422,772 research outputs found

    Online and Distance Education for a Connected World

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    Learning at a distance and learning online are growing in scale and importance in higher education, presenting opportunities for large scale, inclusive, flexible and engaging learning. These modes of learning swept the world in response to the Covid-19 pandemic. The many challenges of providing effective education online and remotely have been acknowledged, particularly by those who rapidly jumped into online and distance education during the crisis. This volume, edited by the University of London’s Centre for Online and Distance Education, addresses the practice and theory of online and distance education, building on knowledge and expertise developed in the University over some 150 years. The University is currently providing distance transnational education to around 50,000 students in more than 180 countries around the world. Throughout the book, contributors explore important principles and highlight successful practices in areas including course design and pedagogy, online assessment, open education, inclusive practice, and enabling student voice. Case studies illustrate prominent issues and approaches. Together, the chapters offer current and future leaders and practitioners a practical, productive, practice- and theory-informed account of the present and likely future state of online and distance higher education worldwide

    S-TREE: Self-Organizing Trees for Data Clustering and Online Vector Quantization

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    This paper introduces S-TREE (Self-Organizing Tree), a family of models that use unsupervised learning to construct hierarchical representations of data and online tree-structured vector quantizers. The S-TREE1 model, which features a new tree-building algorithm, can be implemented with various cost functions. An alternative implementation, S-TREE2, which uses a new double-path search procedure, is also developed. S-TREE2 implements an online procedure that approximates an optimal (unstructured) clustering solution while imposing a tree-structure constraint. The performance of the S-TREE algorithms is illustrated with data clustering and vector quantization examples, including a Gauss-Markov source benchmark and an image compression application. S-TREE performance on these tasks is compared with the standard tree-structured vector quantizer (TSVQ) and the generalized Lloyd algorithm (GLA). The image reconstruction quality with S-TREE2 approaches that of GLA while taking less than 10% of computer time. S-TREE1 and S-TREE2 also compare favorably with the standard TSVQ in both the time needed to create the codebook and the quality of image reconstruction.Office of Naval Research (N00014-95-10409, N00014-95-0G57

    Stochastic network formation and homophily

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    This is a chapter of the forthcoming Oxford Handbook on the Economics of Networks

    Reviews

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    Managing Change in Higher Education: A Learning Environment Architecture by Peter Ford and eight other authors, Buckingham: Society for Research into Higher Education and the Open University Press, 1996. ISBN 0–335–19791–4. 161 pages, paperback. No price indicated

    Innovate Magazine / Annual Review 2010-2011

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    https://scholarworks.sjsu.edu/innovate/1001/thumbnail.jp
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