This week, for #change11, Dave Cormier is facilitating a discussion on rhizomatic learning. I’ve been aware of Dave’s thinking on this topic since his article in Innovate (.pdf). I was the guest editor of this journal issue and Dave’s article is the one that generated the most interest and still continues to resonate with many people. It certainly resonates with me.
One aspect of Cormier’s work on rhizomatic learning that he has not fully resolved is how rhizomes are related to networks. Periodically, he expresses rhizomes as an alternative to the network model. For example, in his discussion on edtechweekly, he suggests that networks are too structured and lack the organic attributes of rhizomes. On other occasions, he has raised concerns about the knowledge or epistemological underpinnings of networked learning (connectivism). I sense a lingering discomfort with networks in Cormier’s view of knowledge and learning. He hasn’t tackled this discomfort directly. Perhaps he is trying to be polite. Or perhaps he’s still thinking through the nature of that discomfort. Personally, I’d like to see him explore in more detail where the networked learning model fails in contrast with rhizomes.
Rhizomes are appealing for several reasons. First, they share the decentralized attributes of networks. There is no centre. Second, rhizomes are organic, they’re living and adaptive. A rhizomatic structure today will be different from a structure that will exist in a few months. Third, each part of a rhizomatic structure is capable of producing a new plant, propagating and replicating itself without central control. Fourth, in contrast with an architected structure such as a road system, rhizomes are not artificially bounded. They continually grow, extend, and develop. It’s easy to see how the decentralized, organic, adaptive, self-replicating, and unbounded characteristics of rhizomes are appealing as metaphors for learning and knowledge.
I’m not sure whether Cormier views rhizomes as a metaphor for learning or whether he is bolder in his claims. Does he think that learning is *like* a rhizome? Or is he making a bolder statement in saying that learning *is* a rhizome? In the blog post I referenced above, he states that rhizomes are “a way of thinking about learning”. If this assertion is central in his work, then I largely agree with Cormier.
However, when rhizomes are considered in contrast with networks, I find the rhizome model begins to lose some appeal. The greatest weakness I see with the rhizome model is that rhizomes do nothing new. They only make more of what already exists. And they can only make more of themselves. This is the opposite of diversity – it is extreme monoversity (it’s not a word, I checked, but it works for me). While rhizomes are diverse in shape and structure – growing, adapting, sprouting, replicating – they are not diverse in substance – i.e. rhizomes do not morph into new organic entities.
In his edtechweekly podcast, Cormier criticizes networks as being “duplicatable”. If someone has a successful network, she is tempted to say “this is how you create your own network”. Suddenly, the network becomes mechanistic. Cormier doesn’t like that. Neither do I. However, networks need not be designed in order to duplicate structure. Networks are organic – consider food webs, ecosystems, and the architecture of the brain. I don’t accept the argument that rhizomes are organic and that networks are not.
Cormier doesn’t seem to appreciate the descriptive attributes of networks – i.e. that we can describe certain network attributes by formula – power laws, propagation of influence, in-degrees and out-degrees. Personally, I think the descriptive and analytic capacities that have been applied to networks are tremendously positive. From the outbreak of cholera to the structure of terrorist cells to the development of the internet, being able to understand, often mathematically, the structure of networks enables us to react to and even build on that structure. It is because we can measure and understand how messages move through networks, the role of hubs, the need for amplification of messages, etc., that we are able to build the internet. The “definiteness” of networks is what gives them their relevance and power in our society today.
Together with Stephen Downes, Cormier and I have had long running discussions about objectivism, subjectivism, knowledge, learning, and a raft of other related concepts. I’m of that rare breed that still believes structure can be good and that some level of objectivity can exist in some situations. Inevitably, when this conversation begins, I find myself arguing with Downes and Cormier. It has never been resolved. And I don’t think it ever will be.
My network view of knowledge is simple: entities (broadly defined as well, anything: people, a chemical substance, information, etc) have attributes. When entities are connected to other entities, different attributes will be activated based on the structure of those connections and the nature of other entities that are being connected. This fluidity of attribute activation appears to be subjective, but in reality, is the contextual activation of the attributes of entities based on how they are related to other entities. Knowledge then is literally the connections that occur between entities.
I don’t see networks as a metaphor for learning and knowledge. I see learning and knowledge as networks. In global, digital, distributed, and complex settings, a networked model of learning and knowledge is critical. Most disciplines in society have become too specialized to function in isolation. Global problems are too intractable to be tackled by any structure other than networks. Generalists have given way to connected specialization (as evidenced in the identification process of the corona virus (SARS)). Everything – form fixing my car, to my morning coffee, to my research, to my mobile phone, to healthcare – is a function of connected specialization. Novelty and innovation arises when we collide ideas or specialties that previously had not been brought in relation to one another.
Much of the world that we live in today can be explained through networks, including education, learning, research, and knowledge development. I have not read Deleuze, though it is on my agenda for my trip to Croatia this weekend, but I don’t see rhizomes as possessing a similar capacity (to networks) to generate insight into learning, innovation, and complexity. Terry Anderson describes a sense of alienation with the rhizomatic learning and states that: “If we really want to CHANGE systems, we have to insure that we don’t grow as rhizomes, reproducing clones of ourselves or establishing gardens in which only certain types of weeds can flourish.”
Rhizomes then, are effective for describing the structure and form of knowledge and learning – bumpy, lumpy, organic, and adaptive. But they fail to describe how learning occurs, how novelty happens, and how a rhizome becomes more than a replication of itself. Rhizomes can be a helpful way to think about curriculum, to think about how we develop educational content when we are connected (dang networks again) to one another and to information sources. However, beyond the value of describing the form of curriculum as decentralized, adaptive, and organic, I’m unsure what rhizomes contribute to knowledge and learning.