August 30, 2009

Educause: Top-Ten IT Issues, 2009

Article


…a wealth of resources from Educause on their 2009 Top Ten IT Issues:

  1. Funding IT
  2. Administrative/ERP Information Systems
  3. Security
  4. Infrastructure / Cyberinfrastructure
  5. Teaching and Learning with Technology
  6. Identity/Access management
  7. Governance, Organization, and Leadership for IT
  8. Disaster Recovery / Business Continuity
  9. Agility / Adaptability / Responsiveness
  10. Learning Management Systems

August 24, 2009

H1N1 Scenarios

Note one of the recommendations:


"Expand or create distance learning programs for those who need to be isolated."


http://www.insidehighered.com/news/2009/08/21/h1n1


http://www.flu.gov/plan/school/higheredguidance.html

August 2, 2009

The End of Theory: The Data Deluge Makes the Scientific Method Obsolete

Just read an article that knocked me over!


Not only should we think about making all higher education data openly and visually accessible through tools like: http://www.gapminder.org


We also need to start thinking about how to leverage the computing power “in the cloud” to find new relationships, in higher ed data, and between higher ed data and others’ data, that we (a) have no theoretical models for and (b) may never have considered.


Read this excellent article (editor of Wired magazine) to get your head around it: http://www.wired.com/science/discoveries/magazine/16-07/pb_theory


This is a world where massive amounts of data and applied mathematics replace every other tool that might be brought to bear. Out with every theory of human behavior, from linguistics to sociology. Forget taxonomy, ontology, and psychology. Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity.


With enough data, the numbers speak for themselves.


The new availability of huge amounts of data, along with the statistical tools to crunch these numbers, offers a whole new way of understanding the world. Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all.


There's no reason to cling to our old ways. It's time to ask: What can science learn from Google?


Welcome to the 21st century … time to unlearn what we "know."

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