CCK09: Notes on Learning Networks and Connective Knowledge

Over the last few days I’ve been working through Stephen Downes’ paper Learning Networks and Connective Knowledge. I often struggle to get through these longer works and actually pull anything relevant out of them, and this is definitely a longer work (over 12,000 words, just over 29 pages pasted in Word). I used Diigo to highlight and comment while reading.

Below are quotes from Stephen’s paper and my comments. Stephen’s words are in block quotes; my comments appear below each snippet.  If you’d like to see my notes in context and don’t use Diigo, use this annotated link for the page.

This is a bit rough and pretty much my “thinking out loud” from when I was reading, so don’t expect a lot of polished thoughts here. It’s also still over 3000 words, so don’t expect a succinct summary either. This is more of “somewhere in the middle of the process,” and I have many questions remaining.

In other words, cognitivists defend an approach that may be called ‘folk psychology’. “In our everyday social interactions we both predict and explain behavior, and our explanations are couched in a mentalistic vocabulary which includes terms like ‘belief’ and ‘desire’.” The argument, in a nutshell, is that the claims of folk psychology are literally true, that there is, for example, an entity in the mind corresponding to the belief that ‘Paris is the capital of France’, and that this belief is, in fact, what might loosely be called ‘brain writing’ – or, more precisely, there is a one-to-one correspondence between a person’s brain states and the sentence itself.

I’ve never heard cognitivism compared to “folk psychology” before. I’m not totally convinced by this argument. Cognitivist methods do have some research support, after all. (Think multimedia learning, Clark & Mayer’s “ELearning and the Science of Instruction.”) But their methods could (at least sometimes) be right even if their explanation of the underlying mechanism is wrong.

We may contrast cognitivism, which is a causal theory of mind, with connectionism, which is an emergentist theory of mind. This is not to say that connectionism (see also) does away with causation altogether; it is not a ‘hand of God’ theory. It allows that there is a physical, causal connection between entities, and this is what makes communication possible. But where it differs is, crucially: the transfer of information does not reduce to this physical substrate. Contrary to the communications-theoretical account, the new theory is a non-reductive theory. The contents of communications, such as sentences, are not isomorphic with some mental state.

From Wikipedia: “A property of a system is said to be emergent if it is more than the sum of the properties of the system’s parts.” If I understand Stephen’s argument correctly, part of what he’s saying here is that rather than knowledge being exactly what we perceive it to be (a sentence like “Paris is a city in France”), what’s happening in our brains is more than that. When a teacher shares knowledge with a learner, it doesn’t work like a copy machine where the teacher gives the learner a duplicate of the original and then both people have discrete copies of that knowledge.

For example (and there are many we could choose from), consider Randall O’Reilly on how the brain represents conceptual structures, as described in Modeling Integration and Dissociation in Brain and Cognitive Development. He explicitly rejects the ‘isomorphic’ view of mental contents, and instead describes a network of distributed representations. “Instead of viewing brain areas as being specialized for specific representational content (e.g., color, shape, location, etc), areas are specialized for specific
computational functions by virtue of having different neural parameters…

I struggle a bit with the neurological arguments, but it does seem to make sense that the brain is divided by the different functions and not by the symbols we’ve created to communicate. And certainly when you look at brain scans of people doing different tasks, the activity isn’t just in one area: multiple areas of the brain are involved in any complex task.But I’m also cautious about the brain evidence because, frankly, I don’t really understand it that well. I’m also aware of research about how people find arguments more convincing when they’re shown with pictures of brain scans, even if it’s the same text. I don’t want to fall prey to that fallacy.

For example, when I say, “What makes something a learning object is how we use the learning object,” I am asserting a functionalist approach to the definition of learning objects (people are so habituated to essentialist definitions that my definition does not even appear on lists of definitions of learning objects).

It’s like asking, what makes a person a ‘bus driver’? Is it the colour of his blood? The nature of his muscles? A particular mental state? No – according to functionalism, what makes him a ‘bus driver’ is the fact that he drives buses. He performs that function.

These are better examples; this makes more sense to me. It does seems to support creating learning environments where content can be used multiple different ways, which fits with connectivism.

To illustrate this concept, I have been asking people to think of the concept ‘Paris’. If ‘Paris’ were represented by a simple symbol set, we would all mean the same thing when we say ‘Paris’. But in fact, we each mean a collection of different things and none of our collections is the same. Therefore, in our own minds, the concept ‘Paris’ is a loose association of a whole bunch of different things, and hence the concept ‘Paris’ exists in no particular place in our minds, but rather, is scattered throughout our minds.

Back to the cognitivist idea of the teacher as mental copy machine handing a student a duplicate copy of knowledge–this is the opposite of that. It’s more like if 20 artists sit down to draw the same scene; there will be similarities and overlaps, but nobody’s picture will be the same. This is, perhaps, part of why connectivism makes more sense when applied to learning complex topics. You don’t need connectivism to explain memorizing the state capitals or multiplication tables; the idea of the mental copy machine is probably a functional enough explanation. But if you’re trying to learn a big, gnarly topic, a model that works for regurgitating facts isn’t enough.

As we examine the emergentist theory of mind we can arrive at five major implications of this approach for educational theorists:

- first, knowledge is subsymbolic. Mere possession of the words does not mean that there is knowledge;
the possession of knowledge does not necessarily result in the possession of the words (and for much more on this, see Michael Polanyi’s discussion of ‘tacit knowledge‘ in ‘Personal Knowledge‘).
- second, knowledge is distributed. There is no specific ‘mental entity’ that corresponds to the belief that ‘Paris is the capital of France’. What we call that ‘knowledge’ is (an indistinguishable) pattern of
connections between neurons. See, for example, Geoffrey Hinton, ‘Learning Distributed Representations of Concepts‘.
- third, knowledge is interconnected. The same neuron that is a part of ‘Paris is the capital of France’ might also be a part of ‘My dog is named Fred’. It is important to note that this is a non-symbolic interconnection – this is the basis for non-rational associations, such as are described in the recent Guardian article, ‘Where Belief is Born‘
- fourth, knowledge is personal. Your ‘belief’ that ‘Paris is the capital of France’ is quite literally different from my belief that ‘Paris is the capital of France’. If you think about it, this must be the case – otherwise Gestalt tests would be useless; we would all utter the same word when shown the same picture.
- fifth, what we call ‘knowledge’ (or ‘belief’, or ‘memory’) is an emergent phenomenon. Specifically, it is not ‘in’ the brain itself, or even ‘in’ the connections themselves, because there is no ‘canonical’ set of connections that corresponds with ‘Paris is the capital of France’. It is, rather (and carefully stated), a recognition of a pattern in a set of neural events (if we are introspecting) or behavioural events (if we are observing). We infer to mental contents the same way we watch Donald Duck on TV – we think we see something, but that something is not actually there – it’s just an organization of pixels.

If this is the case, then the concepts of what it is to know and what it is to teach are very different from the traditional theories that dominate distance education today. Because if learning is not the transfer of mental contents – if there is, indeed, no such mental content that exists to be transported – then we need to ask, what is it that we are attempting to do when we attempt to teach and learn.

I’m finding myself resisting some of these ideas, and I’m not quite sure why. Is it because it’s so different from what I’ve been taught and assumed? Is it because I’m just too used to the folk psychology ideas and I need to unlearn them? I still feel like even cognitivism is a “good enough” explanation for some basic kinds of knowledge that do seem to operate as content transfer. Cognitivism isn’t a perfect model, but a simple knowledge transfer model might be good enough for some areas. But maybe education has focused too much on the simple knowledge transfer because it’s easy and we have an easy model to explan how it works–and education should be about a lot more than the kinds of learning that cognitivism explains well. The learning theories we believe must affect what we choose to teach, and not just how we choose to teach it.

we can identify the essential elements of network semantics.
First, context, that is, the localization of entities in a network. Each context is unique – entities see the network differently, experience the world differently. Context is required in order to interpret signals, that is, each signal means something different depending on the perspective of the entity receiving it.
Second, salience, that is, the relevance or importance of a message. This amounts to the similarity between one pattern of connectivity and another. If a signal creates the activation of a set of
connections that were previously activated, then this signal is salient. Meaning is created from context and messages via salience.
Third, emergence, that is, the development of patterns in the network. Emergence is a process of resonance or synchronicity, not creation. We do not create emergent phenomena. Rather
emergence phenomena are more like commonalities in patterns of perception. It requires an interpretation to be recognized; this happens when a pattern becomes salient to a perceiver.
Fourth, memory is the persistence of patterns of connectivity, and in particular, those patterns of connectivity that result from, and result in, salient signals or perceptions.

Earlier in this section, Stephen says that the constructivist idea of “making meaning” is meaningless. But here he says “Meaning is created from context and messages via salience.” What’s the difference between “making meaning” and “creating meaning”? I don’t get it.

For example, in order to illustrate the observation that ‘knowledge is distributed’ I have frequently appealed to the story of the 747. In a nutshell, I ask, “who knows how to make a 747 fly
from London to Toronto?” The short answer is that nobody knows how to do this – no one person could design a 747, manufacture the parts (including tires and aircraft engines), take it off, fly it properly, tend to the passengers, navigate, and land it successfully. The knowledge is distributed across a network of
people, and the phenomenon of ‘flying a 747’ can exist at all only because of the connections between the constituent members of that network.

This is an example of complicated knowledge, I think, and not complex, but the idea of complicated knowledge being distributed makes sense.

“What happens,” I asked, “when online learning ceases to be like a medium, and becomes more like a platform? What happens when online learning software ceases to be a type of content-consumption tool, where learning is “delivered,” and becomes more like a content-authoring tool, where learning is created?”
The answer turns out to be a lot like Web 2.0: “The model of e-learning as being a type of content, produced by publishers, organized and structured into courses, and consumed by students, is turned on its head. Insofar as there is content, it is used rather than read— and is, in any case, more likely to be produced by students than courseware authors. And insofar as there is structure, it is more likely to resemble a language or a conversation rather than a book or a manual.”

Summary of e-learning 2.0, although so much of what is being developed is still about content consumption

The idea behind the personal learning environment is that the management of learning migrates from the institution to the learner.

Learning therefore evolves from being a transfer of content and knowledge to the production of
content and knowledge.

I’m not sure if learning always has to be about the “production” of content by the learners; it could be about analyzing, summarizing, aggregating, tagging, etc. Am I really “producing content” with my comments on this article? I don’t feel like I’m producing something new, but I definitely feel like this is e-learning 2.0. I’m building on Downes’ work. But maybe my problem is with how I’m defining “content”; if “content” includes tagging and critiquing and commenting, then I am producing content now.

In a distributed environment, however, the design is no longer defined as a type of process. Rather, designers need to characterize the nature of the connections between the constituent entities.

An interesting idea for instructional design. Usually a big part of what we do as instructional designer is think about the structure and order of learning objects. But if the learning objects are scattered in different places and nonsequential, then the support learners need isn’t being told what order to follow: it’s how the objects relate to each other.

In effective networks, content and services are disaggregated. Units of content should be as small as possible and content should not be ‘bundled’. Instead, the organization and structure of content and services is created by the receiver.

The problem from everyone who has tried reusable learning objects is that it’s so hard to get objects that are really independent and free of context. I think this is a very difficult thing to actually achieve.

An effective network is desegregated. For example, in network learning, learning is not thought of as a Separate Domain. Hence, there is no need for learning-specific tools and processes. Learning is instead thought of as a part of living, of work, of play. The same tools we use to perform day-to-day activities are the tools we use to learn.

This is already happening to some extent. Blogs, wikis, and Twitter weren’t designed as learning tools, but lots of people use them as such. A look at Jane Hart’s top tools collection shows lots of tools used by learning professionals that weren’t originally intended for learning.

Knowledge is a network phenomenon. To ‘know’ something is to be organized in a certain way, to exhibit patterns of connectivity. To ‘learn’ is to acquire certain patterns.

If learning is about acquiring patterns, then the “to learn is to practice and reflect” would be ways of following and reinforcing those patterns. I suspect for this to really make sense that “pattern” has to be my individual pattern as a learner; my pattern isn’t the same as Stephen’s, even as I’m learning from him. But my pattern might be similar to Stephen’s or overlap with his, or connect with his.

Downes
Educational Theory

A good student learns by practice, practice and reflection.
A good teacher teaches by demonstration and modeling.
The essence of being a good teacher is to be the sort of person you want your students to become.
The most important learning outcome is a good and happy life.

One thing I’ve been wrestling with a bit lately is the idea of teachers demonstrating and modeling. It seems like demonstrating and modeling are mostly the same thing. What’s the difference between the two? And I do feel like “teacher” implies something a little more active than being a model off in the distance. What if we say that good teachers model and nurture instead? Nurturing doesn’t imply direct instruction or even most of what we think of as teaching, but it does imply interacting with students in ways that supports them and helps bring out the best in them.

In essence, on this theory, to learn is to immerse oneself in the network. It is to expose oneself to actual instances of the discipline being performed, where the practitioners of that discipline are (hopefully with some awareness) modeling good practice in that discipline. The student then, through a process of
interaction with the practitioners, will begin to practice by replicating what has been modeled, with a process of reflection (the computer geeks would say: back propagation) providing guidance and correction.

This description is helpful, but I again don’t see how demonstrating is different from modeling.

These environments cut across disciplines. Students will not study algebra beginning with the first principles and progressing through the functions. They will learn the principles of algebra as
needed, progressing more deeply into the subject as the need for new knowledge is provoked by the demands of the simulation. Learning opportunities – either in the form of interaction with others, in the
form of online learning resources (formerly known as learning objects), or in the form of interaction with mentors or instructors – will be embedded in the learning environment, sometimes presenting
themselves spontaneously, sometimes presenting themselves on request.

This reinforces what Stephen said earlier about tools not being specific to learning; learning tools should be the tools we live and work and play with, integrated in our daily lives.

This does not mean that a ’science’ of learning is impossible. Rather, it means that the science will be more like meteorology than like (classical) physics. It will be a science based on modeling and simulation, pattern recognition and interpretation, projection and uncertainty.

This is in the postscript about the futility of traditional empirical research on learning. Maybe this is where I run into problems reconciling the cognitivist research I’ve read (which is all traditional “change one variable” research) with connectivism. This would also explain why some of the cognitivist research that works OK in a lab environment fails in real classrooms; a lab environment doesn’t actually reflect the chaos of a classroom well enough. I’ve heard Stephen make this argument on a number of occasions, but I’ve always assumed that it meant any educational research would be worthless. That isn’t what he’s saying though; he’s saying that educational research is a different type of research. Now it’s finally making sense to me; of course educational research should be more like psychology, where we have trends and patterns but few absolutes.

CCK09: Connectivism and Constructivism

Magnetix

This was written as a comment on April Hayman’s post comparing Legos and Magnetix as metaphors for constructivism and connectivism. One of her readers, Plain_Gillian, said she was struggling to verbalize the difference between the two learning theories. My response is below, but you should go check out the original post and discussion there too.

I think the table comparing learning theories to connectivism is a good way to start. I admit though that even having gone through CCK08 and having done all this reading that I struggle to summarize connectivism in a sentence or two the way I could crystallize the point of constructivism.

If the idea of the difference between building knowledge with pieces and connecting ideas isn’t significant enough to really help you visualize it, think instead about how you would deal with a really, really complex overabundance of information. In the constructivist view, you would take little pieces out of that overabundance and build them into something new. If you’re thinking more social constructivist, you probably socially negotiate what’s important out of the river of information. But does either of those methods of learning really give you an overall picture of the trends or substance of something really big?

From a connectivist standpoint, the response to a huge amount of information isn’t to look at the individual pieces, but to look at the patterns. The human brain is designed to look for patterns, and that’s a big part of connectivist theory. If you analyze a large text sentence by sentence, deconstructing it and reconstructing a new analysis, that’s a constructivist response. If you analyze a large text with a word cloud to look for trends, that’s a connectivist approach.

Does that help at all? This isn’t all the aspects of the theory (which is part of why it’s hard to summarize in a sentence or two), but you might find it easier to think just about one part of it at a time. (And yes, that is sort of a constructivist approach to understanding connectivism.)

If you’re having trouble verbalizing it, then go with some other medium makes sense. If wrestling with these ideas inspires you to paint or draw or make a mind map or play with Play-Doh, then do that. Connectivism is a complex theory because it’s designed to work best for complex, rapidly changing knowledge. There isn’t a single best way to approach understanding it.

Image Credit:

Magnetix by Guapolo

Brief Intro to Social Networks

These are my notes from the Learn Trends: Networked and Social Learning online mini-conference. This isn’t my usual comprehensive live blogging, just snippets of things that caught my interest. I wanted to actually participate in the chat and watch Twitter too, and I couldn’t juggle all of it at the same time. This is post 1 of 3.

Intro to Social Networks

George Siemens & Tony Karrer

Social & Networked Learning combines psychology and technology

Developing the whole person wasn’t valued–behaviorism didn’t care what people think, just what they do. If you’re running an assembly line, it only matters what employees produce, not what they are thinking.

Cognitivism was the shift to recognizing that what people think matters.

Tony: coming less from the theoretical underpinning, seeing more of this in the changes in how he gets information. What used to be search/research is more conversation now.

A lot of the networking and connecting has been going on with different structures for a long time, but it’s changing.

“We can be more productive with an individual, expert-based model.” –George

More on Tony’s ideas on networked learning.

George’s definition: connecting, often using technology–using the value of other people to be more productive and better at what we do

Moderator (Will Thalheimer): My thoughts at the moment: Learning at a cognitive level is individual. It affects the cognitive neural substrate of the individual. It can be augmented, influenced, controlled by social interactions. STILL, it is an individual thing. Sometimes being social can help learning. Sometimes it can hurt.

CCK08: Iterative, Appreciative Change

Recursive flower

These are my liveblogged notes from this morning’s live session for CCK08. Although I usually aim for a fairly complete transcript of live sessions, I spent more time paying attention to and participating in the backchannel chat today. Therefore, some of the questions and discussion near the end aren’t included in my notes. My comments are in italics. There are a few comments copied directly from the chat. The recording will be available at some point under Week 9.

Nancy White of Full Circle Associates was the guest speaker, with Stephen Downes moderating.

Key Points

Much of this discussion was about how to get change to happen, with (I think) a very practical and realistic acknowledgment of the barriers to change. The idea of iterative change came up several times. Where traditional project and change management follows a linear process, what may be more effective for emerging technology is to do lots of little projects, see how they go, and adjust as needed.

Nancy mentioned another idea that I thought was great: an appreciative approach to change. Basically, you look at what strengths you have and start there, building on what already exists. Extending this idea, I think when we build on those strengths and stretch people to grow more, we can iteratively build on the new strengths.

If you’re looking for the nuts and bolts of the ideas, skip down to the “Change Ideas” list near the bottom of this post. This was the collected brainstorming of the group on how to make change happen.

Notes

Love the intro here—a circle of clip art chairs, asking people to put their names under a chair. Not a perfect solution for huge groups, but nice for imagining community with smaller groups.

Tech + Social
“Technology has fundamentally changed how we can be together”

Learning Communities

Understand the relationships between the me, the we, and the many. The boundaries aren’t always clear.
Me: The individual. Personal identity.
We: Communities. Group identity, bounded membership, shared interest.
Many: Networks. Boundaryless, fuzzy. “intersecting interests”

Networks are about value of weak ties; Obama’s win reflects that value

Not so interested in learning theories; more interested in emerging roles and practices
Enabling people to be stewards of online communities

Stephen: Teachers just trying to change current practice. “How can I apply this in my current practice”
Nancy: You can’t usefully apply the technology unless you understand the teaching. Understand the teaching, then look at why it’s important. BUT sometimes looking at the technology first can spark an idea.

Is it just change, or is it the scale of change?

Stephen: Can we do the change just partway?
Nancy: Value of networks is that we don’t all have to do everything. One teacher doesn’t have to do everything by him/herself. We need safe places for people to explore, and sometimes privacy to try things out.
bradley.shoebottom: “Change is acceptable for only the amount people are willing to allow change. Therefore change needs to be iterative”
Nancy: Different roles for different people within the network

Use an “appreciative approach”—build on the strengths that already exist rather than trying to jump the chasm

Stephen: So many technologies are blocked
Nancy: sometimes people need to be taken outside the firewall to see what’s possible, but sometimes we need to look at workarounds and not our first choice tools. Demonstrating with limited tools can help changes occur.

Jo Ann Hammond-Meiers: @Lisa and others– yes — then it is important to stimulate the right questions from the teacher/learners. What do they want to happen?

How do you get change to happen?
Demonstration, validation
Money can be a pressure point.
Concrete purposes rather than wide solutions can get to a lot of people. What’s the tangible benefit?
Do lots of little experiments on multiple fronts to figure out what works

How do you find the time to make the connections?
Nancy: we connect to people all the time. It’s as natural as breathing. Create the conditions for change to move the direction we want, rather than pushing hard for our own position. Make building community and ties into your meetings and time together with groups, without labeling it as “community building.”

Traditional project management is very linear, but we need something more iterative. Iterative change is a recurring theme here

Change that happens at the edges instead of in the middle

Change Ideas:

• Skunkworks—under the radar
• Small, iterative projects
• Distributed learning
• Start with your personal practices because you have control there, then apply that to your work
• Get outside validation
• Find related projects outside and work together
• Nike Strategy: Just do it
• Put in the infrastructure people need to change your practice. Instead of trying to directly change what people do, make it easier for them to do it—sort of letting them choose the tools
• Give what they WANT in a nonthreatening way
• Listen carefully for needs, not just openings for your own agenda
• Do easy things in an easy way
• Openness: share what you do openly (network value)

It’s not just about technology; it’s about how you see the world and connections and how we interact with each other. It’s about asking the questions to get people to see the world differently.

How do you measure success? Benchmarks? Would another course modeled like CCK08 be a success?
Nancy: it depends on the goals and learning. Not sure how to answer the question. How do you measure success with iterative learning?

Image credit:

‘Recursive Flower, Second Time 100 visits!!!‘
www.flickr.com/photos/98621082@N00/428277413

CCK08: Connectivism, Equity, and Equality

In Groups Vs Networks: The Class Struggle Continues, Stephen Downes makes this statement about assessment:

I want to change the system of assessment in schools because right now we have tests and things like that that are scrupulously fair, particularly distance learning where we outline the objectives the performance metrics and the outcomes and all of that. I want to scrap that system. I want testing to be done by at random by comments from your peers and other people and strangers based on no criteria whatsoever and applied unequally and unfairly.

I found this a little jarring at first. Don’t we want things to be fair, to apply the same rules to everyone?

But applying the rules uniformly to everyone isn’t fair. The rules of baseball require that people run between the bases. Would you ask someone in a wheelchair to get up and run though, just because the rules say so? No, of course not. It’s absurd, not fair.

Most of the time, our educational system is set up with equality held up as the ideal. Everyone should be treated equally; we should hold everyone to the same standards. No exceptions should be made for individuals to bend the rules. In the US, NCLB is a prime example of this: every child is expected to meet the grade level goals, regardless of learning or other disabilities. We start from the assumption that everyone will learn and be assessed equally.

A better ideal for the system would be equity. We can move the emphasis away from applying the rules consistently across the board to giving people what they need as individuals to be successful. We should recognize that people do have obstacles to overcome and provide support for them to get around those obstacles. Being in a wheelchair means someone won’t run, but it certainly doesn’t mean they can’t participate in any sports.

The ALA article Equality and Equity of Access: What’s the Difference? describes equality as “fairness as uniform distribution” and equity as “fairness as justice.”

It occurred to me as I read Stephen’s ideas about assessment that connectivism may be a better way to get to the ideal of equity. It’s better for equity and accessibility when you don’t start from the assumption that everyone will learn and be assessed in the same way. If we start with the assumption that individuals will find their own path in learning, and that our job is to give them lots of opportunities and ways to participate, we’re more likely to help people get past their obstacles.

The CCK08 class is modeling that approach of letting people find their own path and giving them a chance for equity. Everything Stephen talks about with valuing diversity over uniformity reinforces that idea. The 2000 people can figure out what works best for them–lots of time in the Moodle forums or none, multiple blog posts or just reading and lurking, concept maps or word clouds, live sessions or only asynchronous. It’s what allows me to still be a participant in this class even though I knew I’d be out for a few weeks while I moved. I could take that break when I needed and step back in now.

I don’t know whether anyone in the course is visually or hearing impaired, but I can’t see any reason why they couldn’t find ways to actively participate and learn. Not everything is accessible to everyone, but you don’t need to see every image or hear the audio presentations to find value in the course.

I do wonder though–with the course so open and flexible, and with so many people participating, how much diversity is actually represented by the participants of the CCK08 class?

Metaphors and Language of Learning

Oak Tree Seedling

The discussion around whether learning grows or is built has been terrific. I can definitely sympathize with Sarah Stewart’s comment about enjoying the conversation even if I’m not sure I understand it all. I’ve got a nice collection of metaphors for learning now:

• Building: This is the classic constructivist language, constructing and building your own learning.
• Growing: From Stephen Downes’ What Connectivism Is
• “Learning As Advancement Of Ideas”: George Siemens’ suggestion to find a middle ground and avoid the conflict between building and growing
• River or Stream: Virginia Yonkers’ metaphor, shared by Ken Allen, is about the process of change as well as learning. Her idea is that we have a “river path” where the river of learning flows. The paths generated are the connections in the brain. One a path is made, it’s hard to redirect the river.
• Connections: At the chemical-physical level, learning is the connections between neurons in our brains. This is a literal description rather than a metaphor, but is we think of connections as the essential element of learning, it might affect us differently than if we think of learning like bricks in a building.
• Browser Plug-Ins: This isn’t so much a metaphor for learning as a whole, but for what the idea of neural connections actually tells us. As long as the plug-in is working, we don’t need to actually understand how it works to be able to use it. By the same token, we don’t need to necessarily understand the brain at a chemical-physical level in order to learn or help others learn.

Virginia made this observation related to my tag clouds:

It appears to me that you are expanding your “words” to use through the connectivism course.

This really resonated with me. It does seem like I’m looking for a different set of vocabulary to talk about learning.

And I think that’s why the metaphors matter–the metaphor we use to understand learning influences the language with which we talk about learning, teaching, and education.

Build implies structure and order. Ken suggested it seems linear, although Diego Leal disagreed, saying structure isn’t necessarily linear. Virginia pointed out that “building” carries the image of a systemic, external plan. In her comment, Gina Minks used the words “scaffold” and “bridge,” both “building” words. Her language choices reflect the metaphor that makes sense to her.

So what language would we use if our central metaphor for learning was “growing” rather than “building”? Would we say we nurture instead of scaffold? Connect instead of bridge? Feed instead of support? Deeper roots instead of a solid foundation?

What metaphor for learning makes the most sense to you? How does it affect the language you use when you talk about learning?

Image: ‘Oak Tree Seedling‘

Visualizing My Connectivism Learning So Far

Prompted my Michele Martin’s Web 2.0 Wednesday task for this week, I decided to do a Wordle tag cloud of my blog.

Learning is the top word in my cloud, followed by connectivism. Think, information, and networks are all prominent. Growing and grow are both more prominent than build–an interesting observation after my post about whether learning grows or is built.

Although Michele’s task was originally about personal branding, I was hoping to get a better grasp of the discussions about learning and our metaphors for it. Sometimes I just need to look at it a different way to start making the connections and seeing the patterns.

Here’s a different version of the tag cloud, this time with just my post on learning and the comments (all 12 of them, at current count):

The discussion has been great, but I need some more time to process it all and figure out where I want to go next with these ideas. I’m not used to such deep philosophical discussions.

Does Learning Grow or Is it Built?

It’s Week 1 of the Connectivism and Connective Knowledge course. This is a massively open online course led by Stephen Downes and George Siemens. I believe 1900 people have signed up for the course, so it really is huge.

With something this big, no single person can follow all the conversations or absorb all the information. It’s simply not possible. I’m planning to try to delve more deeply into a few conversations, rather than skimming lightly on the surface of many.

This week, I’m taking that to perhaps an extreme level: it’s one particular phrase and a comment about it that caught my attention. I had read Stephen’s What Connectivism Is previously, but was intrigued by the embedded comments today. Gina Minks and Diego Leal used Diigo’s sticky notes to add comments on the reading. Unfortunately, I’m not sure that you can see their annotations unless you have the Diigo toolbar installed. I want to share a snippet of the conversation here though, so anyone can read it.

Stephen argues in his post about understanding being no “more than the process of making connections”:

The point is:
- there are no mental models per se (that is, no systematically constructed rule-based representational systems)
- and what there is (ie., connectionist networks) is not built (like a model) it is grown (like a plant) (Color emphasis mine)

Gina highlighted the phrase at the end, starting with “not built,” and added this comment:

I’m not sure I agree with this. If I need to learn something, sometimes I really need to work at thinking about the new information, trying to tie it to something else I already know, or look for more information to sort the new information out in my head. It is definitely work – I search for the connections in my existing frame of knowledge, and then look through all the relevant networks I have for something to help me learn the new information. To me, his is definitly more than just “growing” a new mental model.

Here’s my response:

If you’re connecting it to existing knowledge, isn’t that sort of like a new branch growing from an existing tree? I’m not sure it’s clear here, but from Downes’ other writing, I think this is more about it growing internally, driven by the learner, rather than constructed externally. I admit I struggle with this metaphor though, and I’m not sure I quite get what he’s saying. I don’t think Downes would deny that learning can be work, but he would likely characterize that work as growing rather than building.

It may be more helpful to think of it in terms of networks of people rather than what’s happening inside your head. If you try to build a network based on a model, from the top down according to rules, is it going to be successful, or will it always be artificial and forced? On the other hand, if you can provide an environment where relationships and connections between people naturally form, you grow an organic network.

What if learning, networks of thoughts or whatever, happens the same way as networks of people grow?

I’m still not convinced that I’m not completely off-base here trying to comprehend Stephen’s argument, let alone whether I think mental models exist or not. It’s an intriguing metaphor though, especially since I do tend to be constructivist and talk about learning in constructivist terms.

What do you think: does learning grow or is it built? What metaphor for learning makes the most sense to you?

Update: This discussion continues at Metaphors and Language of Learning.

Image: ‘Reaching out‘

Continually Improving Courses

Some of the continuing conversation from Will’s It’s Not Just the Read/Write Web post has been about, well, continuing conversations. It’s the idea that learning shouldn’t be about memorizing for a test or completing a project, but lifelong learning. David Warlick wonders if we can focus more on learning and less on just what has been learned. Carolyn Foote writes about how these tools can help us focus on the process rather than just the product.

I admit that my initial reaction to David’s post was pretty much, “That’s nice, but how would we do that in the real world?” David said that in real life, “It’s all ongoing. It’s all conversation,” arguing that it isn’t about creating finished products, whether they are papers or podcasts. I’m not convinced on that though. When I develop a course with a SME, there is a product at the end. All the web pages and Blackboard setup and Flash files are products. I have deadlines, and my products have to be done at a certain point. That my real life. David’s ideas didn’t seem to mesh with the reality of my job.

Fortunately, what Carolyn wrote forced me to look at it from a different perspective.

How can we refocus students on the process? How can we extend the conversation beyond the specific project? And how can we connect cross curricular content so it’s more meaningful, as it is in the “real” world?

I think one of the powerful things about blogs and also about social networks, is that you can create an ongoing community conversation as a class or as a school, which can serve to unite those discrete assignments or efforts into a more unified and continuous learning experience.

When I read David’s post, I saw it as only conversation, without any products or milestones along the way. But I think I was wrong. I don’t think he meant we shouldn’t have these projects and products, just that we should view them as part of a larger conversation and process. Instead of a project being just an independent thing that isn’t connected to any other learning, it’s connected to a process and a network and dialog. I was too focused looking at the trees to see the forest.

Looking at it from that perspective, I can see how it relates to what I do. I know that no course I develop will ever be perfect. I can always do more to improve them: make them more engaging, more relevant, more visually appealing, more usable. We have that idea built into our review process. Yes, we have to have courses in a reasonably “finished” state so students can take them. So I do have deadlines and I do complete projects, and I celebrate the milestones of finishing and launching courses. But they aren’t really done when they launch or even after a field test; we’ll keep working on them and continuously improving them. Our conversations will mostly be internal, between the instructors and our course development team, but it is the same idea. Continuous improvement is my real life experience.

Now that I see how this relates to what I’m doing in my development, it makes me wonder how to create this kind of environment for our students as well. I’m not sure I’m really creating that kind of environment, at least not consistently. That’s a post for another day though.

Technorati Tags: continuous+improvement, learning, conversation, david+warlick, carolyn+foote, will+richardson, instructionaldesign

Sensemaking through Networks

Will Richardson writes that much of the chatter about changes in education due to technology is missing the point. Getting students and teachers and everyone to publish and write–yes, that’s a good start. Improving communication between schools and the community–yep, that’s good too. But Will argues that those are really just ways of doing the same thing we’ve been doing for a while, just with a nicer set of tools.

But here is the bigger question, I think. Through teaching them to use these tools to publish, are we also teaching them how to use these tools to continue the learning once that project is over? Can they continue to explore and reflect on the ideas that those artifacts represent regardless of who is teaching the next class? Can they connect with that audience not simply in the ways that books connect to readers (read but no write) but in the ways that allow them to engage and explore more deeply with an ongoing, growing community of learners? Isn’t that the real literacy here?

Part of what I think Will’s talking about here is connectivism: the idea that learning is about creating connections, both between people and between ideas. When I first heard of connectivism, I didn’t really get it. Actually, I’m not sure that I get it now. However, I think I might have figured out one piece of it.

My initial reaction to connectivism was that it was just about using people in your network basically as sources of information. Instead of looking something up in a search engine or encyclopedia, you ask a person. Will quotes Jay Cross in calling this the “outboard brain.” That didn’t seem very revolutionary to me though; it just seemed like more of the same stuff, just with different tools.

But I don’t think that’s what Will’s really talking about. The network isn’t just a source of information; our connections actually help us make sense of that information. We see patterns in what people talk about and how they discuss it, and that helps us in our sensemaking. We weigh information from trusted sources more heavily than those we don’t trust, and that becomes part of our understanding too. Our networks are part of our filters keeping some information out, but networks also help us connect ideas and dig deeper. We get feedback from others, and hopefully we learn to improve because of that.

What do we want students to be able to do? If I understand Will correctly, he’s hoping we can teach students to use the network as a way to make sense of the vast amounts of information now available to us. What the technology lets us do is connect with people so we can understand more and keep learning. We don’t have to stop learning when a course is finished; we can keep interacting with our network and learning together. Really, that shouldn’t just be a goal for students; lifelong learning should be a goal for everyone.

Technorati Tags: lifelonglearning, connectivism, will richardson, learning, community, patterns, web2.0