概括
这项研究引入了一个新的统计模型来解释复杂的基于计算机的解决问题的数据. 该模型使用隐藏的马尔科夫模型来了解个人差异如何影响问题解决过程,为受访者行为提供了更清晰的见解.
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:
- 来自基于计算机的评估的响应过程数据 (RPD) 为解决问题的行为提供了洞察力.
- 当前的数据驱动特征提取方法产生可解释的特征,但缺乏与原始响应过程的明确联系.
- 这种差距阻碍了对潜在特征如何影响问题解决策略的深入理解.
研究的目的:
- 提出一种用于分析响应过程数据的新型统计模型.
- 为了提高从非结构化过程数据中提取的特征的可解释性.
- 模拟受访者之间解决问题过程的异质性.
主要方法:
- 开发了一个统计模型,将隐藏的特征与隐藏的马尔科夫模型 (HMMs) 集成在一起.
- HMM结构代表了解决问题的阶段,隐藏状态作为子任务.
- 隐藏的特征被纳入来解释响应过程中的变化.
主要成果:
- 拟议的模型为RPD分析提供了一个节和可解释的框架.
- 通过模拟研究证明了模型的有效性.
- 使用来自国际学生评估计划 (PISA) 的现实世界数据验证了模型.
结论:
- 隐藏的特征告知HMM提供了一个强大的工具,以了解个体在解决问题的差异.
- 这种方法弥合了复杂的过程数据和可解释的心理结构之间的差距.
- 促进在教育评估中对认知过程进行更细致的分析.
相关概念视频
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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