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Difference between revisions of "Learning Analytics Workshop White Paper"

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{{Learning Analytics Workshop Whitepaper}}
 
{{Learning Analytics Workshop Whitepaper}}
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'''Complexity Science: Envisioning a future model of learning''' (just a sample title as place holder)<br>
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''Goal of the paper:'' Detail what is needed to shift our current models of education from industrial models to ones rooted in complexity science. <br>
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''Audience:''' Educators/trainers, corporate/higher ed<br>
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''Length:'' ~10-15 pages <br>
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Deadline: Oct 31 for Draft discussion at Sante Fe Institute. Will then be re-structured and shared as a document publicly by all attendees of the workshop (i.e. they’ll all sign our “call to action” after helping to revise and update the paper).<br>
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Sample layout:<br>
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1. Introduction:<br>
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::a. What are complex systems?<br>
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::b. Why is our current education system not meeting our needs<br>
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::::i. Lifelong learning<br>
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::::ii. Learning as a social and interactive process<br>
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::::iii. Blurring boundaries of learning<br>
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2. Understanding networks<br>
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3. How is learning a complex system?<br>
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:: a. Design <br>
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:: b.Teaching<br>
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:: c. multiple -recombinations of ideas/social capital etc.<br>
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4. Imagining a computational model of learning<br>
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:: a. Curriculum is computed at the point of need<br>
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:: b. Sophisticated learner profiles<br>
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5. What do we know about research<br>
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:: a. Network models (connectivism)<br>
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:: b. Social structures and social capital<br>
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:: c. Idea generation and knowledge building (Bereiter...Pentland)<br>
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6. Leadership in CAS<br>
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7. The drawbacks<br>
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:: a. Some people just like instructivism<br>
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8. What’s next? <br>
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:: a. How do we begin changing a system?<br>
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:::: i. Academic and corporate partnerships<br>
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:::: ii. Life long learner profile<br>
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:::: iii. Advocating for complexity models<br>

Revision as of 20:41, 10 October 2016

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Complexity Science: Envisioning a future model of learning (just a sample title as place holder)

Goal of the paper: Detail what is needed to shift our current models of education from industrial models to ones rooted in complexity science.

Audience:' Educators/trainers, corporate/higher ed

Length: ~10-15 pages

Deadline: Oct 31 for Draft discussion at Sante Fe Institute. Will then be re-structured and shared as a document publicly by all attendees of the workshop (i.e. they’ll all sign our “call to action” after helping to revise and update the paper).

Sample layout:

1. Introduction:

a. What are complex systems?
b. Why is our current education system not meeting our needs
i. Lifelong learning
ii. Learning as a social and interactive process
iii. Blurring boundaries of learning

2. Understanding networks
3. How is learning a complex system?

a. Design
b.Teaching
c. multiple -recombinations of ideas/social capital etc.

4. Imagining a computational model of learning

a. Curriculum is computed at the point of need
b. Sophisticated learner profiles

5. What do we know about research

a. Network models (connectivism)
b. Social structures and social capital
c. Idea generation and knowledge building (Bereiter...Pentland)

6. Leadership in CAS
7. The drawbacks

a. Some people just like instructivism

8. What’s next?

a. How do we begin changing a system?
i. Academic and corporate partnerships
ii. Life long learner profile
iii. Advocating for complexity models