CAJ | 학술논문

在计算机化自适应测验（CAT）的研究中,制定既高效又安全的选题策略是一个追求目标。用极大项目信息量准则（MIC）选题使得测验效率高、能力估计准确,缺点是项目调用很不均匀,影响考试的安全;按a分层法通过控制试题曝光率以提高考试的安全性,但该方法可能会使测验效率略有下降,且该方法在各层内部无法实现对区分度的调整。本文针对上述两种选题策略的优缺点,对0-1评分下的CAT,通过引入曝光因子、分阶段自动调整区分度的影响以及提高选题准确性等手段,对MIC和a-STR进行改进,引入了两类新的选题策略。计算机模拟实验显示,新的选题方法效果比较理想。
재계산궤화자괄응측험（CAT）적연구중,제정기고효우안전적선제책략시일개추구목표。용겁대항목신식량준칙（MIC）선제사득측험효솔고、능력고계준학,결점시항목조용흔불균균,영향고시적안전;안a분층법통과공제시제폭광솔이제고고시적안전성,단해방법가능회사측험효솔략유하강,차해방법재각층내부무법실현대구분도적조정。본문침대상술량충선제책략적우결점,대0-1평분하적CAT,통과인입폭광인자、분계단자동조정구분도적영향이급제고선제준학성등수단,대MIC화a-STR진행개진,인입료량류신적선제책략。계산궤모의실험현시,신적선제방법효과비교이상。
As far as Computerized Adaptive Testing （CAT） is concerned, the issue of item selection strategy has received more attention because of its vital role. It is well known that there are two typical selection strategies called Maximum Information Criterion （MIC） and a-Stratification （a-STR）. However, both of the two strategies have their advantages together with their downsides. On the one hand, MIC method can obtain high efficiency and accurate estimation of ability; on the other hand, its uneven item selection may lead to the insecurity of examination. Meanwhile, though a-STR can improve the test security by controlling the item exposure rate, it may result in the inefficiency of the test and failure in adjusting the discrimination within the layers. As a result, the development of both effective and safe item selection strategies has always been a goal to pursue in studies on CAT. According to the previous studies, the test security can be enhanced and the item pool utilization rate can be increased by balancing the item exposure rate. Therefore, in 0-1 scored CAT, two new item selection strategies are proposed in this paper to improve the MIC and a-STR methods by introducing exposure factor, adjusting automatically the discrimination by stage and increasing the accuracy of item selection. One of the new item selection strategies has three prominent characteristics： First, a function of item information （FII） rather than the item information function is set up to combine the advantages of both MIC and a-STR. Second, the effect of the discrimination on different stages in CAT is taken into account and a function of item discrimination is used in the FII to make up for the defect of a-STR for not being able to control the item discrimination in the internal layer. Third, mechanism of online control exposure is adopted. While some specific items in a certain examination process are more frequently exposured than others, with the help of the mechanism, they will turn out to become less likely to be selected in the future tests. At the same time, some specific items in a certain examination process are less frequently exposured than others, with the help of the mechanism, they will turn out to become more likely to be selected in the future tests. Thanks to the mechanism of online control exposure, the whole exposure rate of all the items in the item pool is evened and the utilization rate of the item pool is increased. In order to fill the gap between the exposure rate of each item and mean exposure rate of all the items in the item pool, this paper attempts to treat the item exposure rate directly as a part of the selection strategies expression. And it also tries to equalize the exposure rate by decreasing the exposure rate of the items with high ones and increasing the uses of the items with low ones. The approach differs from the approaches which only control the items with high exposure rate, e.g. SH. The results of Monte Carlo simulations show that compared with other approaches, the approach proposed in this paper is more effective in terms of exposure control and more ideal in the performance of other indexes.