2005; Smith, Wiser, Anderson, & Krajcik, 2006). Several recent policy reports have advocated for the use of LPs as a means of aligning standards, curriculum, and assessment (National Research Council [NRC], 2005, 2007). In some ways, LPs are not a new idea; developmental psychologists have long been examining the development of childrens' ideas over time in several scientific domains. However, the emerging research offers renewed interest, a new perspective, and potentially new applications for this construct. For these reasons, this special issue of the Journal for Research in Science Teaching is timely. Our goal in this introduction is to explain the motivation for developing LPs, propose a consensual definition of LPs, describe the ways in which these constructs are being developed and validated, and finally, discuss some of the unresolved questions regarding this emerging scholarship. © 2009 Wiley Periodicals, Inc. J Res Sci Teach 46: 606–609, 2009
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
Cites
BRUNER, J. S. (1960) · doi:10.4159/9780674028999 · Cited by 6,042
Cited by
Handbook of Research on Learning and Instruction
(2011) · Routledge eBooks · doi:10.4324/9780203839089 · Cited by 916
Learning progressions and teaching sequences: a review and analysis
Duschl, R., Maeng, S., & Sezen, A. (2011) · Studies in Science Education · doi:10.1080/03057267.2011.604476 · Cited by 375
The Model of Educational Reconstruction – a Framework for Improving Teaching and Learning Science1
Duit, R., Gropengießer, H., Kattmann, U., Komorek, M., & Parchmann, I. (2012) · Science Education Research and Practice in Europe · doi:10.1007/978-94-6091-900-8_2 · Cited by 362
Measuring progressions: Assessment structures underlying a learning progression
Wilson, M. (2009) · Journal of Research in Science Teaching · doi:10.1002/tea.20318 · Cited by 329
Towards a learning progression of energy
Neumann, K., Viering, T., Boone, W. J., & Fischer, H. E. (2013) · Journal of Research in Science Teaching · doi:10.1002/tea.21061 · Cited by 301
Metadata
- Authors
- Duncan, R. G., & Hmelo‐Silver, C. E.
- Venue
- Journal of Research in Science Teaching
- Publisher
- Wiley
- Published
- 2009-07-02
- Volume
- 46
- Issue
- 6
- Pages
- 606-609
- ISSN
- 0022-4308 (ISSN-L)
- 1098-2736
- OpenAlex
- W2016295847
- Cited by
- 316
- References
- 1
- FWCI
- 73.24
- Type
- journal article
- Language
- en
- Open access
- bronze
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
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.