Abstract

Classical automated test assembly (ATA) methods assume fixed and known parameters, an hypothesis that is not true for estimates of item response theory parameters which are key elements in test assembly. To account for uncertainty in ATA, we propose a chance-constrained version of the MAXIMIN ATA model which allows to maximize the alpha-quantile of the sampling distribution function of the test information function obtained by bootstrapping the item parameters. An heuristic based on the simulated annealing is proposed to solve the ATA model. The validity of the proposed approach is verified by simulated data and the applicability is verified by an application to real data.