Dron, J. (2024). Learning: A technological perspective. Journal of Open, Distance, and Digital Education, 1(2), Article 2. https://doi.org/10.25619/dpvg4687
My latest paper, Learning: A technological perspective, was published today in the (open) Journal of Open, Distance, and Digital Education. Methodologically, it provides a connected series of (I think) reasonable and largely uncontroversial assertions about the nature of technology and, for each assertion, offers some examples of why that matters to educators. In the process it wends its way towards a view of learning that is firmly situated in the field of extended cognition (and related complexivist learning theories such as Connectivism, Rhizomatic Learning, Networks of Practice, etc), with a technological twist that is, I think, pragmatically useful and theoretically interesting. Much of it repeats ideas from How Education Works but it extends and generalizes them further into the realms of intelligence and cognition through what I describe as the technological connectome.
I wrote this paper to align with the themes of the journal so, as a result, it has a greater focus on education than on the technological connectome, but I intend to write more on the subject some time soon. The essence of the idea is that what we recognize as intelligent behaviour consists largely of intracranial technologies like words, symbols, theories, models, procedures, structures, skills, ways of doing things, and so on – our cognitive gadgets – that we largely share with others, and that exist in vastly interconnected, hugely recursive, massively layered assemblies in and beyond our heads. I invoke Reed’s Law to help explain how and why this makes our intracranial cognition so much greater than the neural networks that host it: it’s not just the neural connections but the groups and multi-scaled clusters of technological entities that emerge as a result that can then be a part of the network that embodies them, and of one another, and so on and so on. In passing, I have a vague and hard-to-express hunch that the “and so on” is at least part of the answer to the hard problem: networks that form other networks that themselves become parts of the networks that form them (rinse and repeat) seems like a potential path to self-consciousness to me. However, the ludicrous levels of intertwingularity implied by this, not to mention an almost total absence of any idea about the underlying mechanism, ties my little mind in knots that I cannot yet and probably will never unravel.
At least as importantly, these private intracranial technologies are in turn parts of even greater assemblies that extend into our bodies, our environments, and above all into the technologies around us, and thence into the minds of others. To a large extent it is our ability to make use of and participate in this extended technological connectome, that is both within us and beyond us, that forms the object, the subject, and the purpose of education. Our technologies as much form a part of our cognition as they enable it. We continuously shape and are shaped by them, assembling and reassembling them as we move into the adjacent possibles that result, creating further adjacent possibles every time we do, for ourselves and others. There is something incredibly awesome about that.
Abstract
This paper frames technology as a phenomenon that is inextricable from individual and collective cognition. Technologies are not “the other”, separate from us: we are parts of them and they are parts of us. We learn to be technologies as much as we learn to use them, and each use is itself a technology through which we participate both as parts and as creators of nodes in a vast technological connectome of awesome complexity. The technological connectome in turn forms a major part of what makes us, individually and collectively, smart. With that framing in mind, the paper is presented as a series of sets of observations about the nature of technology followed by examples of consequences for educators that illustrate some of the potential value of understanding technology this way, ending with an application of the model to provide actionable insights into what large language models imply for how we should teach.