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5.5 Rhizomatic learning

Figure 15

Another learning theory closely associated with MOOCs and open education is that of rhizomatic learning. This invokes the biological metaphor of a rhizome, likening learning to the roots of a plant. The roots can spread out laterally and horizontally, consisting of a series of nodes, with no distinct centre, beginning or end, and no defined boundary – the only restrictions to growth are those that exist in the surrounding habitat. Rhizomes resist organisational structure and chronology and instead grow and propagate in a ‘nomadic’ fashion. Seen as a model for the construction of knowledge, rhizomatic processes hint at the interconnectedness of ideas as well as boundless exploration across many fronts from many different starting points.

The rhizome work develops a metaphor proposed by French post-modern theorists Deleuze and Guattari (1987), but Dave Cormier [Tip: hold Ctrl and click a link to open it in a new tab. (Hide tip)] has done most work on this as a theory in modern education. Cormier suggests that rhizomatic learning is a means by which learners develop problem-solving skills for complex domains.

For the educator, supporting rhizomatic learning requires the creation of a context within which the curriculum and knowledge are constructed by contributions made by members of the learning community, and which can be reshaped and reconstructed in a dynamic manner in response to environmental conditions. As Cormier (2010) puts it, ‘the community is the curriculum’. The possibly open syllabus represents the scope of the local habitat the rhizomatic learning process can explore, and provides the context for a community-negotiated curriculum. The learning experience itself may build on social, conversational processes, as well as on a personal knowledge-creation process, through the creation of large, unbounded personal learning networks that may incorporate formal and informal social media.

Some examples of rhizomatic learning are often found in MOOCs, where students are expected to operate in a networked, open manner and offer peer support. Dave Cormier ran an open course on rhizomatic learning itself, which naturally embodies the approach in its pedagogy.

Work with adolescent gamers by Kathy Sanford, Liz Merkel and Leanna Madill (2011) looked at how adolescent gamers’ experiences revealed the complex learning systems in which they contributed, created and participated in their gaming communities. The authors of the paper conclude that there is ‘no fixed course’ in gaming, and that their subjects actively blurred the boundaries of the following traditional identity categories: producer/consumer, teacher/learner and individual/collective.

The advantages of a rhizomatic approach are that, as with connectivism, it is more ‘network native’ as a theory than many existing pedagogic approaches. It promotes peer support, learner responsibility and an appreciation of the power of the network. You may like to consider the differences and similarities between connectivism and rhizomatic learning.

Activity 20: Exploring rhizomatic learning

Timing: 3 hours
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Embracing Uncertainty: Rhizomatic Learning in Formal Education

Dave Cormier

Embracing uncertainty was a presentation that I gave in New Delhi a couple of weeks ago. I thought it might be useful for me at least to go back right now and to take a look at what some of the ideas were inside of that, and see if I can pull them together in a ten minute piece to give to you guys, and see if I can’t get some feedback. So, Embracing Uncertainty, Rhizomatic Learning in Formal Education– it’s an attempt at trying to envision how to answer the question, ‘Why do we teach?’ And that presentation was really about pulling together five things that I thought, I think, about how to answer that question, and how rhizomatical learning in some ways can be an answer to that question.

So to me the first place that I always start when I think about learning and why I got involved in education and why it’s important to me are these two guys. And this is Posey on the right and Oscar on the left and they’re my little guys. And for me, thinking about the learning process and watching them learn is always a fascinating counterpoint to the work that I do in higher ed and to the work that I do online, and trying to see how they come through their own world.

And one of the things that I was thinking about was how there are some really primal lessons that we get involved in when we teach little kids and really these are lessons that go across cultures and they go across time. So the question of how we deal with fire is one of those things that I’m dealing with with my kids right now. They are three and six years old, or almost six, and they are walking by the stove, and things are hot and you’re trying to explain to them how that goes and we have this expression that ‘the burnt hand teaches best’.

We’re obviously not out there burning children but it does give this sense that there is an experiential nature to learning. That’s something that’s been around for a long time, this expression has been with us for a while. But the problem with that, and I think one of the ways in which our world is complexified and the ways in which our lessons need to be adapted, is that at one time it really was just fire we were talking about, right. There’s really just the places where fire existed. And then it gets more complicated: we get inside houses and there are other things that are hot, and we get into a world where that uncertainty about heat is there so maybe it’s steam that comes out as the hot thing, so it’s not just the fire it’s the steam. And the burnt hand on the fire or on the stove doesn’t quite warn you, you know, for the steam that might be coming out, or the hot car engine or whatever else is out there.

So the question becomes that the burnt hand teaches best – what’s really being taught there? So is it that fire is hot so don’t touch it? So there’s a behaviourist lesson there, I guess. I guess it’s a lesson you’re probably going to learn because we won’t want to touch it. And maybe the second lesson that’s built under there is when you say, ‘a burnt hand teaches best’, to some degree you’re saying you should do what you’re told. You know, had you done what you were told, this thing would not have happened to you. And I think of that as an undercurrent to that message.

But the third piece to that, and the one that I’m interested in for my kids, and the one that sort of is the foundation that I’m presenting here for rhizomatic learning, is this idea of uncertainty – is things are hot and we should check for them. And so in learning that things could be hot you can check for them in the future. You can prepare yourself for an uncertain world where things may or may not be hot. And you can learn how to approach those things and not touch them or get close to them and feel the heat emanating from them and know that those things should be checked for. So, ideally, what I’m doing is preparing my kids – not by letting them touch all the hot things to know that they’re hot, but to realise that things can be hot and that’s one of the various complexities of the world that they live in.

So the five things I think I think:

The best teaching prepares people for dealing with uncertainty and that’s sort of what I’m presenting as one of the potential core pieces of rhizomatic learning, is that what we are doing is trying to prepare people for uncertainty.

EdTechTalk is really an online community of webcasters, educators who come together, talk about their practice. We’ve been doing this for six or seven years. There have been twelve, thirteen, fourteen hundred radio shows. And it started in 2005, when we all got together on the website and got together on these live shows to start talking about our practice. And if you remember, in 2005 we had YouTube just starting up, WordPress was coming in, and we had a really great blogging platform that we could use and we had all these new things that were coming at us in education and technology that nobody really had an answer for. How were we supposed to use that? What’s the best way of doing this in our classroom? How can I make sure that my kids are safe? And these were questions we had no idea about. There were no books to buy, there was no place to go for reference, so reasonably the only thing we could do was come together and talk about it.

What we found out as we went along, is that just by coming together and talking about it we were learning. There was no set pattern for it, there was no agenda, there was no curriculum set out, but yet when I went to a meeting and started having a conversation about something, the things that came into the conversation, the connections I’d made, came together to give me answers. And I think that, that piece that I did at the community can be the curriculum to learning when there’s no answer, when you’re not sure what the answer is going to be, when complexity gets in the way, when you get to the point where nobody knows what the best way is, maybe there isn’t a best way.

And at that point the community really can be the curriculum. You can all come together to learn together. There doesn’t need to be an outside source of knowledge. So the response I normally get at this point is, ‘Yeah, yeah, that’s networked learning. We understand. That’s the sort of thing that lots of people are talking about’. And to some degree I agree. But to me, rhizomatic learning is a particular kind. A rhizome is a particular kind of network and I’d like to sort of drop down into the rhizome metaphor here a little bit and take a look at it.

If you look at these models of networks, and this is just a random page pulled off GoogleImages to try to pull together that idea, you’ll notice that the majority of these networks are very tidy. They are all point to point, all the lines are connected, and it gives you this idea that the connections involved are really clean ones.

There’s one in the top right-hand corner that’s kinda mouched together, but if you zoom in on it you can actually see that it’s all dots and lines. And the same with the bottom left-hand corner; there’s a lot there but it’s dots and lines and all the dots are connected to lines. And there’s a sense of tidiness about that process, that to me somehow implies that the learning process is tidy, that the model is out there, that all we need to do is know what that model is and once we have it we’ll be fine.

The rhizome presents a different kind of model to that. Or at least it focuses in on a special kind of network. So these trees that you’re seeing in front of you, the aspens, they grow. That’s actually one plant, right, and they grow underground. The largest aspen grove is, I think, one hundred and six miles, square miles, and it just kind of spreads out and the shoots go down and they run across, and they shoot up in different locations. There’s no real start to the plant, there’s no real end to it, it’s not a tidy structure, right. You begin wherever you are, you follow the plant around, right, there is no necessary point where all the points are connected to lines. You can cut a whole piece out, move it somewhere else, it will continue to grow, right. It’s not a neat, tidy network.

This is another example of a rhizome: these are bamboo shoots and you can see how/where the rhizomes go out. They’re the sort of medium thick parts. When they come out and spread over, they go in different directions. And you can break off a piece and walk it away and drop it somewhere else, you know, and it will still continue to grow.

So there are some nice qualities about rhizomes that make them interesting to think about as ways in which things are connected. So they can map in any direction from any starting point, so there’s no set beautiful circle or ways in which it’s tidy and neat. They just take off in directions, they fit into an eco system, they adapt to the eco system around them. They grow and spread via experimentation, so they’ll try out this way, maybe they run into a rock, maybe it turns a corner, maybe it hits a wall but it ends up reaching out its tendril and trying to figure out whether it can find a place to grow, whether the nutrients are there, whether that’s a direction that’s gonna work out. And again I think this is a really nice metaphor for the learning process.

And they grow and spread regardless of breakage, so you can snap and twist them. Are there any of you who’ve ever had a nasty rhizome, like a Japanese knotweed or a Bishop’s weed in your garden? You’ll know that the tiniest little bit of it is enough to make it grow, and there’s something really nice about that too in thinking about network models.

I think when we talk about learning, the tidy network model to me gives the sense that when we have a group of learners together and they’re working as a network, if a piece breaks off, that piece that they are connected to has gone away. Whereas if you think of it as something more organic, something that can work when it’s broken or displaced or put in a new location, it gives it a new chance to grow. I like that kind of model as well. So the third thing is the rhizome as a model for learning, for learning for uncertainty.

So, I guess, what are we going to do this kind of learning for? And I’ve heard this probably a half dozen times at presentations, where people will say, ‘I don’t want my doctor learning this way’, ‘I don’t want this kind of community-generated knowledge stuff. There are things that are true and things that aren’t true, and we should be out there learning those things.’

I mean, I’m certainly not saying that there aren’t things we should learn, things that we should memorise, things that are not just about connecting to a community, although a community would be a good place to find out what those things are. But there are some basic ideas, whether they be language or whether they be best practices that underwrite any kind of context.

So this is a model: this is the Cynefin Framework. It’s a simplified version of that model by Dave Snowden, and what it talks about is how people make decisions in management. So we talk about simple, complicated, complex and chaotic decision making. We think about this in the context of learning. A simple piece would be something you can memorise; a simple decision where we can all agree on what’s true and what’s not true. So we can all agree that this thing over here is called a mouse. We can all agree that this is a computer, and that these are words and languages that are useful for us, that we all kind of agree on. And there are ways in which we have sort of automated responses to things that make our lives easier. So we point at things and we agree they are certain things, and that’s a good thing. And I think in any context, in any sort of grouping of learning, it’s important to get those simple things agreed upon. And I think anybody who is moving to a new field for the first time has to gather some of that information. Whether they need to gather it first is a different conversation, but they certainly do need it.

The second zone in the top right-hand corner, ‘Complicated’, is more of a – it’s good practice. So if maybe I’ve hurt my shoulder and we look back to our doctor example, if I’ve hurt my shoulder, well I could have it sewn back together or I could do physio. And they’re both reasonablly good practices and there are reasons to do one or the other. If you look really close at it and you bring an expert in, that person is going to be able to give you an evaluation. And odds are, there’s one or two or three or four different ways to do it, and those things are things that can be sorted out and decided between you. Not necessarily there’s one best answer, but like I’ve broken my leg, I need to put a caste on it but you know there’s a couple of options and choosing between them is something that we can do.

The Complex domain is really the one where the uncertainty lives. You know it’s the place where we don’t know what the answer is, we have to do as Dave describes: probe, sense and respond. You need to try something, check it out, see if this thing is gonna work out. And if it starts to be a little better, you do more of it. If it starts to do less, you do less of it.

So imagine somebody with chronic headache pain, for instance. You don’t necessarily know what the cause is. You don’t necessarily know what’s gonna help. You might try a little bit of medication; you might try a little bit of physio; you might try something else and try bits and pieces, see what works and do a little bit more of that if that goes through.

Those kinds of things are far more about experience, about trial and error and about trying to keep a general sense of what the possibilities are. Now that chaotic domain down there is more about acting right away and I think that there are different kinds of learning where you simply need a simple piece of information, you need it right now, you need to do something. That’s a different phase again.

So, when we look at the literature, when we look at the way some people are starting to talk about it. To take a medical example, ‘successful health services in the 21st century must aim not merely to change for change, improvement and response, but for changeability, improvability and responsiveness’. And again I argue that to have that inside a system we can’t be teaching people what’s right and what’s wrong, we need to be preparing them for uncertainty. We need them to be reaching out as part of that community and think of their learning and their knowledge as part of that community growth and seeing it change along with everyone else around them.

And this is one from management. This comes from Dave Snowden’s Cognitive Edge, written by Gary Wong. ‘When you finally come to grips you can’t solve today’s problems using present methods, you take the lead to venture to the Complex Domain’. You initiate a search, rally followers and try out these different things to see if you can change the paradigm. And again, it’s that same idea that at some point you get to the place where uncertainty is what you’re confronting, and I think of that as the important part of learning. It’s the place where you need to be prepared to be able to make those kinds of decisions. And I think in an education system that has definitive answers, that offers up a scenario in which somebody can get something right rather than make decisions between a variety of options, is one that does not prepare people for those kinds of uncertainties. So that in rhizomatic learning, that sort of exploratory probe/sense/respond kind of learning, where you’re in the complex domain, where answers aren’t clear, is what I’m talking about rhizomatic learning being best for.

So, I guess the final question is, ‘How do you do this on purpose?’

So how do you actually go about structuring an environment where everybody has the ability to probe and sense and respond, and the learners are able to react to their own environment and they are able to follow their own learning paths and still be connected as a community? And you don’t have a pre-established curriculum, and that’s something that gets built out over the course, how do you actually do that in any kind of practical sense?

So with my children (this is a picture of Oscar again) I’m trying to set up scenarios where, you know, it’s not a right and wrong answer, where you can actually grow and develop. And this is something I catch myself doing all the time, right, you know.

My boy is almost six, and I try to set up these, or engage with these really interesting learning experiences with him and I find myself going, ‘What’s the answer to that Oscar? What’s three times three?’ And I set up environments where the right answer is the thing that he needs to sing-song back to me, and again he starts to learn the world is a place where answers are right or wrong. And if he gets them right he gets rewarded; if he’s wrong they’re not rewarded. Where, in my experience, the most valuable things in the world are places where you need to make decisions between things that aren’t right and wrong, you know, and I find myself constantly struggling with that. I think for me the lesson for rhizomatic learning, which I’m constantly trying to relearn, is to try to make those conversations more complex, to offer complexity to him and let him make his own sort of explorations inside that uncertainty.

This ED366 is the course that I teach at the University of Prince Edward Island, Educational Technology and the Adult Learner. If you’re interested, if you do a search for that online you’ll see the syllabus that I have set up for it. Trying to set it up for that course is a challenge because I get students from all over. Some of them are teachers, some of them are trainers, some of them are faculty, some of them are people interested in teaching. So they come from all different walks of life, and we start without a curriculum and really they have to build their own, they have to build their own learning network plan. And the goal for that plan, in that course, is that they’re planning for themselves six months away. So how can you set up a textbook for you so that six months from now, when you’re trying to do something that has to do with technology, or has to do with trying to put together or understand one of these new concepts, that you’ll have something to work from, so that you’ve built it up yourself and it fits for your context?

It’s particularly useful for this group because they come from such different levels of literacy, both digital literacies and all kinds of different stuff, so it ends up being a real challenge. And for those of you who are familiar with the MOOCs, this structure for MOOCs again is designed to allow for that kind of flexibility. So this is from Five Steps to Succeed in a MOOC, which you can see is a four-minute video, that you can see if you search on YouTube: Orient, Declare, Network, Cluster and Focus.

So go out – find yourself a place inside one of these MOOCs, inside one of these open courses. Declare yourself so people know you’re there. Start to find people to work with, find groups, like a community that can slowly start to form. And then focus on your own work so that that community can become your curriculum and then you’re driving yourself towards the goals that you’ve set for yourself.

So, that sense of responsibility, that point where you are setting your own step, where I put Oscar, my son, in the place where he has to make decisions for himself, it’s not just about me parroting the right or wrong solution to him. My students are actually focused on their own learning, and their own goals and where the individual student in a MOOC is looking towards their own focus, as part of that community but the thing they are trying to get done, those are all about putting the responsibility for learning back on top of the student, right, and again it’s not only in their own learning but also when we’re working with communities, it’s the learning of those people around you.

So, as this was a presentation in India the question there is always, ‘How does this scale?’ Maybe you can do it with your son over there, maybe you can do it with those twenty people in your classroom, but what do we do when the numbers get big? What do you do when you bureaucratise that across a country? There’s three million teachers in the United States, how do you do this stuff across the way?

Well, for me, we need to stop measuring. People are always saying that they need to measure learning. And in this kind of scenario, in this kind of environment it’s extraordinarily difficult to do what people call ‘measured learning’. So if everybody’s doing something different how do I know what one person has learned? How do I know how this other person is doing? How can I guarantee that that classroom or that school is actually doing something because they need to measure learning?

And my argument to that is always the same: the fact that you need to measure learning doesn’t mean that it’s possible. I understand that people think they need to measure but I don’t think it’s possible to measure learning. And when I said this in the presentation, somebody said, ‘Well, you can sort of check to see if some of the effects of learning have happened.’ So, you know, if somebody’s learning to drive a car you can tell that they’re driving it. And I was, well – kind of.

You can measure around learning but trying to measure whether or not learning is happening, to me is a red herring, and I think we should stop trying to measure learning altogether, you know. If we’re trying to measure that someone actually has something in their head, we’re getting people to cram, right? So that right before the test they try to jam everything in, and it’s gone three days later. In my mind, that’s like cheating. Like, yes, you were able to produce something in the test but you haven’t actually learned it. You’ve remembered it for a couple of days and now it’s gone, so you never made it part of who you are. You never brought it into your context, you never connected it to those other things you know. You just were able to reproduce it based on the testing structure that I set up for you and that to me is not learning. It does prove that you were able to reproduce it but I don’t think that is learning.

So to me we need to stop that idea of measuring learning and start measuring things like effort and engagement and connection, and people’s ability to talk about the ways in which the things they have connect to the other pieces that they have. And we can let the robots count the rest of those pieces, you know. How many contacts they’ve made and whether or not they’ve researched stuff. There’s a lot of things we can count in terms of clicks but I think we also need to trust those teachers to look at people and say, you know, that person is getting it, and I can understand that, you know. The teachers that I know can answer that question and I think trusting the teacher is another really big part of this. So, if we can make the community the curriculum, membership in that community becomes how we scale them.


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  • Consider your reaction to the video.
    1. Were you convinced by rhizomatic learning as an approach?
    2. Could you imagine implementing rhizomatic learning?
    3. How might rhizomatic learning differ from current approaches?
    4. What issues would arise in implementing rhizomatic learning?

Write a brief blog post discussing your thoughts about rhizomatic learning and if you are content to use Twitter to share your thoughts, Tweet about your blog post using the hashtags #h817open and #Activity20.


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