Chapter 5

Handbook of Learning Analytics
First Edition

Predictive Modelling in Teaching and Learning

Christopher Brooks & Craig Thompson


Abstract

This article describes the process, practice, and challenges of using predictive modelling in teaching and learning. In both the fields of educational data mining (EDM) and learning analytics (LA) predictive modelling has become a core practice of researchers, largely with a focus on predicting student success as operationalized by academic achievement. In this chapter, we provide a general overview of considerations when using predictive modelling, the steps that an educational data scientist must consider when engaging in the process, and a brief overview of the most popular techniques in the field.

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About this Chapter

Title
Predictive Modelling in Teaching and Learning

Book Title
Handbook of Learning Analytics

Pages
pp. 61-68

Copyright
2017

DOI
10.18608/hla17.005

ISBN
978-0-9952408-0-3

Publisher
Society for Learning Analytics Research

Authors
Christopher Brooks1
Craig Thompson2

Author Affiliations
1. School of Information, University of Michigan, USA
2. Department of Computer Science, University of Saskatchewan, Canada

Editors
Charles Lang3
George Siemens4
Alyssa Wise5
Dragan Gašević6

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

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