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相关概念视频

Biostatistics: Overview01:20

Biostatistics: Overview

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Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
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Overview of Biostatistics in Health Sciences01:19

Overview of Biostatistics in Health Sciences

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Biostatistics involves the application of statistical techniques to scientific research in health-related fields, including biology and public health. These techniques are essential for designing studies, collecting data, and analyzing it to draw meaningful conclusions. Given the complexity of biological processes, particularly in studies involving human subjects, biostatistical methods are crucial for effectively organizing and interpreting data that might otherwise obscure underlying patterns...
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Orthogonal Trajectories01:26

Orthogonal Trajectories

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Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...
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Metacognition01:26

Metacognition

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Metacognition is a conscious process where individuals are aware of their cognitive and executive processes, such as planning before solving a problem or self-monitoring during reading. For instance, a writer may need help with composing a piece. The situation involves a writer who is working on a piece of writing, but while doing so, they realize that something is missing. They notice that their characters lack depth or details. This realization occurs because the writer is reflecting on their...
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Longitudinal Studies01:26

Longitudinal Studies

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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Longitudinal Research02:20

Longitudinal Research

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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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从成绩单到轨迹:通过大学学习学术途径的框架.

Jai K Malik1, Fred M Feinberg2, Elizabeth E Bruch3,4

  • 1Middle East and North African Transport, World Bank, Washington, DC 20433, USA.

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概括

这项研究引入了一个新的框架,用于使用成绩单数据分析学生的学术途径. 它揭示了多元化的学生,包括代表性不足的群体,如何导航教育计划和研究领域.

关键词:
计算社会科学 计算社会科学动力学 动力学 动力学高等教育 高等教育不平等 不平等 不平等 不平等

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科学领域:

  • 教育研究教育研究
  • 数据科学在教育中的应用
  • 教育社会学教育的社会学

背景情况:

  • 教育机构正面临着学生的坚持,毕业率和STEM中的代表性不足的问题.
  • 现有的方法缺乏对学生如何通过学术课程进步的细粒度分析.
  • "教育途径科学"是必要的,但缺乏方法论上的严谨性.

研究的目的:

  • 提出一个理论上有基础的,数据驱动的框架,用于将成绩单数据转化为详细的学术途径.
  • 通过课程,使学生进步的细粒度,过程性帐户.
  • 解决分析复杂学生轨迹的统计挑战.

主要方法:

  • 开发了一个数据驱动的框架,将成绩单数据转化为学术途径.
  • 创建了一个基于问题和数据的统计模型来分析路径丰富性.
  • 利用成绩单数据来检查专业内和专业之间学生的移动情况.
  • 纳入学生路径中的时间动态和上下文变化.

主要成果:

  • 该框架提供了详细的洞察力,了解学生在整个学术课程和学术课程内的运动.
  • 分析揭示了来自不同背景的学生,包括代表性不足的群体,如何进入和退出研究领域.
  • 该模型考虑了学生路径中的时间动态和上下文差异.

结论:

  • 开发的框架为教育途径科学提供了一个强大的方法.
  • 这种方法可以为有针对性的干预措施提供信息,以改善学生的坚持和高等教育中的公平.
  • 了解多样化的学生轨迹对于应对STEM和其他领域的挑战至关重要.