PLEM: a Web 2.0 driven Long Tail aggregator and filter for e-learning
Datos de la obra:
Tipo de obra: Article (academic)
Categorías:e-Learning and Instructional Technology | Education
The Personal Learning Environment (PLE) driven approach to learning suggests a shift in emphasis from a teacher driven knowledge-push to a learner driven knowledge-pull learning model. One concern with knowledge-pull approaches is knowledge overload. The concepts of collective intelligence and the Long Tail provide a potential solution to help learners cope with the problem of knowledge overload. Based on these concepts, the paper proposes a filtering mechanism that taps the collective intelligence to help learners find quality in the Long Tail, thus overcoming the problem of knowledge overload. We present theoretical, design, and implementation details of PLEM, a Web 2.0 driven service for personal learning management, which acts as a Long Tail aggregator and filter for learning. The primary aim of PLEM is to harness the collective intelligence and leverage social filtering methods to rank and recommend learning entities.
The PLEM project homepage can be accessed http://eiche.informatik.rwth-aachen.de:3333/PLEM/.