计算机化自适应测验中原始题项目参数的估计

计算机化自适应测验(Computerized Adaptive Testing, 简称CAT)其安全性面临着新的挑战, 小题库的安全更受威胁。如何建设一个大型、优质的题库成为CAT研究中一个非常重要的课题。目前CAT题库的建设存在一些问题, 如成本高且保密性较差。尤其是等值技术较复杂且锚题重复使用容易造成泄露。如能在实施CAT过程中插入未经过参数估计的项目(原始题), 同时对原始题项目参数进行估计, 这对建设大型、优质的CAT题库来说其意义是不言而喻的。本文基于1PLM和2PLM对此进行研究, 提出了原始题在线估计的新方法以及推导出了求区分度参数a迭代初值的计算公式。研究结果表明：无论是模拟研究还是实证研究, 原始题被作答的次数对项目参数估计结果都会产生不同的影响, 并且原始题作答人数越多项目参数估计精度也越高。

Parameter Estimation of the Raw Item in Computerized Adaptive Testing

Along with the development of computer technology and increasing needs of individual learning, Computerized Adaptive Testing (CAT) has received more and more attention. However, the problem of the test security is becoming a new challenge to the CAT, for example, excessive exposure of the test items might weaken the efficiency and equity of the CAT. Therefore, constructing a large-scale and high-quality CAT item bank is highly demanded The traditional method of constructing a CAT item bank involves four steps: first, the items are developed by the experts according to the test specification and blueprint; second, a representative sample of examinees is recruited to take the test; Then, the calibration of item parameters and testing of the model-data fit will be conducted afterward. Finally, items of high-quality and its item parameters will be added into the item bank based on the pre-analysis. However, several problems exist in the above traditional method. First, a large number of test takers are needed, which is time-consuming and expensive. Second, the security of the items cannot be guaranteed in the pilot test. Third, complicated techniques of equating may have to be used to construct a good item bank, which may also affect the security of the items, especially when the anchor items are repeatedly used in the test equating process. Thus, if the raw items can be seeded in the CAT process and the item parameters combined with the examinee abilities estimated at the same time, it will be significant for the construction of CAT item bank. The research in this area has not been widely conducted and reported in the domestic journals, although this issue has been a big topic for foreign researchers. Thus, This study aims to explore how to insert raw items and estimate the item parameters in CAT and investigate the efficient strategies with high security for enlarging the item bank. A new online calibration method is proposed based on the principle of adaption in CAT, provided there is a small-scale item pool. And the formula of initial value during the stage of the iteration is determined. When the ability parameter has been estimated, the conditional maximum likelihood estimation(CMLE) will be implemented to estimate item parameters in the raw items. First, the difficulty parameter could be obtained through CMLE. Second, the discrimination parameter could be estimated with the abilities and difficulty parameter and the initial value of the discrimination parameter could be gained through CMLE. Third, the difficulty parameter could be estimated when the discrimination parameter and abilities are known .Repeat the second and the third steps until the stop condition is satisfied. Simulation of Monte Carlo has been employed to estimate the parameters of raw items with the one-parameter Logistic and two-parameter Logistic models in the study and good result has been gained.