04072nas a2200133   4500008004100000245009300041210006900134300001200203490000700215520357800222100001503800700001603815856010703831       2007                        eng d00aAn exploration and realization of computerized adaptive testing with cognitive diagnosis0 aexploration and realization of computerized adaptive testing wit  a747-7530 v393 a  An increased attention paid to “cognitive bugs behavior,” appears to lead to an increased research interests in diagnostic testing based on Item Response Theory（IRT）that combines cognitive psychology and psychometrics. The study of cognitive diagnosis were applied mainly to Paper-and-Pencil (P&P) testing. Rarely has it been applied to computerized adaptive testing CAT）, To our knowledge, no research on CAT with cognitive diagnosis has been conducted in China. Since CAT is more efficient and accurate than P&P testing, there is important to develop an application technique for cognitive diagnosis suitable for CAT. This study attempts to construct a preliminary CAT system for cognitive diagnosis.With the help of the methods for “ Diagnosis first, Ability estimation second ”, the knowledge state conversion diagram was used to describe all the possible knowledge states in a domain of interest and the relation among the knowledge states at the diagnosis stage, where a new strategy of item selection based-on the algorithm of Depth First Search was proposed. On the other hand, those items that contain attributes which the examinee has not mastered were removed in ability estimation. At the stage of accurate ability estimation, all the items answered by each examinee not only matched his/her ability estimated value, but also were limited to those items whose attributes have been mastered by the examinee.We used Monte Carlo Simulation to simulate all the data of the three different structures of cognitive attributes in this study. These structures were tree-shaped, forest-shaped, and some isolated vertices (that are related to simple Q-matrix). Both tree-shaped and isolated vertices structure were derived from actual cases, while forest-shaped structure was a generalized simulation. 3000 examinees and 3000 items were simulated in the experiment of tree-shaped, 2550 examinees and 3100 items in forest-shaped, and 2000 examinees and 2500 items in isolated vertices. The maximum test length was all assumed as 30 items for all those experiments. The difficulty parameters and the logarithm of the discrimination were drawn from the standard normal distribution N(0,1). There were 100 examinees of each attribute pattern in the experiment of tree-shaped and 50 examinees of each attribute pattern in forest-shaped. In isolated vertices, 2000 examinees are students come from actual case.To assess the behaviors of the proposed diagnostic approach, three assessment indices were used. They are attribute pattern classification agreement rate (abr.APCAR), the Recovery (the average of the absolute deviation between the estimated value and the true value) and the average test length (abr. Length).Parts of results of Monte Carlo study were as follows.For the attribute structure of tree-shaped, APCAR is 84.27%,Recovery is 0.17,Length is 24.80.For the attribute structure of forest-shaped, APCAR is 84.02%,Recovery is 0.172,Length is 23.47.For the attribute structure of isolated vertices, APCAR is 99.16%,Recorvery is 0.256,Length is 27.32.As show the above, we can conclude that the results are favorable. The rate of cognitive diagnosis accuracy has exceeded 80% in each experiment, and the Recovery is also good. Therefore, it should be an acceptable idea to construct an initiatory CAT system for cognitive diagnosis, if we use the methods for “Diagnosis first, Ability estimation second ” with the help of both knowledge state conversion diagram and the new strategy of item selection based-on the algorithm of Depth First Search1 aHaijing, L1 aShuliang, D  uhttp://iacat.org/content/exploration-and-realization-computerized-adaptive-testing-cognitive-diagnosis02399nas a2200241   4500008004100000020002200041245008500063210006900148260002500217300001200242490000700254520161500261653000801876653003401884653002601918653003001944653002601974100001402000700001402014700001602028700001502044856009802059       2006                        eng d  a0439-755X (Print)00aThe comparison among item selection strategies of CAT with multiple-choice items0 acomparison among item selection strategies of CAT with multiplec  bScience Press: China  a778-7830 v383 aThe initial purpose of comparing item selection strategies for CAT was to increase the efficiency of tests. As studies continued, however, it was found that increasing the efficiency of item bank using was also an important goal of comparing item selection strategies. These two goals often conflicted. The key solution was to find a strategy with which both goals could be accomplished. The item selection strategies for graded response model in this study included: the average of the difficulty orders matching with the ability; the medium of the difficulty orders matching with the ability; maximum information; A stratified (average); and A stratified (medium). The evaluation indexes used for comparison included: the bias of ability estimates for the true; the standard error of ability estimates; the average items which the examinees have administered; the standard deviation of the frequency of items selected; and sum of the indices weighted. Using the Monte Carlo simulation method, we obtained some data and computer iterated the data 20 times each under the conditions that the item difficulty parameters followed the normal distribution and even distribution. The results were as follows; The results indicated that no matter difficulty parameters followed the normal distribution or even distribution. Every type of item selection strategies designed in this research had its strong and weak points. In general evaluation, under the condition that items were stratified appropriately, A stratified (medium) (ASM) had the best effect. (PsycINFO Database Record (c) 2007 APA, all rights reserved)10aCAT10acomputerized adaptive testing10agraded response model10aitem selection strategies10amultiple choice items1 aHai-qi, D1 aDe-zhi, C1 aShuliang, D1 aTaiping, D  uhttp://iacat.org/content/comparison-among-item-selection-strategies-cat-multiple-choice-items