Institutional future of Social Media in Higher Ed

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ianeboston

An exploration of the impact of social media on higher education and the

Tecnologia Negócios

Institutional Future
of Social Media
Dr Ian Boston
University of Cambridge

Institutional Context

• We are people not
buildings
• Too late?
• Club Penguin,
Moshi Monsters,
GenY

Outsource
• “Does Kmart ask
MySpace to tell it
what its
customers
buying patterns
are?”
• “Is LinkedIn the
way to build
great researcher
teams?”

Harvard Catalyst

http://catalyst.harvard.edu/home.html

Data Gathering

• Ofﬁcial Sources,
SIS, HR, Finance
• Collaboration
trails
• Starving for raw
data

Outsource by default

• Nature abhors
a vacuum
• Order or
Desire

Its Our Domain

• Surprisingly deep
• Borrow, beg, buy
• Head start

The task ahead

• Encourage
Connections
• Data gathering
• Academic Apps

Encourage Connections

Proﬁles Collaboration
Groups

Colleagues
Publications Sharing

Data Gathering

Proﬁles Collaboration
Groups
Activity Data
Colleagues
Publications Sharing

Sakai 3 App

Proﬁles

Publications
Sakai 3 App

Sakai 3 App

Groups
Sakai 3 App

Colleagues
Sakai 3 App

Sakai 3 App

Sharing
Collaboration Sakai 3 App

Sakai Apps Framework
Sakai 3 App

Sakai 3 App

Google Gadget
Academic Apps

Google Gadget

Google Gadget
Gadget

Activity Data
Container

Google Gadget

Improving Experience

User Event
Activity Records

Enhanced Ofﬂine
Data Analysis

Important Maxims

• Unbiased Observations.
• Original data.
• Analysis without expectations
• Uncertainty.

Example: Identifying
Interests
User authors Content
content Created Event

User sees related Content created event, is picked
content, which authors up by a processor, analyses and
are connected, which are categorises the content based on
not, offered to expand word used, generating links to
network of collaborators. similar content.

Example: Identifying
Behaviour
Usage And Normal Expectation
Student A
Activity
throughout
the system

Student has been identiﬁed as
having erratic engagement with
peers and the course. Mentors,
alerted that there may be an
underlying cause.

5 years hence

• TL&R tools built to support the needs
of the members of all institutions.

• Increasing availability of data sets
within institutions to further the
understanding of TL&R.
• Less resource spent on the mundane,
more on the unique.
Thank you - Questions ?

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Institutional future of Social Media in Higher Ed

1. Institutional Future of Social Media Dr Ian Boston University of Cambridge

2. Social Media ?

3. Some rights reserved by Maschinenraum

4. Institutional Context • We are people not buildings • Too late? • Club Penguin, Moshi Monsters, GenY

5. Outsource • “Does Kmart ask MySpace to tell it what its customers buying patterns are?” • “Is LinkedIn the way to build great researcher teams?”

6. Harvard Catalyst http://catalyst.harvard.edu/home.html

7. Institutional Memory

8. Analysis and Actions

9. Realistic Scale

10. Data Gathering • Ofﬁcial Sources, SIS, HR, Finance • Collaboration trails • Starving for raw data

11. Outsource by default • Nature abhors a vacuum • Order or Desire

12. Its Our Domain • Surprisingly deep • Borrow, beg, buy • Head start

13. The task ahead • Encourage Connections • Data gathering • Academic Apps

14. Encourage Connections Proﬁles Collaboration Groups Colleagues Publications Sharing

15. Data Gathering Proﬁles Collaboration Groups Activity Data Colleagues Publications Sharing

16. Sakai 3 App Proﬁles Publications Sakai 3 App Sakai 3 App Groups Sakai 3 App Colleagues Sakai 3 App Sakai 3 App Sharing Collaboration Sakai 3 App Sakai Apps Framework Sakai 3 App Sakai 3 App Google Gadget Academic Apps Google Gadget Google Gadget Gadget Activity Data Container Google Gadget

17. Improving Experience User Event Activity Records Enhanced Ofﬂine Data Analysis

18. Important Maxims • Unbiased Observations. • Original data. • Analysis without expectations • Uncertainty.

19. Example: Identifying Interests User authors Content content Created Event User sees related Content created event, is picked content, which authors up by a processor, analyses and are connected, which are categorises the content based on not, offered to expand word used, generating links to network of collaborators. similar content.

20. Example: Identifying Behaviour Usage And Normal Expectation Student A Activity throughout the system Student has been identiﬁed as having erratic engagement with peers and the course. Mentors, alerted that there may be an underlying cause.

21. 5 years hence • TL&R tools built to support the needs of the members of all institutions. • Increasing availability of data sets within institutions to further the understanding of TL&R. • Less resource spent on the mundane, more on the unique. Thank you - Questions ?

Institutional future of Social Media in Higher Ed

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