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Cochran's Q Test01:17

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对于多选项的非参数CD-CAT:项目选择方法和Q-优化.

Yu Wang1, Chia-Yi Chiu2, Hans Friedrich Köhn3

  • 1University of Minnesota, Twin Cities, Minneapolis, Minnesota, USA.

The British journal of mathematical and statistical psychology
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概括

这项研究引入了选择多选项 (MC) 项目的新方法,用于用于认知诊断 (CD-CAT) 的计算机自适应测试. 这些方法提高了诊断准确度,特别是当校准样本有限时.

关键词:
这是一个CD-CATCD-CAT.这是一个MC-DINA模型.这是一个Q-优的Q-优.认知诊断是一种认知诊断.多选项非参数分类方法.非参数的项目选择方法.

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科学领域:

  • 教育测量教育的测量
  • 心理测量 心理测量 心理测量
  • 认知科学 认知科学

背景情况:

  • 用于认知诊断的计算机自适应测试 (CD-CAT) 通过量身定制的项目选择来提高估计效率和准确性.
  • 现有的项目选择方法主要集中在二进制答案上,不太强调多选项 (MC) 项目.
  • 詹森-香农差异 (JSD) 指数是MC项目的唯一现有方法,但需要大规模的校准样本.

研究的目的:

  • 解决CD-CAT中现有的MC项目选择方法的局限性.
  • 为MC项目提出新的项目选择算法,这些算法在有限的校准数据下是有效的.
  • 为了提高CD-CAT的诊断准确性和效率,使用MC项目.

主要方法:

  • 为MC项目 (MC-NPS) 提出了一种非参数项选择方法,使用一种新的歧视力度量.
  • 开发了MC项目的Q-最佳程序,以改善CD-CAT的早期分类.
  • 通过模拟研究评估拟议的算法.

主要成果:

  • 对于MC项目,MC-NPS方法在项目选择方面表现出了有效性.
  • 在CD-CAT的初始阶段,Q-最佳程序提高了分类准确性.
  • 模拟研究证实了两种拟议算法的有效性和效率.

结论:

  • 开发的MC-NPS和Q-optimal程序为CD-CAT中的MC项目选择提供了可行的解决方案,特别是在小或没有校准样本的情况下.
  • 这些方法通过利用来自MC项目的更丰富的信息来提高CD-CAT的诊断能力.
  • 这些发现有助于推进用于认知诊断的适应性测试领域.