贝叶斯联合建模的响应时间与动态潜伏能力在教育测试的响应时间
Xiaojing Wang1, Abhisek Saha2, Dipak K Dey1
1Department of Statistics, https://ror.org/02der9h97University of Connecticut, Storrs, United States.
Psychometrika
|December 1, 2025
概括
这项研究引入了新的模型,将项目响应和响应时间结合起来,以改善教育测试. 这些模型减少了偏见,并提高了教育评估中的能力估计准确性.
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:
- 传统的教育测试主要使用项目响应来推断能力,经常忽视响应时间数据.
- 忽视响应时间可能会导致能力估计不那么准确.
研究的目的:
- 开发先进的状态空间模型,整合物品响应和响应时间,以增强能力推断.
- 在教育测试中调查考生能力,项目难度和响应时间之间的关系.
主要方法:
- 开发了一种新的状态空间模型类别,用于联合分析二分的项目响应和响应时间.
- 进行模拟以评估拟议模型的性能.
- 使用EdSphere数据集进行实证研究,以比较不同的响应时间模型.
主要成果:
- 模拟显示,新模型显著减少了能力估计中的偏差.
- 与传统方法相比,拟议的模型在能力估计中显示出更高的精度.
- 能力-难度距离和响应时间之间的反转U形关系为EdSphere数据提供了更好的匹配.
结论:
- 将响应时间数据集成到教育测试模型中可以提高能力估计的准确性和精度.
- 反向的U形关系为评估期间的受试者行为提供了更具心理可信性的解释.
- 这些发现表明,通过将时间数据纳入教育测量的更全面的方法.
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