Posts Tagged ‘Learning’

CCK08 – Week 6: Living and learning with complexity

October 19, 2008

In previous blogs we have affirmed that connectivsm states that knowledge is in the connections and in the networks (neural, conceptual and external).  Connections together with nodes are the fundamental particles of networks. Networks have structure and dynamic. For change we require structures that also change. And networks are these kinds of structures.

Learning is complex to be confined or reduced to a mechanistic model. Instead, we use networks and ecologies as a model for learning, knowledge and managing the complexity of the environment where learning takes place. Ecologies and networks are reflective of chaos and complexity theories main tenets and provide a suitable replacement for the current classroom and hierarchical model of education. This complex environment we find in education explains the emergency of learners understanding, group formation, advancement of a discipline, etc.

 

Science of complexity

Cynefin framework is a sense making device to make sense of the complexities based in three ontological states: order, complexity and chaos. Cynefin is a Welsh word for place for multiple affiliations. All human interactions are strongly influenced and frequently determined by patterns of our multiple experiences both personal and collective expressed in stories. And patterns are what we use to order the world and make sense of things in complex situations.

Cynefin framework is not about “objective” reality but about perception and understanding. It helps us to think about the ways in which different people might be perceiving the same situation. And this understanding can be used to one’s advantage. Even dough we may define the ordered and un-ordered domains, things are both ordered and un-ordered at once, because both intertwine and interact. But a fifth domain still exists, disorder.

 

Un-ordered domain

 

Weak central connection

Ordered domain

 

Strong central connection

 

Strong connection between components

 

 

Complex relationships

 

Knowable causes and effects

 

 

Weak connections between components

 

 

Chaos

 

 

Known causes and effects

Cynefin Framework

In the realm of complexity chaos, unpredictability that occurs in systems that obey predictable laws as defined by Strogratz  may be seen as “deterministic unpredictability” where a type of order is discovered.

Complexity theory is concerned with open, non-linear systems. Non-linear means ecologically embedded, non-additive, inseparable, heterogeneous, interactive, asynchronous, lagged or delayed. In complexity the autonomy of agents adds an additional level of complexity. In learning we have individual parts, dynamic interaction, criticality of feedback in influencing adaptation and openness

Complexity provides a perspective on learning based on non-linearity of thought and variation as a source and outcome of thinking (Bloom). Networks and the web are non linear environment. So we should benefit of complexity and non-linearity in online learning or learning in general. There is no escape. The system behavior brings stability, emergence and adaptation. Structure is linked with behavior.

Emergence is the outcome (understanding) that arises from different agents interacting and producing unanticipated outcomes. Emergence may manifest through collective self-organization, un-programmed functionality, interactive complexity or/and incompressible unfolding

For an autonomously developing system to acquire knowledge about a realistically complex environment, it must be able to interact extensively with that environment. Such interactions require very sophisticated sensors, which bring information into the system so that the system can test its understanding of the outside world.

We may be able to develop a Global Brain, an intelligent system that links all cognitive and physical resources in the most efficient way possible. Let’s leave for another discussion what efficiency may imply in this context.

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