A shadow-test approach to the calibration of field-test items embedded in adaptive testing is presented. The objective function used in the shadow-test model selects both the operational and field-test items adaptively using a Bayesian version of the criterion of $$D_{\mathrm{s}}$$ D s -optimality. The constraint set for the model can be used to hide the field-test items completely in the content of the test as well as to deal with such practical issues as random control of their exposure rates. The approach runs on efficient implementations of the Gibbs sampler for the real-time updating of the ability and field-test parameters. Optimal settings for the proposed algorithms were found and used to demonstrate item calibration with smaller than traditional sample sizes in runtimes fully comparable with conventional adaptive testing.

中文翻译：

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自适应项目校准的影子测试方法

提出了一种用于校准嵌入在自适应测试中的现场测试项目的影子测试方法。影子测试模型中使用的目标函数使用 $$D_{\mathrm{s}}$$ D s -最优性标准的贝叶斯版本自适应地选择操作和现场测试项目。模型的约束集可用于将现场测试项目完全隐藏在测试内容中，以及处理随机控制其暴露率等实际问题。该方法运行在 Gibbs 采样器的有效实现上，用于实时更新能力和现场测试参数。找到了所提出算法的最佳设置，并用于演示项目校准，运行时的样本量小于传统的样本量，与传统的自适应测试完全可比。