ULearn is a meta engine that supports learners in achieving the objective of a “balanced learning” through personalized learning paths in the medical domain, based on specific user requirements. To this end, we consider four different learner categories: “basic”, “deep”, “wide” and “explorer”, each of them with different objectives and needs. ULearn suggests words correlated to each of those learner categories thus supporting the creation of medical learning activities tailored to the real user needs.
Researchers: Marco Alfano, Markus Helfert
Funding:EU H2020 (MSCA COFUND), SFI – Lero
Collaboration / Partners: University of Palermo, Italian National Council of Research