一个潜在的隐藏的马尔科夫模型处理数据
1University of Arizona, 617 N. Santa Rita Ave., Tucson, AZ , 85721, USA. xytang@math.arizona.edu.
Psychometrika
|November 7, 2023
概括
这项研究引入了一个新的统计模型,使用隐藏的马尔科夫模型来解释复杂的基于计算机的解决问题的数据. 该模型阐明了潜在特征中的个体差异如何影响不同的问题解决阶段,以更好地理解反应行为.
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:
- 响应过程数据为解决问题的行为提供了洞察力.
- 目前的特征提取方法缺乏对原始响应过程的解释性.
- 了解问题解决中的受访者异质性至关重要.
研究的目的:
- 提出一个统计模型来描述和分析基于计算机解决问题的响应过程.
- 提供一种可解释的方法来描述问题解决策略中的个体差异.
- 将隐藏的特征与可观察到的解决问题的阶段联系起来.
主要方法:
- 使用隐藏的马尔科夫模型 (HMM) 来表示响应过程.
- 将潜伏特征集成到HMM框架中,以解释受访者异质性.
- 将模型应用于模拟实验和国际学生评估计划 (PISA) 过程数据.
主要成果:
- 提出的基于HMM的模型成功地描述了响应过程及其在受访者之间的变化.
- 纳入潜在特征可以提高模型的节性和可解释性.
- 该模型有效地描述了问题解决阶段的异质性.
结论:
- 统计模型为分析复杂的响应过程数据提供了一种强大而可解释的方法.
- 这种方法促进了对解决问题的认知过程和个人差异的理解.
- 这些发现对教育评估和基于计算机的测试环境的设计有影响.
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