data intensive university

41
The data-intensive university George Siemens, PhD July 27, 2012 Presented to: American Association of State and College Universities San Francisco, CA

Upload: gsiemens

Post on 06-May-2015

29.279 views

Category:

Education


1 download

DESCRIPTION

Presented t

TRANSCRIPT

Page 1: Data Intensive University

The data-intensive universityGeorge Siemens, PhD

July 27, 2012Presented to:

American Association of State and College UniversitiesSan Francisco, CA

Page 2: Data Intensive University

Assumptions

Page 3: Data Intensive University
Page 4: Data Intensive University
Page 5: Data Intensive University

American intelligence communities are interested in your YouTube video, flickr uploads, tweets -- even your online book purchases -- and for over a year they've been laying down some serious cash to get a better look at all of them.

Page 6: Data Intensive University
Page 7: Data Intensive University

“…probably indicates that these sectors face strong systemic barriers to increasing productivity”

Page 8: Data Intensive University

Kron, et al (2011)

Page 9: Data Intensive University

“higher education finds itself on the verge of diving deeply into the analytical end of the education transformation pool”

Wagner & Ice 2012

Page 10: Data Intensive University

“Analytics, and the data and research that fuel it, offers the potential to identify broken models and promising practices, to explain them, and to propagate those practices.”

Grajek, 2011

Page 11: Data Intensive University
Page 12: Data Intensive University

http://www.dataqualitycampaign.org/

A different way of thinking and functioning

Page 13: Data Intensive University

What is a data-intensive university?

Page 14: Data Intensive University

“A university where staff and students understand data and, regardless of its volume and diversity, can use it and reuse it, store and curate it, apply and develop the analytical tools to interpret it.”

Page 15: Data Intensive University

Siemens, Long, 2011. EDUCUASE Review

Page 16: Data Intensive University

Limited efficiency and productivity gains through piecemeal solutions

Page 17: Data Intensive University
Page 18: Data Intensive University
Page 19: Data Intensive University
Page 20: Data Intensive University
Page 21: Data Intensive University
Page 22: Data Intensive University
Page 23: Data Intensive University
Page 24: Data Intensive University

We collect enough data. We need to focus on connecting.

Multiple data sources:

Social mediaUniversity help resourcesLMSStudent information systemCourse progression, etc

Page 25: Data Intensive University

Challenges: Broadening scope of data capture

- data outside of the current model of LMS - sociometer: Choudhury & Pentland (2002)

- classroom/library/support services,- quantified self

Timeliness of data (real-time analytics)

Page 26: Data Intensive University
Page 27: Data Intensive University
Page 28: Data Intensive University
Page 29: Data Intensive University
Page 30: Data Intensive University
Page 31: Data Intensive University
Page 32: Data Intensive University
Page 33: Data Intensive University
Page 34: Data Intensive University
Page 35: Data Intensive University

Principles of a systems-wide analytics tool

1. Algorithms should be open, customizable for context2. Students should see what the organization sees3. Analytics engine as a platform: open for all researchers and organizations to build on4. Specific analytics strategies and tools: APIs5. Integrate and connect with existing open tools6. Modularized and extensible

Page 36: Data Intensive University
Page 37: Data Intensive University

37

Page 38: Data Intensive University

Siemens, Long, 2011. EDUCAUSE Review

Page 39: Data Intensive University

http://edfuture.net/

October 8-November 16, 2012

Page 40: Data Intensive University

http://lakconference.org

Page 41: Data Intensive University

gsiemens @gmailTwitterSkypeFBWherever

www.elearnspace.org

www.connectivism.ca

www.learninganalytics.net