Abstract: 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.

Key words: exposure-control factor, computerized adaptive testing, item selection strategy, 3PLM