Using learning environments as a metaphor for educational change

Purpose – The purpose of this paper is to show that the online learning environment can be seen as the means by which higher education can explore the challenges and opportunities raised by online and digital society. Design/methodology/approach – The paper argues that the online learning environment can be seen as a metaphor for how universities respond to the requirements and challenges of the digital age. Current learning management systems (LMSs) are examined, and compared with the values found in web 2.0 and social media. Current thinking on pedagogy for online learning is then examined. The SocialLearn project at the Open University in the UK is then explained, which seeks to create a disaggregated, decentralised, social system for learners. Findings – The conclusion from the analysis is that there is a conflict between the centralised learning management system (LMS) and the requirements of online pedagogy. The traditional LMS can be seen as embodying the wrong metaphor, that of the traditional classroom. The paper concludes by arguing that such learning environments will be more useful to higher education in coming to understand its response to many of the changes being seen in society, which are facilitated by the new technologies. Originality/value – The paper provides a framework for considering LMSs and their relation to universities and pedagogy, and an argument for the promotion of more decentralised systems

Handbook of Econometrics

This paper outlines recently developed techniques for estimating the primitives needed to empirically analyze equilibrium interactions and their implications in oligopolistic markets. It is divided into an introduction and three sections; a section on estimating demand functions, a section on estimating production functions, and a section on estimating "dynamic" parameters (parameters estimated through their implications on the choice of controls which determine the distribution of future profits). The introduction provides an overview of how these primitives are used in typical I.O. applications, and explains how the individual sections are structured. The topics of the three sections have all been addressed in prior literature. Consequently each section begins with a review of the problems I.O. researchers encountered in using the prior approaches. The sections then continue with a fairly detailed explanation of the recent techniques and their relationship to the problems with the prior approaches. Hopefully the detail is rich enough to enable the reader to actually program up a version of the techniques and use them to analyze data. We conclude each section with a brief discussion of some of the problems with the more recent techniques. Here the emphasis is on when those problems are likely to be particularly important, and on recent research designed to overcome them when they are.