R became a major tool in higher education. Cloud Computing technologies reached maturity, public clouds such as Amazon EC2 and Azure can rapidly provision, on demand and for any web user, large compute and storage capabilities. A Cloud-based collaborative IDE for R enabling new ways of using R in the classroom and within distributed scientific Collaborations will be presented. Unlike classic Software-as-a-Service tools, the collaborative IDE's capabilities are described and built on real-time according to the user needs. A teacher can prepare quickly and autonomously the course-specific environments needed by the students and grant them remote access to those environments within which an R session can be shared the way a document is shared in Google Docs: The teacher can share a session with students and explain new concepts and models interactively in the classroom or remotely in a distant learning context. Students can share their sessions and solve problems collaboratively. The public cloud resources costs are hidden to the students by allowing them to access temporarily shared teacher-owned resources or using tokens that a teacher can generate using the department's cloud account. Those tokens can also be associated to online-courses involving R exercises. The same scenarios described above can involve researchers within a Collaboration, various domain-specific customization of the collaborative IDE are available and a Scientific Gateways rapid prototyping and delivery framework is included.
Users are expected to have working familiarity with R and to have an up-to-date installation of R.
Resources about the tutorial will be available prior to the tutorial at the following address: http://www.elastic-r.net.
Karim Chine (2010). Learning math and statistics on the cloud, towards an EC2-based Google Docs-like portal for teaching / learning collaboratively with R and Scilab, icalt, pp.752-753, 2010 10th IEEE International Conference on Advanced Learning Technologies.
Karim Chine (2010). Open science in the cloud: towards a universal platform for scientific and statistical computing. In: Furht B, Escalante A (eds) Handbook of cloud computing, Springer, USA, pp 453–474. ISBN 978-1-4419-6524-0.