中文摘要：

项目的增补对认知诊断计算机化自适应测验（CD—CAT）题库的开发与维护至关重要。借鉴单维项目反应理论（IRT）中联合极大似然估计方法（JMLE）的思路，提出联合估计算法（JEA），仅依赖被试在旧题和新题上的作答反应联合地、自动地估计新题的属性向量和新题的项目参数。研究结果表明：当项目参数相对较小且样本量相对较大时，JEA算法在新题属性向量和新题项目参数估计精度方面表现不错；而且样本大小、项目参数大小以及项目参数初值都影响着JEA算法的表现。

英文摘要：

Item replenishing is essential for item bank maintenance and development in cognitive diagnostic computerized adaptive testing （CD-CAT）. Compared with item replenishing in regular CAT, item replenishing in CD-CAT is more complicated because it requires constructing the Q matrix （Embretson, 1984; Tatsuoka, 1995） corresponding to the new items （denoted as Qnew_item）. However, the Qnew_item is often constructed manually by content experts and psychometricians, which brings about two issues： first, it takes experts a lot of time and efforts to discuss and complete the attribute identification task, especially when the number of new items is large; second, the Qnew_item identified by experts is not guaranteed to be totally correct because experts often disagree in the discussion. Therefore, this study borrowed the main idea of joint maximum likelihood estimation （JMLE） method in unidimensional item response theory （IRT） to propose the joint estimation algorithm （JEA）, which depended fully on the examinees＇ responses on the operational and new items to jointly estimate the Qnew_item and the item parameters of new items automatically in the context of CD-CAT under the Deterministic lnputs, Noisy ＂and＂ Gate （DINA） model. A simulation study was conducted to investigate whether the JEA algorithm could accurately and efficiently estimate the Qnew_item and the item parameters of new items under different sample sizes and different levels of item parameter range, and the new items were randomly seeded in the random positions of examinees＇ CD-CAT tests. In this study, four samples （sample sizes were 100, 300, 1000 and 3000 respectively） were simulated and each examinee had 50% probability of mastering each attribute. On the other hand, three item banks of 360 items were simulated and their item parameters were randomly drawn from U （0.05, 0.25）, U （0.15, 0.35） and U （0.25, 0.45） respectively, and the three item banks shared the same Q matrix. 20 new items were simulated