分位数
计算机科学
极小极大
启发式
数学优化
模拟退火
计算机化自适应测验
项目反应理论
考试(生物学)
数学
机器学习
人工智能
统计
心理测量学
古生物学
生物
作者
Giada Spaccapanico Proietti,Mariagiulia Matteucci,Stefania Mignani,Bernard P. Veldkamp
标识
DOI:10.3102/10769986231169039
摘要
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.
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