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相关概念视频

Reliability and Validity01:29

Reliability and Validity

Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...

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相关实验视频

Updated: May 13, 2026

Enabling High Grayscale Resolution Displays and Accurate Response Time Measurements on Conventional Computers
06:50

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预测学生测试成绩中的个人屏幕响应时间的价值:来自TIMSS 2019问题的解决和调查任务的证据.

Bin Tan1, Okan Bulut2

  • 1Measurement, Evaluation, and Data Science, Faculty of Education, University of Alberta, Edmonton, AB T6G 2G5, Canada.

Journal of Intelligence
|July 25, 2025
PubMed
概括

学生在测试项目的响应时间变化,而不仅仅是平均时间,显著预测学术表现. 分析精细的时间数据为学生的考试过程提供了更深入的见解,并改善了评估设计.

关键词:
个人差异是个人的差异.处理数据 处理数据.分析个人资料分析分析.响应时间响应时间在人体内变化的变化.

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科学领域:

  • 教育测量教育的测量
  • 认知心理学 认知心理学
  • 心理测量 心理测量 心理测量

背景情况:

  • 学生响应时间 (RT) 在测试项目之间有很大的差异,影响绩效预测.
  • 使用平均RT的传统分析可能会掩盖个人内部的关键变异性.
  • 了解RT的个体内变化是解锁对学生表现更深入洞察的关键.

研究的目的:

  • 为了证明学生测试成绩的RT在人体内变化的预测和解释价值.
  • 将RT的预测能力分解为模式效应 (变量) 和水平效应 (平均值).

主要方法:

  • 在TIMSS 2019数学评估中,利用了来自13,829名四年级学生的响应时间数据的个人资料分析.
  • 分析了屏幕级RT作为项目级定时的代理,捕捉了人内变化.
  • 将RT预测能力分解为模式和水平效应;进行交叉验证.

主要成果:

  • 在预测表现方面,标准化RT (模式效应) 的个人内部变异性明显超过平均RT (水平效应).
  • 个别屏幕RTs表现出独特的预测能力,强度和方向各不相同.
  • 结果在不同成绩组中一致,并通过交叉分析验证.

结论:

  • 响应时间的个体内变化是学生考试表现的关键,经常被忽视的预测因素.
  • 细粒度的RT数据为学生在评估期间的认知过程提供了更丰富的见解.
  • 未来教育评估的建议包括收集和分析详细的RT数据.