Chapter 6

Handbook of Learning Analytics
First Edition

Going Beyond Better Data Prediction to
Create Explanatory Models of Educational Data

Ran Liu & Kenneth R. Koedinger


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 find 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 predictive accuracy, but we argue that the field could benefit from more focus on developing explanatory models. We review examples of educational data mining efforts that have produced 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.

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About this Chapter

Title
Going Beyond Better Data Prediction to Create Explanatory Models of Educational Data

Book Title
Handbook of Learning Analytics

Pages
pp. 69-76

Copyright
2017

DOI
10.18608/hla17.006

ISBN
978-0-9952408-0-3

Publisher
Society for Learning Analytics Research

Authors
Ran Liu
Kenneth R. Koedinger

Author Affiliations
School of Computer Science, Carnegie Mellon University, USA

Editors
Charles Lang1
George Siemens2
Alyssa Wise3
Dragan Gašević4

Editor Affiliations
1. Teachers College, Columbia University, USA
2. LINK Research Lab, University of Texas at Arlington, USA
3. Learning Analytics Research Network, New York University, USA
4. Schools of Education and Informatics, University of Edinburgh, UK

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