概括
本研究介绍了认知诊断模型 (CDM) 的一般框架,这些模型可以处理多种响应类型,证明其可识别性并提供有效的估计方法. 新的一般响应CDM提供了教育评估的范式转变.
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
- 数据科学数据科学数据科学
背景情况:
- 认知诊断模型 (CDM) 传统上分析分类数据.
- 现代评估包括各种响应类型,如响应时间和计数数据.
- 对这些新响应类型的CDM的识别和估计能力在很大程度上仍未被探索.
研究的目的:
- 为任意的多变量响应类型提出一个通用的认知诊断建模框架.
- 确定这些一般响应CDM的可识别性.
- 为这些模型开发一个高效的估计算法.
主要方法:
- 开发了一个具有最小假设的通用CDM框架.
- 使用基于Q矩阵的条件建立了可识别性.
- 为参数估计提出了一个预期最大化 (EM) 算法.
- 在各种响应类型中进行模拟研究.
主要成果:
- 证明一般响应的CDM在Q矩阵条件下是可以识别的.
- 证明了拟议的EM算法的卓越实证性能.
- 模拟结果证实了理论上的可识别性发现.
结论:
- 已经建立了一个可识别的通用响应CDM的新范式.
- 拟议的框架和EM算法对教育评估中的不同类型的响应有效.
- 该方法成功地应用于真实世界的数据集 (TIMSS 2019响应时间).
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