Introduction: Future pathways for science policy and research assessment: metrics vs peer review, quality vs impact

Copyright @ 2007 Beech Tree PublishingThe idea for this special issue arose from observing contrary developments in the design of national research assessment schemes in the UK and Australia during 2006 and 2007. Alternative pathways were being forged, determined, on the one hand, by the perceived relative merits of 'metrics' (quantitative measures of research performance) and peer judgement and, on the other hand, by the value attached to scientific excellence ('quality') versus usefulness ('impact'). This special issue presents a broad range of provocative academic opinion on preferred future pathways for science policy and research assessment. It unpacks the apparent dichotomies of metrics vs peer review and quality vs impact, and considers the hazards of adopting research evaluation policies in isolation from wider developments in scientometrics (the science of research evaluation) and divorced from the practical experience of other nations (policy learning)

Towards a Global Learning Commons: ccLearn

Though open educational resources (OER) promise to transform the conditions for teaching and learning worldwide, there are many barriers to the full realization of this vision. Among other things, much of what is currently considered "free and open" is legally, technically, and/or culturally incompatible. Herein, the authors give a brief history of open education, outline some key problems, and offer some possible solutionsThis article was originally published in Educational Technology 4(6). Nov-Dec 2007

Local search: A guide for the information retrieval practitioner

There are a number of combinatorial optimisation problems in information retrieval in which the use of local search methods are worthwhile. The purpose of this paper is to show how local search can be used to solve some well known tasks in information retrieval (IR), how previous research in the field is piecemeal, bereft of a structure and methodologically flawed, and to suggest more rigorous ways of applying local search methods to solve IR problems. We provide a query based taxonomy for analysing the use of local search in IR tasks and an overview of issues such as fitness functions, statistical significance and test collections when conducting experiments on combinatorial optimisation problems. The paper gives a guide on the pitfalls and problems for IR practitioners who wish to use local search to solve their research issues, and gives practical advice on the use of such methods. The query based taxonomy is a novel structure which can be used by the IR practitioner in order to examine the use of local search in IR

Introduction to the Issue: Assessment and Feedback in the 21st Century: Lessons Learnt from the Past and Future Directions

open3siSpecial issue editorialopenSerbati, Anna; Grion, Valentina; Brown, SallySerbati, Anna; Grion, Valentina; Brown, Sall