@incollection{liu_going_2017,
	address = {Alberta, Canada},
	edition = {1},
	title = {Going {Beyond} {Better} {Data} {Prediction} to {Create} {Explanatory} {Models} of {Educational} {Data}},
	isbn = {978-0-9952408-0-3},
	url = {http://solaresearch.org/hla-17/hla17-chapter1},
	abstract = {In the statistical modelling of educational data, approaches vary depending on whether the goal is to build a predictive or an explanatory model. Predictive models aim to  nd a combination of features that best predict outcomes; they are typically assessed by their accuracy in predicting held-out data. Explanatory models seek to identify interpretable causal relationships between constructs that can be either observed or inferred from the data. The vast majority of educational data mining research has focused on achieving pre- dictive accuracy, but we argue that the  eld could bene t from more focus on developing explanatory models. We review examples of educational data mining efforts that have pro- duced explanatory models and led to improvements to learning outcomes and/or learning theory. We also summarize some of the common characteristics of explanatory models, such as having parameters that map to interpretable constructs, having fewer parameters overall, and involving human input early in the model development process.},
	booktitle = {The {Handbook} of {Learning} {Analytics}},
	publisher = {Society for Learning Analytics Research (SoLAR)},
	author = {Liu, Ren and Koedinger, Kenneth},
	editor = {Lang, Charles and Siemens, George and Wise, Alyssa Friend and Gaševic, Dragan},
	year = {2017},
	pages = {69--76}
}