Connectivism: Human-Computer Relationships in a Digital Age
by Lorraine Ledger
Connectivism was founded in 2004 by theorist George Siemens, with later additions by Stephen Downes in 2010. The theory addresses the role technology plays in the learning process. Connectivism seeks to expand upon social cognitive learning theory to include a unique role for technology (e.g., email, wiki sites, social media, video, etc.) as an equal partner with humans in their collective and social learning process, as well as a stand-alone dynamic and amorphous aggregate of all knowledge (AlDadouh et. al., 2015)
What is Connectivism …Exactly?
At the moment, a static and comprehensive review of Connectivism is not possible, because “the thing itself” is constantly changing. That, I suppose, may be the best way to describe it. As a theoretical foundation for discussion, this “theory” seeks more to explain the current world-wide networking phenomenon than to create a specific framework for instruction.
Similarities to Cognitivism and Constructivism
As an instructional approach, Connectivism can be thought of as a new way of looking at Constructivism and, perhaps, as presenting an epistemology similar to Cognitivism but on a macrocosmic scale.
It is constructivist in nature because it promotes social learning in the construction of knowledge through exploration, discovery, contextual problem-solving, personal interpretation, and the whole network benefits from cultural and intellectual diversity, and language plays a major role.
Because it relies on active inquiry by both human and artificial intelligence, that is exercised through internal processes that are constantly working to filter out and add new stores of information, it is similar to Cognitivism too. People and machines steadily compare, sort, rearrange, analyze, combine, synthesize, perceive and filter information in much the way as the organic human brain.
Characteristics
Some characteristics of Connectivism include the following:
- Networks of multi-directional nodes connect people, content, technology
- Knowledge as a network
- Knowledge is actionable
- Neural, conceptual and external nodes in varying and various relationships
- Recognizing patterns and acting in response
- Network is alive, moving and ephemeral in nature
- Learners are content generators and researchers
- Learners are consumers and producers
- Learners are curators of existing data
- All levels of users interact inside an egalitarian community
- Chaotic uncertainty forces a search for answers
- Criteria for establishing authority is vague and unclear
- Mobility and accessibility limit participation
- Cultural and intellectual diversity benefit the whole network
The ability to connect to existing knowledge is more important than building new knowledge. By opening the internet to everyone equally, individuals benefit from access to information stored all over the world that they can retrieve and apply to addressing their own personal needs.
Benefits and Educational Implications
New things like open source software and collaboration tools have made it possible for humans to solve problems and construct new knowledge through active participation in groups, for personal gain and for commercial enterprises.
MOOCs (massive open online courses) like edX and Coursera are an example of this phenomenon too, where anyone can enroll and take free or low-cost online classes on most any subject you can think of.
Another benefit of Connectivism is its ability to help solve problems too complex for a single human brain to contain. It eliminates the cognitive load limitations of the human working memory by distributing memory and holding complex problems in a distributed manner across networks, with each node holding a part of the entire puzzle (Siemens, 2006).
The role of instructor in such a learning environment is one of coordinator, guide, facilitator, and as a consultant in problem-solving. Learners are encouraged to identify and pursue solutions to their own problems by investigating what is already known seeking out answers to questions in places like online libraries, discussion forums, social media, through email, and even have the opportunity to attempt direct communication with researchers and experts.
Drawbacks and Weaknesses
One drawback of online learning, in particular, is lack of certainty about how to judge information you find. Is it good, is it bad, how do I know? To some extent we have to trust. But, there are ways of determining, to a great extent, whether to trust a source or not.
One way is to only go to places referenced by sources already known to be trustworthy. This issue of trustworthiness is too broad to discuss here, and possibly better confined to the domain of philosophy or rhetoric.
With that in mind, I wanted to take a moment to plug a website I found that has been a good resource to me on this subject, and you might find useful too:
https://contentwriters.com/blog/the-professional-writers-guide-to-authoritative-sources/
References
AlDadouh, A. A., Osório, A. J., & Portugal, S. C. (2015). Understanding knowledge network, learning and connectivism. International Journal of Instructional Technology and Distance Learning, (10), 3-21. Retrieved November 11, 2018 from https://files.eric.ed.gov/fulltext/ED572896.pdf
Bruner, J.S. & Goodman, C.C. (1947). Value and Need as Organizing Factors in Perception. Journal of Abnormal and Social Psychology, 42,33-44. Retrieved from http://psychclassics.yorku.ca/Bruner/Value/
Downes, S. (2007). An Introduction to Connective Knowledge. In Hug, Theo (Ed.) Media, Knowledge & Education–Exploring new Spaces, Relations and Dynamics in Digital Media Ecologies.
Downes, S. (2010). New technology supporting informal learning. Journal of Emerging Technologies in Web Intelligence, 2(1), 27-33.
Ritter, S.E. & Phillips, K. (2014, January 9). An explanation of connectives. Learning Centered Technology.
Sh!ft Learning Blog. https://www.shiftelearning.com/blog
Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1), 310.
Siemens, G. (2006). Knowing knowledge. Lulu. com.
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