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中文摘要：
多级评分可以提供更多关于被试的信息,是计算机化自适应测验的一个发展方向,选题策略是计算机化自适应测验的研究重点。对于多级评分的等级反应模型,本文拟用区间估计的思想改进近期提出的几种选题策略,并且将两级评分b-STR和a-STR推广到多级评分以改进最大信息量选题策略。Monte Carlo模拟实验表明在达到或接近原有选题策略测验精度的基础上,本文提出的几种新选题策略有的能够有效降低测验长度,有的可以极大降低项目曝光率。
英文摘要：
Item selection strategy （ISS） is a core component in Computerized Adaptive Testing （CAT）. Polytomous items can provide more information about examinee compared with dichotomous items, and adopting polytomously scored items in test is a research direction of CAT. As we know, the most widely used ISS is the maximum Fisher information （MFI） criterion, which raises concerns about cost-efficiency of the pool utilization and poses security risks for CAT programs. Chang Ying （1999） and Chang, Qian, Ying （2001） proposed two alternative item selection procedures, the a-stratified method （a-STR） and the a-stratified with b blocking method （b-STR） based on dichotomous model, with the goal to remedy the problems of item overexposure and item underexposure produced by MFI. However, the technology of a-STR and b-STR is static because the items are stratified according to the given information at the beginning of test. Based on graded response model （GRM）, a technique of the reduction dimensionality of difficulty （or step） parameters was employed to construct some ISSs recently. The limitation of this dimension reduction technique is that it loses a lot of information. Thus, in order to improve MFI, two new item selection methods are proposed based on GRM： （1） modify the technique of the reduction dimensionality of difficulty （or step） parameters by integrating the interval estimation; （2） dynamic a-STR and dynamic b-STR methods are implemented in the testing process. On one hand, these new ISSs can avoid and remedy the limitations of MFI and make good use of the advantages of the Fisher information function （FIF）; FIF compresses all item parameters and ability parameters, so it is a comprehensive tool for all parameters in nature.On the other hand, the new ISSs employ the property that FIF could represent the inverse of the variance of the ability estimation, let ε be the square root of the reciprocal of the Fisher information, d be the absolute deviation between the estim
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