@incollection{fazeli_applying_2017,
	address = {Alberta, Canada},
	edition = {1},
	title = {Applying {Recommender} {Systems} for {Learning} {Analytics} {Data}: {A} {Tutorial}},
	isbn = {978-0-9952408-0-3},
	url = {http://solaresearch.org/hla-17/hla17-chapter1},
	abstract = {This chapter provides an example of how a recommender system experiment can be conducted in the domain of learning analytics (LA). The example study presented in this chapter followed a standard methodology for evaluating recommender systems in learning. The example is set in the context of the FP7 Open Discovery Space (ODS) project that aims to provide educational stakeholders in Europe with a social learning platform in a social network similar to Facebook, but unlike Facebook, exclusively for learning and knowledge sharing. In this chapter, we describe a full recommender system data study in a stepwise process. Furthermore, we outline shortcomings for data-driven studies in the domain of learning and emphasize the high need for an open learning analytics platform as suggested by the SoLAR society.},
	booktitle = {The {Handbook} of {Learning} {Analytics}},
	publisher = {Society for Learning Analytics Research (SoLAR)},
	author = {Fazeli, Soude and Drachsler, Hendrik and Sloep, Peter},
	editor = {Lang, Charles and Siemens, George and Wise, Alyssa Friend and Gaševic, Dragan},
	year = {2017},
	pages = {234--240}
}