在线教育数据预测和因果模型的变量构建

Proceedings of the 1st International Conference on Learning Analytics and Knowledge Pub Date : 2011-02-27 DOI:10.1145/2090116.2090123

Stephen E. Fancsali

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引用次数: 21

摘要

我们考虑在线教育设置中由课件收集的数据的预测和因果建模问题，重点关注图形因果模型作为这种建模的形式化方法。我们回顾了先前研究的结果，提出了一项新的试点研究，并建议构建变量分析的新方法可以提高我们推断在线教育学习结果的预测因素和原因的能力。最后，调查了从这些数据中发现因果关系的几个一般问题以及可能的解决方案。

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Variable construction for predictive and causal modeling of online education data

We consider the problem of predictive and causal modeling of data collected by courseware in online education settings, focusing on graphical causal models as a formalism for such modeling. We review results from a prior study, present a new pilot study, and suggest that novel methods of constructing variables for analysis may improve our ability to infer predictors and causes of learning outcomes in online education. Finally, several general problems for causal discovery from such data are surveyed along with potential solutions.

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Proceedings of the 1st International Conference on Learning Analytics and Knowledge

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