Towards Automatic Learning Content Sequence via Linked Open Data

The paradigm of lifelong learning supported by technology is redefining the way we learn as well as the way we search and consume the ever growing corpus of information available in the Web to acquire knowledge on a particular subject. This research addresses the problem of finding and organizing learning content to support self-directed learners in achieving a learning goal through the search, selection and sequencing of Web content that might or might not have been conceived as learning resources. We plan to build an automatic process driven by the knowledge available in datasets belonging to the Linked Open Initiative and open non-structured information such as courses syllabi and books table of contents. Our proposed service have two main components: (i) a graph of interrelated learning concepts from which is possible infer what concepts must be addressed first before others in the learning process (prerequisite relationships), and (ii) a component for the creation of learning resources sequences based on a learning goal and a learner profile.