The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
A BRIEF INTRODUCTION TO EVIDENCE‐CENTERED DESIGN
Open accessAbstract
Evidence‐centered assessment design (ECD) is an approach to constructing educational assessments in terms of evidentiary arguments. This paper provides an introduction to the basic ideas of ECD, as well as some of the terminology and models that have been developed to implement the approach. In particular, it presents the high‐level models of the Conceptual Assessment Framework (CAF) and the Four‐process Delivery Architecture for assessment delivery systems. Special attention is given to the role of probability‐based reasoning in accumulating evidence across task performances, in terms of belief about unobservable variables that characterize the knowledge, skills, and/or abilities of students. This is the role traditionally associated with psychometric models, such as those of item response theory (IRT) and latent class models. To unify the ideas and to provide a foundation for extending probability‐based reasoning in assessment applications more broadly, however, a more general expression in terms of graphical models is indicated. This brief overview of evidence‐centered design provides the reader with a feel for where and how graphical models fit into the larger enterprise of educational and psychological assessment. A simple example based on familiar large‐scale standardized tests such as the Graduate Record Examinations® (GRE®) is used to fix ideas.
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Cited by
Characterizing College Science Assessments: The Three-Dimensional Learning Assessment Protocol
Laverty, J. T., Underwood, S. M., Matz, R. L., Posey, L. A., Carmel, J. H., Caballero, M. D., Fata-Hartley, C. L., Ebert-May, D., Jardeleza, S. E., & Cooper, M. M. (2016) · PLOS ONE · doi:10.1371/journal.pone.0162333 · Cited by 262
Bang, H. J., Li, L., & Flynn, K. (2023) · Early Childhood Education Journal · doi:10.1007/s10643-022-01332-3 · Cited by 91
Culturally Responsive Assessment: Provisional Principles
Walker, M. E., Olivera‐Aguilar, M., Lehman, B., Laitusis, C., Guzman‐Orth, D., & Gholson, M. (2023) · ETS Research Report Series · doi:10.1002/ets2.12374 · Cited by 62
Opportunities and challenges of using generative AI to personalize educational assessment
Arslan, B., Lehman, B., Tenison, C., Sparks, J. R., López, A. A., Gu, L., & Zapata-Rivera, D. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1460651 · Cited by 52
Using machine learning to model trace behavioral data from a game‐based assessment
Auer, E. M., Mersy, G., Marin, S., Blaik, J., & Landers, R. N. (2022) · International Journal of Selection and Assessment · doi:10.1111/ijsa.12363 · Cited by 24
Metadata
- Authors
- Mislevy, R. J., Almond, R. G., & Lukas, J. F.
- Venue
- ETS Research Report Series
- Publisher
- Wiley
- Published
- 2003-06
- Volume
- 2003
- Issue
- 1
- ISSN
- 2330-8516 (ISSN-L)
- OpenAlex
- W1987868810
- Cited by
- 473
- References
- 33
- FWCI
- 2.90
- Type
- journal article
- Language
- en
- Open access
- bronze
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