Learning Analytics Workshop White Paper: Difference between revisions
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{{Learning Analytics Workshop Whitepaper}} | {{Learning Analytics Workshop Whitepaper}} | ||
'''Complexity Science: Envisioning a future model of learning''' (just a sample title as place holder)<br> | |||
''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> | |||
''Audience:''' Educators/trainers, corporate/higher ed<br> | |||
''Length:'' ~10-15 pages <br> | |||
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> | |||
Sample layout:<br> | |||
1. Introduction:<br> | |||
::a. What are complex systems?<br> | |||
::b. Why is our current education system not meeting our needs<br> | |||
::::i. Lifelong learning<br> | |||
::::ii. Learning as a social and interactive process<br> | |||
::::iii. Blurring boundaries of learning<br> | |||
2. Understanding networks<br> | |||
3. How is learning a complex system?<br> | |||
:: a. Design <br> | |||
:: b.Teaching<br> | |||
:: c. multiple -recombinations of ideas/social capital etc.<br> | |||
4. Imagining a computational model of learning<br> | |||
:: a. Curriculum is computed at the point of need<br> | |||
:: b. Sophisticated learner profiles<br> | |||
5. What do we know about research<br> | |||
:: a. Network models (connectivism)<br> | |||
:: b. Social structures and social capital<br> | |||
:: c. Idea generation and knowledge building (Bereiter...Pentland)<br> | |||
6. Leadership in CAS<br> | |||
7. The drawbacks<br> | |||
:: a. Some people just like instructivism<br> | |||
8. What’s next? <br> | |||
:: a. How do we begin changing a system?<br> | |||
:::: i. Academic and corporate partnerships<br> | |||
:::: ii. Life long learner profile<br> | |||
:::: 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
- i. Lifelong learning
- a. What are complex systems?
2. Understanding networks
3. How is learning a complex system?
- a. Design
- b.Teaching
- c. multiple -recombinations of ideas/social capital etc.
- a. Design
4. Imagining a computational model of learning
- a. Curriculum is computed at the point of need
- b. Sophisticated learner profiles
- a. Curriculum is computed at the point of need
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)
- a. Network models (connectivism)
6. Leadership in CAS
7. The drawbacks
- a. Some people just like instructivism
- 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
- i. Academic and corporate partnerships
- a. How do we begin changing a system?