@incollection{kop_critical_2017,
	address = {Alberta, Canada},
	edition = {1},
	title = {A {Critical} {Perspective} on {Learning} {Analytics} and {Educational} {Data} {Mining}},
	isbn = {978-0-9952408-0-3},
	url = {http://solaresearch.org/hla-17/hla17-chapter1},
	abstract = {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 empiri- cal 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.},
	booktitle = {The {Handbook} of {Learning} {Analytics}},
	publisher = {Society for Learning Analytics Research (SoLAR)},
	author = {Kop, Rita and Fournier, Helene and Durand, Guillaume},
	editor = {Lang, Charles and Siemens, George and Wise, Alyssa Friend and Gaševic, Dragan},
	year = {2017},
	pages = {319--326}
}