Learning and learning contexts are multifarious and notoriously difficult to understand in detail. Part of such complexity derives from the difficulty to observe the interactions between subjects, resources, instructors, and to some extent, the mental processes occurring in the participants. This complexity is present in many research areas such as psychology, social studies, etc. The widespread use of technology in education and its capacity to record virtually any electronic event has facilitated the emergence of a new interdisciplinary research space that combines knowledge from business intelligence, computer science, psychology, and pedagogy. This new discipline has been described as learning analytics (LA) or educational data mining (EDM) (Romero & Ventura, 2013; Siemens, 2012). While there are differences between LA and EDM, the fields are very closely related and aim to provide greater insight into the learning process through the analysis of user log data from educational technologies. Essentially, when interactions are mediated by technology, a detailed account of the events making up such interaction can be captured. In a simplistic view, these events may provide an initial insight into what is actually occurring in such context. In areas such as advertising, health, sports, or business intelligence, the use of data about customers has prompted a significant transformation (Lohr, 2012).