01369nas a2200157   4500008003900000022001400039245010400053210006900157300001400226490000700240520085700247100001501104700001901119700001801138856005501156       2011                          d  a1745-398400aRestrictive Stochastic Item Selection Methods in Cognitive Diagnostic Computerized Adaptive Testing0 aRestrictive Stochastic Item Selection Methods in Cognitive Diagn  a255–2730 v483 a<p>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.&#39;s (2008) progressive method and proportional method.</p>
1 aWang, Chun1 aChang, Hua-Hua1 aHuebner, Alan  uhttp://dx.doi.org/10.1111/j.1745-3984.2011.00145.x