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This article aims to introduce the personal knowledge network (PKN) model as an alternative model to knowledge management (KM) and to discuss whether personal knowledge management (PKM) is better adapted to the demands of the new knowledge environments. The PKN model views knowledge as a personal network and represents a knowledge ecological approach to KM.

KM and PKM have attracted attention over the past two decades and are considered as important means to increase organizational and individual performance. In this article, the author reviews previous models of KM and PKM and explores their failure to address the problem of knowledge worker performance and to cope with the constant change and critical challenges of the new knowledge era. The author further highlights the crucial need for new KM models that have the potential to overcome the shortcomings of previous models. In light of these shortcomings, the article introduces and discusses the PKN model as an alternative model to KM and PKM that is better adapted to the demands of the new knowledge environments.

Unlike traditional KM/PKM models which view knowledge as a thing or process, the PKN model views knowledge as a personal network and represents a knowledge ecological approach to KM.

The article focuses on personal knowledge and the links to networks and knowledge ecologies in an innovative way for consideration within KM.

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.

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.

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.

The primary aim of PLEM is to harness the collective intelligence and leverage social filtering methods to rank and recommend learning entities.