Chapter 2

Handbook of Learning Analytics
First Edition

Computational Methods for the Analysis
of Learning and Knowledge Building
Communities

H. Ulrich Hoppe


Abstract

Learning analytics (LA) features an inherent interest in algorithms and computational methods of analysis. This makes LA an interesting field of study for computer scientists and mathematically inspired researchers. A differentiated view of the different types of approaches is relevant not only for “technologists” but also for the design and interpretation of analytics applications. The “trinity of methods” includes analytics of 1) network structures including actor–actor (social) networks but also actor–artefact networks, 2) processes using methods of sequence analysis, and 3) content using text mining or other techniques of artefact analysis. A summary picture of these approaches and their roots is given. Two recent studies are presented to exemplify challenges and potential benefits of using advanced computational methods that combine different methodological approaches.

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About this Chapter

Title
Computational Methods for the Analysis of Learning and Knowledge Building Communities

Book Title
Handbook of Learning Analytics

Pages
pp. 23-33

Copyright
2017

DOI
10.18608/hla17.002

ISBN
978-0-9952408-0-3

Publisher
Society for Learning Analytics Research

Authors
H. Ulrich Hoppe

Author Affiliations
Department of Computer Science and Applied Cognitive Science, University of Duisburg-Essen, Germany

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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