01437nas a2200205   4500008004100000245008800041210007000129300001200199490000700211520067700218653002100895653003000916653002400946653002500970653002600995653005201021100001901073700002301092856011601115       2003                        eng d00aOptimal stratification of item pools in α-stratified computerized adaptive testing0 aOptimal stratification of item pools in αstratified computerized  a262-2740 v273 aA method based on 0-1 linear programming (LP) is presented to stratify an item pool optimally for use in α-stratified adaptive testing. Because the 0-1 LP model belongs to the subclass of models with a network flow structure, efficient solutions are possible. The method is applied to a previous item pool from the computerized adaptive testing (CAT) version of the Graduate Record Exams (GRE) Quantitative Test. The results indicate that the new method performs well in practical situations. It improves item exposure control, reduces the mean squared error in the θ estimates, and increases test reliability. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10aAdaptive Testing10aComputer Assisted Testing10aItem Content (Test)10aItem Response Theory10aMathematical Modeling10aTest Construction computerized adaptive testing1 aChang, Hua-Hua1 avan der Linden, WJ  uhttp://mail.iacat.org/content/optimal-stratification-item-pools-%CE%B1-stratified-computerized-adaptive-testing02086nas a2200229   4500008004100000245012900041210006900170300001200239490000700251520124000258653001801498653002101516653004501537653003001582653001801612653002501630653002601655100001601681700001401697700001901711856012601730       2002                        eng d00aA comparison of item selection techniques and exposure control mechanisms in CATs using the generalized partial credit model0 acomparison of item selection techniques and exposure control mec  a147-1630 v263 aThe use of more performance items in large-scale testing has led to an increase in the research investigating the use of polytomously scored items in computer adaptive testing (CAT). Because this research has to be complemented with information pertaining to exposure control, the present research investigated the impact of using five different exposure control algorithms in two sized item pools calibrated using the generalized partial credit model. The results of the simulation study indicated that the a-stratified design, in comparison to a no-exposure control condition, could be used to reduce item exposure and overlap, increase pool utilization, and only minorly degrade measurement precision. Use of the more restrictive exposure control algorithms, such as the Sympson-Hetter and conditional Sympson-Hetter, controlled exposure to a greater extent but at the cost of measurement precision. Because convergence of the exposure control parameters was problematic for some of the more restrictive exposure control algorithms, use of the more simplistic exposure control mechanisms, particularly when the test length to item pool size ratio is large, is recommended. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10a(Statistical)10aAdaptive Testing10aAlgorithms computerized adaptive testing10aComputer Assisted Testing10aItem Analysis10aItem Response Theory10aMathematical Modeling1 aPastor, D A1 aDodd, B G1 aChang, Hua-Hua  uhttp://mail.iacat.org/content/comparison-item-selection-techniques-and-exposure-control-mechanisms-cats-using-generalized02014nas a2200205   4500008004100000245008200041210006900123300001200192490000700204520132300211653002101534653001501555653003001570653001101600653000901611653004701620100001901667700001301686856010901699       2002                        eng d00aHypergeometric family and item overlap rates in computerized adaptive testing0 aHypergeometric family and item overlap rates in computerized ada  a387-3980 v673 aA computerized adaptive test (CAT) is usually administered to small groups of examinees at frequent time intervals. It is often the case that examinees who take the test earlier share information with examinees who will take the test later, thus increasing the risk that many items may become known. Item overlap rate for a group of examinees refers to the number of overlapping items encountered by these examinees divided by the test length. For a specific item pool, different item selection algorithms may yield different item overlap rates. An important issue in designing a good CAT item selection algorithm is to keep item overlap rate below a preset level. In doing so, it is important to investigate what the lowest rate could be for all possible item selection algorithms. In this paper we rigorously prove that if every item had an equal possibility to be selected from the pool in a fixed-length CAT, the number of overlapping item among any α randomly sampled examinees follows the hypergeometric distribution family for α ≥ 1. Thus, the expected values of the number of overlapping items among any randomly sampled α examinee can be calculated precisely. These values may serve as benchmarks in controlling item overlap rates for fixed-length adaptive tests. (PsycINFO Database Record (c) 2005 APA )10aAdaptive Testing10aAlgorithms10aComputer Assisted Testing10aTaking10aTest10aTime On Task computerized adaptive testing1 aChang, Hua-Hua1 aZhang, J  uhttp://mail.iacat.org/content/hypergeometric-family-and-item-overlap-rates-computerized-adaptive-testing01986nas a2200205   4500008004100000245010100041210006900142300001200211490000700223520124900230653001201479653002101491653003001512653001501542653001601557653004501573100001701618700001901635856012601654       2001                        eng d00aItem selection in computerized adaptive testing: Should more discriminating items be used first?0 aItem selection in computerized adaptive testing Should more disc  a249-2660 v383 aDuring computerized adaptive testing (CAT), items are selected continuously according to the test-taker's estimated ability. Test security has become a problem because high-discrimination items are more likely to be selected and become overexposed. So, there seems to be a tradeoff between high efficiency in ability estimations and balanced usage of items. This series of four studies addressed the dilemma by focusing on the notion of whether more or less discriminating items should be used first in CAT. The first study demonstrated that the common maximum information method with J. B. Sympson and R. D. Hetter (1985) control resulted in the use of more discriminating items first. The remaining studies showed that using items in the reverse order, as described in H. Chang and Z. Yings (1999) stratified method had potential advantages: (a) a more balanced item usage and (b) a relatively stable resultant item pool structure with easy and inexpensive management. This stratified method may have ability-estimation efficiency better than or close to that of other methods. It is argued that the judicious selection of items, as in the stratified method, is a more active control of item exposure. (PsycINFO Database Record (c) 2005 APA )10aability10aAdaptive Testing10aComputer Assisted Testing10aEstimation10aStatistical10aTest Items computerized adaptive testing1 aHau, Kit-Tai1 aChang, Hua-Hua  uhttp://mail.iacat.org/content/item-selection-computerized-adaptive-testing-should-more-discriminating-items-be-used-first01390nas a2200193   4500008004100000245009400041210006900135300001200204490000700216520067900223653002100902653003000923653002500953653005700978100001401035700001901049700001901068856010901087       2000                        eng d00aA comparison of item selection rules at the early stages of computerized adaptive testing0 acomparison of item selection rules at the early stages of comput  a241-2550 v243 aThe effects of 5 item selection rules--Fisher information (FI), Fisher interval information (FII), Fisher information with a posterior distribution (FIP), Kullback-Leibler information (KL), and Kullback-Leibler information with a posterior distribution (KLP)--were compared with respect to the efficiency and precision of trait (θ) estimation at the early stages of computerized adaptive testing (CAT). FII, FIP, KL, and KLP performed marginally better than FI at the early stages of CAT for θ=-3 and -2. For tests longer than 10 items, there appeared to be no precision advantage for any of the selection rules. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10aAdaptive Testing10aComputer Assisted Testing10aItem Analysis (Test)10aStatistical Estimation computerized adaptive testing1 aChen, S-Y1 aAnkenmann, R D1 aChang, Hua-Hua  uhttp://mail.iacat.org/content/comparison-item-selection-rules-early-stages-computerized-adaptive-testing