Linked Data is a set of principles and technologies aimed at using the architecture of the web to share, expose and integrate data in a global, collaborative space. This tutorial intends to provide Learning Analytics practitioners with the basic knowledge and skills required to exploit the new possibilities offered by linked data, especially through exploring the wealth of data sources already available in the linked data cloud. We will therefore introduce the basic technologies and practices generally associated with linked data, including graph-based data modelling with RDF and relevant vocabularies, data discovery on the linked data cloud and the use of linked data endpoints (with SPARQL). Since the focus of the tutorial is on the concrete use of these technologies and practices within a Learning Analytics scenario, a large part of the sessions will be dedicated to hands-on exercises with data and use cases of relevance to Learning Analytics.
In addition, the tutorial will be used as a channel to present initial outcomes of the LinkedUp project, like the LinkedUp data pool and the LinkedUp Evaluation Framework, to compare the outcomes of different Learning Analytics projects. Participants to the tutorial will be encouraged to push further their ideas regarding the possible applications of Linked Data in Learning analytics scenarios through collaborating with members of LinkedUp and participating to the LinkedUp Challenge: the application development competition organized by the project. These particular activities will be concretely materialized through the inclusion as key sessions in the tutorial of activities around the LinkedUp-‐supported “LAK Data Challenge”, as well as interactive brainstorming sessions around possible use cases for linked data in Learning Analytics scenarios, and their possible realisation.