Predicting item exposure parameters in computerized adaptive testing
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{BMSP:BMSP255,
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author = "Shu-Ying Chen and Shing-Hwang Doong",
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title = "Predicting item exposure parameters in computerized
adaptive testing",
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journal = "British Journal of Mathematical and Statistical
Psychology",
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volume = "61",
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number = "1",
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year = "2008",
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pages = "75--91",
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month = may,
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keywords = "genetic algorithms, genetic programming",
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publisher = "Blackwell Publishing Ltd",
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ISSN = "2044-8317",
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language = "en",
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oai = "oai:CiteSeerX.psu:10.1.1.624.6855",
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URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.624.6855",
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broken = "http://www.psych.umn.edu/psylabs/catcentral/pdf
files/ch03-01.pdf",
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DOI = "doi:10.1348/000711006X129553",
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abstract = "The purpose of this study is to find a formula that
describes the relationship between item exposure
parameters and item parameters in computerized adaptive
tests by using genetic programming (GP) - a
biologically inspired artificial intelligence
technique. Based on the formula, item exposure
parameters for new parallel item pools can be predicted
without conducting additional iterative simulations.
Results show that an interesting formula between item
exposure parameters and item parameters in a pool can
be found by using GP. The item exposure parameters
predicted based on the found formula were close to
those observed from the Sympson and Hetter (1985)
procedure and performed well in controlling item
exposure rates. Similar results were observed for the
Stocking and Lewis (1998) multinomial model for item
selection and the Sympson and Hetter procedure with
content balancing. The proposed GP approach has
provided a knowledge-based solution for finding item
exposure parameters.",
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notes = "PMID: 18482476",
- }
Genetic Programming entries for
Shu-Ying Chen
Shing-Hwang Doong
Citations