Interim assessment occurs throughout instruction to provide feedback about what students know and have achieved. Different from the current available cognitive diagnostic computerized adaptive testing (CD-CAT) design that focuses on assessment at a single time point, the authors discuss several designs of interim CD-CAT that are suitable in the learning context. The interim CD-CAT differs from the current available CD-CAT designs primarily because students’ mastery profile (i.e., skills mastery) changes due to learning, and new attributes are added periodically. Moreover, hierarchies exist among attributes taught sequentially and such information could be used during item selection. Two specific designs are considered: The first one is when new attributes are taught in Stage II, but the student mastery status of the previously taught attributes stays the same. The second design is when both new attributes are taught, and previously taught attributes can be further learned or forgotten in Stage II. For both designs, the authors propose an individual prior, which considers a person’s learning history and population learning model, to start an interim CD-CAT. Simulation results show that the Stage II CD-CAT using individual prior outperforms the methods using population priors. The GDINA (generalized deterministic inputs, noisy, “and” gate) diagnostic index (GDI) is extended to accommodate item hierarchies, and analytic results are provided to further illustrate the types of items that are most popular during item selection. As the first study that focuses on the application of CD-CAT in a learning context, the methods and results present herein showed the great promise of using CD-CAT to monitor learning.

中期评估在整个教学过程中进行，以提供有关学生所知道和取得的成就的反馈。与当前可用的认知诊断计算机化自适应测试 (CD-CAT) 设计侧重于单个时间点的评估不同，作者讨论了几种临时设计适合学习环境的 CD-CAT。临时 CD-CAT 与当前可用的 CD-CAT 设计不同，主要是因为学生的掌握概况（即技能掌握）会因学习而改变，并且会定期添加新属性。此外，按顺序教授的属性之间存在层次结构，并且可以在项目选择期间使用此类信息。考虑了两种具体的设计：第一种是在第二阶段教授新属性时，但之前教授的属性的学生掌握状态保持不变。第二种设计是当两个新属性都被教授时，并且以前教授的属性可以在第二阶段进一步学习或忘记。对于这两种设计，作者都提出了一个个人先验，它考虑了一个人的学习历史和人口学习模型，以启动一个临时 CD-CAT。模拟结果表明，使用个体先验的 Stage II CD-CAT 优于使用群体先验的方法。GDINA（广义确定性输入，噪声，“和”门）诊断指数（GDI）被扩展以适应项目层次结构，并提供分析结果以进一步说明在项目选择过程中最受欢迎的项目类型。作为第一项关注 CD-CAT 在学习环境中应用的研究，本文提出的方法和结果显示了使用 CD-CAT 监控学习的巨大前景。并提供分析结果以进一步说明在项目选择过程中最受欢迎的项目类型。作为第一项关注 CD-CAT 在学习环境中应用的研究，本文提出的方法和结果显示了使用 CD-CAT 监控学习的巨大前景。并提供分析结果以进一步说明在项目选择过程中最受欢迎的项目类型。作为第一项关注 CD-CAT 在学习环境中应用的研究，本文提出的方法和结果显示了使用 CD-CAT 监控学习的巨大前景。