Restrictive stochastic item selection methods in cognitive diagnostic computerized adaptive testing

Chun Wang, Hua Hua Chang, Alan Huebner

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

This paper proposes two new item selection methods for cognitive diagnostic computerized adaptive testing: the restrictive progressive method and the restrictive threshold method. They are built upon the posterior weighted Kullback-Leibler (KL) information index but include additional stochastic components either in the item selection index or in the item selection procedure. Simulation studies show that both methods are successful at simultaneously suppressing overexposed items and increasing the usage of underexposed items. Compared to item selection based upon (1) pure KL information and (2) the Sympson-Hetter method, the two new methods strike a better balance between item exposure control and measurement accuracy. The two new methods are also compared with progressive method and proportional method.

Original languageEnglish (US)
Pages (from-to)255-273
Number of pages19
JournalJournal of Educational Measurement
Volume48
Issue number3
DOIs
StatePublished - Sep 1 2011

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