The TIILA project wants to give agency to the users by allowing them to decide on the Learning Analytics that will be done with their data. With this outspoken approach, we intend to raise the user’s data literacy, teach them privacy awareness and enlighten them about dangers and opportunities of learning analytics. The German sociologist Niklas Luhmann defined trust as a way to cope with risk, complexity, and a lack of system understanding. Following this, we believe that users will at some point feel the urge to investigate the Learning Analytics system in place. We want to allow for this by providing automated trust-building features. In our opinion, such features encourage a gain of trust in the system leading to a raise in commitment and engagement with Learning Analytics. The hypothesis of this project is that this will ultimately improve the overall impact of the Learning Analytics.

The infrastructure consists of three core engines and possibly a variety of research project engines docking in.
Literature
  • Ciordas-Hertel, Schneider, Ternier, Drachsler, Towards a Trusted Big Data Learning Analytics Infrastructure, In Review, J.UCS Journal of Universal Computer Science
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