Chance-Constrained Automated Test Assembly

Giada Spaccapanico Proietti, Mariagiulia Matteucci, Stefania Mignani and Bernard P. Veldkamp
Additional contact information
Stefania Mignani: University of Bologna
Bernard P. Veldkamp: University of Twente

Journal of Educational and Behavioral Statistics, 2024, vol. 49, issue 1, 92-120

Abstract: 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.

Keywords: automated test assembly; uncertainty; chance-constrained; simulated annealing (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.3102/10769986231169039 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:49:y:2024:i:1:p:92-120

DOI: 10.3102/10769986231169039

Access Statistics for this article

More articles in Journal of Educational and Behavioral Statistics
Bibliographic data for series maintained by SAGE Publications ().