标题： 大规模研究小学生学习成绩与其学习管理系统活动和经济社会人口统计特征的关系，使用包含随机效应的贝叶斯加法回归树 显示英文标题

标题： Large scale study of primary school student performance relative to their LMS activity and socioeconomic demographics using a Bayesian Additive Regression Trees containing random effects

摘要： 使用在乌拉圭几乎所有9-12岁学生中收集的数据，我们展示了如何使用具有随机效应的贝叶斯加性回归树（BART）来研究学习管理系统（LMS）活动和经济社会地位与表现之间的关联。 表现数据与LMS活动模式数据相结合。 选择BART是因为可以包含学校层面的随机效应。 该模型可用于早期识别有风险的学生，并突出表现良好或需要干预的学校。 一个有趣的发现是，在低经济社会地位的学生中，高水平的LMS使用对表现有更大的积极影响。 显示英文摘要

摘要： Using data collected on almost every 9-12 years old student in Uruguay, we show how to apply Bayesian Additive Regression Trees (BART) with random effects to study performance association with Learning Managment System (LMS) activity and socioeconomic status. Performance data is joined with LMS activity pattern data. BART is chosen because it is possible to include school-level random effects. The model can be used for early identification of at-risk students, and highlights schools that are successful or need intervention. An interesting finding is that high levels of LMS usage show larger positive effects on performance in low socioeconomic status.