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Measuring progressions: Assessment structures underlying a learning progression
Abstract
This article describes some of the underlying conceptualizations that have gone into the work of the BEAR Center in the development of learning progressions. The core of all of these developments has been the construct map, which is the first building block in the BEAR Assessment System (BAS). After introducing the concept of a learning progression, the article summarizes the elements of the BAS, emphasizing the central concept of a construct map. The article then describes a series of several different ways to see the relationship between the idea of a construct map and the idea of a progression (which I call the “assessment structure”), and also gives illustrative examples from recent BEAR projects. The article then discusses some strengths and limitations of these conceptualizations, focusing on both educational and measurement issues. The article concludes with some general reflections. © 2009 Wiley Periodicals, Inc. J Res Sci Teach 46: 716–730, 2009
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Metadata
- Authors
- Wilson, M.
- Venue
- Journal of Research in Science Teaching
- Publisher
- Wiley
- Published
- 2009-07-07
- Volume
- 46
- Issue
- 6
- Pages
- 716-730
- ISSN
- 0022-4308 (ISSN-L)
- 1098-2736
- OpenAlex
- W2114433744
- Cited by
- 329
- References
- 355
- FWCI
- 71.83
- 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 ›
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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.