概括
本研究介绍了适用于各种响应类型的认知诊断模型 (CDM) 的一般框架,证明了它们的可识别性,并为高级教育评估提供了高效的估计算法.
科学领域:
- 教育测量和心理测量学
- 统计建模 统计建模
- 认知科学 认知科学
背景情况:
- 认知诊断模型 (CDM) 传统上分析分类数据来评估技能掌握.
- 现代评估包括多种响应类型 (例如响应时间,计数),需要新的建模方法.
- 对这些新型反应类型的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.
- 建立这些一般响应CDM的理论识别能力.
- 为这些模型的广泛类别开发一个高效的估计算法.
主要方法:
- 开发一个通用的认知诊断建模框架,使用最小的假设.
- 在类似于传统的CDM条件下的识别能力的数学证明.
- 实现一个预期最大化 (EM) 算法用于参数估计.
主要成果:
- 一般响应CDM的识别性是在新的理论条件下确定的.
- 拟议的EM算法有效地估计了指数式家族基一般响应CDM的参数.
- 模拟研究证实了可识别性理论,并证明了估计算法的优越性能.
结论:
- 建立了可识别的一般响应认知诊断模型的新范式.
- 拟议的框架和算法将CDM扩展到更广泛的教育评估数据.
- 该方法通过模拟和应用到现实世界的响应时间数据来验证.
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