00515nas a2200133 4500008003900000245012900039210006900168300000900237490000600246100001800252700002700270700002400297856006000321 2018 d00aFactors Affecting the Classification Accuracy and Average Length of a Variable-Length Cognitive Diagnostic Computerized Test0 aFactors Affecting the Classification Accuracy and Average Length a1-140 v61 aHuebner, Alan1 aFinkelman, Matthew, D.1 aWeissman, Alexander uhttp://iacat.org/jcat/index.php/jcat/article/view/55/3001281nas a2200133 4500008003900000245009500039210006900134300001200203490000700215520083800222100001801060700001601078856005301094 2012 d00aA Stochastic Method for Balancing Item Exposure Rates in Computerized Classification Tests0 aStochastic Method for Balancing Item Exposure Rates in Computeri a181-1880 v363 a

Computerized classification tests (CCTs) classify examinees into categories such as pass/fail, master/nonmaster, and so on. This article proposes the use of stochastic methods from sequential analysis to address item overexposure, a practical concern in operational CCTs. Item overexposure is traditionally dealt with in CCTs by the Sympson-Hetter (SH) method, but this method is unable to restrict the exposure of the most informative items to the desired level. The authors’ new method of stochastic item exposure balance (SIEB) works in conjunction with the SH method and is shown to greatly reduce the number of overexposed items in a pool and improve overall exposure balance while maintaining classification accuracy comparable with using the SH method alone. The method is demonstrated using a simulation study.

1 aHuebner, Alan1 aLi, Zhushan uhttp://apm.sagepub.com/content/36/3/181.abstract01281nas a2200133 4500008003900000245009500039210006900134300001200203490000700215520083800222100001801060700001601078856005301094 2012 d00aA Stochastic Method for Balancing Item Exposure Rates in Computerized Classification Tests0 aStochastic Method for Balancing Item Exposure Rates in Computeri a181-1880 v363 a

Computerized classification tests (CCTs) classify examinees into categories such as pass/fail, master/nonmaster, and so on. This article proposes the use of stochastic methods from sequential analysis to address item overexposure, a practical concern in operational CCTs. Item overexposure is traditionally dealt with in CCTs by the Sympson-Hetter (SH) method, but this method is unable to restrict the exposure of the most informative items to the desired level. The authors’ new method of stochastic item exposure balance (SIEB) works in conjunction with the SH method and is shown to greatly reduce the number of overexposed items in a pool and improve overall exposure balance while maintaining classification accuracy comparable with using the SH method alone. The method is demonstrated using a simulation study.

1 aHuebner, Alan1 aLi, Zhushan uhttp://apm.sagepub.com/content/36/3/181.abstract01369nas 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

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.

1 aWang, Chun1 aChang, Hua-Hua1 aHuebner, Alan uhttp://dx.doi.org/10.1111/j.1745-3984.2011.00145.x