从多站点随机试验中设计最佳的数据驱动策略
1Department of Human Development, Teachers College, Columbia University, 525 West 120th Street, New York, NY, 10027, USA. ysuk@tc.columbia.edu.
Psychometrika
|October 24, 2023
概括
本研究引入了在教育环境中为最佳治疗方案 (OTRs) 提供新的方法,解决了层次数据结构. 修改后的Q学习和权重方法显著提高了多站点随机试验中的OTR性能.
科学领域:
- * 教育心理学 教育心理学
- * 数据科学数据科学
- * 生物统计学
背景情况:
- *最佳治疗方案 (OTR) 是基于数据的建议,用于计算机科学和个性化医学.
- *现有的OTR研究往往忽视了在教育环境中常见的等级依赖关系 (学生在学校内嵌).
研究的目的:
- * 提出一个框架,用于设计针对教育领域多站点随机试验 (MRT) 的OTR.
- * 适应和评估Q学习和权重方法,以提高教学数据层次的表现.
主要方法:
- * 开发了12个修改 (6个Q学习,6个权重) 使用多层模型,调节器和增强.
- *研究了结合随机治疗效应和集群级调节者的影响.
- * 在权重方法中应用集群人偶和增强术语.
主要成果:
- *所有修改后的Q学习方法都在MRT中提高了性能.
- * 随机治疗效应的Q学习修改在处理集群级别主持人方面表现出色.
- *表现最好的权重方法包括集群人偶和增强术语.
结论:
- * 拟议的框架有效地适应了OTR方法用于MRT中的等级教育数据.
- * 修改后的Q学习和权重方法为个性化教育干预提供了改进的策略.
- * 在优化有条件现金转账计划以提高教育成绩方面有所应用.
关键词:
这就是Q-learning.聚类数据是聚类数据.有条件的现金转移计划治疗效果的异质性 治疗效果的异质性多层次数据多层次数据最佳的治疗方案是最佳的治疗方案.最佳治疗规则的最佳治疗规则个性化学习个性化学习权衡权衡权衡权衡权衡权衡权衡权更多相关视频
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