Hot spot

The changing boundaries of the MOOC identity

4 ottobre 2016

Redazione Federica.euRedazione, Notizie dallo staff

Pressure has been mounting over the last three years to introduce innovative forms of online delivery and methodology and create avant-garde learning content in the form of MOOCs. MOOCs initiatives have mushroomed in diverse areas, including the creation of learning content; the development of purpose-built platforms and aggregators; the definition of quality assessment systems; advanced tools and services provision; knowledge delivery and educational publishing. The MOOCs explosion has also opened the way to debate – at times heated – regarding traditional Higher education business models, the importance of educational technology in self-learning, the role and status of teachers, and issues regarding assessment and accreditation where more personalized courses are concerned.

The MOOC landscape is fast changing.  As the MOOC market continues to grow, and new players continue to enter, do Universities still hold a dominant role? How is their strategy evolving? The original MOOC academic format seems to be under pressure from various sides to better adapt to the web environment. But also to respond to a more professional – and less scholarly – target. The changing boundaries of the MOOC identity is the main theme of the Anacapri 2016 International Colloquium.

The development of Federica, by far the largest single-University platform in Italy and one of the largest in Europe, has been possible thanks to generous EU funding and an innovative cultural vision. More than 9 years in the making, Federica is not just a course provider, but a comprehensive educational environment, with special emphasis on interface design and guided access to on-line reference sources. With 300 courses published on its original platform, and more than 5 million accesses in 2015.  Federica Weblearning is the first Italian University Center fully devoted to innovation, experimentation and dissemination of multimedia distance learning.


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