试管婴儿的诊断分类模型:方法和理论
Xin Xu1, Guanhua Fang2, Jinxin Guo3
1Beijing Normal University, Beijing, China.
Psychometrika
|March 26, 2024
概括
本研究引入了一种新的诊断分类模型 (DCM),该模型考虑了教育评估中属性配置文件和测试小组效应之间的相关性. 与现有方法相比,改进后的模型显示了与现有方法相比,适合度的显著改善.
科学领域:
- 教育测量教育的测量
- 心理测量建模 心理测量建模
- 认知诊断是一种认知诊断.
背景情况:
- 诊断分类模型 (DCM) 对于形成性评估至关重要.
- 基于测试片的DCM涉及复杂的潜在结构.
- 现有的模型通常假定属性配置文件和测试小组效应之间的独立性.
研究的目的:
- 扩展试卷DINA (T-DINA) 模型,将属性配置文件和试卷效应之间的潜在相关性纳入其中.
- 为拟议的扩展T-DINA模型建立模型识别条件.
- 用现实世界的评估数据来评估新模型的性能.
主要方法:
- 开发一个扩展的测试器DINA (T-DINA) 模型.
- 对模型识别能力的调查和足够条件的推导.
- 申请2015年国际学生评估计划数据集.
- 与标准DINA和T-DINA模型进行比较分析.
- 模拟研究用于评估模型性能.
主要成果:
- 提议的扩展T-DINA模型容纳了潜在结构之间的相关性.
- 建立了足够的模型识别条件,包括T-DINA标准.
- 与DINA和T-DINA相比,新型号显示了与DINA和T-DINA相比,适合度的大幅提高.
- 模拟结果证实了模型在各种环境中的有效性.
结论:
- 扩展的T-DINA模型在基于试卷的评估中提供了更准确的复杂潜伏结构表示.
- 考虑属性-测试小组相关性可以提高模型的合适性和诊断准确性.
- 这些发现对改善形成性评估和教育测量实践有影响.
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