Let's meet: integrating social and learning worlds

The widespread welcome to the social web in recent years has led to the emergence of a new source of user information which is a result of users' everyday activities and contributions on social networks. Leveraging this information within learning environments bears beneficial opportunities that can i) bring the educational (what learners learn in educational settings) and social (what they perform within their social networks) worlds closer, and ii) support further recommendation and personalization services based on more 'real' data about learners. This information, however, is neither explicitly available nor formally represented on the social web paradigm, an attribute of the social web regarded as being a "walled garden". In this paper, we propose a framework that leverages semantic web technologies to locate and extract such data, originating from multiple resources on the social web, to support advanced learning services such as personalized peer recommendation.