关键词: 关键词:变加权法, GPCM模型, a-分层, CAT, 选题策略

Abstract:

Abstract: For dichotomous scoring , the a-stratified item selection strategy(a-STR) is an effect and safe method for computerized adaptive testing(CAT).But it could not be applied to the polytomous scoring CAT, because there are too many parameters in the polytomous item response model to be comprehensive. Some domestic researchers employed the mean, the mode or some one of the difficulties (or location parameters) to represent all of the difficulties or location parameters. In a word, the difficulty parameter in the dichotomous score item is replaced with some descriptive statistic of the difficulties or the location parameters then a-stratified item selection strategy is employed to polytomous CAT. It is well known that the information function is a good comprehension of all item parameters as well as the ability parameter. But the item selection strategy of maximum information index derogates the safe if MII is used at the early stage during the course of CAT for every examinee. Since the items with higher discrimination parameter may be frequently used and it causes the higher exposure rate for these items. A new item selection strategy named as variable-weighted item selection strategy is proposed in this paper. Some functions of the information are employed to replace the a-stratified strategy. The new item selection strategy is comprehension of all information of the item parameters for polytomous items and plays the role of a-STR. Some comparisons of the new selection strategy with the other selection strategies combined with a-stratified based on GPCM model. The results of Monte Carlo study show that the new selection strategy has the best effect.

Key words: Key words: variable-weighted method, GPCM, a-stratified, CAT, Item Selection Strategy