The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Assessing Knowledge Integration in Science: Construct, Measures, and Evidence
Abstract
In response to the demand for sound science assessments, this article presents the development of a latent construct called knowledge integration as an effective measure of science inquiry. Knowledge integration assessments ask students to link, distinguish, evaluate, and organize their ideas about complex scientific topics. The article focuses on assessment topics commonly taught in 6th- through 12th-grade classes. Items from both published standardized tests and previous knowledge integration research were examined in 6 subject-area tests. Results from Rasch partial credit analyses revealed that the tests exhibited satisfactory psychometric properties with respect to internal consistency, item fit, weighted likelihood estimates, discrimination, and differential item functioning. Compared with items coded using dichotomous scoring rubrics, those coded with the knowledge integration rubrics yielded significantly higher discrimination indexes. The knowledge integration assessment tasks, analyzed using knowledge integration scoring rubrics, demonstrate strong promise as effective measures of complex science reasoning in varied science domains.
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Cites
Inside the Black Box: Raising Standards through Classroom Assessment
Black, P., & Wiliam, D. (2010) · Phi Delta Kappan · doi:10.1177/003172171009200119 · Cited by 4,231
A Rasch model for partial credit scoring
Masters, G. N. (1982) · Psychometrika · doi:10.1007/bf02296272 · Cited by 3,782
Weighted Likelihood Estimation of Ability in Item Response Theory
Warm, T. A. (1989) · Psychometrika · doi:10.1007/bf02294627 · Cited by 1,216
From principles to practice: An embedded assessment system
Wilson, M., & Sloane, K. (2000) · Applied Measurement in Education · doi:10.1207/s15324818ame1302_4 · Cited by 333
Cited by
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
Zhu, M., Liu, O. L., & Lee, H.-S. (2020) · Computers & Education · doi:10.1016/j.compedu.2019.103668 · Cited by 229
Self-citationGuiding collaborative revision of science explanations
Gerard, L., Kidron, A., & Linn, M. C. (2019) · International Journal of Computer-Supported Collaborative Learning · doi:10.1007/s11412-019-09298-y · Cited by 58
Self-citationTeijema, J. J., Hofstee, L., Brouwer, M., de Bruin, J., Ferdinands, G., de Boer, J., Vizan, P., van den Brand, S., Bockting, C., van de Schoot, R., et al. (2023) · Frontiers in Research Metrics and Analytics · doi:10.3389/frma.2023.1178181 · Cited by 36
Distributed practice in classroom inquiry science learning
Svihla, V., Wester, M. J., & Linn, M. C. (2018) · Learning: Research and Practice · doi:10.1080/23735082.2017.1371321 · Cited by 8
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Metadata
- Authors
- Liu, O. L., Lee, H.-S., Hofstetter, C., & Linn, M.
- Venue
- Educational Assessment
- Publisher
- Informa UK Limited
- Published
- 2008-01
- Volume
- 13
- Issue
- 1
- Pages
- 33-55
- ISSN
- 1062-7197 (ISSN-L)
- OpenAlex
- W2029885956
- Cited by
- 163
- References
- 4
- FWCI
- 35.77
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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