Universities can offer eLearning 2.0 tools and services to learners while obtaining clear benefits from releasing the control over some learning content. This means a shift from the institution centred and monolithic model of traditional virtual learning environments (VLEs) to a more heterogeneous and open model. This article tries to plot an architecture to be put in practice by universities to give learners the control of their learning processes by using eLearning 2.0. We propose an institutionally powered personal learning environment (iPLE) that constitutes our vision of how Web 2.0 tools (blogs, wikis, starting pages), services (del.icio.us, Flickr, YouTube) and people arrangement and data sharing (social networking, learn-streaming) could be applied in an integrated manner to learning processes. First, this article justifies the suitability for a PLE in the context of eLearning 2.0 and European Higher Education Area. Second, an overview of the evolution from VLEs to PLEs is given, and different PLE approaches are reviewed. Third, the concept of an iPLE is presented, and a conceptual architecture for it is detailed. Finally, this article also describes the technological aspects of a prototype of iPLE Network designed for the University of the Basque Country, which will be used as a test-bed for a case study in the Faculty of Medicine.

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An interesting aspect in the current literature about learning networks is the shift of focus from the understanding of the “whole network” of a course to the examination of the “personal networks” of individual students. This line of research is relatively new, based on small‐scale studies and diverse analysis techniques, which demands for more empirical research in order to contextualize the findings and to meta‐analyze the research methods. The main objective of this paper is to review two research questions posed by a previous British Journal of Educational Technology contribution by Shane Dawson in order to know whether the differences in personal network composition impact on the performance of students. The two questions were defined by Dawson as follows: (1) Are there significant differences in personal network composition between high‐ and low‐performing students? and (2) Do high‐performing students have larger personal networks than their low‐performing peers? In addition, the “clustered graphs” method used in this study allows the inclusion of the structural analysis of personal networks. In doing so, a new research question is addressed: (3) Are there significant differences in personal network structure between high‐ and low‐performing students? This paper tries to answer these questions in the context of two undergraduate, inter‐university and fully online courses, and two different technology‐enhanced learning environments (a virtual learning environment and a personal learning environment) where interactions took place indirectly through shared resources. The results show that the network behaviors of high‐ and low‐performing students' are strongly correlated, and that high‐performing students developed larger personal networks than low performers.