01947nas a2200277   4500008004100000020004100041245007300082210006900155250001500224260000800239300001000247490000700257520099700264653001601261653002901277653004801306653006201354653001101416653002401427653004601451653003101497653001301528100001401541700001501555856009901570       2008                        eng d  a0007-1102 (Print)0007-1102 (Linking)00aPredicting item exposure parameters in computerized adaptive testing0 aPredicting item exposure parameters in computerized adaptive tes  a2008/05/17  cMay  a75-910 v613 aThe purpose of this study is to find a formula that describes the relationship between item exposure parameters and item parameters in computerized adaptive tests by using genetic programming (GP) - a biologically inspired artificial intelligence technique. Based on the formula, item exposure parameters for new parallel item pools can be predicted without conducting additional iterative simulations. Results show that an interesting formula between item exposure parameters and item parameters in a pool can be found by using GP. The item exposure parameters predicted based on the found formula were close to those observed from the Sympson and Hetter (1985) procedure and performed well in controlling item exposure rates. Similar results were observed for the Stocking and Lewis (1998) multinomial model for item selection and the Sympson and Hetter procedure with content balancing. The proposed GP approach has provided a knowledge-based solution for finding item exposure parameters.10a*Algorithms10a*Artificial Intelligence10aAptitude Tests/*statistics & numerical data10aDiagnosis, Computer-Assisted/*statistics & numerical data10aHumans10aModels, Statistical10aPsychometrics/statistics & numerical data10aReproducibility of Results10aSoftware1 aChen, S-Y1 aDoong, S H  uhttp://www.iacat.org/content/predicting-item-exposure-parameters-computerized-adaptive-testing03119nas a2200277   4500008004100000020002200041245010900063210006900172250001500241260000800256300001200264490000700276520221700283653002902500653002002529653002502549653002102574653001502595653002802610653001102638653002502649100001702674700001502691700001202706856012302718       2006                        eng d  a0214-9915 (Print)00aMaximum information stratification method for controlling item exposure in computerized adaptive testing0 aMaximum information stratification method for controlling item e  a2007/02/14  cFeb  a156-1590 v183 aThe proposal for increasing the security in Computerized Adaptive Tests that has received most attention in recent years is the a-stratified method (AS - Chang and Ying, 1999): at the beginning of the test only items with low discrimination parameters (a) can be administered, with the values of the a parameters increasing as the test goes on. With this method, distribution of the exposure rates of the items is less skewed, while efficiency is maintained in trait-level estimation. The pseudo-guessing parameter (c), present in the three-parameter logistic model, is considered irrelevant, and is not used in the AS method. The Maximum Information Stratified (MIS) model incorporates the c parameter in the stratification of the bank and in the item-selection rule, improving accuracy by comparison with the AS, for item banks with a and b parameters correlated and uncorrelated. For both kinds of banks, the blocking b methods (Chang, Qian and Ying, 2001) improve the security of the item bank.Método de estratificación por máxima información para el control de la exposición en tests adaptativos informatizados. La propuesta para aumentar la seguridad en los tests adaptativos informatizados que ha recibido más atención en los últimos años ha sido el método a-estratificado (AE - Chang y Ying, 1999): en los momentos iniciales del test sólo pueden administrarse ítems con bajos parámetros de discriminación (a), incrementándose los valores del parámetro a admisibles según avanza el test. Con este método la distribución de las tasas de exposición de los ítems es más equilibrada, manteniendo una adecuada precisión en la medida. El parámetro de pseudoadivinación (c), presente en el modelo logístico de tres parámetros, se supone irrelevante y no se incorpora en el AE. El método de Estratificación por Máxima Información (EMI) incorpora el parámetro c a la estratificación del banco y a la regla de selección de ítems, mejorando la precisión en comparación con AE, tanto para bancos donde los parámetros a y b correlacionan como para bancos donde no. Para ambos tipos de bancos, los métodos de bloqueo de b (Chang, Qian y Ying, 2001) mejoran la seguridad del banco.10a*Artificial Intelligence10a*Microcomputers10a*Psychological Tests10a*Software Design10aAlgorithms10aChi-Square Distribution10aHumans10aLikelihood Functions1 aBarrada, J R1 aMazuela, P1 aOlea, J  uhttp://www.iacat.org/content/maximum-information-stratification-method-controlling-item-exposure-computerized-adaptive