Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Optimal structure identification with greedy search
The Journal of Machine Learning Research
Learning the Structure of Linear Latent Variable Models
The Journal of Machine Learning Research
Identifying independencies in causal graphs with feedback
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
A discovery algorithm for directed cyclic graphs
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Goal-oriented visualizations of activity tracking: a case study with engineering students
Proceedings of the 2nd International Conference on Learning Analytics and Knowledge
A reference model for learning analytics
International Journal of Technology Enhanced Learning
Temporal learning analytics for computer based testing
Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
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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.