01541nas a2200157   4500008003900000022001400039245009700053210006900150300001400219490000700233520104800240100001901288700001601307700001901323856004101342       2017                          d  a1745-398400aDual-Objective Item Selection Criteria in Cognitive Diagnostic Computerized Adaptive Testing0 aDualObjective Item Selection Criteria in Cognitive Diagnostic Co  a165–1830 v543 aThe development of cognitive diagnostic-computerized adaptive testing (CD-CAT) has provided a new perspective for gaining information about examinees' mastery on a set of cognitive attributes. This study proposes a new item selection method within the framework of dual-objective CD-CAT that simultaneously addresses examinees' attribute mastery status and overall test performance. The new procedure is based on the Jensen-Shannon (JS) divergence, a symmetrized version of the Kullback-Leibler divergence. We show that the JS divergence resolves the noncomparability problem of the dual information index and has close relationships with Shannon entropy, mutual information, and Fisher information. The performance of the JS divergence is evaluated in simulation studies in comparison with the methods available in the literature. Results suggest that the JS divergence achieves parallel or more precise recovery of latent trait variables compared to the existing methods and maintains practical advantages in computation and item pool usage.1 aKang, Hyeon-Ah1 aZhang, Susu1 aChang, Hua-Hua  uhttp://dx.doi.org/10.1111/jedm.1213902198nas a2200145   4500008003900000245007900039210006900118300001100187490000700198520173800205100001501943700001901958700002301977856005202000       2013                          d00aDeriving Stopping Rules for Multidimensional Computerized Adaptive Testing0 aDeriving Stopping Rules for Multidimensional Computerized Adapti  a99-1220 v373 a<p>Multidimensional computerized adaptive testing (MCAT) is able to provide a vector of ability estimates for each examinee, which could be used to provide a more informative profile of an examinee&rsquo;s performance. The current literature on MCAT focuses on the fixed-length tests, which can generate less accurate results for those examinees whose abilities are quite different from the average difficulty level of the item bank when there are only a limited number of items in the item bank. Therefore, instead of stopping the test with a predetermined fixed test length, the authors use a more informative stopping criterion that is directly related to measurement accuracy. Specifically, this research derives four stopping rules that either quantify the measurement precision of the ability vector (i.e., minimum determinant rule [D-rule], minimum eigenvalue rule [E-rule], and maximum trace rule [T-rule]) or quantify the amount of available information carried by each item (i.e., maximum Kullback&ndash;Leibler divergence rule [K-rule]). The simulation results showed that all four stopping rules successfully terminated the test when the mean squared error of ability estimation is within a desired range, regardless of examinees&rsquo; true abilities. It was found that when using the D-, E-, or T-rule, examinees with extreme abilities tended to have tests that were twice as long as the tests received by examinees with moderate abilities. However, the test length difference with K-rule is not very dramatic, indicating that K-rule may not be very sensitive to measurement precision. In all cases, the cutoff value for each stopping rule needs to be adjusted on a case-by-case basis to find an optimal solution.</p>
1 aWang, Chun1 aChang, Hua-Hua1 aBoughton, Keith, A  uhttp://apm.sagepub.com/content/37/2/99.abstract00427nas a2200121   4500008004100000245005900041210005900100260001500159100001700174700001900191700001200210856008300222       2001                        eng d00aDeriving a stopping rule for sequential adaptive tests0 aDeriving a stopping rule for sequential adaptive tests  aSeattle WA1 aGrabovsky, I1 aChang, Hua-Hua1 aYing, Z  uhttp://mail.iacat.org/content/deriving-stopping-rule-sequential-adaptive-tests00408nas a2200121   4500008004100000245005400041210005300095300001200148490000700160100001700167700001900184856008300203       2000                        eng d00aDoes adaptive testing violate local independence?0 aDoes adaptive testing violate local independence  a149-1560 v651 aMislevy, R J1 aChang, Hua-Hua  uhttp://mail.iacat.org/content/does-adaptive-testing-violate-local-independence00485nas a2200109   4500008004100000245007800041210006900119260004600188100001700234700001900251856010500270       1998                        eng d00aDoes adaptive testing violate local independence? (Research Report 98-33)0 aDoes adaptive testing violate local independence Research Report  aPrinceton NJ: Educational Testing Service1 aMislevy, R J1 aChang, Hua-Hua  uhttp://mail.iacat.org/content/does-adaptive-testing-violate-local-independence-research-report-98-33