Echo chamber for your own predilections

Building on a previous post where I describe the lack of community in our NGL course and in response to David Jones’ post about Understanding and using the idea of “network learning”, I want to explore using a network for learning rather than a learning community. I now understand that by looking at membership, roles and structures of a learning communities that communities are just a component of networked learning. It is clear that it is not just about people or technology. I want to move to “…the understanding of learning networks as assemblages of tools, artefacts, people, ideas and practices” (Carvalho & Goodyear, 2014: p.14).

But first…

an easy explanation of network versus community. In a community, everyone interacts with everyone else, has a role to play and there are expectations. In a network however “A interacts with B, B interacts with C, C does not necessarily know A” (Carvalho & Goodyear, 2014: p.10). The network is created by us and we create its identity. In the following video, The Machine is Us/ing Us by Michael Wesch, this is very powerfully represented.

What I find most fascinating is that apart from this course each participant is creating their own viewpoint about networked learning and seeking out the information that is relevant to them. Their identity is prescribing which information, people, artefacts, tools people people connect with and what they create. The quote take from Vaughan (1986: p.71) by David Jones in Week 6, really struck  a cord with me:

[In the situations we deal with as humans, we use] a frame of reference constructed from integrated sets of assumptions, expectations and experiences. Everything is perceived on the basis of this framework. The framework becomes self-confirming because, whenever we can, we tend to impost it on experiences and events, creating incidents and relationships that conform to it. And we tend to ignore, misperceive, or deny events that do not fit it. As a consequence, it generally leads us to what we are looking for. This frame of references is not easily altered or dismantled, because the way we tend to see the world is intimately linked to how we see and define ourselves in relation to the world. Thus, we have a vested interest in maintaining consistency because our own identity is at risk.

Thus each person connects, follows people and information that fits in with the way you think, that confirm your point of view. Extreme examples of this might be political extremists, nazis, vegans, religious fanatics who are unable to accept contrary views, absorb new ideas or dismiss/negate points of view contrary to their own. So even though a shared knowledge network is created with knowledge distributed across different minds this is inhibited by the tendency for people to interact with people who are like themselves (Carvalho & Goodyear, 2014), creating an echo chamber for your own predilections.

Echo chamber for your own predilections

But there is more to this than immediately meets the eye. You might think, I wouldn’t do that, I would explore other points of views, I want to be challenged, I want to know what is important even if it does not confirm my point of view. So on top of the fact that we search out our own network, the network also chooses what is relevant for us. Eli Pariser’s view, in a New York Times article (2011):

Personalization on the Web, he says, is becoming so pervasive that we may not even know what we’re missing: the views and voices that challenge our own thinking.

The web is filtering our view of the world by what ‘it’ sees as relevant to us. The following TED talk by Eli Pariser describes this very clearly:

If we are constraint by the personalised filtering that is done automatically on the web and that we do ourselves, how do we create our own learning network that goes beyond relevance and adds important, uncomfortable, challenging, different points of view to our mix? How do we teach this to our students? The more varied network ties we have to information, people, organisations, the more knowledge we accumulate, distribute and recommend that could lead to transformative change.

As a teacher…

I am an educational designer, I help to create things that support the learning of others. So in my role ‘as a teacher’ [an educational designer], I need to design for networked learning. This is why my main purpose is to acquire knowledge in how to design effective learning networks that takes into account the echo chamber of your own predilections and tries to prevent this by design. A quote from de Botton in The Architecture of Happiness (2006: p.20) as quoted by Carvalho & Goodyear (2014) describes best how I hope a learning network will be designed:

Architecture may well possess moral messages; it simply has no power to enforce them. It offers suggestions instead of making laws. It invites, rather than orders, us to emulate its spirit and cannot prevent its own abuse.

 


References

de Botton, A. (2006) The Architecture of Happiness. London: Hamish Hamilton.

Carvalho, L., & Goodyear, P. (2014). The architecture of productive learning networks. New York: Routledge.

Singer, N. (2011) The Trouble With the Echo Chamber Online. Retrieved from http://www.nytimes.com/2011/05/29/technology/29stream.html.

TED Talks (2011) Eli Pariser: Beware online “filter bubbles”. Retrieved from https://www.youtube.com/watch?v=B8ofWFx525s.

Vaughan, D. (1986) Uncoupling: Turning Points in Intimate Relationships. Melbourne: Oxford University Press.

Wesch, (2007) The Machine is Us/ing Us. Retrieved from https://www.youtube.com/watch?v=NLlGopyXT_g.

2 thoughts on “Echo chamber for your own predilections

  1. The transition from seeing NGL as a community, to perhaps a network, and then onto frames had me wondering if perhaps your initial view of it as a community might be coming from your existing frames of reference. The network perspective – as outlined above – is so different from community that the measures of community aren’t appropriate and are always going to find flaws, because a network works on a different set of assumptions.

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