Giorgio Maria Di Nunzio

Department of Information Engineering, University of Padua, Padua, Italy


Educational data mining (EDM) is an emerging discipline that studies methods for exploring the data that come from educational environments and uses those methods to better understand students and the environments in which they learn, as discussed by Baker and Yacef (2009, 1). A recent survey by the US Department of Education (2012) gives a detailed overview of how EDM is currently applied in institutions, what kinds of questions it can answer, and its relationships with other research fields like learning analytics (LA). In general, EDM is more focused on the process of breaking down learning into small components that can be analyzed and then adapted into software designed for students rather than understanding entire systems and supporting human decision‐making (Siemens & Baker, 2012). Student learning data collected by online learning systems are then explored to develop predictive models by applying EDM methods that classify data or find relationships. Indeed, computer‐supported interactive learning methods and tools have opened up opportunities to collect and analyze student data, to discover patterns and trends in those data, and to make new discoveries and test hypotheses about how students learn. LA is a closely related field with more emphasis on simultaneously investigating ...

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