Big data has seen significant returns across numerous sectors including health care, public sector administration, manufacturing, retail, and global personal data location tools. Educational data mining and learning analytics are starting to become more refined, and in the 2012 NMC Horizon report, it was classified as a mid term horizon field.
Even dating back to the 1940’s, Paul Lazarsfeld started to define learning analytics devices as tools that would help shape and improve the education system and their operation. So the question remains, what do we know about educational data mining and its relevance to education? There are five different types of educational data. Identity, user interaction, inferred content, system-wide and inferred student data are integral components to learning analytics systems.
The most basic systems use simple user interaction data to provide recommendations to its users to direct learning. The most advanced systems use all of this data together combined with complex machine learning algorithms to create probabilistic graphical models to provide more meaningful advice.
Learning analytics systems allows teachers to quantify what they have learned on a more personalized and adaptive fashion. They create spaces for collective in dwelling that can be incorporated into numerous different learning platforms. Whether it is just an educational app, blended learning environment, or gamified learning experience, learning analytics can be incorporated into numerous learning environments.
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An Infographic by Open Colleges