Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, No. 19, Xin Jie Kou Wai Street, Hai Dian District, Beijing, 100875, China.
Behav Res Methods. 2024 Oct;56(7):7026-7058. doi: 10.3758/s13428-024-02406-3. Epub 2024 Apr 12.
This paper presents a novel approach known as the cross estimation network (CEN) for fitting the datasets obtained from psychological or educational tests and estimating the parameters of item response theory (IRT) models. The CEN is comprised of two subnetworks: the person network (PN) and the item network (IN). The PN processes the response pattern of individual respondent and generates an estimate of the underlying ability, while the IN takes in the response pattern of individual item and outputs the estimates of the item parameters. Four simulation studies and an empirical study were comprehensively and rigorously conducted to investigate the performance of CEN on parameter estimation of the two-parameter logistic model under various testing scenarios. Results showed that CEN effectively fit the training data and produced accurate estimates of both person and item parameters. The trained PN and IN adhered to AI principles and acted as intelligent agents, delivering commendable evaluations for even unseen patterns of new respondents and items.
本文提出了一种新的方法，称为交叉估计网络（CEN），用于拟合心理或教育测试获得的数据集，并估计项目反应理论（IRT）模型的参数。CEN 由两个子网组成：个体网络（PN）和项目网络（IN）。PN 处理个体应答者的应答模式，并生成潜在能力的估计，而 IN 则接受个体项目的应答模式，并输出项目参数的估计。进行了四项模拟研究和一项实证研究，以全面严格地研究 CEN 在各种测试情况下对二参数逻辑模型的参数估计的性能。结果表明，CEN 有效地拟合了训练数据，并对个体和项目参数进行了准确的估计。经过训练的 PN 和 IN 遵守 AI 原则，充当智能代理，即使对新应答者和项目的未见模式也能提供值得称赞的评估。
