Complexity Science and Social Media Learning

Rather than march into the Infinite, explore the finite in all directions -
Johann Goethe

Complexity Science is a relatively new study. I was introduced to it early this year at a NZCER sponsored seminar in Wellington on Complexity and Education. Some of this post is written from the notes I took, with a focus on learning through social media.

What is Complexity Science about? Complexity is difficult to define and there is no unified definition. Complexity research in education is a way of studying learning systems. The study of complexities is really about its objects of study rather than its modes of investigation. It’s to do with what's studied not how it's studied. Complexity Science is the study of complexity systems.

The presenters, Brent Davis and Denis Sumara, outlined in broad terms the properties of complexity systems with specific reference to education. They suggested that Complexity Science can and should be embraced by educators and educational researchers as a properly educational discourse.

Among important points emphasised were the distinctions between ‘complicated’ systems and ‘complex’ systems. When the properties of complicated entities and their complex counterparts are compared there are similarities and differences. I summarise some of those here:


In a complexity system there is no centre to speak of, nor is there a periphery as such. Complex systems are decentrally networked and are said to be decentralised.


If there exists anything identifiable that is like a centre in a complexity system it is wherever you happen to be within the system, either actually or metaphorically.

Social learning that develops through interaction between members of a community has qualities that categorise it as a complexity system. This relates to the learning that happens within the community in distinction to the learning achieved by an individual, though that too has properties that resemble those of a complexity system.

Learning systems:

adaptive

A complexity system has two key qualities that can be used as identifiers, one of which is described as adaptive. Davis describes this quality as being more like Darwinian evolution than Newtonian mechanics. A system with this quality resembles a living system more than it does a mechanical system, for it has the ability to change its structure - it can transform.

emergent

The other key quality is described as emergent. What arises within a complex system is a synergy from the activity between and within its individual components so that the effect of the whole is greater than the sum of the single effects from each of its parts.

Systems that exhibit these key qualities, adaptive and emergent, are described as ‘learning systems’.

We often read of how learners create their own version of knowledge through social interaction. In doing this, learners are creative in their own way. Creativity speaks through the spontaneity of emergence and through the power of self-organisation within a community. It invariably develops through evolving processes within a community.

Self-organising and self-governing:

Social learning activities tend to be emergent and self-organising. They also tend to be self-governing, in much the same manner as the movement of a shoal of fish. In this way, rational understandings can arise through social learning without the need for a central controller.

In studies of the way individuals integrate into a community, Etienne Wenger and Jean Lave report, “initially people have to join communities and learn at the 'periphery'. As they become more competent they move more to the ‘centre’ of the particular community. Learning is, thus, not seen as the acquisition of knowledge by individuals so much as a process of social participation.”

Self-transforming:

Social learning activities can be self-transforming, so that the way learning responds to a situation is determined by the learning itself, not the situation. Learning does not respond mechanically to a situation. Instead its response depends on previous learning and can be unpredictable as a result.

A simple and metaphorical example of this is what happens when a bull confronts a group of people walking through a field. The response of the group will depend not only on the previous experiences of the individuals but also on the previous experiences of the group itself.

Social learning does not operate in static situations. If interactive activity stops, the learning also stops.

Studies on virtual communities:

Recent studies on different types of virtual communities are significant in that driven, assigned working groups show less of the qualities of a complexity system than do virtual communities of practice (CoP). These findings are important to the application of learning through social media where efficiency and effectiveness are critical to success.

Assigned work teams:

Studies on assigned virtual work teams showed the same variations that are found in studies on groups in education. Patricia Sobrero reports that some groups “sometimes failed to develop the social capital needed for trust and joint decision-making. This led to increased competition and failure. However, the successful virtual teams were found to be high performing, and they provided value to organizations for learning, research, product development, and cost-benefit to the organization. Research found that teams often disbanded after the task or product was developed, and the learning, social capital, and intellectual capital built, dissipated.”

Communities of practice:

On the other hand, virtual communities of practice, where groups of people share a concern or passion for something they do, show a greater degree of permanence. Sobrero reports that they tend to be self-organised, self-governing and develop “social learning that crosses structures, cultures, organisations, time, and space to learn from each other, develop new knowledge, and continuously improve know-how”.

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