Stephen Downes
Knowledge, Learning, CommunityUnedited Automated Transcription by Microsoft Word 360.
Speaker 2
Hello and thank you for inviting me. It's a pleasure to be here as always to speak with you today. I'm, I'm looking forward to to being able to meet with you and talk about the topic. Connectivism, we'll talk about what it is and how to apply it, so I'm going to share my screen, which hopefully will share my slides. We shall see. So whoops, the content of this is being. Yep, no problem. OK. Uh. OK, share screen. And screen 2, they're all screen. I can never figure out which screen. Yeah, this screen I think. Alright, here we go. Right, so now you're all reading my e-mail? Not really what I had in mind. Oh, I see what's going to happen to me. You'd think I'd be used to this by now, but no. So let's try it anyways, right? So I'm going to stop, share and start sharing the other screen. Sorry about that. We'll be alright. There we go. So now you should be seeing my my presentation. Nice, big beautiful pictures.
Speaker 1
Yes. Excellent. Yes, definitely. May I have your permission to record this session, Sir?
Speaker 2
Yes, please, absolutely. And I will record as well.
Speaker
Recording in progress.
Speaker 2
OK. There we go. So it's a pleasure to be with. You all. And what we're going to talk about today is connectivism, what is it, how to apply it? UM. And basically what it means for you. So I've got a ton of content. These slides will be available later. So you don't need to worry about remembering all of the content. That's not what this is about. It's not what Connectivism is about. So. We're we're going to take it from there. Sorry. I'm just. There, that's better. That's what I want. All right. OK. The objective in this session is to present the core ideas of CONNECTIVISM in both a knowing and a learning context. So talk about both knowledge and learning, and in a sense. Using connectivism to unify or bring together the ideas of discovery, interaction and education. That's a mouthful. That's an awful lot of stuff to try to do in one hour. Obviously, we're going to skip through some subjects, but again, this is about getting the overall idea. So here's the plan. We're going to look at what knowledge is briefly. Going to look at what Connectivism is, and we'll bring these two ideas together to talk for a bit about how we actually learn. And that's where connectivism is. I'm pointing to the how we how do we learn with my finger and then from that we're going to draw out some implications for practice. So with respect to knowledge, the proposal of connectivism is that. The core idea. Of education or learning, or a science broadly conceived. Is to learn a discipline is to become like a practitioner of that discipline. It's not to learn a whole pile of facts and knowledge. But is to actually be or become like another person who is already an expert in that discipline, we'll need to. Expand on that a fair bit in this presentation, but that's the core idea of knowledge in connectivism. We define knowledge literally as the set of connections between entities. It's pretty vague in a person, a human being. We're talking about the set of connections between the neurons in our brain. In the computer we're talking about the set of connections between virtual neurons or digital neurons in a society. We're talking about the set of connections between people, or maybe people and things, or maybe people and ideas. The idea here is that. Where you have a set of connections between things can know, it can become a thing that knows and learning for us, for society, for anything is the growth and the development of those connections. I don't think these ideas are unique to connectivism. But Connectivism takes these ideas and transforms them into an approach for learning and discovery. Either on our own or in a classroom. On this model is the idea that we learn and grow by becoming connected. And again, there's two senses of that right. The one sense as people in a society, in a world, in an environment, we learn and grow by becoming connected to other people or things, or ideas or concepts or languages or whatever surround. And as well as a person. It's the idea that we learn and grow by developing the the network of neural connections in our mind through activities, through experience, through immersion in a subject. This is a goal of both science and education, kind of interestingly. The practice. Is the howl of becoming connected? This is the actual application of the theory into a learning context. The practice is. What we need to do in order to become connected? I'll talk about some elements of that. I'll talk about what I call the ARF process model, aggregate, remix, repurpose, feed forward. We'll talk about success criteria, how we know we've developed a network. That will be successful in learning. And I'll talk a bit about the critical literacies the skills and the capacities. That we are looking toward as both ways of becoming better learners and as indicators that we have become better learners. Let's begin with the first part. What is knowledge? What it isn't is this building and this statue. We can think of knowledge. As if you will a domain of discourse. It's something that we talk about. And in a domain of discourse, usually we're talking about set of objects, which I'll call AB and C, But they might be this cup, this water bottle fuzzed up a duck, you know. And then the set of properties they share, it's round it's square, it's blue. It's annoying. It's bigger than something else, etcetera. That's the typical way we talk about knowledge. What is it then? To know things well? On the traditional picture. You know the old. European logical positivist in Anglo American tradition. You take all of the things and all of the properties that they can have and you organize them. Into what is called a state space. So you have, you know, here's the picture of that. So you have. A. A thing that is a that could have property P or property Q, et cetera. It's all very organized. It's all very logical. And then. What we count as? Actuality or reality is those members of the set of possibilities that actually exist. So in the world and a could be P or Q or R or S but in our world it's actually P. And B is Q&R et cetera. So we distinguish between all of the possibilities of the world and then the things that actually are the case in all of those possibilities. And that gives us a logic of probability that gives us. The philosophy of science, if you will. Working on this theory we've got. The traditional theory of science. Called the deductive nomological model, and you've seen this before. Even if you didn't see the name before. And that's the idea that. We see something in the world. We see Rabbit tracks and then we see a rabbit. And we come up with a hypothesis, A generalization about a state of affairs in the world. If I see rabbit tracks, then I'll see a rabbit. And then you go out and you test your theory. You go out into the world, you look for rabbit tracks. And you predict OK at the end of these tracks, I'm going to see a rabbit. And that's your test. And if you test. Correct. If you actually do see a rabbit, you've confirmed your theory, but if you see something else instead. Maybe somebody with sticks making a rabbit track. Then you've disconfirmed or falsified your theory. And that's, we are told, is how science progresses. So on this view known as positivism, knowledge is the generation of general principles based on inference from observations. And you've seen this before. It's the sort of stuff that they ask you to memorize in a classroom, E equals MC squared is one of those generalizations Newton's laws, the first and second third law of motion, the laws of thermodynamics. Any sort of general principle? You know that you're supposed to remember crowds behave badly. Is an example of this sort of approach. It's a knowledge consists of a set of generalizations. We remember that set of generalizations so that we can apply a generalization any time we see something in the world. We see Rabbit tracks, we know that if you see Rabbit tracks then you'll see a rabbit. You apply that you begin to expect a rabbit. I don't know why I pick rabbit tracks. I'm I'm looking outside in my backyard and there's rabbit tracks there. I like rabbits. All right, moving on. The problem with this model is that it's wrong. It works really well in limited circumstances, but as soon as things get complex, this becomes wrong first of all. You can't reduce the world to a set of entities and property. The world is way more complex than that. You can't reduce the world to a set of statements about. What we observe. The world again is way too complex for that. And there's no distinction. That we can draw between what we see and the laws of logic that allow us to draw these conclusions to make these predictions. Another way of putting that is that. Everything we see. What they call theory laden. We don't just have raw perceptions. Anytime we look at the world, it is informed by all the things we already know or think we know or have experienced about the. And so knowledge isn't this set of analytical statements. It's more like something really fuzzy. The languages we use, the practices that we undertake, the questions that we ask. Vicki Enstein called it a way of life. The the German word for it is Dalton Sumlin. A world view. Or if you want to be more technical and more precise a model. Know something? Is really to belong to a community. That knows that thing. Is that how does that sounds? But if you think about it? The knowledge of, say, physics. Is defined by. All of the things that all of the people who we call physicists, all of the things that they do, they think they, they talk about. That's what knowledge of physics is. It's not a set of principles and a set of experiments. It's all of this knowledge. Here on the screen we have a A community. It's it's kind of hard to define, but it's sort of like an open education resource, web development community. It's a bit fuzzy because communities looked at this way are a bit. Fuzzy and you can see all the links between the different parts of the community, the things that members of the community think about. So we think about the community and the practice of the Community is essentially the same thing. So we have this idea, then that knowledge is in network. And the communities and network and the knowledge of a specific domain is the network describing that community. That's the thing with connectives and it comes down to its own networks in the end. This isn't just me making it up. If you go out into the world and look for evidence, you see it all over. This on the screen now. Is a map of the sciences. And it's a map. Based not on whether they agree on statements or principles or laws of nature or questions or anything like that, but on how they're connected with each other. And the way this particular graph was created is to look at citations in scientific papers. And if you cited somebody in the scientific paper, you draw a line, and the more people cite each other's papers, you can draw these lines thick enough, and eventually you get this diagram here that shows how all of the different sciences. Everything from nursing to music to biochemistry to plant agriculture. They're all linked to each other. How do we explain this? How? How do we describe? How this comes to be? You know, how does it happen in a person? How does it happen in a society? Well, we could say that knowledge is construction. A few weeks ago. I actually talked about that, Sir. And if you look back into the archives. Of the Mesquite ZH maszkiewicz. Micro learning series. You'll see that talk. It's also on my website an introduction to constructivism. And we get this idea from people like Basman Frasson or David Chalmers. That knowledge is constructing the world. Out of sentences, propositions, words, systems of language. But the problem with that approach, at least to me, is that it takes us, in a roundabout way, right back to that, that idea that we're talking about, things and properties of things. The way we name them with words. That's not really what knowledge is. We reach a point of decision. And this is what distinguishes to my mind. One of the things that distinguishes connectivism from constructivism. We could view well, just construction. And so it's something that we make intentionally. It's founded in language representation in models. Knowledge is something we build, either individually or in social constructivism as a society. But I see. In my own experience and in my own thoughts on this subject, knowledge as being something that is discovered. It's discovered in the sense that it's founded in experience, in immersion in practice. By joining this community of practitioners trying to do the same sorts of things that they do and over time. By changing our neural connections, becoming like the members of that community. So if this is what knowledge is. Then learning is. Reorganizing our neural network such that we become like a person who's already an expert in that discipline. That takes us to connectivism. What is connectivism? Well, it begins with a theory of computer science. Called connectionism. Connectionism is a way of designing computer programs. That can work with. Fuzzy and ambiguous input data. You put the fuzzy and ambiguous input data into one end. You have a network of connections. I'm pointing at my screen which is not really helpful, but you have a network of connections and then out the other end you generate an interpretation of whatever went in in the first place. We can contrast it with. A symbol system model based representation that we construct. That's what we see on the left here. And this is constructed we've. Created a mechanism of representing that Apple using words. So we'll talk about the origin in an apple tree, the kind of thing that it is. It has a body, it has a shape, has a size, colour, taste. It's a kind of thing, a fruit, et cetera. On the right hand side, however. We don't have that. What we have is this network of connectivity. That defines. And Apple as the connections between the entities in those networks. And I've kind of tried to indicate that with the layering of the network diagram with the connectionist Apple diagram. And these connections are represented with numbers. And that would describe the strength or the weight of the connection. It's a numerical representation of an apple rather than a symbolic representation. When we show. A number of things when we train one of these neural networks. Our representation of our different concepts say an apple or a pear, is what we call a vector. The series of numbers representing the strengths of the connections or the values of properties of the entities that are connected in this network. And what's important here is that. Two things. First of all this. These individual values don't stand for a property that we would normally think like roundness, squareness, or whatever. And there aren't just seven of them. There can be hundreds of them. There can be thousands of them, way more factors than we would think about as a person. And these vectors. Can define something as similar to something else. You you look at these numbers here, see how the numbers for Apple and pear are sort of similar. Compare them to the members for Bear and Wolf. They're very different. The numbers for Bear and Wolfe are sort of similar to each other. The numbers for pair are not very similar to the numbers for bear. Set they rhyme, but that's about it. OK, you might say, well, how does this apply to me? This doesn't seem to be. How I think. Yes, it is. Let's play a game. We're going to play the projection game. Because what Connectivism proposes is that this is what you actually have. In your brain. These sets of values. Of properties of your neural connections, and we're using numbers to represent them, but they're actually physical properties. So how does this? How do we show that this is the case well. Let's play the projection game. Try this yourself. What word comes next? Bacon and. Probably right. I'd say so. All right, let's try another one. What word comes next, Wayne? Ah, you're all in Alberta. There should be only one word that comes to mind. Gretzky, interestingly, though, if you lived in Las Vegas. You'd probably be thinking Wayne Newton. If you're a film buff, you might be thinking Wayne's World. OK, how about this one, American? So you see what's happening here, right? I'm giving you a word in your mind. That's a vector. And what your mind is doing is coming up with a word that has the most similar vector. In my case. It's American Idol. Fells older it might be American graffiti. You may have thought of something different yourself. Hubby Justin. Well, Canadians are all going to say. Trudeau. Americans are probably going to say Justin Timberlake. Unless they're really young, in which case they don't know who Justin Timberlake is. One more tried and. I picked true and this is an important thing, right? What Monisha says and what I say is actually different here. We come up with a different concept. And the reason for that is we've had. Different backgrounds and different experiences. Now, the way we fill what word comes next. Is representative of our knowledge. Our knowledge. Is in this case it's kind of like social cultural knowledge. The answers that I've given you are specifically the answers that a Canadian perhaps of my age might give you. Perhaps not. It's really hard to define. The the actual games, the actual projection games that a community would practice. Are much more complex than this. And they come in the form of questions. What questions are important to ask? They come in the form of jargon and terminology. They come in the form of images etcetera. In Connectivism and in Connectionism for that matter, a concept. Traditionally, so-called is essentially just a pattern of connections described by vectors in the network. So what's really interesting here is we have a tree, a dog, and a couch. They're all represented in that network somewhere. We use one network to represent many things. And the individual neuron. That's these square boxes here. They don't actually stand for anything. They don't stand for a dog or a couch or anything like that. They're just numerical values. But the connections between them to find whether we respond with a dot or with tree or with. I'm looking for my or with dog or with couch. And the key point of connection connectivism is. This network is learned. Or, more accurately, grown, developed through practice, reflection, experience and immersion. We can talk about these networks in many, many different ways. We can talk about them as linked data, which is what they're trying to do in the semantic web. We can talk about them in terms of our own personal learning environments or our own. Personal learning networks, or the communities that we live in, the sorts of things that we recognize here. This might be me. It's not me. It's some red haired guy. But you know. But I recognize all of these images and logos and concepts and these people and these other people and this company. Your personal network is very different. As through the development of this network that you will be exposed to and have experience in different concepts, ideas, practices. And problems. In connectivism. A course. Is not an organization of facts. It's not like telling a story or presenting a bunch of content that you need to remember. A course is a network, literally a network. When George Seamans and I built the first massive open on my course, this is what we built. This is a drawing that our friend Matthias Melcher created of our course. So a mook is a website, not a web. And if I were building a MOOC the way I really would like to build a MOOC. All of these different parts would not be on one single service, but they would be created by different people about different things and then all connected together by all of us. And that's the sort of thing that you see, for example, in a blonde network. Where people write about topics but they link to each other. It's the sort of thing that we see in a network of scientific publications. Where they write about topics but they reference or cite each other. It's the sort of thing that we see in a community where people know each other, talk to each other, talk about each other. Pretty much any place where we have knowledge. This knowledge is distributed. It's not held in one place by one thing, or even in one person. It exists across a large number of sources, a large number of places that are interconnected with each other. So a mook. Instead of seeing a course as a series of contents to be presented, we think of a course as a network of participants who find and exchange resources with each other. So what we did when we built a MOOP. Is we developed that structure and we seeded it with a few resources. But the mook actually came into being when participants joined together with us in this community. And then we used a tool. I'll use the tool called Grasshopper to connect them together to allow people to refer to each other and find each other's comments. So here we come back to the idea of community again, this community of people. In a quote UN quote course. Who are talking about similar things, who are creating their own resources, who are linking to these resources and who are learning together as a community? But what's interesting here, and This is why I have the Detour group versus network, they're not learning the same. They're they're not. They don't all have the same learning objectives. They have different reasons for taking part in this course. Just as and I see there's there's a number of people here in this presentation, each one of you has a different reason for being in this presentation. You will want to take away different things from it and different things are important to you. You have a different objective for being here. And this gives us clues as to sort as to the sorts of values. Or success criteria. Of of course, like a mook. In our view, not, you know, the the traditional content based course. So now let's look at what learning is on this picture. We reached this point of decision. He said learning. Is founded in experienced immersion practice into this community and the result of that is that it is something we become. You might think, well, how how does that happen? Right. Why don't we just get some stuff and remember it? I mean, that's what we test for, isn't it? We test for tests. And you know, we see how well they remember something. Part of the argument of of Connectivism is we're probably testing for the wrong thing as. Well, if we think. We know somebody. Sorry if we think that. If we can test whether somebody remembers a set of facts, we know whether they are, say, a doctor or a pilot. We figure that's enough, but we know that that's enough. I would never trust a doctor. Who did nothing but passed the test. I would never trust a pilot who did nothing but pass a test. I want to know that they had experience. In the cockpit or in the operating theatre before I'll trust them. So I want to know that they learned what they learned through experience, immersion and practice, and not just being told stuff. Really important distinction. How does this work? Well, we we could talk about this for a long time, but basically the theory behind this describes the creation and the growing of connections between entities. That's what learning theory really is. You you may have heard of learning theory as. Constructivism or behaviorism, or instructive, wism or whatever. But really, learning theory is the description of the methods, the forms and methods of creating and growing connections between entities. And there are some common mechanisms at work here. Things that we call associationism, back propagation, etcetera, we don't need to get into these details here and this probably won't help us at this point. The main point is there are mechanisms that we know about that create these connections. So network learning is the development of these networks. Looking at both personal experience and social networks. Getting people into these networks and so learning using the mechanisms described by learning theory. Becomes a matter of practice and reflection. And how this comes out the other end? Is that we say to know is to recognize something. Now I know that I said earlier knowing is to have a certain. Set of connections. And the way we know we have the right set of connections is that that set of connections is able to recognize the sorts of things we want it to recognize. So you'd see the distinction. In here. Remembering recognizing 2 different ways of thinking about learning and connectivism, it's recognizing, not remembering. How do we build that? Well, here's an example of a learning theory that describes. The set of connections between 2 neurons. There are all kinds of parameters that we can play with to shape our learning network. These networks are described by the connections, in this case, on this slide between individual neurons. Here they are. Here they are being organized that connections are being formed and we see out the top. The vector that I previously described. Here are some networks. We have two separate networks. Maybe your personal neural network and the social network. If a pattern exists in one network, we can say that it emerges. And if there is a pattern in the network that is detected that is recognized, it's kind of a rough concept here, but the main thing here is that one network can recognize a pattern in another network. What do I mean by that? Well, emergence is the creation of what we might call order out of patterns order out of chaos. But emergence depends on perception. Here are some examples. Here we have what's called a murmuration of birds. You've probably seen them. They fly around in a flock.
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It looks like one single connected flock making all of these different shapes as it goes through the air, but really it's just a collection of 1002 thousand blackbirds. We also see it in art, where somebody draws very simple shapes and forms, but we look at that and we perceive a complete scene. You look at this diagram, what do we see here? Well, it looks like somebody's climbing out of a hole. Or did you notice the woman looking at the person climbing out of the hall? Depends on perception. Depends on us seeing the pattern in a certain way. Recognition is the skill of being able to do that. Is seeing something in the pattern in front of us. And this can vary depending on our context. On our background, on our state of mind in any given point, this is a very famous example here. It's called the duck rabbit. And keep coming back to rabbits. But if you look at it one way, here's the bill, right? It's a dog. If you look at it another way, here's the ears. Here's the mouth. It's a rabbit. And your mind can go back and forth on that. And that's you picking out or actually experiencing different sets of connections as you look at this phenomenon? To wrap things up, what are the implications for practice? What does this actually mean for us in a concrete way in our own educational experiences? To present. Collectivism in this ways, in a sense, as I said earlier, unifying the ideas of discovery, interaction in education. They're all part of the same thing. They all it all. We're not talking about 3 separate things here. We're talking about one single thing. So one way we can talk about it is method as discovery. To discover something. On our account is to be immersed in it, to speak it, to listen to the people speaking in it, you discover a new country, a new city, a new environment. You don't look at it on a map. You don't listen to stories about it. Yeah, you might, but it's nothing compared to actually going there and being in that place. And it's not just passive, right? This is something early scientists recognized. To immerse yourself in the world is to try listening and to try speaking, and for that matter, to try doing things, testing things out. It's not formal and organized the way a logical positivist might do it, but you're still trying to do things. How do we know that our network that we're trying to get people to participate in is a well formed network? Well, again, I could go into a long digression here and talk about. What would count as a well formed network, but basically the idea is a network is well formed. If it's the kind of network that actually can learn from experience. If it can't learn. Then it's just static. It's like a rock. It can't change. Nothing would change it. There is no experience that would change its point of view. A learning network has properties that allow it to learn. I have a list of these properties. I have four properties. Experience may teach us that these are the right properties, or that a slightly different set of properties matter. We'll see, you know, it's a matter of experience. One property of the network though. Is that each entity in the network is autonomous. Now, that doesn't mean it's alone. Right. That doesn't mean it's, you know, an Ann Rand self-sufficient. Forget the rest of society kind of autonomy, no. It has its own values, its own objectives, and decides for itself. It's still connected. To every other or or to many other entities in the network. Right, it's still influenced. By the experiences that it has by the signals that it receives from other entities in the network, but it makes up its own mind about things and decides for itself what it will pass on or what it would share with other entities in the network. For a network to learn. The members in the network can't all be the same. Because if they're all the same. There's nothing that would cause any of them to change. They're just stuck static. Everybody's the same. So for a network to learn. The individuals in that network need to be different from each other. And there are many ways. Count the ways. It's a good game that a member of a network, a person in a classroom, and you're on in your brain can be unique and different from other members of that network. That can be unique in form of roles, functions, perspectives, background, history. Physical constitution, whatever. There's a huge range of diversity. That goes into creating a network that has the capacity to change and develop based on the varying and diverse. Signals that one Member will send to another. Again, you think about it. If the network is completely closed. No new Members, no new experiences, nothing. Else is allowed to enter the network, then the network can't learn. There's nothing that would disrupt that network. There's nothing that would cause a change in that network. So for a network to be able to learn. It needs to be open. Membership in the network might be fluid. There aren't hard boundaries. You either are or you're not a member of this community. The types of content signals that enter and accept the network can change new experiences, new knowledge. New discoveries all of these. Form part of the input to a network and being open to this network. This input is what allows a network to be able. To learn. And then finally interactivity. What this means is that the knowledge in the network is created by the interactions of members of the network. Not by the transmission of a signal. The specific piece of content from one to another to another, where you end up with everybody having the same content, no. Everybody has different content. Different people have different thoughts in their minds. Everybody's thoughts are different. And in a community, the knowledge of the community isn't all held in one individual person. It's distributed across that community. If you think about it, no one person knows how to fly a 747 aircraft from. Ottawa to Paris. Not that we'd ever see a 747 in Ottawa, but I digress. You need people to build the plane. You need people to fly the plane. You need people to repair the plane. These are all different people. These are all different skill sets. Nobody can do everything. Right. That's very different from a network where one person makes all the decisions and everybody else obeys. Right. The capacity of a network where one person makes all the decisions and everybody else will base is very limited. It's limited to whatever that one person can know. But the capacity of a network. Where everybody knows something different is that much greater? And then the pattern of connectivity, for example flying an airplane from Ottawa to Paris, emerges from the network. For us. As an individual learner, it's a process of being a neuron in the network. In the social net. Where our role? Isn't to try to become everything that the social network is. Our role is to bring in. That which is relevant to us, the experiences, the content, the messages. To aggregate. And then. To reform this these experiences in our own way. And I describe that in two terms to remix and repurpose to remix is to bring together. Content from many different sources. And to repurpose is then to shape that content in a way that's relevant to us. So for example, if I was researching the the history of the Peloponnesian War, I would read a number of different books about that war. So I'd first I would aggregate them, I'd collect them from the library and I'd read them and combine all of their contents into one set of documents. So there I've remixed it, but that's not going to be good enough. I'm going to want to repurpose it to shape what I've remixed and I'm. Put it in my own words. So I would start writing to history of the Peloponnesian War. Herodotus said this the Quiddity said that. And then the last. Is to feed forward or to share what I've created and the sharing is an important part of learning because in the process of sharing I receive feedback. Minimally, I'll receive a message from somebody else. Yeah, I got it. That gives me a little rush because it's kind of cool that somebody read what I've shared. And makes me want to share more but also I get feedback because. They might say, well, did you consider this source? Did you consider that fact? Is your interpretation informed by this worldview instead of that world? Whatever, right, you get feedback and now your knowledge of the Peloponnesian War begins to shape and reform. According to this feedback. Thus, the process, think of yourself as a neuron in a network, be the neuron. There's the the process written out, aggregate, remix, repurpose, feed forward. So we can represent this as a model. It's the seventy 2010 model if you wish. The 70, 2010 model of learning and development is 70% experience, 20 social. And 10 in the. It's a very different model than we have now, isn't it? Where you know most of it happens in the classroom, and then you have homework and social forget social. But this is a model for connectivism. You see this expressed all over the place. Again, it's not new to me. Incorporate learning. For example, they've done studies. He asked somebody who's actually working in a discipline ask real physicists or real managers or real farmers how they learn. Most of it's through experience. Some of us, through talking to other people and the last 10 per cent, is stuff they learn in the classroom, maybe some. It often doesn't even get as high as 10 per cent. So the model of cognition matches that. The 70, 2010 model of cognition, most of it is recognition. This is what our experience brings us. Some of it is reasoning, which is the social practice of argumentation and discussion back and forth. An educational theory. They would call them discursive models of learning and then remembering. It's not like you. Don't need it. But it's not nearly as important as it is made out to be. If you can recognize the situation, then you only need to remember a small fraction of that. Recognition is experience, practice, reflection, creation, reasoning involves the creation of models, inference, representation, theorizing, and then remembering is just the the stuff that we need to use in order to do the. Rest of this. Really. And I talked about this, I've always talked about it this way. Learning is, in a sense, reading the. World, but it's not reading the world like a nice neat ordered language like logic and mathematics. It's a messy and complex way of reading the world. There's no one language that describes the world. There's no one set of experiences that describe the world, and it's always always in how these different languages and these different experiences are connected to. And learning is always always about how we put these together. I talked in another presentation about the critical literacies that we need to be able to read the world in this way. Literacies not like the traditional literacies of grammar or of mathematics, but the underlying mechanisms that describe the principles. Learning in a collectivist way, things like pattern recognition, things like deciding what's important and yes, things like drawing conclusions and making inferences. So that's what I had to say for today. This can't be the end of this subject. If this interests you, this is only the beginning. These notes will be available to you as slides later on, and you might want to go back and reflect through. But reflect through them. If you do this not as a body of content to be learned and remembered, I don't care about that. You shouldn't care about that. But rather. Use this as a list of practices that you might be able to try for yourself. Practice describing your community. Practice playing recognition games, practice playing the projection game and kind of look behind these this. You know when you expect to, you go out there, you're going to go out there. After this presentation, you are going to expect something to happen. I don't know. Maybe you expect that lunch will be ready. Maybe you expect that somebody will be in an office. Ask yourself, why would you expect that? And it's not going to be some rule or principle. It's going to be something very fuzzy and overall world view that informs that single projection. Thank you very much. I'm Stephen Downes and I appreciate the time that you've taken to be with me today and I hope we have a chance to talk again.
Speaker
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Speaker 1
Thank you Stephen Downs for doing an excellent presentation. As always. You know when you present it, it changes my mindset like I think 7020. And what impact it is having on current education, you know, how is education changing right now for for everyone? You know, I don't think small colleges are different from larger colleges because when it becomes like trained, it impacts everything. Like you know, COVID made sure that we all are in the same. Boat, I think. So thank you so much. You mentioned pattern recognition. And I'm trying to link it with education and leadership like insights, reading the world. You know. If we are able to recognize some patterns, what changes like how does this link with change management? Because what what is your opinion? How did this connectivism like emerge experience emotion? Social. Then it's a network connected. It's not just one individual. It's happening all across the world. You know, whether it's organizations, individuals. So how in this environment, how does change management? What impact does this connectivism have on change management, because everything is.
Speaker 2
Huge question. Huge huge question. One thing I can say, you know, I mean and and you know there's I can't. You know I can't cover it all in a in a short account. Change management. Typically involves. Working with people and having them change their perspective. You know. For example, there's there's a whole range of theories called technology acceptance models. They talk about how you can convince people to accept a new technology. What this discussion should say is that. We're not going to be able to argue our way into getting people to change, you know, and I think this is a really important life lesson, generally not, and not just a change management lesson and not just a leadership lesson. You know, if you walk into a room and you want somebody to use a new software program, you could sit there, you could argue you could list all the points up, you know, all the reasons why they should use it, and then leave the room and, you know the person saying, yeah, yeah, I get you. Yeah, I totally understand. You know, that's a great argument and you leave the room. They turn around, they use the same system they were using before. All the argument in the world isn't going to change. What software are they going to use? All the argument in. The world. Isn't going to change. They believe they know about the world just generally. You know, I mean you you can, you know, see, suppose you see a person. Who believes that unicorns are real, or the earth is flat? That's one that's been in the news a lot recently. You can't change their mind by arguing. Even if all the facts are on your side, they will ignore the facts, and in fact, there have been studies that have shown that the more facts that you show somebody to get them to change their mind. The more entrenched they become in their original position, they become defensive. So change management. Isn't a rational process. It isn't a process of description, argument theory, explanation or anything like that. It's a much more complex process. If I could wave my hands, I'd say something like it's a social and community process. It involves immersion, it involves trust. It involves experiences. If I wanted to change somebody's mind about something. Really I would need to get to know that person. I would need to live alongside that person for a while. I would need to begin having experiences in common with that person and discussing my experiences with them. And if I'm trying to change their mind, I'm probably thinking of OK, how can I set up experiences that will be helpful in changing their mind this way? So if I'm trying to get them to use. I might work with them together on a project. And just very gradually, high experiences using the two different kinds of software. And not try to convince them to use the different kind of software, but just let them see what I'm doing with that software and connectivism I often talk about the role of the educators to model and to demonstrate. So don't try to convince them. I just do what I do with my software and I do it openly. Sometimes called open working and people can see what I'm doing. And when they ask, and that's what I'm waiting for, right when they ask, then I can explain. Then I can say, well, I did this by doing such and such. And at this point we're in a conversation. Where we've already established through a process community what language we're using to talk about things. What common problems we might have the sorts of things that we think are important, you know? Do we think that finishing a project fast is important? Do we think creating a better outcome is important? And once all of that has been established, then I can say I did such and such. This way you can see how this met our objectives. That's a very different model. Of change than than is is typically thought about. You know you take political campaigns, people think Oh yeah, party. The party should have a point of view on all of the issues and then they get into a debate and each party leader argues for the merits of their position. And then the voters will decide. But elections never work that way, do they? You know, George Lakoff said that. An election, you know, a party begins. By finding frames is what he called them ways of viewing the world, ways of talking about viewing the world, metaphors that lead people to view the world in the same way that you do, and only then can you begin to talk about the sorts of things that you might do. So that's how this feeds into a theory of change management, and I've I've waved my hands a lot because it's fuzzy, right? It's I I can't cover all of the details, but as a quick overview that that's how I would answer that question.
Speaker 1
Well, I find it very fascinating. It's part of this connectivism when you speak about frames, you know there are. If I take it into leadership perspective, there is so much to it. It's influence like you're painting a picture you're making someone think. When I look at frames is like storytelling. You know you're drawing real life examples. It's like immersion. You were speaking about, you know, from experience, you're frames means you want people to be immersed into looking. If it's a picture which you're trying to paint in the mind like, you know so. James I I think it's it's a really good way. I love your presentations because I love the words like reading the world, which is getting insights. You know the words which you use. To describe your topic, is is like poetry like. I just find it very fascinating. So I want to thank all the attendees. Thank you for being with us. Katrina is in my class research class, which I'm she's taking an independent study. Katrina, do you have any questions you would like to ask? What stood out to you in this presentation?
Speaker 3
I have no questions. It's just. I guess the networking part of it. Would have stood out to me because of like my business. And like the connectivity of everything of, like, maybe like between people and stuff.
Speaker
And I can.
Speaker 3
I can think of like the networking between people. And like between businesses and different things, I guess.
Speaker 1
Thank you. Thanks for sharing, Katrina. So maybe we'll, I'll create a reflection journal and I'll put the video link and then you can you know, you can just expand on what you said with some examples. Maybe just one paragraph. Thank you. Katrina. If Stella, if you'd like to say something, you're welcome to share your thoughts. What's that out to you? So thank you to all the attendees. Yeah, go ahead. Yeah.
Speaker 4
I am here. I I have my background is in connectionism as in the neural net side of things and I think it's fascinating how. UM, really the way that the neural net side of things has gone in terms of totally moving away from the symbolic side of things with the generative AI approaches that we have at the moment and how? How really that is an example of. Doing that pattern matching that recognition piece that you were talking about in the 702020 cognition and that so you know the most sort of intelligent behaviours or pseudo intelligent behaviours that we've that we've created from that technical point of view. Actually are the most prominent bit in connectivism perspective and as well you know in in what we're trying to achieve. And I think it's an, it's a. A quite a nice metaphor for or a realisation perhaps, of how we do a lot more connectivism in the way that we learn than we think we do. So yeah, anyway, so thank you very much for like drawing that comparison and making it super clear in today's presentation. Thanks, Stephen.
Speaker 2
Yeah, it's been interesting that the, the, the whole emergence over the last few years of generative AI. You know, when we started talking about this back in the early 2000s, you know, the main objection would be, well, there's there's no way you can understand without language. And yet it it seems that that language is mostly a communications tool and the the understanding itself is as they would say, is sub symbolic it it exists below the language.
Stephen Downes, Casselman, Canada
stephen@downes.ca
Last Updated: Nov 21, 2024 07:11 a.m.
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