http://it.coe.uga.edu/itforum/paper92/paper92.html

In practice, these principles may be realized in the following design principles. It is worth noting at this juncture that these principles are intended to describe not only networks but also network learning, to show how network learning differs from traditional learning. The idea is that each principle confers an advantage over non-network systems, and that the set, therefore, may be used as a means of evaluating new technology. This is a tentative set of principles, based on observation and pattern recognition. It is not a definitive list, and indeed, it is likely that there cannot be a definitive list.

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We begin with the nature of a network itself. In any network, there will be three major elements:

–        Entities, that is, the things that are connected that send and receive signals

–        Connections, that is, the link or channel between entities (may be represented as physical or virtual)

–        Signals, that is, the message sent between entities. Note that meaning is not inherent in signal and must be interpreted by the receiver

In an environment of this description, then, networks may vary according to a certain set of properties:

–        Density, or how many other entities each entity is connected to

–        Speed, or how quickly a message moves to an entity (can be measured in time or ‘hops’)

–        Flow, or how much information an entity processes, which includes messages sent and received in addition to transfers of messages for other entities

–        Plasticity, or, how frequently connections created, abandoned

–        Degree of connectedness – is a function of density, speed, flow and plasticity

Given this description of networks, we can identify the essential elements of network semantics.

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First, context, that is, the localization of entities in a network. Each context is unique – entities see the network differently, experience the world differently. Context is required in order to interpret signals, that is, each signal means something different depending on the perspective of the entity receiving it.

Second, salience, that is, the relevance or importance of a message. This amounts to the similarity between one pattern of connectivity and another. If a signal creates the activation of a set of connections that were previously activated, then this signal is salient. Meaning is created from context and messages via salience.

Third, emergence, that is, the development of patterns in the network. Emergence is a process of resonance or synchronicity, not creation. We do not create emergent phenomena. Rather emergence phenomena are more like commonalities in patterns of perception. It requires an interpretation to be recognized; this happens when a pattern becomes salient to a perceiver.

Fourth, memory is the persistence of patterns of connectivity, and in particular, those patterns of connectivity that result from, and result in, salient signals or perceptions.

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Connective semantics is therefore derived from what might be called connectivist ‘pragmatics’, that is, that actual use of networks in practice. In our particular circumstance we would examine how networks are used to support learning. The methodology employed is to look at multiple examples and to determine what patterns may be discerned. These patterns cannot be directly communicated. But instances of these patterns may be communicated, thus allowing readers to (more or less) ‘get the idea’.

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For example, in order to illustrate the observation that ‘knowledge is distributed’ I have frequently appealed to the story of the 747. In a nutshell, I ask, “who knows how to make a 747 fly from London to Toronto?” The short answer is that nobody knows how to do this – no one person could design a 747, manufacture the parts (including tires and aircraft engines), take it off, fly it properly, tend to the passengers, navigate, and land it successfully. The knowledge is distributed across a network of people, and the phenomenon of ‘flying a 747’ can exist at all only because of the connections between the constituent members of that network.

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Or, another story: if knowledge is a network phenomenon, then, is it necessary for all the elements of a bit of knowledge to be stored in one’s own mind? Karen Stephenson writes, “I store my knowledge in my friends.” This assertion constitutes an explicit recognition that what we ‘know’ is embedded in our network of connections to each other, to resources, to the world. Siemens writes, “Self-organization on a personal level is a micro-process of the larger self-organizing knowledge constructs created within corporate or institutional environments. The capacity to form connections between sources of information, and thereby create useful information patterns, is required to learn in our knowledge economy.”

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This approach to learning has been captured under the heading of ‘connectivism’. In his paper of the same name, George Siemens articulates the major theses:

• Learning and knowledge rests in diversity of opinions.

• Learning is a process of connecting specialized nodes or information sources.

• Learning may reside in non-human appliances.

• Capacity to know more is more critical than what is currently known

• Nurturing and maintaining connections is needed to facilitate continual learning.

• Ability to see connections between fields, ideas, and concepts is a core skill.

• Currency (accurate, up-to-date knowledge) is the intent of all connectivist learning activities.

• Decision-making is in itself a learning process. Choosing what to learn and the meaning of incoming information is seen through the lens of a shifting reality. While there is a right answer now, it may be wrong tomorrow due to alterations in the information climate affecting the decision.

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Is this the definitive statement of network learning? Probably not. But it is developed in the classic mold of network learning, through a process of immersion into the network and recognition of salient patterns. What sort of network? The following list is typical of what might be called ‘network’ practices online (I won’t draw these out in detail because there are dozens of papers and presentations that do this):

Practice: Content Authoring and Delivery

–        Numerous content authoring systems on the web…

–        Weblogs – Blogger, Wordpress, LiveJournal, Moveable Type, more

–        Content Management Systems – Drupal, PostNuke, Plone, Scoop, and many more…

–        Audio – Audacity – and audioblogs.com – and Podcasting

–        Digital imagery and video – and let’s not forget Flickr

–        Collaborative authoring – Writely, Hula, the wiki

Practice: Organize, Syndicate Sequence, Deliver

–        Aggregation of content metadata – RSS and Atom, OPML, FOAF, even DC and LOM

–        Aggregators – NewsGator, Bloglines – Edu_RSS

–        Aggregation services – Technorati, Blogdex, PubSub

–        More coming – the Semantic Social Network

Practice: Identity and Authorization

–        A raft of centralized (or Federated) approaches – from Microsoft Passport to Liberty to Shibboleth

–        Also various locking and encryption systems

–        But nobody wants these

–        Distributed DRM – Creative Commons, ODRL…

–        Distributed Identification management – Sxip, LID…

Practice: Chatting, Phoning, Conferencing

–        Bulletin board systems and chat rooms, usually attached to the aforementioned content management systems such as Drupal, Plone, PostNuke, Scoop

–        Your students use this, even if you don’t: ICQ, AIM, YIM, and some even use MSN Messenger

–        Audioconferencing? Skype…Or NetworkEducationWare…

–        Videoconferencing? Built into AIM… and Skype

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“What happens,” I asked, “when online learning ceases to be like a medium, and becomes more like a platform? What happens when online learning software ceases to be a type of content-consumption tool, where learning is "delivered," and becomes more like a content-authoring tool, where learning is created?”

The answer turns out to be a lot like Web 2.0: “The model of e-learning as being a type of content, produced by publishers, organized and structured into courses, and consumed by students, is turned on its head. Insofar as there is content, it is used rather than read— and is, in any case, more likely to be produced by students than courseware authors. And insofar as there is structure, it is more likely to resemble a language or a conversation rather than a book or a manual.”

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In the days since this shift was recognized a growing community of educators and developers has been gathering around a model of online learning typified by this diagram authored by Scott Wilson (and remixed by various others since then):

Figure 1: Future VLE

http://www.cetis.ac.uk/members/scott/blogview?entry=20050125170206

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In an environment such as this, the nature of design changes. In a typical computer program, the design will be specified with an algorithm or flowchart. Software will be described as performing a specific process, with specified (and often controlled) inputs and outputs. In a distributed environment, however, the design is no longer defined as a type of process. Rather, designers need to characterize the nature of the connections between the constituent entities.

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What are the core principles that will characterize such a description? The internet itself illustrates a sound set of principles, grounded by two major characteristics: simple services with realistic scope. “Simple service or simple devices with realistic scope are usually able to offer a superior user experience compared to a complex, multi–purpose service or device.” Or as David Weinberger describes the network: small pieces, loosely joined.

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1. Effective networks are decentralized. Centralized networks have a characteristic ‘star’ shape, where some entities have many connections while the vast majority have few. This is typical of, say a broadcast network or the method of a teacher in a classroom. Decentralized networks, by contrast, form a mesh. The weight of connections and the flow of information is distributed. This balanced load results in a more stable network, with no single point of failure.

2. Effective networks are distributed. Network entities reside in different physical locations. This reduces the risk of network failure. It also reduces need for major infrastructure, such as powerful servers, large bandwidth, and massive storage. Examples of distributed networks include peer-to-peer networks, such as Kazaa, Gnutella and content syndication networks, such as RSS. The emphasis of such systems is on sharing, not copying; local copies, if they exist, are temporary.

3. Effective networks disintermediated. That is, they eliminate ‘mediation’, the barrier between source and receiver. Examples of disintermediation include the bypassing of editors, replacing peer review prior to publication with recommender systems subsequent to publication. Or of the replacement of traditional news media and broadcasters with networks of news bloggers. And, crucially, the removal of the intermediate teacher that stands between knowledge and the student. The idea is to, where possible, provide direct access to information and services. The purpose of mediation, if any, is to manage flow, not information, to reduce the volume of information, not the type of information.

4. In effective networks, content and services are disaggregated. Units of content should be as small as possible and content should not be ‘bundled’. Instead, the organization and structure of content and services is created by the receiver . This allows the integration of new information and services with the old, of popular news and services with those in an individual’s particular niche interests. This was the idea behind learning objects; the learning object was sometimes defined as the ‘smallest possible unit of instruction’. The assembly of learning objects into pre-packaged ‘courses’ defeats this, however, obviating any advantage the disaggregating of content may have provided.

5. In an effective network, content and services are dis-integrated. That is to say, entities in a network are not ‘components’ of one another. For example, plug-ins or required software to be avoided. What this means in practice is that the structure of the message is logically distinct from the type of entity sending or receiving it. The message is coded in a common ‘language’ where the code is open, not proprietary. So no particular software or device is needed to receive the code. This is the idea of standards, but where standards evolve rather than being created, and where they are adopted by agreement, not requirement.

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6. An effective network is democratic. Entities in a network are autonomous; they have the freedom to negotiate connections with other entities, and they have the freedom to send and receive information. Diversity in a network is an asset, as it confers flexibility and adaptation. It also allows the network as a whole to represent more than just the part. Control of the entities in a network, therefore, should be impossible. Indeed, in an effective network, even where control seems desirable, it is not practical. This condition – which may be thought of as the semantic condition – is what distinguishes networks from groups (see below).

7. An effective network is dynamic. A network is a fluid, changing entity, because without change, growth and adaptation are not possible. This is sometimes described as the ‘plasticity’ of a network. It is through this process of change that new knowledge is discovered, where the creation of connections is a core function.

8. An effective network is desegregated. For example, in network learning, learning is not thought of as a Separate Domain. Hence, there is no need for learning-specific tools and processes. Learning is instead thought of as a part of living, of work, of play. The same tools we use to perform day-to-day activities are the tools we use to learn. Viewed more broadly, this condition amounts to seeing the network as infrastructure. Computing, communicating and learning are not something we ‘go some place to do’. Instead, we think of network resources as similar to a utility, like electricity, like water, like telephones. The network is everywhere.

It should be noted that though some indication of the justification for these methodological principles has been offered in the list above, along with some examples, this list is in essence descriptive. In other words, what is claimed here is that successful networks in fact adhere to these principles. The why of this is the subject of the next few sections.

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But it should be self-evident that mere organization is not the only determinate of what constitutes, if you will, 'good' knowledge as opposed to 'bad' (or 'false') knowledge. Consider public knowledge. People form themselves into communities, develop common language and social bonds, and then proceed to invade Europe or commit mass suicide. Nor is personal knowledge any reliable counterbalance to this. People are as inclined to internalize the dysfunctional as the utile, the self-destructive as the empowering. Some types of knowledge (that is, some ways of being organized, whether socially or personally) are destructive and unstable.

These are examples of cascade phenomena. In social sciences the same phenomenon might be referred to as the bandwagon effect. Such phenomena exist in the natural world as well. The sweep of the plague through medieval society, the failure of one hydro plant after another, the bubbles in the stock market. Cascade phenomena occur when some event or property sweeps through the network. Cascade phenomena are in one sense difficult to explain and in another sense deceptively simple.

The sense in which they are simple to explain is mathematical. If a signal has more than an even chance of being propagated from one entity in the network to the next, and if the network is fully connected, then the signal will eventually propagate to every entity in the network. The speed at which this process occurs is a property of the connectivity of the network. In (certain) random and scale free networks, including hierarchal networks, it takes very few connections to jump from one side of the network to the other. Cascade phenomena sweep through densely connected networks very rapidly.

The sense in which they are hard to explain is related to the question of why they exist at all. Given the destructive nature of cascade phenomena, it would make more sense to leave entities in the network unconnected (much like Newton escaped the plague by isolating himself). Terminating all the connections would prevent cascade phenomena. However, it would also prevent any possibility of human knowledge, any possibility of a knowing society.

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Network Semantics and Connective Learning

If we accept that something like the network theory of learning is true, then we are faced with a knowledge and learning environment very different from what we are used to. In the strictest sense, there is no semantics in network learning, because there is no meaning in network learning (and hence, the constructivist practice of ‘making meaning’ is literally meaningless).

Traditionally, what a sentence ‘means’ is the (truth of falsity of) the state of the world it represents. However, on a network theory of knowledge, there is no such state of the world to which this meaning can be affixed. This is not because there is no such state of the world. The world could most certainly exist, and there is no contradiction in saying that a person’s neural states are caused by world events. However, it does mean that there is no particular state of the world that corresponds with (is isomorphic to) a particular mental state. This is because the mental state is embedded in a sea of context and presuppositions that are completely opaque to the state of the world.

How, then, do we express ourselves? How do we distinguish between true and false – what, indeed, does it even mean to say that something is true and false? The answer to these questions is going to be different for each of us. They will be embedded in a network of assumptions and beliefs about the nature of meaning, truth and falsity. In order to get at a response, therefore, it will be necessary to outline what may only loosely be called ‘network semantics’

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As we examine the emergentist theory of mind we can arrive at five major implications of this approach for educational theorists:

- first, knowledge is subsymbolic. Mere possession of the words does not mean that there is knowledge; the possession of knowledge does not necessarily result in the possession of the words (and for much more on this, see Michael Polanyi's discussion of 'tacit knowledge' in 'Personal Knowledge').

- second, knowledge is distributed. There is no specific 'mental entity' that corresponds to the belief that 'Paris is the capital of France'. What we call that 'knowledge' is (an indistinguishable) pattern of connections between neurons. See, for example, Geoffrey Hinton, 'Learning Distributed Representations of Concepts'.

- third, knowledge is interconnected. The same neuron that is a part of 'Paris is the capital of France' might also be a part of 'My dog is named Fred'. It is important to note that this is a non-symbolic interconnection - this is the basis for non-rational associations, such as are described in the recent Guardian article, 'Where Belief is Born'

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- fourth, knowledge is personal. Your 'belief' that 'Paris is the capital of France' is quite literally different from my belief that 'Paris is the capital of France'. If you think about it, this must be the case - otherwise Gestalt tests would be useless; we would all utter the same word when shown the same picture.

- fifth, what we call 'knowledge' (or 'belief', or 'memory') is an emergent phenomenon. Specifically, it is not 'in' the brain itself, or even 'in' the connections themselves, because there is no 'canonical' set of connections that corresponds with 'Paris is the capital of France'. It is, rather (and carefully stated), a recognition of a pattern in a set of neural events (if we are introspecting) or behavioural events (if we are observing). We infer to mental contents the same way we watch Donald Duck on TV - we think we see something, but that something is not actually there - it's just an organization of pixels.

This set of features constitutes a mechanism for evaluating whether a cognitivist theory or a connectivist theory is likely to be true. In my own mind (and in my own writing, as this was the subject of my first published paper, ‘Why Equi Fails’), the mechanism can be summed in one empirical test: context sensitivity.

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ith the nature of a network itself. In any network, there will be three major elements:

–        Entities, that is, the things that are connected that send and receive signals

–        Connections, that is, the link or channel between entities (may be represented as physical or virtual)

–        Signals, that is, the message sent between entities. Note that meaning is not inherent in signal and must be interpreted by the receiver

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If, as asserted above, what counts as knowledge of even basic things like the meanings of words and the cause of events is sensitive to context, then it seems clear that such knowledge is not a stand-along symbolic representation of that knowledge, since representations would not be, could not be, context sensitive. Rather, what is happening is that each person is experiencing a mental state that is at best seen as an approximation of what it is that is being said in words or experienced in nature, an approximation that is framed and indeed comprehensible only from which the rich set of world views, previous experiences and frames in which it embedded.

If this is the case, then the concepts of what it is to know and what it is to teach are very different from the traditional theories that dominate distance education today. Because if learning is not the transfer of mental contents – if there is, indeed, no such mental content that exists to be transported – then we need to ask, what is it that we are attempting to do when we attempt to teach and learn.

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In an environment of this description, then, networks may vary according to a certain set of properties:

–        Density, or how many other entities each entity is connected to

–        Speed, or how quickly a message moves to an entity (can be measured in time or ‘hops’)

–        Flow, or how much information an entity processes, which includes messages sent and received in addition to transfers of messages for other entities

–        Plasticity, or, how frequently connections created, abandoned

–        Degree of connectedness – is a function of density, speed, flow and plasticity

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