Educational Complexity and Open Systems

The fourth property of complexity that both Taborga and Lawrimore discuss within the context of modern organizations is openness or, as Lawrimore terms it, adaptability. This is, I think, a particularly critical property of complexity and most relevant to my current conversations about education, especially the conversation about MOOCs. Openness and the concomitant adaptability overturn so much of how we normally think about the world, especially education, that it is difficult to decide where to start, so I'll begin with what our two interlocutors say and see where it leads:

Taborga says:

The fourth property of complexity is that the system is open. An open system responds to its environment. Complexity Theory posits that all systems are open with the possibility that the total universe is the only closed system. Continuing with the project team example, there are numerous external influences that determines the project’s course. Funding would be an important external variable for a project team.

Lawrimore speaks of adaptation rather than openness, but clearly he is approaching the same property of complexity:

Adaptive refers to the fact that living systems constantly adapt to their changing environments. (Adapt means "fit to.") In organizations people adapt to each other, to customers, the economy, competitors and many other things. They are able to adapt through learning. Continuous learning is very important in Complexity organizations.

A view of time, temporality, lies at the heart of what they are both saying. All complex systems have an evolutionary arc that we can trace fairly well looking back on it, but that we find very difficult to trace looking forward. This seems such an obvious thing to say—especially in light of Darwin and our common, everyday experience—but it isn't. Basically, we humans do not like an open future. We spend great resources and energy on trying to fix the future, in all senses of the word fix. This is, I think, the heart of fundamentalism, especially the scientific and religious varieties, both of which posit a deterministic view of reality. The fundamentalist scientists believe that if they can know the position and speed of all the particles of a system (and only they can), then they can determine any past or future disposition of that system. As Lee Smolin describes it in Time Reborn (2013), they believe that systems are path independent and totally controlled by eternal natural laws expressible in timeless mathematical formulae that work each time, every time to accurately describe reality both past and present. The fundamentalist religious believers, on the same hand, believe that if they can properly interpret scripture (and only they can) then they can determine the past and future disposition of all humans on the planet and predict the End of Times. For them, all history is path independent, its arc ordained by the divine from the Beginning and all controlled by eternal divine law expressible in timeless ritual formulae that work each time, every time to accurately describe reality both past and present.

Complexity says otherwise. Eternal scientific laws are not much improvement over eternal religious laws because reality is NOT absolutely determined by either set of laws, certainly not by both. At the heart of every atom and every galaxy lies enough probability to provide wiggle room for almost anything to emerge, including somewhat conscious and intelligent apes, and whatever emerges comes with new laws to follow, as laws, physical and otherwise, are also emergent properties of emergent systems. All complex systems—and for me that includes everything from the most inert rocks to gravity—are open to change. Heraclitus would be proud.

So, one might complain, anything goes in this open-ended, relativistic universe? Don't be absurd, and don't jump off the Golden Gate Bridge to test it. The probability of your death is near certain (about 98%), and as one of the few known survivors (the fortunate effect of just the tiniest wiggle room) is likely to tell you: on the way down, you might change your mind. No, open adaptability is that zone between an anything-goes chaos and an only-one-thing-goes determinism, and life and all that we humans hold dear emerges in this open, temperate zone poised between hot, erratic chaos and cold, fixed determinism. We humans can enjoy the heat of chaos for a time, but too close for too long and we die a hot death. We can depend on the fixed cold of determinism for a time, but too close for too long and we die a cold death. Frost had it right when he said in his poem Fire and Ice that the world would end with either fire or ice and that either "would suffice."

Unfortunately, too much of modern education is based on a scientific determinism, which replaces a religious determinism. Some may see that as progress, and perhaps so, but not much, I think, and certainly not enough progress. We need a complex view of learning based on an understanding of open, adaptable systems. Indeed, if people were not open, adaptable, and changeable, then what would be the point of education? Fortunately, people are open and adaptable systems; thus, education is a worthwhile endeavor. Why, then, have we constructed schools on the factory system for the purpose of batch producing a consistent product? I think I can understand why we did it in the late 19th and early 20th centuries when industrialism was triumphing in business and reductionism in science, but why are we persisting into the 21st century? Ford's assembly line was a fairly decent system for outputting an endless line of black-only Model Ts, but even the assembly line has moved way beyond Ford's early iteration of it. As an aside, I think his fascination with the assembly line goes a long way in explaining Ford's attraction to fascism. The assembly line is fascism for business, and while it  may have narrow benefits for a narrow time, one shouldn't base a life or a nation on it.

Morin helps me understand the implications of this openness and adaptability, especially for education. First, open systems can be understood most completely only if we account for their relationships with the environment. As Morin says,  "Reality is therefore as much in the connection (relationship) as in the distinction between the open system and its environment" (On Complexity, 11). If you want to understand a student, you don't get very far looking merely at GPA, though GPA does have real, if limited, utility. In other words, you cannot reduce a student to one, or even a collection, of objective characteristics and expect to know very much. Students are not closed systems, definable from the outside-in. They are open systems, definable from the inside-out to all their connections and interactions with their ecosystems over time. All complex, open systems must be understood from the inside-out and not reduced to some handful of essential characteristics from the outside-in. This includes school subjects, which cannot be closed into disciplines but must be explored from the inside out into. Morin's thoughts about studying open systems are most apropos to education, I think:

Methodologically, it becomes difficult to study open systems as entities that can be radically isolated. Theoretically and empirically, the concept of an open system opens the door to a theory of evolution, that can only come from the interaction of system and eco-system, and, in its most significant organizational leaps, can be conceived of as the "going beyond," the surpassing, of the system into a meta-system. The door is, therefore, open for a theory of self-eco-organizing systems. These systems are themselves open, of course, because far from escaping 'openness,' evolution toward complexity increases it. In other words, it is a theory of living systems. (11)

 Let me add here that this approach to openness is perhaps what most attracted me to the conversation about Connectivism, a theory of education first formally suggested by George Siemens and Stephen Downes in about 2005. Some have questioned whether or not Connectivism is really a theory, but they are mostly trying to define Connectivism from the outside, to reduce it to a few canonical characteristics. Both Siemens and Downes have also performed this kind of definition in their writings (and, by the way, I do value this kind of definition—I just think it is radically limited when applied to complex, open systems), but what is really brilliant about what Siemens and Downes have done is to pretty much abandon defining Connectivism from the outside as objective observers and turn to defining it from the inside as engaged participants. The result of this switch has been MOOCs, among other things. Connectivism is now defining itself from inside-out, and the definition has gone in directions that I suspect Siemens and Downes would have never explored or pursued. It is a messy definition, but much more real, I think, and infinitely more engaging. Connectivism, then, is being defined not merely on the basis of what characteristics separate it from all other theories but mostly on the basis of what it connects us to, on the affordances that it provides. The boundaries of the theory are better seen from the inside as the outward limits of how far we can take these particular set of ideas.

Of course, I think all theories are better seen from the inside, especially if you want to understand them, but we are in the reductionistic habit of separating to define, not connecting to define. The complexity theory behind open systems will change that, I think. And again: I am not saying that defining from the outside-in has no value. It does. But it is only part of the story, and a small part at that. You will never really understand rap music if you stay on the outside. You have to get inside it to understand it. You have to see what it can connect you to, where it can take you, and that is seen from the inside. Open systems are like that. Everything is like that.

When Morin says that "methodologically, it becomes difficult to study open systems as entities that can be radically isolated", I am reminded of the marvelous work that Jenny Mackness and her associates Williams and Gumtau are doing with emergent learning. Of course, they are studying open systems, but I suspect, their tools are still based too much on a reductionist approach to science that works from the outside-in. Mackness has said to me that merely coming up with the new terms and concepts to describe what they are seeing has been a struggle. If you want to see better how to approach complex, open educational systems, then you should follow their work.

MOOCs, Transdisciplinarity, and Thinking Big

In his 2004 Phi Beta Kappan essay entitled Thinking Big: A Conceptual Framework for the Study of Everything, self-described contrarian educator Marion Brady writes that "the main task of educating is to help students make more sense of the world, themselves, and others" (p. 277). He attacks the current state of knowledge as represented in the plethora of academic subjects and disciplines and insists that such a fragmented approach to knowledge will, in the words of Buckminster Fuller, "be the undoing of the society." He quotes Fuller again in a marvelous 1980s complaint to American educators: "What you fellows in the universities do is make all the bright students into experts in something. That has some usefulness, but the trouble is it leaves the ones with mediocre minds and the dunderheads to become generalists who must serve as college presidents . . . and presidents of the United States." I truly wish I had said that, but … well, he was Buckminster Fuller.

Brady then identifies the basic theory of education that underlies the fragmented, disciplinary approach to knowledge:

The present curriculum, made up as it is of separate, specialized studies, exerts considerable pressure on teachers to make major use of what could be called “Theory T.” Theory T dominates American education. … T stands for “transfer.” Those who accept Theory T believe that knowledge is located in teachers’ heads, textbooks, reference materials, and on the Internet and that the instructional challenge is to transfer it from these locations into the empty space in students’ heads. The degree of success of the transfer process can be measured with relative ease, which helps explain its broad appeal. … Evaluating performance is simple enough to allow student responses to be scored by a machine. (pp. 279, 280)

 He then contrasts Theory T with what he calls Theory R:

Theory R assumes not that students’ heads are empty but that they are full. The primary instructional challenge, then, is not to transfer new knowledge but to help students reorganize existing knowledge to make it more useful, consistent, or true and to supplement it with insights and skills that will help explain more fully what they already know.… Students in Theory R classrooms must be active processors of information. Theory T emphasizes recall; Theory R requires students to engage in every known thought process. … Theory R requires students to make connections, to perceive relationships, and to synthesize ideas. It sends students searching the far corners of their minds without regard for the artificial, arbitrary boundaries imposed by academic disciplines.

Brady gives here a neat precursor to Connectivism, I think. First, he emphasizes that each student already possesses all the neuronal networks needed for making connections, perceiving relationships, and synthesizing ideas. We teachers do not transfer anything into the imagined empty memory slots of student brains; rather, we present them with a, hopefully, coherent and engaging series of artifacts and experiences to which they may connect, perceive relationships, and synthesize ideas, or not. All too often what they connect to, perceive relationships among, and synthesize are ideas that we never taught, or didn't know we were teaching. And each student makes these connections and patterns within an ecosystem (their own life stories) that we teachers know little to nothing about, and that ecosystem, that context, provides almost all of the meaning for whatever new connections and patterns the student is weaving. This reminds me much of Paul Cilliers' definition of knowledge as "information that is situated historically and contextually by a knowing subject" (Why We Cannot Know Complex Things Completely in Capra, Juarrero, Sotolongo, and van Uden's Reframing Complexity: Perspectives from North and South, 2007, p. 85). In other words, while information may exist apart from our students as data, it does not become knowledge until the student situates that information within a context that includes themselves and that necessarily informs the information in ways we teachers cannot predict or control.

But what most impressed me about Brady's article was his observation that this process of making connections (mapping, as Deleuze and Guattari say) "sends students searching the far corners of their minds without regard for the artificial, arbitrary boundaries imposed by academic disciplines." It seems to me that Connectivism and MOOCs are wonderful vehicles for transdisciplinarity, which transcends the "boundaries imposed by academic disciplines." I know that the MOOCs I have joined have had a marvelous, transdisciplinary reach in content and participants. Though I have had some of the most engaging and rewarding conversations of my professional life, as far as I know, I have actually had no conversation with another writing teacher, aside from one colleague who shared an office with me and a few MOOCs. Almost all of my conversations have been with scholars and practitioners outside my discipline, which makes my engagement in the MOOCs most transdisciplinary.

I think I will explore this a bit more in the next few posts.

The Unconscious Reality

The second slippery aspect of the question do we know all of Reality refers to how we conceive knowledge. If knowledge is something conscious and mostly intellectual, then I don't think we can know all of Reality, or even much of Reality. In other words, we have experiences of the Real that we are not conscious of and can hardly represent in any language. We engage and know many things with our minds and bodies long before we become conscious of them, if we ever become conscious. For instance, if you breathe in an unhealthy swarm of influenza virus, your immune system will know it and begin mobilizing a defense long before you are conscious of the infection. Or ask a gifted soccer player how he knows where the ball will be two touches before it arrives, and he likely cannot tell you, but he knows to be at that spot on the pitch anyway. Intimations of things long before we are conscious of them are a common experience in life.

We all know this, but we educators often behave as if we don't. We assume that, and behave as if, knowledge is strictly referential, based solely on our representations, descriptions, images, or mathematical formulations, to use Nicolescu's list. Knowledge is something we can put on the test next Tuesday. It isn't (you'll get a much better discussion of the issues with representational views of knowledge from Stephen Downes' blog Half an Hour). Knowledge extends beyond conscious knowledge.

But is this extended view of knowledge useful to education? I think it is extremely useful for those who envision education as a complex process of traversing networks—not as a walk to be taken (traced), but as a walking (mapping). I'm playing here with ideas that I've gleaned from Morin and Deleuze and Guattari.  Morin's concept of interdisciplinary research suggests that the path to knowledge is not followed, it is forged. He amplifies this idea with a line I've often quoted in this blog: we must learn to define from the inside out, not from the outside in. Deleuze and Guattari suggest that engagement of the rhizome, the Real, is a process of mapping structures and pathways, not tracing given structures and pathways. To my mind, these ideas position the Knower at the center of the zone of engagement as a knowmad who chooses to engage some aspect of the rhizome. Or not.

This tack positions me with the knowmad and suggests questions about what prompts a knowmad to engage or disengage some aspect of the rhizome. This reverses the usual pedagogical question of how to motivate students, as if motivation is a trigger we can pull, a response we can stimulate. I'm not sure it is. What then prompts a student to engage a teacher, a given curriculum, and a class? Where does this come from? And is there anything a teacher can do to facilitate that engagement?

Let's ask from the knowmad's point of view: why would a twenty-year-old studying to be a physical therapist want to engage a sixty-year-old in a course about writing? Why would they want to avoid such an engagement? In his book The Art of Changing the Brain (2002), James Zull says that most students unconsciously decide within the first 30 seconds of entering a class whether or not they will like it. Or like me. I probably know within the first 30 seconds whether or not I will like a particular class. These largely emotional engagements with the Real set the parameters of the Reality of the class, and they are difficult to change, in large part because we never quite make them conscious, or explicit. We just have a feeling that some classes work and some don't. However, some very heavy, precise neurological sensing and cognitive processing has gone on underneath the conscious surface to cause this particular Reality to emerge in the zone of engagement I and my students call Composition 1. Peering into the collective unconscious of the class to determine why a class is not working is more work than most of us care to take on, but it is extremely important for the success of the class.

There are plenty more questions to ask from the view of the knowmad: does this twenty-year-old have any sense of where I want to take them in the class? And do they want to go there? Do they have any hope of success? Any desire for success? Does this connect in any way with the path they are already on, or is this a side-trek they had just as soon avoid? And mostly: do they really want to connect to this sixty-year-old, short, white guy with his corny jokes told in a slightly southern accent?

The willingness to engage always comes from the knowmad themselves. The knowmad must see some path worth traversing, because mapping the rhizome is hard work. And it is the rare knowmad, especially young knowmads, who know why they want to engage or not. Much of our willingness to engage or not with a particular aspect of the rhizome, the Real, is decided prior to or completely outside of consciousness. As educators, we overlook this unconscious aspect of reality at our peril.

Quantum Random Walks and MOOCs

If Reality is an emergent property of the zone of engagement between the Knower and the Real, then what does that say about education in general and educational practices such as MOOCs in particular?

I think I should draw out some of the implications of this arrangement: a zone of engagement between the Knower and the Real. Most of the Real is hidden from us, both because it is beyond the horizons of possible engagement and because there is more Real than we can engage even within those horizons. Still, there is plenty of Real that we can know (we won't run out), and we are ever extending our horizons through technologies that allow us to engage more and more. So there's lots to learn, even if we can never learn it all. That's extremely good news, I think. A fine gospel for educators. Our vocations, if not our jobs, are secure.

That's our status in terms of the Real, but what about Reality, or the stuff that we can engage and can know, the stuff that "resists our experiences, representations, descriptions, images, or mathematical formulations" (Manifesto, 20)? Do we know all of that? This seems to me a tricky question. The first glib answer is that, of course, we don't know all of Reality, but I immediately want to counter that while I or you alone don't know all of Reality, maybe we do. Is it useful to define Reality as the sum total of what humans know and have known? I think so, and I think it points us to a most useful feature of MOOCs.

It seems to me that MOOCs, especially of the Connectivist variety, help us to approach the question of what we know rather than what I know. This is an important question because it undermines traditional education and its strict grading economy: 1 student = 1 grade, no cheating.

As electronically networked entities, MOOCs function similarly to quantum walks, a concept I first heard about in MIT quantum computing engineer Seth Lloyd's talk on Quantum Life, or how organisms have evolved to make use of quantum effects. At one point (11:50), Lloyd asks how photosynthesis can be so efficient (about 99%) when a photon that strikes a leaf must go through a maze of molecules to find the central photosynthesis processing unit. Apparently the photon engages in some very special kind of quantum multi-tasking, or quantum superposition, called a quantum algorithm. I don't have the science and mathematical knowledge to go into the details, and as Lloyd notes himself, even quantum scientists find superposition counter-intuitive, but basically the photon is able to explore all pathways at once, quickly locating the correct path to bliss.

For me, a better, more manageable, image of quantum walks is how bees search for a new hive. The bees start from the hive and then search all paths (approximately) at once, bringing back reports about each path, which is subsequently processed by the hive. This is what MOOCs can do, and this is what makes them so powerful, at least for me. Like bees searching for a new hive, MOOCers can begin in the middle with a given curriculum, but then they fan outward making a thousand different connections at once before bringing that new information back to the MOOC. Rather than exploring the single path of a single instructor, a functioning Connectivist MOOC explores nearly all paths at once, making connections to more knowledge, more contexts, than any single instructor, even a really bright and gifted instructor, can make. In other words, like bees in a hive, MOOCs use a process something like a quantum random walk to search all paths (approximately) at once, aggregate that knowledge with something like GRSShopper, and thereby create actionable knowledge available to the entire MOOC.

I like this image. For me, it captures some of the best of what happens within a successful Connectivist MOOC. This is good stuff.

Rhizomatic Thought, #etmooc

I came across a video and a couple of quotes today that illuminated and expanded for me some ideas I've been discussing in #etmooc about rhizomatic learning.

Brian Rose shared the NASA video Fiery Looping Rain on the Sun:



This kind of inspirational video leads to comments (270+ when I looked last, 4:00 pm EDT, Fri, Feb 22). For instance, Jeremy Ellwood quoted Neil Degrasse Tyson's view about feeling small in the light of such enormous power:

I look up at the night sky, and I know that, yes, we are part of this Universe, we are in this Universe, but perhaps more important than both of those facts is that the Universe is in us. When I reflect on that fact, I look up—many people feel small, because they’re small and the Universe is big, but I feel big, because my atoms came from those stars.

Then Brian Rose quoted Apollo 14 astronaut Edgar Mitchell, who spoke about looking back at the Earth from the Moon:

"You develop an instant global consciousness, people orientation, an intense dissatisfaction with the state of the world, and a compulsion to do something about it. From out there on the moon, international politics look so petty. You want to grab a politician by the scruff of the neck and drag him a quarter of a million miles out and say, ‘Look at that, you son of a bitch.”

These comments make explicit why I like the rhizome: it allows me to think over and beyond networks based on simple connectivity. The rhizome is networks+, connectivity on steroids.

More accurately, the rhizome is connectivity across multi-scale networks, across what Basarab Nicolescu calls different levels of reality. This connectivity—not just within a network but across sub-networks and super-networks—is important for my rhizomatic thinking as it helps me grasp and visualize the extent of rhizomatic structures, or assemblages to use Deleuze and Guattari's term (I suspect they wanted to avoid the more rigid, mechanistic overtones of the term structures). D&G speak of asignifying ruptures within a rhizome, in which our naming, labeling, and definition of a thing suffers a rupture, a line of flight, that unnames the thing as it moves from one scale of network to another scale, from one level of reality to another. Asignifying ruptures, deterritorialization, reterritorialization, and the logic of the included middle all seem abstract and obtuse concepts until you hear an Edgar Mitchell say it so plainly: "From out there on the moon, international politics look so petty." When you view the political arguments which seem so important here on Earth from a different level of reality, from the Moon, then you see that that the contradictions fade away, and the arguments become completely asignified, meaningless, void, not even a playground squabble. These concepts, then, help me understand one of the heuristics available to rhizomatic thinking: that whatever we are learning must be viewed from more than one level of reality, from more than one scale of the network. When we view things in this complex, rhizomatic manner, then contradictions often fade in lines of flight into the included middle.

Then, the comment by Neil Degrasse Tyson captures a second heuristic of rhizomatic thinking, what Edgar Morin calls the holographic principle. The patterns of the Universe echo in my cellular structures. We are composed of stardust, and we are the dust of the stars. I am not speaking poetically here, nor am I alluding to Joni Mitchell (though Woodstock remains one of my favorite songs, especially the version by Mathew's Southern Comfort). I am being literal. The patterns of energy and information exchange that work in the stars also work in me. The information in my DNA and cells come from the stars and feed back into it. I fancifully think that if the entire Universe were to blink out, leaving only me floating alone, then any reasonably intelligent, technologically adept species from another universe that found me could use the data in my cellular structures to pretty much recreate a universe that works more or less like this one (okay, that last part isn't literal, but it might be the start of a good science fiction story). This echoing of information throughout a network and across network scales echoes the fifth principle of the rhizome: decalcomania. As connectivism says, learning has much to do with embodying and recognizing patterns. Yes, "the Universe is big, but I feel big, because my atoms came from those stars."

Why Rhizomatic Learning? Pt. 3 #etmooc

So does the rhizome bring anything to connectivism that it doesn't already have? I don't really know, but I do know that the rhizome helps me think about connectivism in ways that I otherwise find difficult. I also find rhizomatic thinking familiar and evocative for a teacher of writing and literature. To my mind, the rhizome is a metaphor, not a model. A model is created to represent something else, often eliminating much detail and changing the scale to focus on some salient aspects of interest to the model creator and to make handling easier. The rhizome does not model the educational process (or any other process) in this way. Rather, the rhizome is more a metaphor that evokes the way reality works by comparing it, in some points but not all, to the way a rhizome works. The rhizome is evocative rather than descriptive, and it is in no way prescriptive. Evocation works well for me, but the more explicit minded may find the rhizome irrelevant and distracting.

One cannot take the rhizome as metaphor too literally, then. For instance, some have complained that rhizomes are a multiplication of the same plant over and over, and they find little appeal in this kind of mindless repetition, especially when applied to learning. These people are taking the metaphor too literally. The rhizome of Deleuze and Guattari is not a homogeneous botanical system; rather, heterogeneity is one of the six characteristics of their rhizome. As they say, "Any point of a rhizome can be connected to anything other, and must be" (7). Homogeneity, then, is not one of the points at which Deleuze and Guattari's compare rhizomes to reality. A metaphor invites one to explore all connections between the two things compared, but not all connections will prove useful or enlightening. Love is a rose, but not in all aspects.

If I understand the rhizome correctly, then, it is a metaphor of reality similar to the Enlightenment metaphor of the clock. Just as Galileo, Newton, and Descartes gave us the image of a clock to help us envision how the way too big Universe works, Deleuze and Guattari give us the image of a rhizome to help us make the shift from a mechanistic universe to an organic universe and to the math, science, and technology that make sense of that much expanded, different universe. Both the clock and the rhizome, then, are conceptual metaphors or frames, as Lakoff calls them, that describe reality in terms of either a piece of machinery or a plant; however, reality is neither a clock nor a rhizome. Still, I want to say that Deleuze and Guattari's marvelously twisted rhizomatic prose is about as close as one can get to the quantum, relativistic universe without way more math than I have. The rhizome is a wonderful metaphor in almost natural language for the complex systems that physics has almost completely accepted but still largely describes in mathematical terms—terms that I don't understand.

This may be one of the most important contributions that the rhizome of Deleuze and Guattari makes to connectivism: it emphasizes the shift from a mechanistic, reductionist reality to an organic, relativistic, quantum reality and it captures in natural language something like this new reality. In his definitions of connectivism, George Siemens talks about complexity and chaos theories, but his language does not capture complexity and chaos the way Deleuze and Guattari do. Of course, Siemens has a different audience and different objectives than did Deleuze and Guattari. Still, there are things you can come to understand only by jumping in over your head, and as Mark Twain wisely observed, "If you a hold a cat by the tail you learn things that you cannot learn any other way." Reading Deleuze and Guattari is like holding two cats by the tail. Most people are willing to forgo that joy, but I have found it an endless source of enlightenment.

My friend Dave Cormier makes a most important contribution here by connecting rhizomatic thinking to Dave Snowden's Cynefin framework, which posits five contexts for thinking and decision making, particularly in organizations: simple, complicated, complex, chaotic, and disorder. In his post Seeing rhizomatic learning and MOOCs through the lens of the Cynefin framework, Cormier says that both MOOCs and rhizomatic thinking and teaching match best with the complex domain. As Cormier says:

That description of how to act in a MOOC sounds just about right as a description of rhizomatic learning. The knowledge lives in the community, you engage with it by probing into the community, sensing the response and then adjust. Just like the rhizome. It is a learning approach that is full of uncertainty… not least for the educator. But its one that allows for the development of the literacies that will allow us to sharpen our ability to participate in complex decision making. Dealing with the uncertainty is what the learning is all about.

This, then, is a second important contribution of rhizomatic learning to connectivism: a focus on complexity. Rhizomatic thinking enriches the connectivist conversation, and it has allowed me to say things that I could not say otherwise. Deleuze and Guattari have given me language to speak of complexity.

The rhizome also helps me understand why I share Cormier's discomfort with learning in the simple domain. Cormier says:

I think most of what i criticize or, at least, what concerns me about education is the movement between the complicated and simple domains. Our bureaucracies encourage simple domain learning, things that can be tracked and analyzed. Research goals seem to attempt to take things from complicated domains and shove them down into the simple one. Our world is increasingly one where complex decisions need to be made… and thats the kind of education i’m interested in being involved in.

Most of education seems calculated to force all knowledge into the simple domain, with one source for truth and one answer on the test. Sophisticated instructors and some graduate programs allow for the complicated domain where "the relationship between cause and effect requires analysis or some other form of investigation and/or the application of expert knowledge" (Wikipedia). Traditional education, by and large, eschews the complex domain, where "the relationship between cause and effect can only be perceived in retrospect, but not in advance." Our traditional testing regimes demand clear answers and outcomes, and complexity refuses to play that game. Thus, our curricula try to make reality as simple as possible throughout most of K-16 education, only grudgingly admitting the complicated and almost totally denying the complex. The problem here is that most of reality is complex or chaotic. As near as I can tell, the truly simple is extremely rare in Reality and the merely complicated is almost as rare. Everything else is complex and chaotic (about 99.999% by my calculations). If 99% of education is forced into the simple and complicated domains and 99% of life is complex/chaotic, then it appears that we have a mismatch between what we are teaching and what we need to learn. Rhizomatic learning can help address this mismatch.

Why Rhizomatic Learning, Pt. 2 #etmooc

In my last post, I said that networking is the lens through which I see most everything, or at least I try. I confess that I still have some old habits of mind, mostly that I'm unaware of, but when brought to mind, I do try to address them. I quoted Olaf Sporns comments that science is increasingly using the networking metaphor to guide both its subject matter and its research. I want to comment on a couple of points he made, and I quote:

Increasingly, science is concerned with the structure, behavior, and evolution of complex systems such as cells, brains, ecosystems, societies, or the global economy. To understand these systems, we require not only knowledge of elementary system components but also knowledge of the ways in which these components interact and the emergent properties of their interactions. (1)

Note first that he although he started with the term networks, mid-stream he switches to complex systems, the same term that Edgar Morin uses. In my reading, these terms have often been used interchangeably, and I will likely do so in my own discussion, unless I find some reason to distinguish between them.

Second, Sporns captures neatly the distinction between the reductionist, mechanistic clockwork type of science with its focus on "knowledge of elementary system components" and the evolutionary, organic networking type of science with its focus on "knowledge of the ways in which these components interact and the emergent properties of their interactions." This shift in metaphors, or paradigms if you prefer, is extremely important for me.

Although Sporns and I do not share similar disciplines—he studies and teaches neuroscience and I study and teach writing and literature—his work has a critical, core benefit for me: Sporns insists and demonstrates through exhaustive research that cognition is a network phenomenon. I accept his argument, as I have not found a better, more detailed, more precise description of how the brain works. I was pleased, then, to read James Zull's educational book The Art of Changing the Brain (2002) which applies the networking paradigm to learning and draws out some implications for teaching and pedagogy. Networking, of course, is at the heart of Connectivism. Throughout his writings, Stephen Downes makes a number of statements that express knowledge and learning as network phenomena. For instance, in the 2011 post What Networks Have In Common, Downes says, "the state we call 'knowledge' is produced in (complex) entities as a consequence of the connections between and interactions among the parts of that entity." I could no doubt find even more pointed pronouncements in Downes' writing, but this is sufficient. In his online book Knowing Knowledge (2006), George Siemens says, "Knowing and learning are today defined by connections ... connectivism is the assertion that learning is primarily a network-forming process" (15).

For me, then, learning is the ability of an entity to recognize, build, and traverse networks. Moreover, the tools entities use to recognize, build, and traverse networks are themselves networks, and knowledge is an emergent property of the interactions among and across those networks of neurons, sensory organs, sound waves, light waves/particles, classrooms, social groups, languages, the Universe … as far out or in as you wish to take it.

Anyone who has read Downes and Siemens will see that nothing I say here is new. Those fellows have already said it, and in general, I agree with them (details are always problematic, but that doesn't concern me here). Learning is networking across multi-scale networks, and that has huge implications for the way we teach, but is learning rhizomatic? Perhaps a better way to ask this question is what does the concept of the rhizome as developed in Deleuze and Guattari's book A Thousand Plateaus bring to connectivism that it doesn't already have? This is similar to a question I have heard Siemens ask of Cormier in some of our previous MOOCs, and it merits an investigation, if not an answer. I'll try to do that. Tomorrow.

Why Rhizomatic Learning? #etmooc

Okay, so I enjoyed the conversation about rhizomatic education over at Christina Hendricks' blog, You're the Teacher. In the conversation, I'm definitely championing rhizomatic, connectivist education, but why? I've been writing about this for a couple of years now, but can I state my point of view succinctly and reasonably clearly? Well, I can try.

Learning is a network phenomenon.

That's rather succinct, and owes deep apologies to neuroscientist Olaf Sporns, but I can say it with a bit more texture: learning is a function of our complex interactions across multi-scale physical, cognitive, technological, and social networks. For me, this is the DNA of a connectivist and rhizomatic view of learning, and everything else I say about learning will follow from this core idea. At least, I hope so.

But can I defend my claim that learning is a network phenomenon? I think so, but in some ways, starting points always carry with them assumptions that one either accepts or doesn't, and they carry assumptions that the believer is quite often unaware of. I think my use of networks falls into this category. However, I can point to some reasons why I use the concept.

Networks provide me a most useful model of how the Universe/Reality/Everything works, including learning. Of course, as soon as I say that, I am reminded of George E. P. Box's famous dictum that "essentially, all models are wrong, but some are useful" (Empirical Model-Building and Response Surfaces, 1987). I am convinced that, despite how right network thinking feels to me, eventually people will come to see the faults with the network metaphor just as we are coming to see the faults with the mechanistic clockwork metaphor that we inherited from Galileo, Newton, and Descartes. As Edgar Morin has pointed out in his book On Complexity (2008), the mechanistic, clockwork model of reality and the science and technology built upon it has been spectacularly successful, but over the past century, cracks have begun to appear as we have come to see more of Reality, especially at the macro and micro levels. As we peer into our scopes, bits of reality emerge that no longer fit the clockwork model. Reality is stubborn, so we change our model. But slowly, sometimes too slowly.

The model that appears to be replacing the mechanistic clockwork model is networking. Of course, not everyone uses that term. Edgar Morin speaks of systems, especially complex systems. In his book Interaction Ritual Chains (2005), Randall Collins defines the core sociological unit not an individual but the situation, a dynamic nexus of intersecting vectors which to my mind requires a network structure. James Lovelock calls it Gaia, the movies call it The Matrix. All of these sources have valid reasons for using the term that they do, but to my mind, networking (and here I'm using the verbal form intentionally to capture the complex dynamics in my concept) is the most convenient and natural-feeling term. I spent many years of my professional life building campus networks and connecting students, faculty, and staff to the Internet, so it just works for me; however, I also frequently use the term rhizomatics or rhizomics to play off Deleuze and Guattari's concept of the rhizome (A Thousand Plateaus, 1988), a more free-form, complex, dynamic, and inclusive form of networking that reveals some properties that I find particularly useful and fun.

So the networking model in all its various iterations and apellations appears to be the emerging model of how things work. I like Olaf Sporns' comments about this in his book Networks of the Brain (2011), so I'll end this post with a long quote that has a decidedly scientific bias that I think will inform our thinking in the humanities:

Over the last decade, the study of complex networks has dramatically expanded across diverse scientific fields, ranging from the social sciences to physics and biology. This expansion reflects modern trends and currents that have changed the way scientific questions are formulated and research is carried out. Increasingly, science is concerned with the structure, behavior, and evolution of complex systems such as cells, brains, ecosystems, societies, or the global economy. To understand these systems, we require not only knowledge of elementary system components but also knowledge of the ways in which these components interact and the emergent properties of their interactions. (1)

Rewiring Student Brains

James Zull's book The Art of Changing the Brain contends that neuroscience can guide our teaching practice by revealing to us how our brains actually learn. I think his insight is reliable, and I'm particularly satisfied that he views the brain as a complex, multi-scale network and learning as changing, extending, and strengthening the connections within those networks. This fits quite nicely with connectivism, which defines learning in similar networking terms.

This definition of learning puts the student/learner at the center of the learning process, unlike traditional education, which puts the teacher/authority at the center of the learning process. Why? Because if learning is the development of new connections within existing neuronal networks, then learning depends overwhelmingly on the engagement of the student. No teacher can directly touch a student's brain. Development of neuronal networks absolutely depends on the student exercising her own brain, and her teachers cannot do it for her, any more than a fitness trainer can exercise her muscles for her. The student must sweat and exert herself and must want to sweat and exert. If the student is emotionally, physically, or intellectually incapable of learning a given lesson at a given time, then there is little the teacher can do. At best, teachers can create an environment that is engaging for a student and that encourages them to exert themselves, but the teacher cannot do it for them.

Then, each student comes with different neuronal networks. We teachers can often rely on rather gross similarities among student perceptions, neuronal processes, and responses, but the multi-cultural, inclusive nature of many modern classes shows how unreliable our dependence on these gross similarities can be. Our brilliant lectures and lessons, then, may engage one student and not the next. Neuroscience tells us why. If learning is a process of developing existing neuronal networks, then learning must start with each student's existing neuronal networks, and they ain't all the same. Some are positively alien, and ALL are different from the teacher's. Ground Zero for learning is NOT the teacher's knowledge, then, but her students' alien neuronal structures.

Traditional education views the teaching/learning process as a teacher writing a concept on the chalkboard of the student's mind. This is a radically false notion of education, and yet it is still the basis for too much instruction, even if the chalkboard is now a computer screen and the lecture involves a PowerPoint. The teacher's job is crucial but not essential to learning. The skillful teacher can create an environment that focuses, encourages, and enables students to stretch their minds to create new neuronal networks, but the teacher cannot create those neuronal networks for the student.

Moreover, the teacher cannot prevent students from learning. Most of the stuff that I remember learning in middle school—dealing mostly with sex—was never taught in the classroom. I suspect that most of what is learned in school is never taught from a lesson plan.

So what's the lesson for this teacher? First, I must start with the student and with their existing neuronal networks. That means that each program of study should begin not with what I know (the course content) but with what they know. I must build in to my classes time to discern what my students already know and do not know. The flipped classroom and just-in-time teaching techniques allow for this, and I use them.

Second, given the variety of neuronal structures I'm likely to encounter in any class, I must create a flexible environment that allows for a variety of engagements, processes, and responses. There is no one-size fits all. I know where I want my students to end-up, but I cannot assume one highway to travel or one vehicle. Some come from the cane fields near Belle Glade, some from the tenements of Lake Worth, and quite a few from Haiti, Jamaica, and Europe. Some take the bus, some walk, and some drive different cars. Some are here in a few minutes, some drive an hour or more each way. Getting everyone to West Palm Beach is a very messy business, and many teachers simply don't want to take that on. I think that's why they focus on simply delivering their content. It's much easier. It's also largely ineffective.

Third, I must monitor frequently, and not only to discern what they are not learning, but also what they are learning. I must check their progress along their different highways, monitoring where they are going and how fast they are travelling. If a particular lesson calls for a very specific destination, then I must be able to encourage those on-track to continue even if they can't see the destination, and I must be able to nudge those off-track to make new turn. If a particular lesson has no specific destination, as is the case with many cMOOCs, then I must delight in learning where they are going and encourage them to share their snapshots with me.

Rewiring the Neuron

My good friend Bruce recommended that I read James E. Zull's book The Art of Changing the Brain (2002), and I'm glad that I followed his advice. The book has some important implications for connectivist, rhizomatic thinking.

The first section of the book establishes a direct correlation between brain form and functions and teaching and learning. As Zull says in the very first sentence: "Learning is about biology." For Zull, learning is the process of changing neuronal structures, and good teaching is aware of and works with the brain's innate structures and functions to enable those changes. I'm a bit uneasy about reducing learning to physical changes in the brain, but I can accept this as a useful focus for better understanding this aspect of learning. So what does this connection between brain structure and learning imply for connectivism and rhizomatic learning?

First, Zull understands the brain as a network. This is implicit in the first part of his book, but he later makes it explicit, devoting Chapter 6 to an exploration of neuronal networks. Moreover, he conceives the brain as a multi-scale, complex networking structure. For instance, individual neurons are networked to process certain sensory inputs, but then those individual networks are networked into larger networks to perform various integrative and meaning-making functions. Zull does not extend those networks into the larger networks of the body or our social and natural ecosystems, but that may be a result of his particular focus for this book rather than a specific belief. I find nothing in what he says that would exclude such an extension of our neuronal networks. It's easy for me to say, then, that Zull, like connectivists, sees learning as a network phenomenon. For Zull, learning is the process of shaping neuronal networks that map more or less well to reality. This process of neuronal mapping is quite compatible with Deleuze and Guattari's notions of decalcomania and cartography, and network structures are at the heart of both connectivism and the rhizomatics.

Zull provides some specifics about neuronal processes that are useful for connectivism and rhizomatics, that make cartography and decalcomania more practical. First, he outlines the basic sequence of learning:

1. sensing,
2. integrating (2 parts), and
3. acting.

For Zull, then, learning is grounded in sensing the physical world, integrating those sense impressions into our existing neuronal networks first through reflection and then abstraction, and then acting on, or testing, that new knowledge. This is a dynamic, complex process because the results of acting/testing is then fed back into the loop as we sense the consequences of our actions/tests, integrate those consequences, and act/test again. Through this process, our brains develop themselves, strengthening those neuronal networks that lead to somehow satisfactory results and weakening those networks that lead to unsatisfactory results. Of course, the loop is not this simple. We humans are often confused about which neuronal networks map satisfactorily to reality and which don't, and our brains can create and adhere to some awful mappings, but mostly it seems to work for us, and according to the latest neuroscience, this is the process most of us use.

The takeaway for me is that the educational process is improved if it begins with a concrete, sensory experience, includes time for reflection and abstraction of the experience, allows for action/testing of the new knowledge, and allows for feedback of the testing results into the loop. A teacher doesn't need to know anything about connectivism or rhizomatics to follow this teaching process, but this process can usefully inform connectivism and rhizomatics.

First, at the start of any lesson, students favor concrete, sensory experience. This is what they are learning. Unfortunately, that means most of our students are learning whether or not they like their teachers, whether or not the teacher is boring, whether or not the classroom is comfortable, whether or not this class fits into their eating schedule, and so forth. Our brains are wired this way, and our brilliant lectures can seldom overcome this sensory bias. Perhaps, then, we should capitalize on the sensory bias, and begin there. For instance, in my writing classes, I could start by having my students text someone on their smartphones about what they are doing in my class, and then launching into a classroom discussion of why people write to each other.

Second, students need time to integrate their new sensory experiences into their existing neuronal networks. Implications: if we march tenaciously through the content, we will lose most students. At best they will manage to register the content into short-term memory, which is a necessary step in learning, but hardly sufficient for any useful learning. The brain quickly flushes short-term memory to make room for the next short-term memory. Reflection and abstraction are the neuronal processes that move sensory experience into long-term memory and integrate it into existing neuronal networks. Most of the classes that I took provided little to no time for reflection and abstraction or for feedback of tests; thus, I have forgotten most of what I learned. What a sad waste of everybody's efforts and investments. So in my writing classes, I might allow my students to blog about the concepts I'm trying to teach, encouraging them to make the connections between the new stuff and what they already know. Class discussion is also a good tool for this reflection. I might encourage abstraction by having student bring in writing assignments from other classes and plan in groups how they might solve those assignments. We might also allow for feedback from those assignments, assuming there is time.

Then, short, shared writings in class are a good way to test new knowledge and feed the results back into our brains. Actually, I don't see why this wouldn't work in most any class. Maybe I'm just biased, but I think that writing is one of the best tools we have for integrating new information into our existing neuronal networks, thereby turning it into knowledge.

Simple vs. Complex Definitions

Dave Cormier's post A review of rhizomatic learning in Mendeley led me to an engaging conversation about definitions, especially a definition of rhizomatic learning. This is a topic that keeps returning to me, and I have not sufficiently worked through it, but I keep trying because I think it is important to the discussions about connectivism and rhizomatic learning.

Definitions are important to any discussion, for without a working definition, conversation is almost impossible. In many ways, definitions are the bedrock of traditional education, especially to what Cormier calls "the basics." Much class time from kindergarten all the way through baccalaureate higher education is spent on defining colors, times tables, history dates, science terms, parts of speech, and literary genres. The problem, it seems to me, stems from our standard method of defining, which I believe is reductionist and essentialist in nature. What do I mean by that?

First, traditional definitions reduce a concept—let's pick the four major learning theories—to a few usually salient and so-called essential features. A recent infographic from Edudemic entitled A Simple Guide To 4 Complex Learning Theories lists and defines the theories this way:

1. Behaviorism: Learning is a process of reacting to external stimuli.
2. Constructionism: Learning is a process of acquiring and storing information.
3. Cognitivism: Learning is a process of constructing subjective reality based ???
4. Connectivism: Learning is a process of connecting specialized nodes or information sources.

For the moment, let's ignore whether or not we think these definitions reliably capture the essential features of each theory. Let's note instead that they are standard definitions. They reduce complex theories to a couple of essential characteristics. A traditional education course might say: if you want to understand connectivism, at least well enough and long enough to pass the test, then learn an eleven-word formula that effectively reduces the life's work of Siemens, Downes, Cormier, and countless other scholars to a process of connecting specialized nodes or information sources. Well, if that's all it is, then why didn't George just say that to begin with and save us all this work and talk? Of course, this is not connectivism, and if you are like me, you find this kind of reductionism repugnant and inadequate. It takes us almost nowhere. It takes those poor students almost nowhere, at least, nowhere beyond a grade on a test.

So why do we do this? I think we are seeking clarity, stability, and control. The flux of life is troubling and troublesome to most of us, and we want to define it away. We want clarity, or clear boundaries between this and that. So we say that behaviorism is reacting to external stimuli, and connectivism is connecting nodes—as if behaviorists can't connect things and connectivists can't react to external stimuli. This is little more than arranging your sock drawer—not a bad thing to do, but hardly sufficient to build a life or an education around. And we want stability. Once we define light socks on this side and dark socks on that side, then we don't want anybody messing with those categories. We can be outraged at those who will re-arrange our drawers (I'm thinking of a certain spouse here) putting cotton socks here and synthetics there, or dress socks for dark shoes, dress socks for brown shoes, casual socks, and athletic socks all in different piles. Don't these other people understand the inviolable order of the Universe? And finally, we seek control. When I reach for a certain pair of socks, I want to know exactly where they are and just which pants they go with.

So am I ridiculing this drive toward clarity, stability, and control? Absolutely not. Life is much easier with clarity, stability, and control, and much of our time and energy is spent blocking out well-ordered social, economic, political, religious, and familial spaces for ourselves. In terms of Dave Snowden's Cynefin framework, which Dave Cormier uses in his post Seeing rhizomatic learning and MOOCs through the lens of the Cynefin framework, we are constantly trying to move as much of life as possible from the chaotic, complex, and complicated zones into the simple zone. If my socks are arranged as I want them in my sock drawer, then I don't have to think much about them anymore. The sock realm of my life is now simple, which reduces my expenditure of cognitive and physical energy for an issue that I don't really want to engage much anyway. As with our socks, we want to move connectivism into the simple zone. Once I've made connectivism simple by reducing it to couple of essential characteristics and creating distinct, stable boundaries between it and other theories, then I don't have to think about it much anymore. I've nailed it. This gives me a sense of clarity, stability, and control.

So what's wrong with clarity, stability, and control? Nothing, except the Universe doesn't seem to be arranged that way. Reality has a way of becoming un-nailed and slipping out of our comfortable categories, of fleeing down lines of deterritorialization in what Deleuze and Guattari call asignifying ruptures. Modern physics seems to suggest that very little of the Universe is simple and that all of that most interesting and salient part of the Universe that we call Life exists in the complex zone between the merely complicated and the radically chaotic. Certainly, connectivism and rhizomatic education exist in the complex zone. By the time they have moved securely into the simple zone, most of us will have lost interest in them. And look at what has happened to MOOCs. We early participants in cMOOCs thought we had them fairly well defined, and then Coursera and edX came along to re-arrange the sock drawer. Life does that, and we shouldn't have tried to reduce MOOCs to a single thing anyway.

So the big problem for me is that definitions based on reductionism and essentialism try to move complex things into the simple zone, obscuring a thing rather than clarifying it. How so? First, a reductionist definition treats the definition as an end point rather than a beginning point. It stops conversation rather than starts it. Most teachers know that the quickest way to end a classroom discussion is for the teacher to give the answer. After you have the right answer, what else needs to be said? That issue is closed. Unlike simple, reductionist definitions, complex definitions take a definition as a starting point, as DNA which is constantly unfolding into a new entity, like a snowflake: recognizable as a snowflake, and yet totally unique. If snowflakes are that complex an entity, then how much more complex are humans and societies? We need definitions of snowflakes and people and theories that start with a few elements and processes (DNA) and work outward to an infinity of forms, not definitions that work inward toward one form with a few distinguishing features. Of course, this approach to defining really messes with the whole regime of multiple-choice tests.

Then, simple definitions remove the person from the definition. One of the great lessons of modern physics is that no observation or calculation or definition can be made aside from an observer, calculator, or defining agent. We can't leave people out. Any definition of connectivism must account for the point of view of the person defining it. I value the Oxford English Dictionary for its inclusion of writers who have used a given word in a given way. That's a step in the right direction. Complex definitions recognize the issue of point of view and build it into the definition.

I'll write more tomorrow about definitions.

Thinking Like Grass

Like most everyone else for the past six months, I've been thinking about MOOCs (note that on Susan Bainbridge's current Connectivism Scoop page, easily half of the scooped articles are about MOOCs (2nd note: if you are at all interested in Connectivism and MOOCs, then you should follow Susan's Scoop. It's invaluable, and I deeply appreciate her work.)). I've introduced a good friend of mine to MOOCs and Connectivism, and he read the things I sent him. He was interested in the concept, but he had two immediate concerns about MOOCs:

• Social sharing can legitimize any kind of knowledge, like racism, sexism, imperialism. Without an ethical standard, knowledge is free to kill as well as to cure. (Which is not to say that traditional education is ethical—I don’t think it is. But there are other options.)
• And the second is the danger of elitism. I don’t see my students getting very far in their rhizomatic education. (Which is not to say that they will get very far in traditional education either.) I guess I would call this feature the “appearance of democratic education.”

He concluded by asking if I have read "Morris' News from Nowhere—a late 19th-century British utopian novel" in which the citizens "have no theory of education at all, and no specific practices either." I have not read the novel, but I will—after all, turnabout is fair play, but I want to respond to Dan's concerns.

First, I have not thought much about the ethical aspects of Connectivism and MOOCs, nor have I read much about ethics from anyone else in the connectivist discussion, but I think Connectivism and cMOOCs have an ethical perspective built into the first O in MOOC: Open. MOOCs are open in any number of ways, but especially in terms of network connectivity. Anyone is free to connect to and engage a MOOC, and they will do so IF they perceive value in the connection. No one has to connect, and in fact, most of the people who sign-up for a MOOC do not engage the MOOC in any degree that might be significant to an observer—say a college administrator looking for the ROI. This should not bee sting as a bad thing. Rather, it should be seen as bee efficiency. Apparently, when bees want to move their hive, the scout bees fan out in all directions. Most of them find nothing, but a few find something, and through their connections, they channel the other bees into these promising pathways until finally the way to a new hive emerges. What starts as chaos (MOOCers will be familiar with this sense of early chaos in a MOOC) turns out to be a highly efficient way to create new meaning for the hive. Still, it's highly wasteful, like most MOOCs. Fortunately, the cost of each connection to a MOOC is almost nil, so the waste is functionally irrelevant. But the waste identifies quite efficiently those students who are in some way ripe for learning whatever emerges from the MOOC. Those who are not ripe simply fade away with little to no damage to the MOOC. I like this bee efficiency.

This openness to connectivity is an aspect of network dynamics, I think, and it has to do with a shift in the way value is created in a network as opposed to a hierarchical structure. In a hierarchy, one's relative value is measured by the number of people under one and subject to one. In a network, one's relative value is measured by the number of people willing to connect to one. This is an obvious oversimplification, but it points to a seriously different dynamic in the relationships among people in a functional group. The relationships in hierarchical groups are based more on power, benevolent or otherwise, while the relationships in network groups are based more on mutual attraction. Engagement or not is up to the agent, and this is a powerful kind of agency.

This radical shift in agency demands an equally radical shift in ethics. It seems to me that ethics for the past few hundred years has been based on the need to manage exchanges across discrete boundaries. In other words, reductionist thought makes each of us a position within a hierarchy—a "cog in something turning" as Joni Mitchell put it—with quite distinct boundaries between positions, or agents, and agency has been defined in terms of who gets to tell whom what to do and how to think and how to reward and punish those exchanges. This kind of ethics, this Lockean social contract, does not work if, as a node in a network, you have no fixed position, if you are free to engage or disengage connections, and if the connections depend on mutual attraction, as they do in MOOCs. We need an ethics of complex, multi-scale networks, which is partly how I define a MOOC. Perhaps such an ethics exists, but I don't know about it (any philosopher out there willing to enlighten me. I'm a fairly quick read.)

So I revise what I said earlier about connectivism having a built-in ethics. It doesn't. Rather, it seems to me that the openness of connectivism and its MOOCs calls for a new ethics based on a rethinking of agents, their boundaries, and their exchange processes. The ethics that works for an agent occupying a position in a reductionist hierarchy will not work for an agent acting as a node in a dynamic, complex, multi-scale network. The networked, connectivist agent needs a new ethics that guides the dynamic choices that help identify useful connections and cultivate those connections and eventually close some of those connections. To put this in MOOC terms, MOOCers need a new ethics that guides their choices about which MOOCs to engage, which agents and content within the MOOC to engage, and how to engage: how to both give and take value within their networks. Actually, I think give and take are the wrong terms, too strongly tied to the reductionist, hierarchical ethics with its exchanges across discrete boundaries. We need an ethics that helps us become value within a network, increasing the value of the network to the benefit of the entire network. I suspect, then, that ecological movements may be working out the details of the kinds of ethics that I'm looking for. I'll have to check into that.

This leads me to Dan's comments about elitism and that he doesn't see his "students getting very far in their rhizomatic education." If he means that, unlike elite students, most college students lack the internal motivation and skills to engage an open network of inquiry and discussion, such as cMOOCs, then he's probably correct. Aside from the graduate courses at elite universities, too much of our education is an exercise in what Deleuze and Guattari (A Thousand Plateaus, 1987) call tracing, a careful, meticulous repetition of patterns and truths already laid out for us in a curriculum and watched over by proctors keen on sameness and competence. Open cMOOCs call for mapping, or a process of "active construction based on flexible and functional experimentation, requiring and capitalizing on feedback" (Cheun-Ferng Koh, 1997). Thus, our students have learned to trace well, but they see no advantage in going outside the line, in mapping new territory for themselves or others. The last thing a successful student wants to do on a test is to tell the teacher something that she doesn't already know. That is largely and by default defined as failure. Tracing well does not prepare one for success in a MOOC. Actually, that skill frustrates both the MOOC and the student.

If, on the other hand, Dan means that in the open network of a MOOC a few students will attain more status and value than most others, then he is also correct. The power laws of scale-free networks express the strong probability that some nodes will be more well connected than most other nodes. This happens in every MOOC that I have engaged. Often, the teacher or weekly leader in a MOOC is a highly connected node, but I suspect that this is in some part residue from traditional education, in which the teacher is the ONLY well-connected node in the hierarchy (too often connections among students—talking—are censured and censored). In the best MOOCs, sub-networks develop as students connect to each other in their engagement of a mutually interesting and enriching discussion. MOOCs encourage this kind of networking within the network, and often enough, one or two nodes of those sub-networks gain more status, become elite, through more connections from other nodes. I do not see a problem with this, but I do think it is distracting to those students who are looking for the correct content to trace competently rather than for the new content to map usefully.

Finally, like Dan, I wonder if education can do without theory and practice. I think it can, but only if we are thinking of theory and practice as mechanisms for promoting tracing rather than mapping. When many first-time MOOCers move from tracing in the traditional classroom to mapping in a MOOC, then they feel a loss of theory and practice. They are disoriented. The lines drop out from under their feet, and this causes real stress and grief for many, which those students have expressed in blog posts, tweets, and feedback in many of the MOOCs I've engaged. And these are elite students, by the way.

So as with the call for a new ethics, I think MOOCs call for a new theory and practice in education especially, and I'm fairly certain that this new theory and practice will strike many of us as NO theory and practice. I think Deleuze can offer some suggestion here. I read an article by Xiao-Jiu Ling called Thinking like Grass, with Deleuze in Education? (Journal of the Canadian Association for Curriculum Studies, Vol 7, Num 2, 2009) in which Ling draws so tempting implications from Deleuzianal thought:

Then, what could Deleuze mean to the field of Education? My first temptation is to simply boldly borrow his phrase above and to propose thus: There is no need for education: it is necessarily produced where each activity gives rise to its line of deterritorialization. To get out of education, to do never mind what so as to be able to produce it from outside! [italics in the original] Perhaps, it is indeed a Deleuzian repetition that we can aim for in education, a kind of repetition that is a transgression, in which its possibility hinges on opposing as much to moral (nomos) law as to natural (physis) law (DR, p. 2-3). By working in opposition to the order of the always already-existing laws, in the spirit of parrhēsia prefigured by Diogenes the Cynic, Deleuze is proposing new possibilities of working in the direction of creating artistic realities; that is, to treat philosophy itself as an artistic endeavour in its essential nature. And if one is to realize the fundamental role that education plays in forming our frames of thinking, that is, providing existing and always the dominant images of thought of our society in general, the relevance of Deleuze’s analysis and his “anecdotes” of philosophizing is hard to deny. Or, at least we are tempted to make this parallel: that if philosophy can be made fecund with the open-mindedness of an artist, then the work of education can also be made fertile through the exigency of treating it as an artistic engagement, something that not only demands creativity but more importantly a critical consciousness of the ethical dimension that is inherent in education. (43,44)

Well, let's talk about this some more, later.

Why Connectivism?

I've not blogged in over a month, and I have a serious case of disconnection. I'm irritable about it. Really. I'm not so pleasant just now, and I think a large part of it has to do with my loss of connection with my writing, my thoughts, my conversation. Of course, I have good reasons for being disconnected—mostly demands from other connections: family, vacation, a personal blog, work, an election, and more—but those reasons do not ameliorate the dissatisfaction. The only solution is to reconnect. Perhaps a better way to say it is: it's time for me to refire a dormant neuron.

And it occurs to me how differently I think about things now and how I owe much of that difference to this conversation about connectivism.This has been a most important conversation for me, and I'm uncomfortable when I don't exercise it regularly. So what is it that makes connectivism important? If I had to chose one word for you, then I would say networks, networks in both their formal sense as mathematical, scientific structures and their informal sense as rhizomatic, literary structures. I admire and respect the first way to think about networks, but I love the second. The first is a revered teacher, the second a passionate lover. Both aspects of networks have reshaped my thinking about most everything in life, but especially the way I view education and rhetoric. Networking is an archetypal meme that radically changes the way I see my world. For me, this has been big stuff.

I've been reminded from several sources just this past week about the revolution that is occurring all around us as the networking meme (virus might be a better term) spreads. The webzine Edge had a great conversation with Albert-lázló Barabási about thinking in network terms. Barabási notes first that "we always lived in a connected world, except we were not so much aware of it." We became aware of networks as technologies gave us a way of better viewing them. This is the same as our using telescopes to learn that the Earth is but a speck of dust on the outer edge of the Milky Way galaxy. We had always been a speck of dust on a speck of dust, but the telescope helped us to see that. Similarly, we have always been nodes in physical, chemical, biological, social, spiritual, intellectual, rhetorical networks, but it took computers and computer networks for us to appreciate that fact and to address it. Now, as Barabási notes, "We never perceived connectedness as being quantifiable, as being something that we can describe, that we can measure, that we have ways of quantifying the process. That has changed drastically in the last decade, at many, many different levels." New technology has given us the ability to approach rationally a phenomenon that has always been here and sensed on some (usually spiritual or artistic) level, but not quite graspable outside of poetry and prophecy. At last, science has the tools to systematically deal with networking, and it's going to change everything.

The network meme is in the DNA of connectivism. As far as I know, connectivism is the most coherent  and vibrant attempt in educational theory to deal with education as a network structure, and for me, that is connectivism's greatest value. Just as many scholars are doing in physics, chemistry, biology, sociology, mathematics, and other disciplines, connectivism places networking at the core of its methodology and thinking. Networking guides its practice and preaching. Connectivism has converted me, and like the Apostle Paul, I hope to take this new thinking to my home town: Rhetoric. I believe that networking will revolutionize rhetoric and writing instruction.

I'd better start writing. Wow. I do feel better. I hope you do, too.

Agency and the Death of Steve Jobs

A Sunday School Lesson:

The common attitude about Steve Jobs reflects the old view of agency, especially in business: that one person causes things to happen. Most of us so hope that is true, but the reality is that Apple was much more than Steve Jobs and that Jobs could not have done iPads without Apple and Apple could not have done them without Jobs. Steve Jobs became for most people a handy reduction of the complexity of Apple.

The iPad is an emergence of all the parts working at Apple, much like this post, which is an emergence of the billions of underlying calculations and logical processes within my MacBook Pro. Whatever meaning you derive from the words in this post absolutely depends upon the regular mathematical and logical processes at work in the heart of my 2.4 GHz Intel Core i5 processor. These processes are causal: one process leads logically, predictably, and necessarily to the next process.

One might be tempted, then, to extrapolate the causality at work on the processor scale to the social blog post scale. This is a big mistake. The processes at work in my CPU do not cause the meaning that you and I see in this post. Those underlying electronic processes are necessary for meaning to emerge at this scale – a post on our computer screens – but they are not sufficient to explain it. Try it: follow the flow of electrons, translate them into a stream of 1s and 0s, watch as those trillions of 1s and 0s combine, split, store, dump, and interact, and you will never see any evidence – not even a glimmer – of the meaning in this post. You would see a fantastic light show, but you would not see this post or its meaning. You can't get from there to here.

At least not without a gradual process of emergence from network scale to network scale through the rhizome of this blog. The 1s and 0s aggregate into bytes to form letters (and I'm doing a lot of glossing here), but even the letter you think you see on your screen is an emergent entity. You are really looking at millions of pixels interacting in certain ways so that what you accept as letters emerge on your screen. As you see these emergent letters, the vaguest wisps of what we commonly think of as meaning are starting to emerge, but even here at the scale of morpheme and grapheme, you would be hard pressed to actually see the meaning in this post. You cannot find causality even at this nearby scale: morphemes and graphemes are necessary for meaning, but they, too, do not cause meaning. As the morphemes and graphemes aggregate in certain ways into words, then we get closer to our common concept of meaning, but even at this scale – so close at hand – it's very difficult to find the cause of the meaning that we see in this post.

Common meaning does seem to emerge as words aggregate into sentences. Ahh, this is it, we say. Now we have meaning, now we can explain meaning. Perhaps. But just for fun, take any sentence out of this post to stand alone:

At least not without a gradual process of emergence from network scale to network scale through the rhizome of this blog.

I chose the first sentence (yes, I know it is not a complete sentence) of the previous paragraph. It was handy. By itself, it means almost nothing, or almost anything, which is the same thing as far as meaning goes. Of course, it's difficult for you and me to read that sentence outside of the context of this blog post because we've already read it within context, but if you want some small fun, share that sentence with someone who hasn't read this post and see what meaning they make of it. I'm willing to bet not much.

So we have to move up to the paragraph level, then, for meaning to emerge? That helps, but not as much as we might hope. So move up to the post level? That's better, perhaps. If I've written well, then this post might have some hope of standing alone as a meaning-bearing artifact, but really, if you don't also have some sense of this blog in general and of the conversations about emergence, rhizomes, and connectivism, then does this post make much sense? I don't think so. If any of my Composition I students read this, they will not likely understand much of it. And of course, we have not yet considered our own brains and the rhizome of meanings that we each bring to this post. Is that where the meaning is? In our several heads?

The answer – to my mind, at any rate – is quite clear: the meaning isn't in any one of those places. Rather, the meaning is distributed across all the rhizome from the neat march of 1s and 0s in my CPU to the cacophony of conversations across the ages about what it means to mean something. And here's the magic: the more meaning you perceive across the rhizome, then the more meaning you can perceive in any one location.

The meaning of the iPad isn't located in Steve Jobs, either. Rather, the meaning of the iPad is distributed across the rhizome: in the thousands of people across the globe who helped create it to the millions of people who use it and the billions who don't, to those who love it and those who hate it. Steve Jobs is necessary for understanding the iPad, but he is not sufficient. He is part of the DNA of the iPad, just as electronic processes are part of the DNA of this post, but he is not the meaning of the iPad. Rather, the iPad cannot be reduced to Steve Jobs, ultimately not even to Apple. The meaning is distributed throughout the rhizome, and if you want to map that meaning, then you must go know/mad.

More Agency, Rhetoric, and Connectivism

If agency is the ability to recognize and respond to the surround, then does agency fit with connectivism? Yes. In fact, I can't think of an educational theory that agency does not complement. Education is hardly understandable without some notion of agency on either the teacher's part, the student's part, or usually both parts. But is agency in connectivism distinguishable from agency in behaviorism, cognitivism, and constructivism? I think so.

In her article Rhetorical Agency as Emergent and Enacted (CCC 62:3, 420-449), Marilyn M. Cooper locates her concept of rhetorical agency in the same theoretical framework as connectivism: complexity theory, and for her the acting agent is a complex system within a complex system. She is quite clear that the agent is not a classical subject, or a "centered, conscious, rational self", and she defines her task as rescuing the notion of agency from the "death of the subject" (420). She begins this rescue by denying the existence of the subject: "a workable theory of agency requires the death not only of the modernist subject but of the whole notion of the subject" because "the subject is inescapably defined by an agonistic relation to the object/other: the subject attempts to control the object/other in order to escape being controlled" (423).

As I understand her, Cooper is troubled by the inherent and unavoidable power struggle in a modern, reductionist view of the subject – a view I associate with behaviorism and cognitivism. As long as we view agents as discrete subjects distinct from and independent of the objects/others around them, then we cannot avoid sliding into issues of power. Indeed, the agency of a modernist subject can only be viewed in terms of power: the ability of the subject to effect change in an object through the exercise of power, however benign or well-intentioned. This idea of agency as power neatly captures the traditional notion of education: a teacher/school effecting change in students through the exercise of power, usually well-intentioned. We then measure the amount of change in the students to determine the efficacy of the teacher/school. It's a wonderfully simple model that seems as if it should work. One could almost wish it did.

Cooper replaces subject with actor/agent, which she borrows from Bruno Latour's Actor-Network Theory: "Unlike subjects, agents are defined neither by mastery, nor by determination, nor by fragmentation. They are unique, embodied, and autonomous individuals in that they are self-organizing, but by virtue of that fact, they, as well as the surround with which they interact, are always changing" (425). Agency is an emergent property of the interactions of agents with their surround, "the process through which organisms create meanings through acting into the world and changing their structure in response to the perceived consequences of their actions" (426). In other words, we perturb the world, Prufrock notwithstanding, and the world perturbs us in return. In the patterns of this interaction, we come to recognize and know our intentions and agency. Does this complex agency have a place in connectivism? I think so.

Both Cooper's concept of agency and connectivism, then, are grounded in complexity theory. In his blog post What is the unique idea in Connectivism?, George Siemens says that connectivists "find support for connectivism in the more nebulous theories of complexity and systems-based thinking." In his article Learning Networks and Connective Knowledge, Stephen Downes notes that knowledge itself is an emergent property of the interactions of neurons: "human thought amounts to patterns of interactions in neural networks." Knowledge does not lie in any neuron or any grouping of neurons but in the emergent patterns of neuronal interactions and networking.

While Siemens and Downes both ground their thinking in complexity theory, I find the most useful treatment for agency in David Cormier's concept of the community as curriculum. In his 2008 article Rhizomatic Education: Community as Curriculum, Cormier describes how curriculum itself can be an emergent property of a community of learners: "In the rhizomatic model of learning, curriculum is not driven by predefined inputs from experts; it is constructed and negotiated in real time by the contributions of those engaged in the learning process. This community acts as the curriculum, spontaneously shaping, constructing, and reconstructing itself and the subject of its learning in the same way that the rhizome responds to changing environmental conditions."

To my mind, Cooper's concept of agency fits quite nicely into Cormier's community as curriculum, particularly as expressed in connectivist MOOCs. Agency, curriculum, and knowledge all emerge as properties of the interactions of a community of learners, and all three are the trajectories of the patterns of interactions among the various nodes of the community. As learners join a community such as a MOOC, their very presence (even lurkers) perturbs the community, which in turn feeds back into the learner's own mind. The dynamic interplay and interaction of learners, artifacts, and network enables patterns of intention and mapping, in Deleuze's terms, to emerge, and our awareness of these patterns helps us to articulate, mentally and physically, the agencies, knowledges, and curricula that bubble up out of the brew.

I think I have more to say about agency and the rhizome, especially in light of Cormier's use of the term and Cooper's dismissal of Deleuze and Guattari, but not tonight.

The Nodes and Edges of Connectivism

I've just finished reading Scott Weingart's Demystifying Networks, Parts I & II, in which Mr. Weingart tries to correct the misuse of networks by humanities scholars. He provides a basic and quite clear explanation of what networks are and what they are not, how current technology can analyze networks, and what technology can say about networks, and most importantly, what it cannot say about networks.

Along the way, he provides the DNA for networks, and this reminds me of how fractal and complex networks are, with networks nested within networks and interacting across multiple scales. If networking is part of the DNA of connectivism, then what is the DNA of networking? Weingart gives me a few handles to work with.

He starts his discussion by defining networks in a typical fashion: networks "stand for any complex, interlocking system. Stuff and relationships [emphasis in the original]." He calls the stuff nodes and the relationships edges, in keeping with common terminology. A network, then, is a collection of nodes that are in some way related along recognizable edges. Stuff and relationships, or nodes and edges, are part of the DNA of networks. This is simple and intuitive enough, and Weingart makes this definition directly applicable to connectivism when he notes that

generally, network studies are made under the assumption that neither the stuff nor the relationships are the whole story on their own. If you’re studying something with networks, odds are you’re doing so because you think the objects of your study are interdependent rather than independent. Representing information as a network implicitly suggests not only that connections matter, but that they are required to understand whatever’s going on.

Starting with these basic concepts, then, I immediately think that there isn't any information – or any other thing, for that matter – that cannot be represented as a network. Indeed, I agree with Weingart "not only that connections matter, but that they are required to understand whatever's going on." I think this is Downes' point when he says on his blog, "At its heart, connectivism is the thesis that knowledge is distributed across a network of connections, and therefore that learning consists of the ability to construct and traverse those networks."

Both the Universe and our knowledge of it are network phenomena. Actually, I already object to that statement as it seems to suggest that our knowledge is something apart from the Universe. It isn't. Our knowledge is somehow an active node in the universal network, connected along multiple edges, just as natural an emergent phenomenon as rocks are.

I don't know that either Weingart or Downes would completely agree with the statement that everything is a network phenomenon, but it makes sense to me. I can usefully model everything as a network – this very post, for instance.

This post is a multiscale network: nodes joined by edges to form the nodes joined by other edges at other scales. As nodes, letters network to form morphemes, which network to form words, which network to form sentences, which network to form paragraphs, which network to form posts, which network to form blogs, which network to form conversations, and so on. And this covers just the syntactical view of the network. We can then look at the semantic networks in which subject, verbs, objects, and other nodes network together to create concepts, which network together to create arguments and explanations, which network together to create a belief system, which network with other belief systems to create a conversation, and so on.

It is quite useful to me, then, to think of this post as a network structure, and whatever knowledge emerges herein "is distributed across a network of connections" that shifts in scale from letters and morphemes to conversations about networks, education, and knowledge. This knowledge emerges for me as I write – adding, rearranging, deleting, and shifting words and sentences about – and for you as you read – scanning for patterns of meaning using the syntactical cues I've left behind. I hope that the pattern of knowledge that emerges for you as you read this post is reasonably similar to the pattern of knowledge that is emerging for me as I write this post. If the two patterns are reasonably self-similar (and I'm not exactly sure how we can ever establish that for certain), then I will believe that I have communicated my message to you, that I have somehow transferred knowledge from me to you, even though we both know that nothing was transferred.

But there is no guarantee. Every speaker, writer, or actor is aware that an audience can perceive very different patterns in the same configuration of words, sentences, and paragraphs. My father – an old-fashioned, hellfire-and-brimstone preacher – frequently remarked that most of his congregation heard sermons that he never preached. The message they heard depended more on their own sense of guilt and sinfulness than on his choice and arrangement of words.

Once this post is published, then, it becomes a node in a network of people (you and I) who each engage the overall network of writer, reader, and text from different vantages. It should be no surprise, therefore, that knowledge emerges in different ways for each of us, sometimes slightly different, sometimes radically.

If nothing else then, the complex, multi-scale, network nature of writing helps me understand and explain why even a simple piece of writing such as one of my brilliantly clear and concise writing assignments can receive such divergent interpretations, with almost every student doing something different. If knowledge was a discrete, transferrable thing, then I could give all my students the same thing, but because knowledge is an emergent pattern scattered across a network of words, I can hope only for a reasonably similar pattern blossoming in the minds of my students. Sometimes the magic happens. Sometimes it doesn't.