Context-aware recommender for mobile learners

Abstract

As mobile technologies become widespread, new challenges are facing the research community to develop lightweight learning services adapted to the learner’s profile, context, and task at hand. This paper attempts to solve some of these challenges by proposing a knowledge-driven recommender for mobile learning on the Semantic Web. The contribution of this work is an approach for context integration and aggregation using an upper ontology space and a unified reasoning mechanism to adapt the learning sequence and the learning content based on the learner’s activity, background, used technology, and surrounding environment. Whenever context change occurs, the system identifies the new contextual features and translates them into new adaptation constraints in the operating environment. The proposed system has been implemented and tested on various mobile devices. The experimental results show many learning scenarios to demonstrate the usefulness of the system in practice.