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相关概念视频

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Hindsight Biases01:12

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Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
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相关实验视频

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Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
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通过创新措施检测预知作弊:一种混合层次模型,用于联合建模项目响应,响应时间和视觉固定计数.

Kaiwen Man1, Jeffrey R Harring2

  • 1University of Alabama, Tuscaloosa, USA.

Educational and psychological measurement
|September 4, 2023
PubMed
概括

这项研究引入了一种新的模型,通过分析项目响应,响应时间和眼睛跟踪数据 (凝视定位) 来检测测试中的预知作弊. 该模型准确地识别了具有先前知识的异常测试者.

关键词:
用眼睛追踪来进行追踪.凝视固定计数计数时间项目响应理论是物品响应理论.联合建模 联合建模响应时间 响应时间技术增强评估技术增强评估

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Measuring Attentional Biases for Threat in Children and Adults
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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
  • 认知心理学 认知心理学

背景情况:

  • 预知作弊损害了测试的有效性.
  • 现有的方法分析项目响应和响应时间.
  • 眼睛跟踪数据,特别是凝视定位,为增强检测异常测试行为提供了潜在的潜力.

研究的目的:

  • 提出一种新的混合层次模型,整合多个数据源,用于预知作弊检测.
  • 为了识别异常的考生,具有不同程度的预知.
  • 为了区分正常和异常受试者之间的行为模式.

主要方法:

  • 混合物层次模型的开发.
  • 整合物品响应,响应时间和视觉固定计数从眼睛跟踪.
  • 用马尔科夫链蒙特卡洛 (MCMC) 应用贝叶斯方法进行参数估计.

主要成果:

  • 拟议的模型有效地检测出异常的考生.
  • 该模型解释了预先知识水平的差异.
  • 异常受试者的行为模式与正常受试者的行为模式有区别.

结论:

  • 综合模型提高了检测预知作弊的准确性.
  • 眼睛跟踪数据为考试行为提供了有价值的见解.
  • 这种方法提供了一种可靠的方法来确保测试完整性.