在COVID-19大流行期间,用隐藏的马尔科夫模型推断学区学习模式
Mark J Panaggio1, Mike Fang1, Hyunseung Bang1
1Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland.
PloS one
|October 4, 2023
概括
在COVID-19大流行期间,学校改变了学习模式. 隐藏的马尔科夫模型 (HMM) 分析显示,在2020年9月至2021年6月期间,完全面对面的学习率从40.3%上升到54.7%.
科学领域:
- 公共卫生 公共卫生
- 教育政策 教育政策
- 数据科学数据科学数据科学
背景情况:
- 由于COVID-19大流行,美国各地的公立学校学习模式需要改变.
- 学校转向混合和完全远程学习,影响教育提供和公共卫生监督.
- 现有的关于学习模式的数据来源不完整,有时是矛盾的.
研究的目的:
- 从2020年8月到2021年6月,在全国范围内追踪提供完全面对面,混合和完全远程学习的学校比例的变化.
- 开发和验证一个模型,用于结合和消除有关学校学习模式的多样化,不完整的数据源的冲突.
- 在疫情期间提供更全面,更可靠的全国性学习模式描述.
主要方法:
- 从多个来源收集了14688个学区的数据 (Burbio,MCH战略数据,AEI回归学习追踪器,州仪表板).
- 采用隐藏的马尔科夫模型 (HMM) 来推断每个地区每周最可能的学习模式.
- 与单个数据源对HMM的性能进行了验证,实现了更高的时空覆盖率和一致性.
主要成果:
- 提供完全面对面学习的地区比例从2020年9月的40.3%增加到2021年6月的54.7%.
- 在45个州和城市和农村地区都观察到这种亲自学习的增加.
- HMM提供了更全面的数据,与任何单一来源相比,与多个来源更好地达成一致.
结论:
- 一个隐藏的马尔科夫模型有效地将不完整和矛盾的数据融合在一起,用于强大的公共卫生监测和研究.
- 该研究突出显示,在2020-2021学年期间,美国公立学校的完全面对面学习增加的显著趋势.
- 概率模型为分析教育和公共卫生环境中复杂的多源数据集提供了有价值的工具.
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