M.S. Vijay Kumar

  • Opening Up Education

    Opening Up Education

    The Collective Advancement of Education through Open Technology, Open Content, and Open Knowledge

    Toru Iiyoshi and M.S. Vijay Kumar

    Experts discuss the potential for open education tools, resources, and knowledge to transform the economics and ecology of education.

    Given the abundance of open education initiatives that aim to make educational assets freely available online, the time seems ripe to explore the potential of open education to transform the economics and ecology of education. Despite the diversity of tools and resources already available—from well-packaged course materials to simple games, for students, self-learners, faculty, and educational institutions—we have yet to take full advantage of shared knowledge about how these are being used, what local innovations are emerging, and how to learn from and build on the experiences of others. Opening Up Education argues that we must develop not only the technical capability but also the intellectual capacity for transforming tacit pedagogical knowledge into commonly usable and visible knowledge: by providing incentives for faculty to use (and contribute to) open education goods, and by looking beyond institutional boundaries to connect a variety of settings and open source entrepreneurs.

    These essays by leaders in open education describe successes, challenges, and opportunies they have found in a range of open education initiatives. They approach—from both macro and micro perspectives—the central question of how open education tools, resources, and knowledge can improve the quality of education. The contributors (from leading foundations, academic institutions, associations, and projects) discuss the strategic underpinnings of their efforts first in terms of technology, then content, and finally knowledge. They also address the impact of their projects, and how close they come to achieving a vision of sustainable, transformative educational opportunities that amounts to much more than pervasive technology.

    Through the support of the Carnegie Foundation for the Advancement of Teaching, an electronic version of this book is openly available under a Creative Commons license at The MIT Press Web site, http://mitpress.mit.edu.

    Contributors Richard Baraniuk, Randy Bass, Trent Batson, Dan Bernstein, John Seely Brown, Barbara Cambridge, Tom Carey, Catherine Casserly, Bernadine Chuck Fong, Ira Fuchs, Richard Gale, Mia Garlick, Gerard Hanley, Diane Harley, Mary Huber, Pat Hutchings, Toru Iiyoshi, David Kahle, M. S. Vijay Kumar, Andy Lane, Diana Laurillard, Stuart Lee, Steve Lerman, Marilyn Lombardi, Phil Long, Clifford Lynch, Christopher Mackie, Anne Margulies, Owen McGrath, Flora McMartin, Shigeru Miyagawa, Diana Oblinger, Neeru Paharia, Cheryl Richardson, Marshall Smith, Candace Thille, Edward Walker, David Wiley

    • Hardcover $25.95 £17.95
    • Paperback $20.95 £16.99


  • Advances in Distributed and Parallel Knowledge Discovery

    Advances in Distributed and Parallel Knowledge Discovery

    Hillol Kargupta and Philip Chan

    This book presents introductions to DKD and PKD, extensive reviews of the field, and state-of-the-art techniques.

    Foreword by Vipin Kumar Knowledge discovery and data mining (KDD) deals with the problem of extracting interesting associations, classifiers, clusters, and other patterns from data. The emergence of network-based distributed computing environments has introduced an important new dimension to this problem—distributed sources of data. Traditional centralized KDD typically requires central aggregation of distributed data, which may not always be feasible because of limited network bandwidth, security concerns, scalability problems, and other practical issues. Distributed knowledge discovery (DKD) works with the merger of communication and computation by analyzing data in a distributed fashion. This technology is particularly useful for large heterogeneous distributed environments such as the Internet, intranets, mobile computing environments, and sensor-networks.When the data sets are large, scaling up the speed of the KDD process is crucial. Parallel knowledge discovery (PKD) techniques addresses this problem by using high-performance multiprocessor machines. This book presents introductions to DKD and PKD, extensive reviews of the field, and state-of-the-art techniques.

    Contributors Rakesh Agrawal, Khaled AlSabti, Stuart Bailey, Philip Chan, David Cheung, Vincent Cho, Joydeep Ghosh, Robert Grossman, Yi-ke Guo, John Hale, John Hall, Daryl Hershberger, Ching-Tien Ho, Erik Johnson, Chris Jones, Chandrika Kamath, Hillol Kargupta, Charles Lo, Balinder Malhi, Ron Musick, Vincent Ng, Byung-Hoon Park, Srinivasan Parthasarathy, Andreas Prodromidis, Foster Provost, Jian Pun, Ashok Ramu, Sanjay Ranka, Mahesh Sreenivas, Salvatore Stolfo, Ramesh Subramonian, Janjao Sutiwaraphun, Kagan Tummer, Andrei Turinsky, Beat Wüthrich, Mohammed Zaki, Joshua Zhang

    • Paperback $12.75 £9.99