PLEM: a Web 2.0 driven Long Tail aggregator and filter for e-learning

Purpose – The personal learning environment 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. The paper aims to address these issues. Design/methodology/approach – 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. Findings – The paper presents 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. Originality/value – The primary aim of PLEM is to harness the collective intelligence and leverage social filtering methods to rank and recommend learning entities.