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从学习损失中学习:贝叶斯更新在学习中断期间学术普遍选中的贝叶斯更新

Garret J Hall¹, Emma Doyle¹

¹Department of Educational Psychology and Learning Systems, Florida State University, United States.

Journal of school psychology

|April 3, 2025

概括

贝叶斯顺序回归证实,在COVID-19学校中断后,阅读和数学选仍然具有预测性. 预测准确度因年级而异,但不受学习中断的影响.

科学领域:

教育心理学教育心理学
统计统计统计统计
数据科学数据科学数据科学

背景情况:

由于COVID-19大流行,造成了广泛的学习中断,影响了学生的学术需求.
在此类中断期间评估教育选工具的预测有效性至关重要.

研究的目的:

评估Covid-19学习中断之前和之后的阅读和数学查的预测力和准确性.
调查长期非计划性学校关闭 (PUSCs) 对查有效性的影响.

主要方法:

使用贝叶斯式顺序回归模型来分析查数据.
采用贝叶斯更新程序,将前一年的估计值作为后年的先验值.
检查了不同年级水平的预测性表现.

主要成果:

阅读和数学选在COVID-19 PUSC测试之前和之后都表现出一致的预测能力 (赔率比:15-25).
预测强度和准确性因年级不同而有所不同.
预测准确度的年级水平变化没有受到学习中断的显著影响.

结论:

贝叶斯更新是一种通用查预测的实用方法,特别是在像PUSC这样的中断期间.
教育选工具尽管存在重大学习中断,但仍然保持了预测有效性.
这些发现支持在教育评估中继续使用和适应贝叶斯方法.

关键词:

贝叶斯的多层次建模.数学数学数学算法算法算法长期无计划的学校关闭.阅读阅读阅读阅读全球性选检查普遍选检查

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