Chance-Constrained Automated Test Assembly

Giada Spaccapanico Proietti, Mariagiulia Matteucci, Stefania Mignani, Bernard P. Veldkamp

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Classical automated test assembly (ATA) methods assume fixed and known coefficients for the constraints and the objective function. This hypothesis is not true for the estimates of item response theory parameters, which are crucial elements in test assembly classical models. To account for uncertainty in ATA, we propose a chance-constrained version of the maximin ATA model, which allows maximizing the α-quantile of the sampling distribution of the test information function obtained by applying the bootstrap on the item parameter estimation. A heuristic inspired by the simulated annealing optimization technique is implemented to solve the ATA model. The validity of the proposed approach is empirically demonstrated by a simulation study. The applicability is proven by using the real responses to the Trends in International Mathematics and Science Study (TIMSS) 2015 science test.

Original languageEnglish
Pages (from-to)92-120
Number of pages29
JournalJournal of educational and behavioral statistics
Issue number1
Early online date9 May 2023
Publication statusPublished - Feb 2024


  • automated test assembly
  • chance-constrained
  • simulated annealing
  • uncertainty
  • 2023 OA procedure


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