Connectivism
is a theoretical framework for understanding learning in a digital age.
It emphasises how internet technologies such as web browsers, search
engines, wikis, online discussion forums, and social networks
contributed to new avenues of learning. Technologies have enabled people
to learn and share information across the World Wide Web and among
themselves in ways that were not possible before the digital age.
Learning does not simply happen within an individual, but within and
across the networks. What sets connectivism apart from theories such as constructivism
is the view that "learning (defined as actionable knowledge) can reside
outside of ourselves (within an organization or a database), is focused
on connecting specialized information sets, and the connections that
enable us to learn more are more important than our current state of
knowing". Connectivism sees knowledge as a network and learning as a process of pattern recognition. Connectivism has similarities with Vygotsky's zone of proximal development (ZPD) and Engeström's Activity theory. The phrase "a learning theory for the digital age"
indicates the emphasis that connectivism gives to technology's effect
on how people live, communicate, and learn. Connectivism is an
integration of principles related to chaos, network, complexity, and self-organization theories.
History
Connectivism was introduced in 2005 by two publications, Siemens’ Connectivism: Learning as Network Creation and Downes’ An Introduction to Connective Knowledge.
Both works received significant attention in the blogosphere and an
extended discourse has followed on the appropriateness of connectivism
as a learning theory for the digital age. In 2007 Kerr entered into the
debate with a series of lectures and talks on the matter, as did
Forster, both at the Online Connectivism Conference at the University of
Manitoba.
In 2008, in the context of digital and e-learning, connectivism was
reconsidered and its technological implications were discussed by
Siemens' and Ally.
Nodes and links
The central aspect of connectivism is the metaphor of a network with nodes and connections.
In this metaphor, a node is anything that can be connected to another
node such as an organization, information, data, feelings, and images.
Connectivism recognizes three node types: neural, conceptual (internal)
and external.
Connectivism sees learning as the process of creating connections and
expanding or increasing network complexity. Connections may have
different directions and strength.
In this sense, a connection joining nodes A and B which goes from A to B
is not the same as one that goes from B to A. There are some special
kinds of connections such as "self-join" and pattern.
A self-join connection joins a node to itself and a pattern can be
defined as "a set of connections appearing together as a single whole".
The idea of organisation as cognitive systems where knowledge is distributed across nodes originated from the Perceptron (Artificial neuron) in an Artificial Neural Network, and is directly borrowed from Connectionism,
"a software structure developed based on concepts inspired by
biological functions of brain; it aims at creating machines able to
learn like human".
The network metaphor allows a notion of "know-where" (the
understanding of where to find the knowledge when it is needed) to
supplement to the ones of "know-how" and "know-what" that make the
cornerstones of many theories of learning.
As Downes states: "at its heart, connectivism is the thesis that
knowledge is distributed across a network of connections, and therefore
that learning consists of the ability to construct and traverse those
networks".
Principles
Principles of connectivism include:
- 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.
- Learning is more critical than knowing.
- Maintaining and nurturing connections is needed to facilitate continuous learning. When the interaction time between the actors of a learning environment is not enough, the learning networks cannot be consolidated.
- Perceiving connections between fields, ideas and concepts is a core skill.
- Currency (accurate, up-to-date knowledge) is the intent of learning activities.
- Decision-making is 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.
Teaching methods
Summarizing
connectivist teaching and learning, Downes states: "to teach is to
model and demonstrate, to learn is to practice and reflect."
In 2008, Siemens and Downes delivered an online course called "Connectivism and Connective Knowledge".
It covered connectivism as content while attempting to implement some
of their ideas. The course was free to anyone who wished to participate,
and over 2000 people worldwide enrolled. The phrase "Massive Open Online Course" (MOOC) describes this model. All course content was available through RSS feeds, and learners could participate with their choice of tools: threaded discussions in Moodle, blog posts, Second Life and synchronous online meetings. The course was repeated in 2009 and in 2011.
At its core, connectivism is a form of experiential learning
which prioritizes the set of formed by actions and experience over the
idea that knowledge is propositional.
Criticisms
The
idea that connectivism is a new theory of learning is not widely
accepted. Verhagen argued that connectivism is rather a "pedagogical
view."
The lack of comparative literature reviews in Connectivism papers
complicate evaluating how Connectivism relates to prior theories, such
as Socially Distributed Cognition
(Hutchins, 1995), which explored how connectionist ideas could be
applied to social systems. Classical theories of cognition such as Activity theory
(Vygotsky, Leont’ev, Luria, and others starting in the 1920s) proposed
that people are embedded actors, with learning considered via three
features – a subject (the learner), an object (the task or activity) and
tool or mediating artifacts. Social cognitive theory (Bandura, 1962) claimed that people learn by watching others. Social learning theory (Miller and Dollard) elaborated this notion. Situated cognition
(Brown, Collins, & Duguid, 1989; Greeno & Moore, 1993) alleged
that knowledge is situated in activity bound to social, cultural and
physical contexts; knowledge and learning that requires thinking on the
fly rather than the storage and retrieval of conceptual knowledge. Community of practice
(Lave & Wenger 1991) asserted that the process of sharing
information and experiences with the group enables members to learn from
each other. Collective intelligence (Lévy, 1994) described a shared or group intelligence that emerges from collaboration and competition.
Kerr claims that although technology affects learning environments, existing learning theories are sufficient. Kop and Hill
conclude that while it does not seem that connectivism is a separate
learning theory, it "continues to play an important role in the
development and emergence of new pedagogies, where control is shifting
from the tutor to an increasingly more autonomous learner."
AlDahdouh
examined the relation between connectivism and Artificial Neural
Network (ANN) and the results, unexpectedly, revealed that ANN
researchers use constructivism principles to teach ANN with labeled
training data. However, he argued that connectivism principles are used to teach ANN only when the knowledge is unknown.
Ally recognizes that the world has changed and become more
networked, so learning theories developed prior to these global changes
are less relevant. However, he argues that, "What is needed is not a new
stand-alone theory for the digital age, but a model that integrates the
different theories to guide the design of online learning materials.".
Chatti notes that Connectivism misses some concepts, which are
crucial for learning, such as reflection, learning from failures, error
detection and correction, and inquiry. He introduces the Learning as a
Network (LaaN) theory which builds upon connectivism, complexity theory,
and double-loop learning. LaaN starts from the learner and views
learning as the continuous creation of a personal knowledge network
(PKN).
