I frequently emphasize the substrates at which learning and knowledge are connected or networked. As more attention is paid to learning networks and personal learning, it’s important to highlight that most of the discussion is focused on the social/external substrate, ignoring other dimensions of networkedness (I’m sure that’s a word).
By quick review:
1. Neuronal – brains don’t hold knowledge in chunks – it’s networked. A simple task, such as picking up a pencil, requires numerous areas of the brain to harmonize their distributed activity (sometimes referred to as the “binding problem”) in order to produce the intended action. Recognizing a human face is an astonishingly complex distributed neural activity – an image of a face doesn’t exist in our brains. Instead, different regions of the brain contributed to producing recognition. Olaf Sporns has explored the similarity between some network attributes of the neocortex and other scale-free networks.
2. Conceptual – connections generate meanings. When two or more concepts are brought into some type of relationship, they produce something different than their individual attributes would suggest. Conceptual blending attempts to describe what’s involved in the process of bringing concepts in relation to each other. Burkes’ Knowledge Web is similarly based on trying to find how knowledge is connected/related. As does Danny Hillis’ article Aristotle: The Knowledge Web. Or consider a tool like Brainscanr that attempts to detail relationships between concepts in psychology. We are constantly forming and blending concepts. When we are involved in formal learning, we are more conscious of the process as we’re bringing together our life experiences and current understanding of a topic with new information provided by a course or program of study.
3. Social and technological networks – we live these daily and tools like Facebook and Twitter have made these more explicit. Publications from mathematicians and physicians over the last decade have increased attention on networks (Barabási, for example). However, sociologists have been playing in the domain of networks long before the current hype drove networks into popular society. Barry Wellman, Mark Granovetter, and Paul Lazerfeld lay much of the foundation for what is now being “discovered” about social networks. Researchers are beginning to take a multi-disciplinary approach to networks, realizing that network attributes exists in food chains, transportation systems, etc. Basically, networks underpin life and human existence. The internet, web, and now social media raise the profile of networks because we now experience them daily. When directed toward learning, networks (web, citations, social, etc) are inescapable. As human knowledge becomes more explicit – i.e. stored in a database, waiting analysis – analytics becomes increasingly important in order to understand complexity. The discovery of the corona virus (SARS) was accomplished in a period of a month – an extremely short period of time considering the complexity involved. This was enabled by researchers connecting to each other and sharing information. Understanding how and why people and information connect is a key task of analytics (have I mentioned TEKRI is organizing a conference on Learning and Knowledge Analytics?). Knowledge in any moderately complex task or activity is networked (building a plane, designing a road system, printing a book).