A Semantic Approach to Mobile Learning Analytics

Mobile learning has reached a considerable level of maturity in recent years, and its role is widely acknowledged in school contexts, university, vocational training, formal and non-formal learning settings, and more generally as an opportunity for lifelong learning. Despite its maturity, evaluation of mobile learning remains an open research issue, especially as regards the activities that take place outside the classroom. In this context, Learning Analytics can provide answers, and offer the appropriate tools to enhance mobile learning experiences. In recent years Learning Analytics has been highly successful in different contexts, but mobile learning exhibits particular characteristics related to the technologies used, student mobility, the possibility of having localized data and information and the social dynamics that characterize the context in which learning takes place. In this paper we propose an innovative approach to support analytics of learners' activities in a mobile learning setting based on the Semantic Web paradigm and on the semantic relationships expressed in the Linked Open Data cloud. MeLOD, a mobile environment for learning with Linked Open Data, is also introduced as a demonstrator for the ideas illustrated in the paper. Potentials and pitfalls of the proposed approach, both for teachers and learners, are reported in the conclusions.