Special Issue on knowledge transformation, design and technology

Does technology enhance learning? Despite the intervention of numerous researchers in both the learning and computing sciences, this is by far the most cited question in the field on the part of practitioners and policymakers alike, as well as a steady stream of researchers. Slowly but surely, however, the last decade or so has seen some acknowledgement of the question’s limitations, and its technocentrism in ascribing roles to technology that ignore context and activity. Attention has shifted to how technology mediates learning, how different technologies can shape learning and teaching in different ways, and how technology can be designed to influence the behaviours of teaching and teachers. The successor to the Teaching and Learning Research Programme, the Technology-Enhanced Learning Research Programme, funded jointly by the Economic and Social Research Council and the Engineering and Physical Sciences Research Council, flagged this awareness, stressing the need for interdisciplinary research that incorporates research on design, implementation and evaluation, thus recognizing explicitly how complex research in the field of TEL could and should be. As part of this work, attention is implicitly turning to the limitations of the enhancement metaphor, which encourages a belief that technology enters into the culture of learning like some addition of an ingredient to a recipe. More importantly, the metaphor of enhancement takes for granted the immutability of what is to be enhanced. It leads to a tendency to render knowledge itself as an invariant in the transformation of learning, asking whether the acquisition of some knowledge is easier, or faster, or more efficient with technology than without it. The limitations of this view lie in a failure to recognize that knowledge itself is mediated by the computer presence, and that understanding how knowledge is reshaped with technology, rethinking what can be learned, is every bit as important as asking how effectively a given piece of learning can be effected. This is what Seymour Papert calls the ‘10% challenge’ – the need for researchers to spend at least a little time – 10% will do – thinking about epistemology rather than only cognition or pedagogy. One approach is to stress metacognitive themes, looking for evidence that technology-based learning focuses attention on problem solving, the development of heuristic strategies or the affective dimensions of engagement and motivation. However, this is only a partial recognition of Papert’s challenge. The alternative is to consider the epistemological rather than the (meta-)cognitive, asking what happens to the knowledge at stake as it is transformed by the advent of new representational infrastructures made possible by technology; how technology can be transformational not only in terms of how knowledge is accessed, but also of how the landscape of knowledge is potentially reconfigured if technology is designed and deployed thoughtfully (Kaput et al. 2002; Wilensky & Papert 2010). An important corollary to this approach is that there is a reciprocal role for technology, in which the study of new representational forms offers a window (Noss & Hoyles 1996) on current practice and pedagogy. Any transformative technology takes time to adopt its own conventions and establishes its distinctive cultures – the much-cited ‘filming the stage’ of early film is a good example: the camera transformed the notion of performance, of direction, of plot and so on. The study of this transformation can tell us much about learning, how far established pedagogic practice – or even innovatory practice is in fact constrained and shaped by existing technology, and how new kinds of pedagogies become possible with new technologies (and, in fact, how old ones can be realistically implemented for the first time). A good example of this last point is in the first paper in the trio that make up the special issue. The Ensemble project sets out to explore the ways in which semantic web technologies could enable case-based learning, doi: 10.1111/j.1365-2729.2011.00466.x Editorial