Hi, this is an article I found rabbit-holing and it's quite technical but also fun. If you are already into this topic (see below) you might enjoy it: "Connectivism", from the asian distance education journal.
Abstract / Summary
Connectivism is the thesis that knowledge is constituted of the sets of connections between entities, such that a change in one entity may result in a change in the other entity, and that learning is the growth, development, modification or strengthening of those connections. This paper presents an overview of connectivism, offering a connectivist account of learning and a detailed analysis of how learning occurs in networks. It then offers readers an interpretation of connectivism, that is, a set of mechanisms for talking about and implementing connectivism in learning networks, and finally, pedagogy.
My brief thought
This paper takes some ideas related to the "Artificial Neural Networks" which if you never heard about, were inspired by how neurons connect to each other in the brain, and uses to define concepts such as Knowledge and Learning. I found it fun; it's very technical in some parts, and very sloppy in others, but overall quite insightful. It's based off work in the 2000s by another scholar.
In a sense, it models what happens inside the individual, rather than just observing its behaviour, which is how most theories prior to 2000s would talk about learning and knowledge (he calls those "black box models"). Their definition of learning implies that many things "learn" (such as social networks), which seems quite useless but also quite curious.
One of my favourite paragraphs in the article is:
[network-like] phenomena (and others like them) are observable as physical phenomena and formally described by the mathematics of graph theory (Euler, 1995) and artificial neural networks. Connectivism describes phenomena related to teaching and learning in these same terms, appealing to the same underlying principles and abstractions. Thus, one of the advantages of a network way of looking at the world is parsimony, that is, being able to apply the same theory in multiple domains, thus creating broader explanations for related phenomena. A second is transference. If the same logic underlies different domains, then a discovery in one domain can lead to insights in another domain. Thus, if we learn something about how networks behave by studying computer networks, this may lead us to insights about human neural networks.
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The article is: "Connectivism", from the asian distance education journal.