Handbook of Learning Analytics
A Critical Perspective on Learning
Analytics and Educational Data Mining
Rita Kop, Helene Fournier & Guillaume Durand
In our last paper on educational data mining (EDM) and learning analytics (LA; Fournier, Kop & Durand, 2014), we concluded that publications about the usefulness of quantitative and qualitative analysis tools were not yet available and that further research would be helpful to clarify if they might help learners on their self-directed learning journey. Some of these publications have now materialized; however, replicating some of the research described met with disappointing results. In this chapter, we take a critical stance on the validity of EDM and LA for measuring and claiming results in educational and learning settings. We will also report on how EDM might be used to show the fallacies of empirical models of learning. Other dimensions that will be explored are the human factors in learning and their relation to EDM and LA, and the ethics of using “Big Data” in research in open learning environments.
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A Critical Perspective on Learning Analytics and Educational Data Mining
Handbook of Learning Analytics
Society for Learning Analytics Research
1. Faculty of Education, Yorkville University, United Kingdom
2. Information and Communications Technologies, National Research Council of Canada, Canada
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