Semantic Recommendation of Information Sources for Lifelong Learning

Abstract

Lifelong learning (LLL) has been debated in educational environments for more than a decade, but with the proliferation of mobile and wireless communication technologies and pervasiveness of educational environments, it has penetrated into our everyday lives and changed our perception on formal education. If we accept that LLL will have a bigger role in traditional education, and remove barriers between formal and informal learning, then we may open a range of new possibilities for modern learners, who decide what, when, why and how they wish to learn. In this paper we address the problem of choosing suitable learning sources in various situations we may encounter in LLL. We propose a semantic recommendation of learning sources, based on OWL/SWRL enabled computations for the purpose of supporting learners in lifelong learning environments. We use the example of learners in the healthcare domain in order to demonstrate both: the availability of online information and learning sources which support lifelong learning in healthcare, and the way we can address the needs of individual learners who are in a situation to learn informally. We focus on OWL/SWRL enabled computations because they (a) secure semantic interpretation of environments where lifelong learning takes place and (b) guarantee reasoning which results in recommendations of suitable learning sources in a particular situation in LLL.