Tuesday, October 7, 2008

CCK08 - week 3 – Properties of networks

Networks for Newbies .ppt
Stephen Downes: Learning Networks: Theory and Practice .ppt and audio
George Siemens Introduction to Networks
Other useful resources for this week:
CCK08: Valdis Krebs on Networks
Intentionalism and Meaning – Stephen Downes
Emergent Networks PDF – Valdis Krebs

Optional Reading
Introduction to social network methods - Chapter 7 - Connection and Distance – Robert A Hanneman (Dept of Sociology, Uni of California, Riverside and Mark Riddle (Dept of Sociology, Uni of Nthn Colorado)

Notes from readings:

CCK08: Valdis Krebs on Networks
“… what you know depends on who you know … “

“You have to have certain skills and intelligence, etc., but that’s not sufficient. You need to be able to connect, who to rely on, who to work with. And that’s not just who you know, but who knows you.

Often, your success in a company depends on your visibility in a network.

So, I think that learning is social and learning is iterative, so those with a better network have the potential to learn better and more.”

You are how you know

Networks for Newbies .ppt Barry Wellman
“A network is more than the sum of its ties” … “that form distinct analyzable patterns”
“To discover how “A”, who is in touch with “B” and “C”, is affected by the relation between “B” & “C”.
“Networks are a major source of social capital: mobilizable in themselves and from their contents”
“The networked society”
“We dream in graphs: We analyse in matrices”
“How does information flow through a village?”
“People link Groups: Groups link people”

PaulPam2 - Social networks... does anyone notice when people haven't placed a comment for a week or two weeks or even longer...... @PaulPam2 I think it would depend on the amount of people within the network and the contribution to the network by the person
Intentionalism and Meaning – Stephen Downes
“intentionalism - The thesis that all mental states are representational states. Specifically, raw feels and qualia, are said to have representational content.”
“Associationism - is at heart a theory of inference: Ideas, regarded rather as sensations or as mental images, were associated in the mind according to certain laws, mainly concerning contiguity and resemblance, and thereby led to further ideas, and to the functioning of mental life in general.
The position has resolved regarding the principles of association:
Aside from similarity and contiguity, other governing principles have been proposed to explain how ideas become associated with each other. These include temporal contiguity (ideas or sensations formed close together in time), repetition (ideas that occur together repeatedly), recency (associations formed recently are the easiest to remember), and vividness (the most vivid experiences form the strongest associative bonds).
I have advanced a position in my own work proposing four major principles of association:
resemblance - a.k.a. Hebbian associationism
contiguity or proximity - a.k.a. salience
feedback or back propagation
balance, or entropy aka Boltzmann mechanisms
”Realism - is essentially the thesis that there is some (external or underlying) reality to which all of our perceptions (statements, whatever) refer (or represent, whatever).
Pat Parslow’s statement reflects a commonly held belief: “Without the consensual reality of negotiated meaning, the network has little or no basis for its foundation - whilst the negotiation of that reality cannot occur without the network. The two are part and parcel of the same overall system.”
“Learning –
Pat Parslow says, ” Yes, learning is about growing our network, both internally in our brains (and bodies) and externally in terms of the connections we make through associating with others, but these are both intimately tied to negotiating the meaning of concepts with the external (and possibly internal?) networks.”
“Connectivism is a non-intentional theory of learning and knowledge.
What this means is that, in connectivism, learning is not about content. It is not about entering a certain representational state with respect to the world.
It allows - indeed, encourages - the idea that people may have different, and individual, accounts of the external world.
Which means that what is negotiated is not some set of statements about the nature of that world - not representational states, not meanings - but mechanisms for communication, protocols for interaction (which may indeed be, and probably are, understood differently by each person engaged in communication).”

Stephen Downes: Learning Networks: Theory and Practice .ppt and audio
Networks: Basic Elements
- Entities: the things that are connected, sends and received signals
- Connections: links between entities – links, channel, my be physical or virtual
- Signals: message sent between entities – physical, meaning not inherent in signal, must be interpreted

Some Properties of Networks
- Density – how many other entities each entity is connected to
- Speed – how quickly a messages moves to an entity, can be measured in ‘hops’
- Flow – how much info an entity processes, includes messages sent, received plus transfers
- Plasticity – how frequently, connected created, abandoned

Network Design Principles
- specifies how networks differ from traditional learning
- the idea is that each principle confers an advantage over non-network systems
- can be used as a means of evaluating new technology

Centralised vs Decentralised networks
Distributed networks – reside in different physical locations, peer2peer/RSS, sharing not coping
Disintermediate – barrier between source & receiver ie editors/media, to manage flow of information, reduce volume of info, not the type of info
Disaggregated – units of content should be as small as possible – content not bundled, integration of new and old info, ie learning objects, smallest possible unit of instruction
Dis-integrate – entities of a network are not ‘components’ of one another ie avoid ‘required’ software, message coded in a ‘common’ language,
Democratize – entities in a network are autonomous – freedom to connect, send, receive info, diversity important, control is impossible
Dynamize – a network is a fluid, changing entity, it is through the process of change that new knowledge is discovered
Desegregate – do not need learning-specific tools/process and is a part of living/work/play – the network is everywhere

Elements of Network Semantics:
- Context; Relevance; Patterns; Memory, Stability, Weighting

Knowledge is shared understanding

Connectivism: Network Pedagogy
- Network ‘Pragmatics’, how to use networks to support learning, distributed knowledge, recognizes explicitly that what we ‘know’ is embedded in our network of connections to each other/resources/the world

Principles of Connectivism
- learning is a process of connecting entities
- nurturing and maintaining connections is needed to facilitate continual learning
- ability to see connections between fields, ideas and concepts is a core skill
- capacity to know more is more critical than what is currently known
- decision-making is itself a learning process

Stop trying to do online what you do in the classroom …. It’s a different world online

Introduction to social network methods - Chapter 7 - Connection and Distance – Robert A Hanneman (Dept of Sociology, Uni of California, Riverside and Mark Riddle (Dept of Sociology, Uni of Nthn Colorado)

“Highly connected individuals may be more influential, and may be more influenced by others.”

How a network is connected determines the breadth of the network – if the network is closely linked to each other – the network is ‘limited’ – disbursely connected networks means access to larger amounts of information

How influential you are in a network is determined by who you are actually connected to, ie other influential nodes within your network

“In a sense, actors with many ties (at the center of a network) and actors at the periphery of a network (few ties) have patterns of behavior that are more constrained and predictable. Actors with only some ties can vary more in their behavior, depending on to whom they are connected.”

Being connected to others is simply not enough – it’s what you do with the information you receive and whether you send information or more it on – by understanding networks we can have greater influence in the network, understand how to ‘work/build the network’ and who to connect with within the network – direct connection is not always required – but who your network is connected to – single or bi-directional connectivity paths

Networks can be mathematically depicted through charts and graphs – would be interesting to map your own network –

Walks, trails, paths – distances and directions between connections – one way, both ways, passed onwards/forward within the network

George Siemens Introduction to Networks
Networks are everywhere and all we need is an eye for them.
Those who are most easily influenced are those who contributed to the development of new trends.
Networks are an underlying structure that are exhibited in all aspects of our learning at any level that we might consider (social, conceptual, neural).
Are our educational systems designed to appropriately take advantage of network opportunities – curriculum too linear – education being a one way flow.
Connectivism - Assertion that knowledge is distributed and learning is the process of creating those networks – aided through the use of technology – learning networked – knowledge distributed

No comments: