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So, connective knowledge is knowledge OF the connections that exist in the world. It is knowledge about how such connections are created, and what impact, or effect, such a system of connections has.

So we have two types of connective knowledge, the knowledge that we have OF networks, that we obtain by looking at networks, and knowledge that is created and stored BY networks in the world.<br><br>Summary: Connective knowledge is both:<br>- knowledge OF networks in the world<br>- knowledge obtained BY networks

Summary:<br><br>Active participation in the network:<br>- as a node in the network, by participating in society<br>- as a whole network, by perceiving with the brain (the neiural network)<br>Reflective participation in the network:<br>- by observing society as a whole<br>- by reflecting on our mental states and processes

However, nobody would assume that in order to know that wood one has to know exactly every tree, one by one, its shape, age and location. Every plant. Every leave of every plant. Every animal and where every animal is and what every animal is doing at any instant. Every stone. Every particle.

We argue that learning occurs in networks, and therefore, that the properties of successful networks are also the properties of successful learning environments. We don't 'apply' this in any strict sense - we would never force people to be connectivists. Indeed, within the learning environment, we believe there should be diversity; we believe people should be free to choose their own form of learning.

To me, <span style="font-style: italic;">far more</span> complex - and insightful - forms of reasoning are being created through the interplay among thousands, or millions, of individual content elements. Where each content element may by itself appear to be <span style="font-style: italic;">simple</span>, it is the interconnections between them that creates a much more complex, deep, and <span style="font-style: italic;">rich</span> tapestry of meaning, <span style="font-style: italic;">far more</span> than could be created merely using linguistic devices.

It is substantially <span style="font-style: italic;">harder</span> to work with the disorder and complexity we see within a connectivist network. Because linguistic (syntactical and semantical) descriptions of the concepts and entities in such a network just barely touch the surface, and students must therefore immerse themselves in the process of reasoning in such a system, rather than merely reading about it.

Is the term Instructional Designer a dead description, title, category or classification?<br><br>In light of web or learning 2.0, there are theories that in fact a better name would be an <span style="font-weight: bold; font-style: italic;">information and instruction architect</span>.

<p>Downes’ and Siemens’ discussions shed new light on fundamental concepts, such as rules versus patterns, complicated vs. complex, equivalence vs. similarity, and coping with ambiguity and uncertainty. And these consideration render many entrenched practices of the entire knowledge industry questionable.</p> <p>All these aspects have one thing in common: that they can be illustrated by the neuronal <strong>metaphor</strong>, the metaphor of a network with nodes and connections, where</p> <blockquote><p><em>“Not all connections are of equal strength in this metaphor”</em> (<a href="http://en.wikipedia.org/wiki/Connectivism_%28learning_theory%29">Wikipedia</a>)</p></blockquote>

So, connectivism and its neuronal connections metaphor, allow to distinguish more clearly between two types of knowledge, one of which is the more adequate one for coping with complexity and uncertainty: <em>connective</em> knowledge.