中文摘要：

计算机化自适应测验（CAT）中的选题策略，一直是国内外相关学者关注的问题。然而对多级评分的CAT的选题策略的研究却很少报导。本研究采用计算机模拟程序对等级反应模型（Graded Response Model）下CAT的四种选题策略进行研究。研究表明：等级难度值与当前能力估计值匹配选题策略的综合评价最高；在选题策略中增设“影子题库”可以明显提高项目调用的均匀性；并且不同的项目参数分布或不同的能力估计方法都对CAT评价指标有影响。

英文摘要：

Item selection strategy （ISS） is an important component of Computerized Adaptive Testing （CAT）. Its performance directly affects the security, efficiency and precision of the test. Thus, ISS becomes one of the central issues in CATs based on the Graded Response Model （GRM）. It is well known that the goal of IIS is to administer the next unused item remaining in the item bank that best fits the examinee＇s current ability estimate. In dichotomous IRT models, every item has only one difficulty parameter and the item whose difficulty matches the examinee＇s current ability estimate is considered to be the best fitting item. However, in GRM, each item has more than two ordered categories and has no single value to represent the item difficulty. Consequently, some researchers have used to employ the average or the median difficulty value across categories as the difficulty estimate for the item. Using the average value and the median value in effect introduced two corresponding ISSs. In this study, we used computer simulation compare four ISSs based on GRM. We also discussed the effect of ＂shadow pool＂ on the uniformity of pool usage as well as the influence of different item parameter distributions and different ability estimation methods on the evaluation criteria of CAT. In the simulation process, Monte Carlo method was adopted to simulate the entire CAT process; 1000 examinees drawn from standard normal distribution and four 1000 - sized item pools of different item parameter distributions were also simulated. The assumption of the simulation is thai a polytomous item is comprised of six ordered categories. In addition, ability estimates were derived using two methods. They were expected a posteriori Bayesian （EAP） and maximum likelihood estimation （MLE）. In MLE, the Newton -Raphson iteration method and the Fisher Score iteration method were employed, respectively, to solve the likelihood equation. Moreover, the CAT process was simulated with each examinee 30 times to eliminate r