LEARNING NETWORKS

A network requires at minimum two elements: NODES and CONNECTIONS.

 

a NODE is any element that can be connected to any other element. A CONNECTION is any type of link between nodes. Once a network has been established, the flow of information can move from one domain to another with relative ease.  The stronger the connection between nodes, the more rapidly information will flow. The information system underlying network creation includes: Data – a raw element or small meaning neutral element.

Information – data with intelligence applied. Knowledge – information in context and internalized. Meaning –implications of knowledge.  Virtually any element that we can scrutinize or experience can become node. Thoughts, feelings, interactions with others, and new data and information can be seen as nodes. The aggregation of these nodes results in a network. Networks can combine to form larger networks (each node in a larger network can be a network of nodes itself).  A community, for example is a rich learning network of individuals who in themselves are completed learning networks. Nodes are characterized by a general sense of autonomy/independence. A node may exist within a network, even if it is not strongly connected. Each node has the capacity to function in its own manner. The network itself is the aggregation of nodes, but can only exert limited influence on the nature of each node in the network. While networks are simple in nature, numerous elements impact the flow and dynamics of connection creation.   Elements and characteristics of a network include:

1. Content (data or information)

2. Interaction (tentative connection forming)

3. Static nodes (stable knowledge structure)

4. Dynamic nodes (continually changing based on new information and data)

5. Self-updating nodes (nodes which are tightly linked to their original information source, resulting in a high level of currency (i.e. up to date)

6. Emotive elements (emotions that influence the prospect of connection and hub formations).

Connections can be formed based on a number of factors as follows:

1. Motivation

Motivation occurs when there is;

•        Existence of clear goal

•        Relevance

•        Sense of competence

•        Satisfaction

2. Exposure

This can make a node to in popularity an so more nodes link (or will like) to it. Exposure can make a node have more potentials.

3. Patterning

Equal pattern mean similar and similarities usually creates connection very easily

4. Experience

Learners who graduate from university or college often have the information and knowledge nodes, but connections between themselves do not form fully until the learner is active within his/her field (gain experience).

Meaning creation in learning network

Meaning in a network is created through the encoding nodes. For a node to learn and get advantage of knowledge transfer it must first be encoded

Uses of learning networks

The main use is adoption as node will adopt what other node have. When an individual works for an organization, they bring their network with them, combining as part of the larger network of the corporation.