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Designing a computerized adaptive testing chain for the Force Concept Inventory
written by Jun-ichiro Yasuda, Michael M. Hull, and Kentaro Kojima
The use of computer adaptive testing (CAT)-based assessment tests has inherent issues associated with the pre- and post-paradigm, such as the limited ability to observe the progression of student conceptual understanding throughout the course. To address these issues, we propose increasing the frequency of CAT-based assessments during the course, while reducing the test length per administration, thus decreasing the total number of test items during the course. The feasibility of this idea depends on how far the test length per administration can be reduced. To reach this goal, we designed a CAT algorithm, which we call Chain-CAT. This algorithm sequentially links the results of each CAT administration using collateral information. We analyzed the advantages of this algorithm by numerical simulations. Although preliminary, we found that collateral information significantly improved the test efficiency, and the total test length could be shorter than the pre-post method.
Physics Education Research Conference 2024
Part of the PER Conference series
Boston, MA: July 10-11, 2024
Pages 456-461
Subjects Levels Resource Types
Education - Applied Research
- Technology
= Computers
Education - Basic Research
- Assessment
= Conceptual Assessment
= Instruments
- Research Design & Methodology
= Evaluation
= Statistics
- Lower Undergraduate
- Reference Material
= Research study
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Format:
application/pdf
Mirror:
https://doi.org/10.1119/perc.2024…
Access Rights:
Free access
License:
This material is released under a Creative Commons Attribution 4.0 license. Further distribution of this work must maintain attribution to the published article's author(s), title, proceedings citation, and DOI.
Rights Holder:
American Association of Physics Teachers
DOI:
10.1119/perc.2024.pr.Yasuda
Keyword:
PERC 2024
Record Creator:
Metadata instance created September 6, 2024 by Lyle Barbato
Record Updated:
September 12, 2024 by Lyle Barbato
Last Update
when Cataloged:
September 12, 2024
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