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.
这项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卷效应之间的相关性. 与现有方法相比,改进的模型显示了更好的适应性.
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
- 隐性变量分析 隐性变量分析
背景情况:
- 诊断分类模型 (DCM) 对于形成性评估至关重要.
- 试卷响应理论 (TRT) 模型,就像试卷DINA (T-DINA) 一样,结合了项目分组效应.
- 现有的T-DINA模型假设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T-DINA模型,允许属性配置文件和测试卷效应之间的相关性.
- 调查拟议的扩展T-DINA模型的可识别性.
- 用现实世界的评估数据来评估模型的性能.
主要方法:
- 开发一个扩展的试管婴儿DINA (T-DINA) 模型,其中包含相关的潜伏结构.
- 模型可识别性的理论分析,建立足够的条件.
- 该模型应用于2015年国际学生评估计划 (PISA) 数据集.
- 与标准DINA和T-DINA模型进行比较分析.
- 模拟研究用于评估不同条件下的模型性能.
主要成果:
- 与DINA和标准T-DINA相比,提议的扩展T-DINA模型显示了与DINA和标准T-DINA相比,适合度的显著改善.
- 建立了足够的条件来识别扩展模型.
- 标准T-DINA模型的可识别性也被证实为次要结果.
- 该模型在不同环境的模拟研究中表现出强的性能.
结论:
- 扩展的T-DINA模型为教育和心理测量中复杂的数据结构提供了更准确的表示.
- 考虑到属性配置文件和测试卷效应之间的相关性,可以提高模型的合适性,并提供更深入的见解.
- 这些发现支持使用这种先进的DCM来改进形成性评估和数据分析.
更多相关视频
09:00Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
Published on: August 16, 2024
12:18A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
相关概念视频
Sensitivity, Specificity, and Predicted Value
Sensitivity is the...
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
Formulating and Validating Nursing Diagnosis II
Risk nursing diagnoses represent clinical judgments of an individual, family, or community more vulnerable to developing the health problem than others...
Formulating and Validating Nursing Diagnosis I
There are thirteen domains...
Measures of Intelligence
Validity refers to how well a test measures what it claims to measure. An intelligence test should accurately assess intelligence rather than another characteristic, like anxiety. Criterion validity is one way to evaluate this;...
Receiver Operating Characteristic Plot
