Connectivism¹⁾ is a new learning paradigm and a learning theory introduced in 2004 by George Siemens. This theory attempts to approach learning and knowledge in context of technological development during the last few decades, since the impact of technological achievements on learning and knowledge cannot be ignored.

Motivation for introduction of connectivism comes from notion that learning theories in frames of behaviorism, cognitivism, constructivism²⁾ promote the understanding that learning occurs only inside a person. According to Siemens,

• “These theories do not address learning that occurs outside of people (i.e. learning that is stored and manipulated by technology). They also fail to describe how learning happens within organizations… We can no longer personally experience and acquire learning that we need to act. We derive our competence from forming connections.³⁾”

Siemens' connectivism incorporates ideas from three areas:

• Chaos theory - Recognizing complex patterns and deep sensitivity on small changes in initial conditions are important properties of learning and decision-making as well as key aspects of chaos theory.
• Self-organization - This term usually refers to “the spontaneous formation of well organized structures, patterns, or behaviors, from random initial conditions”⁴⁾

, networks and complexity and self-organization to explain his theory and expands terms of learning and knowledge. Chaos theory idea that minimal change in initial conditions can result in relevant changes in the outcome here refers to the fact that knowledge changes over time and network models were acquired because of their applicability and simplicity.

Learning is, according to Siemens, “applicable knowledge” that can also reside outside a person (within a database or an organization). He also views on the learning process, in terms of nodes and links between them, as on establishing connections (links) to specialized nodes (information sources). Purpose of connectivist learning is current, up-to-date knowledge, since it can change in time.

Nodes can represent virtually anything (a community, individual, …), and the stronger the connection is, the faster the information will flow between the nodes. Aggregated nodes form the network, but the network itself can only have limited influence on the nodes. According to Siemens' “Connectivism: Learning as Network-Creation”, elements and characteristics of a network include:

• Content (data or information)
• Interaction (tentative connection forming)
• Static nodes (stable knowledge structure)
• Dynamic nodes (continually changing based on new data, since knowledge can and does change over time)
• Self-updating nodes (nodes tightly linked to original information source)
• Emotive elements (emotions that influence the prospect of connection)

Connections between the nodes can depend on various factors which make them stronger or weaker:

• Motivation - impacts individuals determination to foster deeper connections
• Emotions - affect our evaluation of nodes and allow existence of contradictory perspectives
• Exposure - nodes grow and develop through forming connections to other nodes
• Patterning -
• Logic
• Experience

• George Siemens, Stephen Downes

Some authors like professor of educational design Bijdrage van Pløn Verhagen criticize connectivism for being a pedagogical approach rather than a learning theory, since it doesn't really attempt to explain processes of how people learn.

APA Citation: Davis, C, Edmunds, E, & Kelly-Bateman, V. (2008). Connectivism. In M. Orey (Ed.), Emerging perspectives on learning, teaching, and technology.

Siemens, G. Connectivism: Learning as Network-Creation - ASTD. 2005.