一种两步的Q矩阵估计方法
Hans-Friedrich Köhn1, Chia-Yi Chiu2, Olasumbo Oluwalana3
1University of Illinois at Urbana-Champaign, IL, USA.
Applied psychological measurement
|November 15, 2024
概括
这项研究引入了一种新的两步算法,用于在认知诊断模型中准确估计Q矩阵,改进了现有的教育测量方法.
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:
- 在教育测量中的认知诊断模型 (CDM) 依赖于Q矩阵,该矩阵将测试项目映射到潜在的技能.
- 准确的Q矩阵规范对于有效的认知诊断至关重要,但专家判断是错误的.
- 现有的数据驱动的Q矩阵估计方法面临着计算方面的挑战.
研究的目的:
- 提出一个新的,计算效率高的两步算法来估计Q矩阵.
- 为了提高Q矩阵估计CDM的准确性和可行性.
- 为任何认知诊断模型提供适用于任何认知诊断模型的强大方法.
主要方法:
- 为Q矩阵估计开发了一种两步算法.
- 使用模拟来评估算法的性能.
- 该方法被应用到Tatsuoka的分数减法数据集.
主要成果:
- 与现有方法相比,拟议的算法表现出优越的性能.
- 这种新方法在计算上比以前的方法更有效.
- 对真实世界数据集的成功应用验证了算法的实用性.
结论:
- 开发的算法为Q矩阵估计提供了更准确,更有效的方法.
- 这一进步对教育环境中的认知诊断具有重要的理论和实践意义.
- 这些发现支持使用数据驱动方法来提高诊断评估的有效性.
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