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.
PNAS nexus
|October 9, 2025
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.
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.
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