This link has been bookmarked by 16 people . It was first bookmarked on 18 Nov 2007, by Clay Burell.
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25 Oct 09
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Networks avoid informational cascades - and hence, are reliable - only if they satisfy the following four criteria (known collectively as 'the semantic condition'):
- Diversity - Did the process involve the widest possible spectrum of points of view? Did people who interpret the matter one way, and from one set of background assumptions, interact with people who approach the matter from a different perspective?
- Autonomy - Were the individual knowers contributing to the interaction of their own accord, according to their own knowledge, values and decisions, or were they acting at the behest of some external agency seeking to magnify a certain point of view through quantity rather than reason and reflection?
- Openness - Is there a mechanism that allows a given perspective to be entered into the system, to be heard and interacted with by others?
- Connectivity - Is the knowledge being produced the product of an interaction between the members, or is it a (mere) aggregation of the members' perspectives? A different type of knowledge is produced one way as opposed to the other. Just as the human mind does not determine what is seen in front of it by merely counting pixels, nor either does a process intended to create public knowledge.
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28 Sep 08
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26 Mar 08
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17 Mar 08
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28 Nov 07
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Learners do not 'receive' information which they then 'store', they gain experiences which, over time, result in the formation of neural structures. To learn is to instantiate patterns of connectivity in the brain. These connections form as a result practice and experience. They are not constructed; a student does not 'make meaning' or 'construct meaning', as sometimes depicted in the literature. Connections are grown, not created; meaning is, therefore, grown, not constructed.
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To *teach* is to model and demonstrate, and to "learn* is to practice and reflect. To teach is, essentially, to provide or to make possible the having of experiences by students. These models and demonstrations, by virtue of their structural similarities with other models and demonstrations, allow students to form relevant networks of connections. Students then actively begin to learn by practicing - first by imitating, then later by creating something novel.
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25 Nov 07
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18 Nov 07
Clay Burell
To *teach* is to model and demonstrate, and to "learn* is to practice and reflect. To teach is, essentially, to provide or to make possible the having of experiences by students. These models and demonstrations, by virtue of their structural similaritie-
To *teach* is to model and demonstrate, and to "learn* is to practice and reflect. To teach is, essentially, to provide or to make possible the having of experiences by students. These models and demonstrations, by virtue of their structural similarities with other models and demonstrations, allow students to form relevant networks of connections. Students then actively begin to learn by practicing - first by imitating, then later by creating something novel. The point of practize is to improve performance by receiving feedback. They then reflect on what they have experience and practiced - this is (somewhat) analagous to the Boltzmann mechanism.
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Networks can be trusted, as James Surowiecki shows in The Wisdom of Crowds. "Many cognitive, coordination and cooperation problems are best solved by canvassing groups (the larger the better) of reasonably informed, unbiased, engaged people. The group's answer is almost invariably much better than any individual expert's answer, even better than the best answer of the experts in the group." It is this wisdom we see not only in the sudience picking the right answer in "Who Wants to Be a Millionaire" but also in picking stocks in the stock market and picking governments in elections.
However, not just any network can be trusted. Networks can sometimes run away with themselves - for example, if one person in a community catches a fatal virus, it can spread to every other member, and kill the entire community. Such phenomena are known as cascade phenomena. In the realm of information networks (such as the brain, or a community) these are known as informational cascades. They are like 'jumping to a conclusion' or 'groupthink'.
Networks avoid informational cascades - and hence, are reliable - only if they satisfy the following four criteria (known collectively as 'the semantic condition'):
- Diversity - Did the process involve the widest possible spectrum of points of view? Did people who interpret the matter one way, and from one set of background assumptions, interact with people who approach the matter from a different perspective?
- Autonomy - Were the individual knowers contributing to the interaction of their own accord, according to their own knowledge, values and decisions, or were they acting at the behest of some external agency seeking to magnify a certain point of view through quantity rather than reason and reflection?
- Openness - Is there a mechanism that allows a given perspective to be entered into the system, to be heard and interacted with by others?
- Connectivity - Is the knowledge being produced the product of an interaction between the members, or is it a (mere) aggregation of the members' perspectives? A different type of knowledge is produced one way as opposed to the other. Just as the human mind does not determine what is seen in front of it by merely counting pixels, nor either does a process intended to create public knowledge. -
6. Examples
How does the discussion above help us understand about and design learning technologies? They show us not only what to design but also help us understand what would be a better (or worse) design.
We begin with the principle, 'To *teach* is to model and demonstrate, and to "learn* is to practice and reflect.' This gives us a set of four types of things to create:
- Things that model - such as the wiki, concept maps, diagram tools such as gliffy, video / 2L 3D representation, and the like
- Things that demonstrate - such as code libraries, image samples, articles describing thought processes, case studies and stories
- Things that help us practice - such as games, sandboxes, job aides, simulations and ebnvrionments
- Things that help us reflect - such as presentations and seminars, blogs, wikis, discussion groups, and other ways of sharing and communicating
For any given application in each of the four categories, we can apply the remaining principles to provide an assessment of it likely effectiveness.
For example, consider the wiki. Does it support network learning? Yes - it provides examples to follow, allows correction and criticism, and rethinking and rewriting. Does it support personal learning? Yes, it engages interaction. It supports a genuine voice, experiences, opinions. It is a simple and consistent interface. It is (mostly) accessible where and when I need it.
Is the wiki reliable? Do I have diversity of sources? Yes - but only if there is a threshold number of users. Are the sources autonomous? They can be. And wikis support connectedness with links, etc, and can be open to a large number of contributors. These considerations argues against closed or private wikis, but suggest that wikis can be useful for large groups.
As another example, consider image libraries. They provide examples to follow, but our study suggests that image libraries should have (like Flickr communication channels, ratings and reviews, and ways to link images, such as tags. And an image library will be 'reliable' if it allows contributions from numerous photographers. We also see that we want people to have individual identities on Flickr, rather than just contributing to a pool, to preserve autonomy and diversity.
As a third example, consider Second Life. We can see why people are attracted to it. It allows us to create examples to follow, corrections and criticisms. It engages interaction and supports a genuine voice. But we also see weaknesses. Is Second Life a good place for reflection? There are limits on reusing what other people have created. It is also semantically weak. There is only one world, not a large number of diverse worlds. Autonomy is limited - you can't even pick your own name - and there are questions about governance. There is connectedness, through slurls, but it is not clear that it is an open platform.
7. Concluding Remarks
The purpose of this paper was to describe how network learning works and to show how an understanding of network learning can inform the design and evaluation of online learning applications.
Admittedly, there is room for debate and discussion regarding the nature and precise statement of the principles. What remains, however, is that the model of learning as a personal and a network activity provides us with concrete insights into the sort of learning environments that are most likely to be successful online.
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16 Nov 07
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13 Nov 07
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31 Oct 07
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18 Oct 07
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michelemmartinNetworked learning from Stephen Downes
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