The main objective for Learning Analytics is to unveil so far hidden information out of the educational data to gain new insights and prepare those for the different educational stakeholders (learners, teachers, parents, and managers).
This new kind of information can support individual learning, enhance teaching quality, but also improve organisational knowledge management processes and system administration. It involves various pre-processing steps from basic information models to structure data such as data harvesting, storing, cleaning, anonymisation, analysis, mining and visualisation. Open Data movements such as Linked Data provide sematic structures that are important for those pre-processing steps. It contributes to make the results of Learning Analytics research and big data initiatives more comparable and contributes to a knowledge base about the effects of Learning Analytics in K12, Higher Education and the corporate sector.