It is widely believed that item selection methods using the maximum information approach (MI) can maintain high efficiency in trait estimation by repeatedly choosing high discriminating (alpha) items. However, the consequence is that they lead to extremely skewed item exposure distribution in which items with high alpha values becoming overly…

Information based item selection methods in computerized adaptive tests (CATs) tend to choose the item that provides maximum information at an examinee's estimated trait level. As a result, these methods can yield extremely skewed item exposure distributions in which items with high "a" values may be overexposed, while those with low…

Item selection methods in computerized adaptive testing (CAT) can yield extremely skewed item exposure distribution in which items with high "a" values may be over-exposed while those with low "a" values may never be selected. H. Chang and Z. Ying (1999) proposed the a-stratified design (ASTR) that attempts to equalize item…

The multistage alpha-stratified computerized adaptive testing (CAT) design advocated a new philosophy of pool management and item selection using low discriminating items first. It has been demonstrated through simulation studies to be effective both in reducing item overlap rate and enhancing pool utilization with certain pool types. Based on…