在COVID-19期间关闭学校和有效的面对面学习
1Drexel University, LeBow College of Business, School of Economics, 3220 Market Street, Philadelphia, PA 19104, United States of America.
Economics of education review
|June 7, 2023
概括
在COVID-19期间跟踪面对面学习的差异导致了一项新的措施,有效的面对面学习 (EIPL). EIPL揭示了与学生人口统计,学校资源和区域政治偏好相关的差异.
科学领域:
- 教育政策 教育政策
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 在全球范围内,COVID-19大流行扰乱了传统的学校教育.
- 现有的面对面,混合和远程学习的追踪器表现出显著的差异.
- 准确地衡量亲自学习对于政策评估至关重要.
研究的目的:
- 在COVID-19大流行期间突出目前教育跟踪方法的不一致性.
- 引入一种新的,更准确的有效面对面学习 (EIPL) 的测量方法.
- 通过使用新的EIPL指标,分析影响面对面学习差异的因素.
主要方法:
- 记录了美国知名学校教育追踪者的时间和地理差异.
- 通过将学校模式与手机移动数据相结合,开发了有效的面对面学习 (EIPL) 度量.
- 美国公立和私立学校的大型,代表性样本的估计EIPL.
主要成果:
- 拟议的EIPL措施解决了现有追踪器中发现的差异.
- 证实了较少的亲自学习与非白人学生的更高比例,较低的疫情前成绩和更大的学校规模之间的联系.
- 在富裕,受过教育的地区发现EIPL较低,疫情前支出和紧急资金较高,主要是由于区域政治差异.
结论:
- EIPL指标提供了一种更可靠的方法来量化面对面学习.
- 疫情期间的教育差异受到学生人口统计,学校资源和社会政治因素的影响.
- 系统的区域差异,特别是政治偏好,在人与人之间的学习变化中发挥了重要作用.
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