01948nas a2200277   4500008004100000020004100041245007300082210006900155250001500224260000800239300001000247490000700257520099700264653001601261653002901277653004801306653006201354653001101416653002401427653004601451653003101497653001301528100001401541700001501555856010001570       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://mail.iacat.org/content/predicting-item-exposure-parameters-computerized-adaptive-testing01994nas a2200193   4500008004100000020002200041245011400063210006900177260004100246300001200287490000700299520125600306653003401562653002501596653002601621100001401647700001901661856012001680       2004                        eng d  a0022-0655 (Print)00aEffects of practical constraints on item selection rules at the early stages of computerized adaptive testing0 aEffects of practical constraints on item selection rules at the   bBlackwell Publishing: United Kingdom  a149-1740 v413 aThe purpose of this study was to compare the effects of four item selection rules--(1) Fisher information (F), (2) Fisher information with a posterior distribution (FP), (3) Kullback-Leibler information with a posterior distribution (KP), and (4) completely randomized item selection (RN)--with respect to the precision of trait estimation and the extent of item usage at the early stages of computerized adaptive testing. The comparison of the four item selection rules was carried out under three conditions: (1) using only the item information function as the item selection criterion; (2) using both the item information function and content balancing; and (3) using the item information function, content balancing, and item exposure control. When test length was less than 10 items, FP and KP tended to outperform F at extreme trait levels in Condition 1. However, in more realistic settings, it could not be concluded that FP and KP outperformed F, especially when item exposure control was imposed. When test length was greater than 10 items, the three nonrandom item selection procedures performed similarly no matter what the condition was, while F had slightly higher item usage. (PsycINFO Database Record (c) 2007 APA, all rights reserved)10acomputerized adaptive testing10aitem selection rules10apractical constraints1 aChen, S-Y1 aAnkenmann, R D  uhttp://mail.iacat.org/content/effects-practical-constraints-item-selection-rules-early-stages-computerized-adaptive