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Restrictive Stochastic Item Selection Methods in Cognitive Diagnostic Computerized Adaptive Testing
Author(s) -
Wang Chun,
Chang HuaHua,
Huebner Alan
Publication year - 2011
Publication title -
journal of educational measurement
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.917
H-Index - 47
eISSN - 1745-3984
pISSN - 0022-0655
DOI - 10.1111/j.1745-3984.2011.00145.x
Subject(s) - computerized adaptive testing , computer science , selection (genetic algorithm) , item response theory , cognition , index (typography) , artificial intelligence , statistics , data mining , machine learning , mathematics , psychometrics , psychology , neuroscience , world wide web
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 Barrada et al.'s (2008) progressive method and proportional method .