Learning Analytics Workshop White Paper

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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. Outline several experiments and opportunities for utilizing network and complexity theory to demonstrate progress.

Abstract: The educational ecosystem, government, academia and workforce are a complex, temporal adaptive system. A deep understanding of this structure (exponential complexity encountered as knowledge is distributed through the organization) is required in order to transcend subcultural boundaries and meld a unified framework. (Lemke, J.L. and N. Sabelli, 2008).

Audience:' Higher Ed Educators & Administration, Corporate Learning, Industry Leaders, Non-profit educational leaders

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

Why is our current education system not meeting our needs? The educational system that produces the critical talent for the United States’ future security and prosperity is complex …It is composed of systems nested within subsystems, each operating on multiple temporal scales where observable causality is often hidden… Changes to this system emerge through evolutionary processes and are encumbered by complex physical, behavioral, and social phenomena as well as competing interests. Faced with overwhelming complexity in the learning ecosystem (including shifting economic, political, and business environments), we tend to focus primarily on issues that are relevant to the cultural boundaries within which we operate… (Stephens & Richey, 2011)

2. Understanding networks

a. Complexity Networks
b. Two and Multi-sided networks

3. How is learning a complex system?

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

4. Imagining a computational model of learning

a. Skills maps and learning pathways as objective domains for real-world competencies
b. DNA of effective learning design
c. Sophisticated learner profiles
d. Instruction or Learning 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)
d. efficacy of Adaptive Learning
e. efficacy of Blended/Hybrid classroom's.
f. Efficacy of multi-sided networks.

6. Leadership in CAS

a. Experiments underway in Universities (Blended classroom..)
b. Apprenticeship programs and their value

7. The drawbacks

a. Some people just like instructivism
b. Are our educational institutions and professors set up to acquire new competencies and to teach job skills?

8. What’s next?

a. How do we begin changing a system?
1. Non-profit for overall system guidance and collaboration, including skills maps and accreditation based on learner outcomes.
i. Academic and corporate partnerships
ii. Universal "learning record" Life long learner profile
iii. Advocating for complexity models