http://it.coe.uga.edu/itforum/paper92/paper92.html

Christy Tucker's personal annotations on this page

Christyinsdesign bookmarked on 2009-10-06 CCK09 connectivism teaching education learning networks learningtheories e-learning

Long paper by Stephen Downes on the nature of knowledge, connectivism, learning, and e-learning 2.0

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.
- Christyinsdesign on 2009-10-06
  
  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.
- Christyinsdesign on 2009-10-06
  
  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...
- Christyinsdesign on 2009-10-06
  
  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.
- Christyinsdesign on 2009-10-06
  
  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.
- Christyinsdesign on 2009-10-06
  
  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.
- Christyinsdesign on 2009-10-06
  
  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.
- Christyinsdesign on 2009-10-06
  
  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.
- Christyinsdesign on 2009-10-06
  
  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.”
- Christyinsdesign on 2009-10-07
  
  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.
- Christyinsdesign on 2009-10-07
  
  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.
- Christyinsdesign on 2009-10-07
  
  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.
- Christyinsdesign on 2009-10-07
  
  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.
- Christyinsdesign on 2009-10-08
  
  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.
- Christyinsdesign on 2009-10-08
  
  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.
- Christyinsdesign on 2009-10-08
  
  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.
- Christyinsdesign on 2009-10-08
  
  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.
- Christyinsdesign on 2009-10-08
  
  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.
- Christyinsdesign on 2009-10-08
  
  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.

This link has been bookmarked by 81 people . It was first bookmarked on 11 Nov 2006, by Ole C Brudvik.

25 Nov 09

adolfo arrieta
connectivism
17 Oct 09

Ruby Vine
The purpose of this paper is to outline some of the thinking behind new e-learning technology, including e-portfolios and personal learning environments. Part of this thinking is centered around the theory of connectivism, which asserts that knowledge - and therefore the learning of knowledge - is distributive, that is, not located in any given place (and therefore not 'transferred' or 'transacted' per se) but rather consists of the network of connections formed from experience and interactions with a knowing community. And another part of this thinking is centered around the new, and the newly empowered, learner, the member of the net generation, who is thinking and interacting in new ways. These trends combine to form what is sometimes called 'e-learning 2.0' - an approach to learning that is based on conversation and interaction, on sharing, creation and participation, on learning not as a separate activity, but rather, as embedded in meaningful activities such as games or workflows.

connectivism downes pedagogy cognitivism ple
16 Oct 09

Dwayne Harapnuik
connectivism networks downes elearning learning theory
15 Oct 09

Ryan Bretag
research networks connectivism pedagogy articles
David Bill
learning networks knowledge downes
11 Oct 09

Graham Perrin
unread 2009
09 Oct 09

jlearn 2.0
connectivism elearning pedagogy networks learning pln ple personal_learning professionaldevelopment professionalnetworks
06 Oct 09

Christy Tucker
Long paper by Stephen Downes on the nature of knowledge, connectivism, learning, and e-learning 2.0

CCK09 connectivism teaching education learning networks learningtheories e-learning
- 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.
  - Christy Tucker on 2009-10-06
    
    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.
  - Christy Tucker on 2009-10-06
    
    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.
- 18 more annotations...
- - 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...
    Christy Tucker on 2009-10-06
    
    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.
    Christy Tucker on 2009-10-06
    
    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.
    Christy Tucker on 2009-10-06
    
    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.
    Christy Tucker on 2009-10-06
    
    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.
    Christy Tucker on 2009-10-06
    
    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.
    
    Gillian Watson on 2009-10-08
    
    I think what he is getting at is that meaning is infered from the perceiver based on context and salience vs a meaning that is constructed by the perceiver alone. So it is not that I construct meaning for something, meaning is created based on the interaction of variables and my perception.
  - 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.
    Christy Tucker on 2009-10-06
    
    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.”
    Christy Tucker on 2009-10-07
    
    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.
    Christy Tucker on 2009-10-07
    
    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.
    Christy Tucker on 2009-10-07
    
    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.
    Christy Tucker on 2009-10-07
    
    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.
    Christy Tucker on 2009-10-08
    
    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.
    Christy Tucker on 2009-10-08
    
    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.
    Christy Tucker on 2009-10-08
    
    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.
    Christy Tucker on 2009-10-08
    
    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.
    Christy Tucker on 2009-10-08
    
    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.
    Christy Tucker on 2009-10-08
    
    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.
05 Oct 09

gobibijou
- nowledge - and therefore the
  learning of knowledge - is distributive, that is, not located in
  any
  given place (and therefore not 'transferred' or 'transacted'
  per se) b
- connectivism
- 27 more annotations...
- - that knowledge - and therefore the
    learning of knowledge - is distributive, that is, not located in
    any
    given place
  - but rather consists of the network of connections formed from
    experience and interactions with a knowing community
  - In any network, there will be
    three major elements:
  - Entities
  - hings that are connected that send and receive signals
  - the link or channel between entities
  - Connections
  - Signals
  - message sent between entities
  - networks may vary according to
    a certain set of properties
  - Density
  - how many other entities each entity is connected to
  - Speed
  - ow quickly a message moves to an entity
  - Flow,
  - how much information an entity processes
  - Plasticity
  - how frequently connections created, abandoned
  - Degree
    of connectedness
  - function of density, speed, flow and
    plasticity
  - The
    Move to 2.0
  - As the web
    surged toward 2.0 the educational community solidified its hold on
    the more traditional approach
  - Even so,
    as traditional instructional software became entrenched, it became
    difficult not to notice the movement in the other direction. First
    was the exodus from commercial software in favour of open source
    systems such as Moodle, Sakai
    and LAMS. Others
    eschewed educational software altogether as a wave of educators began
    to look at the use of blogging
    and the wiki
    in their classes. A new, distributed, model of learning was emerging,
    which came to be characterized as e-learning
    2.0.
  - “What
    happens,” I asked, “when online learning ceases to be
    like a medium, and becomes more like a platform? W
  - hat 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:
  - 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.
17 Sep 09

miz minh
- selves, 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
06 Jul 09

John Rodgers
This is an excellent overview of research which supports in some ways the concept of network learning theory

connectivism elearning Downes learning theory networks
30 Jun 09

Tania Sheko
Learning Networks and Connective Knowledge
by Stephen Downes

Stephen_Downes learning networks connective knowledge
27 May 09

Paula Antunes
04 May 09

guilana sonia
elearning downes connectivism eportfolio
03 Apr 09

Ian Guest
Stephen Downes outlines some of the thinking behind new e-learning technology, including e-portfolios and personal learning environments. Part of this thinking is centered around the theory of connectivism, which asserts that knowledge - and therefore the

elearning pedagogy learning theory research Education
19 Dec 08

Angelo Mar
elearning
- connectivism, which asserts that knowledge - and therefore the
  learning of knowledge - is distributive, that is, not located in
  any
  given place (and therefore not 'transferred' or 'transacted'
  per se) but rather consists of the network of connections formed from
  experience and interactions with a knowing community
- communication consists of information that flows through a
  channel
09 Dec 08

Vic Jenkins
T&L1 msc
08 Dec 08

Joh Fra03
connectivism elearning downes networks theory pedagogy
30 Nov 08

Tim Fawns
Stephen Downes

constructivism elearning
16 Nov 08

Stephan Ridgway
networkedlearning education connectivism
04 Nov 08

Lynne Jones
The purpose of this paper is to outline some of the thinking behind new e-learning technology, including e-portfolios and personal learning environments. Part of this thinking is centered around the theory of connectivism, which asserts that knowledge - a

network_literacy Stephen_Downes connective_knowlege learning_networks theory PLEs e_portfolios
03 Nov 08

Kristina Hoeppner
paper by Stephen Downes; October 16, 2006

connectivism networking network learning theory stephendownes research ple
Will Richardson
The purpose of this paper is to outline some of the thinking behind new e-learning technology, including e-portfolios and personal learning environments. Part of this thinking is centered around the theory of connectivism, which asserts that knowledge - a

network_literacy stephen_downes networks
21 Oct 08

Benjamin Stewart
S. Downes

Connectivism
- We begin
  with the nature of a network itself. In any network, there will be
  three major elements:
  
  –        Entities,
  that is, the things that are connected that send and receive signals
  
  –        Connections,
  that is, the link or channel between entities (may be represented as
  physical or virtual)
  
  –        Signals,
  that is, the message sent between entities. Note that meaning is not
  inherent in signal and must be interpreted by the receiver
- 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.

29 Sep 08

Rob Miller

connectivism networks downes elearning pedagogy web2.0 theory learning

connectivism, which asserts that knowledge - and therefore the
learning of knowledge - is distributive, that is, not located in
any
given place (and therefore not 'transferred' or 'transacted'
per se) but rather consists of the network of connections formed from
experience and interactions with a knowing community
Cognitivism is probably best thought of as a response to
behaviourism
53 more annotations...

. It is founded
on the view that the behaviourist assertion that there are no mental
events is in a certain sense implausible
His view is that the effectiveness of communication is
improved through interaction. Instead of viewing communication as a
one-time event, in which information is sent from a sender and
received by a receiver, the transfer of information is enabled
through a series
of communications, such that the receiver sends messages back to
the sender, or to third parties
What is happening is that
information theorists, such as Dretske, along with educational
theorists, such as Moore, are transferring the properties of a
physical medium, in this case, the communication of content via
electronic or other signals, to the realm of the mental
We may contrast cognitivism, which is a
causal theory of mind, with connectionism, which is an
emergentist
theory of min
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
second,
knowledge is distributed
hird,
knowledge is interconnected
fourth,
knowledge is personal.
fifth,
what we call 'knowledge' (or 'belief', or 'memory') is an emergent
phenomenon
For
the presumption of these theories is that, when you believe that
'Paris is the capital of France' and when I believe that 'Paris is
the capital of France', that we believe the same thing, and
that, importantly, we share the same mental state, and hence
can be reasonably relied upon to demonstrate the same semantic
information when prompted.
If we
accept that something like the network theory of learning is true,
then we are faced with a knowledge and learning environment very
different from what we are used to. In the strictest sense, there is
no semantics in network learning, because there is no meaning
in network learning (and hence, the constructivist practice of
‘making
meaning’ is literally meaningless)
ith the nature of a network itself. In any network, there will be
three major elements:

–        Entities,
that is, the things that are connected that send and receive signals

–        Connections,
that is, the link or channel between entities (may be represented as
physical or virtual)

–        Signals,
that is, the message sent between entities. Note that meaning is not
inherent in signal and must be interpreted by the receiver
In an
environment of this description, then, networks may vary according to
a certain set of properties:

–        Density,
or how many other entities each entity is connected to

–        Speed,
or how quickly a message moves to an entity (can be measured in time
or ‘hops’)

–        Flow,
or how much information an entity processes, which includes messages
sent and received in addition to transfers of messages for other
entities

–        Plasticity,
or, how frequently connections created, abandoned

–        Degree
of connectedness – is a function of density, speed, flow and
plasticity
What it is to 'know' is, if you will, a natural
development that occurs in the mind when it is presented with certain
sets of phenomena; other things being equal, present the learner with
different phenomena and they will learn different things
What it
means to 'know' then is based on organization and connectedness in
the brain
if knowledge is a network phenomenon, then, is it
necessary for all the elements of a bit of knowledge to be stored in
one’s own mind?
This assertion
constitutes an explicit recognition that what we ‘know’
is embedded in our network of connections to each other, to
resources, to the world.
The capacity
to form connections between sources of information, and thereby
create useful information patterns, is required to learn in our
knowledge economy.”
- Learning
  and knowledge rests in diversity of opinions.
- Learning
  is a process of connecting specialized nodes or information sources.
- Learning
  may reside in non-human appliances.
- Capacity
  to know more is more critical than what is currently known
- Nurturing
  and maintaining connections is needed to facilitate continual
  learning.
- Ability
  to see connections between fields, ideas, and concepts is a core
  skill.
- Currency
  (accurate, up-to-date knowledge) is the intent of all connectivist
  learning activities.
- Decision-making
  is in itself a learning process. Choosing what to learn and the
  meaning of incoming information is seen through the lens of a
  shifting reality. While there is a right answer now, it may be wrong
  tomorrow due to alterations in the information climate affecting the
  decision.
Practice:
Content Authoring and Delivery

–
Numerous
content authoring systems on the web…

–
Weblogs –
Blogger, Wordpress, LiveJournal, Moveable Type, more

–
Content
Management Systems – Drupal, PostNuke, Plone, Scoop, and many
more…

–
Audio –
Audacity – and audioblogs.com – and Podcasting

–
Digital
imagery and video – and let’s not forget Flickr

–
Collaborative
authoring – Writely, Hula, the wiki
Practice:
Organize, Syndicate Sequence, Deliver

–
Aggregation
of content metadata – RSS and Atom, OPML, FOAF, even DC and LOM

–
Aggregators –
NewsGator, Bloglines – Edu_RSS

–
Aggregation
services – Technorati, Blogdex, PubSub

–
More coming –
the Semantic Social Network
ractice:
Identity and Authorization

–
A raft of
centralized (or Federated) approaches – from Microsoft Passport
to Liberty to Shibboleth

–
Also various
locking and encryption systems

–
But nobody
wants these

–
Distributed
DRM – Creative Commons, ODRL…

–
Distributed
Identification management – Sxip, LID…
Practice:
Chatting, Phoning, Conferencing

–
Bulletin
board systems and chat rooms, usually attached to the aforementioned
content management systems such as Drupal, Plone, PostNuke, Scoop

–
Your students
use this, even if you don’t: ICQ, AIM, YIM, and some even use
MSN Messenger

–
Audioconferencing?
Skype…Or NetworkEducationWare…

–
Videoconferencing?
Built into AIM… and Skype
The
‘future VLE’ is now most commonly referred to as the
‘Personal Learning Environment’, or PLE. As described by
Milligan,
PLEs “would give the learner greater control over their
learning experience (managing their resources, the work they have
produced, the activities they participate in) and would constitute
their own personal learning environment, which they could use to
interact with institutional systems to access content, assessment,
libraries and the like.”
the PLE connects to a number of remote services, some that
specialize in learning and some that do not. Access to learning
becomes access to the resources and services offered by these remote
services.
idea
behind the personal learning environment is that the management of
learning migrates from the institution to the learne
PLE allows the learner not only to consume learning
resources, but to produce them as well. Learning therefore evolves
from being a transfer of content and knowledge to the production of
content and knowledge.
E-learning
2.0 promises
a lot.
, these principles may be realized in the following design
principles. It is worth noting at this juncture that these principles
are intended to describe not only networks but also network learning,
to show how network learning differs from traditional learning
he
idea is that each principle confers an advantage over non-network
systems, and that the set, therefore, may be used as a means of
evaluating new technology. This is a tentative set of
principles, based on observation and pattern recognition. It is not a
definitive list, and indeed, it is likely that there cannot be a
definitive list.
.
Effective networks are decentralized
Effective networks are distributed
liminate
‘mediation’, the barrier between source and receiver
.
Effective networks disintermediated.
In
effective networks, content and services are disaggregated
entities in a network are not ‘components’
of one another.
In an
effective network, content and services are dis-integrated
t the structure of the
message is logically distinct from the type of entity sending or
receiving it
message is coded in a common ‘language’
where the code is open, not proprietary.
An
effective network is democratic
a fluid, changing
entity, because without change, growth and adaptation are not
possible
An
effective network is dynamic.
An
effective network is desegregated.
o 'know' something is to be organized in a
certain way, to exhibit patterns of connectivity. To 'learn' is to
acquire certain patterns
Knowledge
is a network phenomenon
The distinction between groups and networks. Drawn in Auckland by
Stephen Downes.

First,
diversity. Did the process involve the widest possible
spectrum of points of view? Did people who interpret the matter one
way, and from one set of background assumptions, interact with people
who approach the matter from a different perspective?

Second,
and related, autonomy. Were the individual knowers
contributing to the interaction of their own accord, according to
their own knowledge, values and decisions, or were they acting at the
behest of some external agency seeking to magnify a certain point of
view through quantity rather than reason and reflection?

Third,
interactivity, or connectedness. Is the knowledge being
produced the product of an interaction between the members, or is it
a (mere) aggregation of the members' perspectives? A different
type of knowledge is produced one way as opposed to the other. Just
as the human mind does not determine what is seen in front of it by
merely counting pixels, nor either does a process intended to create
public knowledge.

Fourth,
and again related, openness. Is there a mechanism that allows
a given perspective to be entered into the system, to be heard and
interacted with by others?

f the
network theory applies to individual minds as well as to societies,
then the network pedagogy I am proposing may be summarized
as follows (and I know it’s not original, or even
substantial enough to be a theory properly So Called):

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.

n
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
earning,
in other words, occurs in communities, where the practice of learning
is the participation in the community. A learning activity is, in
essence, a conversation undertaken between the learner and
other members of the community. This conversation, in the web 2.0
era, consists not only of words but of images, video, multimedia and
more. This conversation forms a rich tapestry of resources, dynamic
and interconnected, created not only by experts but by all members of
the community, including learners.
What needs
to be understood is that learning environments are
multi-disciplinary. That is, environments are not constructed in
order to teach geometry or to teach philosophy. A learning
environment is an emulation of some 'real world' application or
discipline: managing a city, building a house, flying an airplane,
setting a budget, solving a crime, for example. In the process of
undertaking any of these activities, learning from a large number of
disciplines is required.
These
environments cut across disciplines
The
‘pedagogy’ behind the PLE – if it could be still
called that – is that it offers a portal to the world, through
which learners can explore and create, according to their own
interests and directions, interacting at all times with their friends
and community. “New forms of learning are based on trying
things and action, rather than on more abstract knowledge. ‘Learning
becomes as much social as cognitive, as much concrete as abstract,
and becomes intertwined with judgment and exploration.’”

26 Sep 08

Kimberly McCollum
- We may contrast cognitivism, which is a
  causal theory of mind, with connectionism, which is an
  emergentist
  theory of mind.
- We begin
  with the nature of a network itself. In any network, there will be
  three major elements:
21 Sep 08

Steve Sorden
connectivism cck08
Rene Meijer
cck08
- e-learning 2.0
- emergent
  phenomenon.
- 3 more annotations...
- - there is no meaning
  - Dreyfus talk about 'levels' of knowledge
  - Ability
    to see connections between fields, ideas, and concepts is a core
    skill.
20 Sep 08

Alf Hinojosa
elearning downes networks connectivism
Daniel Craig
Stephen Downes writes about networked learning theories, in particular connective knowledge and connectivism

connectivism connective connectiveknowledge knowledge knowledgemanagement network learningnetworks danielcraig StephanDownes web2.0 theory pedagogy learning stephendownes research
17 Sep 08

glen gatin
CCK08
15 Sep 08

Asako Yoshida
Connectivism Downes theory elearning
- It provides an explicit description of the ‘inner
  workings’ of the mind that behaviourism ignores. It is founded
  on the view that the behaviourist assertion that there are no mental
  events is in a certain sense implausible, if only by introspection.
- What is happening is that
  information theorists, such as Dretske, along with educational
  theorists, such as Moore, are transferring the properties of a
  physical medium, in this case, the communication of content via
  electronic or other signals, to the realm of the mental.
- 12 more annotations...
- - These technologies, in other words, would empower
    students in a way previous technologies didn’t.
  - The traditional approach to e-learning…
    tends to be structured around courses, timetables, and testing. That
    is an approach that is too often driven by the needs of the
    institution rather than the individual learner. In contrast,
    e-learning 2.0 takes a 'small pieces, loosely joined' approach that
    combines the use of discrete but complementary tools and web services
    - such as blogs, wikis, and other social software - to support the
    creation of ad-hoc learning communities.”
  - resources and services are organized in
    order to offer learning opportunities in a network environment.
  - a description of an environment intended to support a
    particular pedagogy.
  - the ecosystem, a collection of
    different entities related in a single environment that interact with
    each other in a complex network of affordances and dependencies, an
    environment where the individual entities are not joined or sequenced
    or packaged in any way, but rather, live, if you will, free, their
    nature defined as much by their interactions with each other as by
    any inherent property in themselves.
  - What is needed is to attain a middle point, where full
    connectivity is achieved, but where impulses in the network ebb and
    flow, where impulses generated by phenomena are checked against not
    one but a multitude of competing and even contradictory impulses.
  - diversity
  - autonomy
  - connectedness
  - openness
  - frames or
    points of view from with one may approach the creation of
    learning environments
  - Learning,
    in other words, occurs in communities, where the practice of learning
    is the participation in the community. A learning activity is, in
    essence, a conversation undertaken between the learner and
    other members of the community. This conversation, in the web 2.0
    era, consists not only of words but of images, video, multimedia and
    more. This conversation forms a rich tapestry of resources, dynamic
    and interconnected, created not only by experts but by all members of
    the community, including learners.
13 Sep 08

Pat Parslow
- thought his
- This was a significant difference between
  computation and neural networks. In computation (and computational
  methodology, including traditional causal science) we look for
  specific and predictable results. Make intervention X and get result
  Y. Neural network (and social network) theory does not offer this.
  Make intervention X today and get result Y. Make intervention X
  tomorrow (even on the same subject) and get result Z.
28 Aug 08

Carla Arena
networks learning theory elearning connectivism downes pedagogy web2.0 article
23 Aug 08

Irmeli Aro
Knowledge_management
21 Aug 08

Cheng Gang
- connectivism
- not located in
  any
  given place (and therefore not 'transferred' or 'transacted'
  per se) but rather consists of the network of connections formed from
  experience and interactions with a knowing community
- 10 more annotations...
- - "A
    distributed
    representation is one in which meaning is not captured by a
    single symbolic unit, but rather arises from the interaction of a set
    of units, normally in a network of some sort."
  - emergent
    phenomenon
  - learning is not
    the transfer of mental contents
  - context, that is, the localization of entities in a network
  - salience, that is, the relevance or importance of a message
  - emergence, that is, the development of patterns in the
    network
  - memory is the persistence of patterns of connectivity
  - 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
  - The
    Personal Learning Environment (PLE)
  - knowledge and learning are emergent phenomena
18 Aug 08

ghazala ansari
learning connectivism theory networks
17 Aug 08

Charles Nelson
network connectivism
28 Jul 08

Silvia Andreoli
connectivism
11 Jul 08

Zsolt Kulcsár
Stephen Downes, 2006 - tanuláselméletek és az e-learning/web viszonyáról. Hogyan alkalmazzuk a konnekcionista modellt?

connectivism downes elearning web2.0 pedagogy networks
27 Jun 08

Maryanne Burgos
Downes cck08
01 Jun 08

Todd Suomela
paper by Stephen Downes

education web2.0 knowledge elearning theory pedagogy networks connectivism
- The
  purpose of this paper is to outline some of the thinking behind new
  e-learning technology, including e-portfolios and personal learning
  environments. Part of this thinking is centered around the theory of
  connectivism, which asserts that knowledge - and therefore the
  learning of knowledge - is distributive, that is, not located in
  any
  given place (and therefore not 'transferred' or 'transacted'
  per se) but rather consists of the network of connections formed from
  experience and interactions with a knowing community. And another
  part of this thinking is centered around the new, and the newly
  empowered, learner, the member of the net generation, who is thinking
  and interacting in new ways. These trends combine to form what is
  sometimes called 'e-learning 2.0' -
  an approach to learning that
  is based on conversation and interaction, on sharing, creation and
  participation, on learning not as a separate activity, but rather, as
  embedded in meaningful activities such as games or workflows.
29 May 08

Steven Smith
downes elearning
27 May 08

Jeff Cross
connectivism pedagogy elearning articles
Mike Wesch
learning theory knowledge connectivism networks Downes pedagogy
25 May 08

Tom Layton
Stephen Downes
October 16, 2006

YB Theory Stephen Downes
Jose Luis Cabello
pedagogía e-learning conectivismo
Barbara Lindsey
web2.0 research elearning beyondwebct aroundtheworld grants
20 May 08

Carlos Vaz
learning_objects
14 May 08

andrewc
networks psychology education
25 Apr 08

Allison Kipta
The purpose of this paper is to outline some of the thinking behind new e-learning technology, including e-portfolios and personal learning environments. Part of this thinking is centered around the theory of connectivism, which asserts that knowledge - a

connectivism learning-networks downes.stephen
20 Apr 08

hilary
todo connectivism downes elearning2.0 pedagogy
14 Apr 08

Fernando S
Parts of this paper are drawn from previous papers (especially Connective Knowledge and Basics of Instructional Design, neither of which are published). Parts are drawn from talks and seminars. This is the best current version of the theory I could manage today. It is my hope that the ensuing discussion will add to the depth and the accuracy of the content. Please do not think of this as a definitive statement. There won’t be a definitive statement.

conectivismo e-learning pedagogy web2.0 research investigación e-portfolio portafolios socialsoftware networks conocimiento theory semantics knowledge learning downes
Concepción Abraira Fernández
Stephen Downes, octubre de 2006:
Parts of this paper are drawn from previous papers (especially Connective Knowledge and Basics of Instructional Design, neither of which are published). Parts are drawn from talks and seminars. This is the best current ve

conectivismo e-learning pedagogy web2.0 research investigación e-portfolio portafolios socialsoftware networks conocimiento
28 Mar 08

sandra farmer
14 Feb 08

Lino Oliveira
Learning Networks and Connective Knowledge

article communities e-learning Education elearning2.0 web2.0 social_software
08 Feb 08

Britt Watwood
elearning Learning web3.0 stephendownes
18 Dec 07

Jason Myers
connective knowledge learning links networks
- Learning Networks and
  Connective Knowledge
05 Oct 07

lorenz77
del.icio.us elearning imported theory
25 Sep 07

elisabetta cigognini
del.icio.us eknowledge elearning elearning2.0 imported web2.0
28 Aug 07

Mike Nall
downes emergence knowledge collectiveintelligence
22 Jul 07

huaihao Chang
edu
15 Jun 07

Wisely
knowledge learning semantics
- 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').
- 98 more annotations...
- - second,
    knowledge is distributed.
  - What
    we call that 'knowledge' is (an indistinguishable) pattern of
    connections between neurons.
  - 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'.
  - 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'
  - 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
    learning is context-sensitive then the 'language of thought'
    hypothesis fails, and the rest of folk psychology along with it. For
    the presumption of these theories is that, when you believe that
    'Paris is the capital of France' and when I believe that 'Paris is
    the capital of France', that we believe the same thing, and
    that, importantly, we share the same mental state, and hence
    can be reasonably relied upon to demonstrate the same semantic
    information when prompted.
  - - Wilbert
    Quine, in 'Two
    Dogmas of Empiricism' and in 'Word
    and Object', shows that observation itself is context-sensitive,
    that there is no knowable one-to-one matching between sense-phenomena
    and the words used to describe them; in 'On
    the Indeterminacy of Translation' he illustrates this with the
    famous 'gavagai' example: when a native speaker uses the word
    'gavagai' there is no empirical way to know whether he means 'rabbit'
    or 'the physically incarnate manifestation of my ancestor'
  - Norwood
    Russell Hanson, in 'Patterns
    of Discovery', argues, in my view successfully, that causal
    explanations are context-sensitive. 'What was the cause of the
    accident?' It depends on who you ask - the police officer will point
    to the speed, the urban planner will point to the road design, the
    driver will point to the visibility.
  - George
    Lakoff, in 'Women,
    Fire and Dangerous Things', shows that categories are context
    sensitive (contra Saul Kripke);
    that what makes two things 'the same' varies from culture to culture,
    and indeed (as evidenced from some of his more recent political
    writings) from 'frame' to 'frame'
  - Bas C.
    van Fraassen in ‘The
    Scientific Image’ shows that explanations are context
    sensitive. 'Why are the roses growing here?' may be answered in a
    number of ways, depending on what alternative explanations are
    anticipated. 'Because someone planted them.' 'Because they were well
    fertilized.' 'Because the chlorophyll in the leaves converts the
    energy of the Sun into glucose' are all acceptable answers, the
    correct one of which depends on the presuppositions inherent in the
    question.
  - David
    K. Lewis and Robert
    C. Stalnaker argue that the counterfactuals and modalities are
    context sensitive (though Lewis, if asked, would probably deny it).
    The truth of a sentence like 'brakeless trains are dangerous'
    depends, not on observation, but rather, on the construction of a
    'possible world' that is relevantly similar (Stalnaker uses the word
    'salience') to our own, but what counts as 'relevant' depends on the
    context in which the hypothetical is being considered.
  - ach person is experiencing a
    mental state that is at best seen as an approximation of what
    it is that is being said in words or experienced in nature, an
    approximation that is framed and indeed comprehensible only from
    which the rich set of world views, previous experiences and frames in
    which it embedded.
  - 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
  - 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.
  - Network
    Semantics and Connective Learning
  - Traditionally,
    what a sentence ‘means’ is the (truth of falsity of) the
    state of the world it represents. However, on a network theory of
    knowledge, there is no such state of the world to which this meaning
    can be affixed. This is not because there is no such state of the
    world. The world could most certainly exist, and there is no
    contradiction in saying that a person’s neural states are
    caused by world events. However, it does mean that there is no
    particular state of the world that corresponds with (is
    isomorphic to) a particular mental state. This is because the mental
    state is embedded in a sea of context and presuppositions that are
    completely opaque to the state of the world.
  - How, then,
    do we express ourselves? How do we distinguish between true and false
    – what, indeed, does it even mean to say that something
    is true and false? The answer to these questions is going to be
    different for each of us. They will be embedded in a network of
    assumptions and beliefs about the nature of meaning, truth and
    falsity. In order to get at a response, therefore, it will be
    necessary to outline what may only loosely be called ‘network
    semantics’.
  - we can identify the essential elements of
    network semantics.
  - First,
    context,
  - 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.
  - 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.
  - what does it mean, then, to say that a sentence has
    semantical import? To say, similarly, that we 'know' something?
  - The knowledge needs to be, in some way, in our mind (or
    in our society); it needs to be a 'belief'. And (so goes the
    argument) it needs to be in some way justified, through a process of
    verification, or at the very least, says Popper, through the absence
    of falsification.
  - to 'know' that 'snow is white' is to be organized in a
    certain way (one that is evidenced by uttering 'snow' when
    asked). To be organized in such a way as to have neural and mental
    structures corresponding to the words 'snow', 'is' and 'white', where
    those structures are such that the concept 'snow' is closely
    associated with (in certain contexts) the concept 'white' (obviously
    this is a gloss, since there is no real correspondence). Knowing that
    'snow is white' is therefore being organized in some certain
    way, but not in a specific particular way (we couldn't examine
    one's neural organization and be able to say whether the person knows
    that snow is white).
  - Whether
    something counts as 'knowledge' rather than, say, 'belief' or
    'speculation', depends less on the state of the world, and more on
    the strength or degree of connectedness between the entities. To
    'know' something is to not be able to not know. It's like finding
    Waldo, or looking at an abstract image. There may be a time when we
    don't know where Waldo is, or what the image represents, but once we
    have an interpretation, it is not possible to look without seeing
    Waldo, without seeing the image.
  - Dreyfus and Dreyfus talk about 'levels' of knowledge, up to and
    including an almost intuitive ‘expert’
    knowledge. As a particular organization, a particular set of
    connections, between neural structures is strengthened, as this
    structure becomes embedded in more and more of our other concepts and
    other knowledge, it changes its nature, changing from something that
    needs to be triggered by cue or association (or mental effort) into
    something that is natural as other things we 'know' deeply, like how
    to breathe, and how to walk, structures entrenched through years,
    decades, or successful practice. Contrast this to a cognitivist model
    of knowledge, where once justification is presented, something is
    'known', and cannot later in life be 'more known'.
  - 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.
  - if knowledge is a network phenomenon, then, is it
    necessary for all the elements of a bit of knowledge to be stored in
    one’s own mind? Karen Stephenson writes,
    “I store my knowledge in my friends.” This assertion
    constitutes an explicit recognition that what we ‘know’
    is embedded in our network of connections to each other, to
    resources, to the world.
  - This
    approach to learning has been captured under the heading of
    ‘connectivism’.
    In his paper of the same name, George Siemens articulates the major
    theses:
  - Learning
    and knowledge rests in diversity of opinions.
  - Learning
    is a process of connecting specialized nodes or information sources.
  - Learning
    may reside in non-human appliances.
  - Capacity
    to know more is more critical than what is currently known
  - Nurturing
    and maintaining connections is needed to facilitate continual
    learning.
  - Ability
    to see connections between fields, ideas, and concepts is a core
    skill.
  - Currency
    (accurate, up-to-date knowledge) is the intent of all connectivist
    learning activities.
  - Decision-making
    is in itself a learning process. Choosing what to learn and the
    meaning of incoming information is seen through the lens of a
    shifting reality. While there is a right answer now, it may be wrong
    tomorrow due to alterations in the information climate affecting the
    decision.
  - The following list is typical of what might be
    called ‘network’ practices online
  - Practice:
    Content Authoring and Delivery
  - Practice:
    Organize, Syndicate Sequence, Deliver
  - –
    Aggregation
    of content metadata – RSS and Atom, OPML, FOAF, even DC and LOM
    
    –
    Aggregators –
    NewsGator, Bloglines – Edu_RSS
    
    –
    Aggregation
    services – Technorati, Blogdex, PubSub
    
    –
    More coming –
    the Semantic Social Network
  - Practice:
    Identity and Authorization
  - –
    Also various
    locking and encryption systems
    
    –
    But nobody
    wants these
  - Practice:
    Chatting, Phoning, Conferencing
  - The
    Move to 2.0
  - As the web
    surged toward 2.0 the educational community solidified its hold on
    the more traditional approach. The learning management system became
    central (and centralized, with Blackboard purchasing
    WebCT).
  - as traditional instructional software became entrenched, it became
    difficult not to notice the movement in the other direction. First
    was the exodus from commercial software in favour of open source
    systems such as Moodle, Sakai
    and LAMS. Others
    eschewed educational software altogether as a wave of educators began
    to look at the use of blogging
    and the wiki
    in their classes. A new, distributed, model of learning was emerging,
    which came to be characterized as e-learning
    2.0.
  - 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 idea
    behind the personal learning environment is that the management of
    learning migrates from the institution to the learner.
  - The 2.0
    Architecture
  - ts fundamental architecture, which may be called ‘learning
    networks
  - We
    don't present these learning objects, ordered, in a sequence, we
    present randomly, unordered. We don't present them in classrooms and
    schools, we present them to the environment, to where students find
    themselves, in their homes and in their workplaces. We don't present
    them at all, we contribute them to the conversation, and we become
    part of the conversation. They are not just text and tests; they are
    ourselves, our blog posts, our publications and speeches, our
    thoughts in real-time conversation.
  - This is a tentative set of
    principles, based on observation and pattern recognition
  - 1.
    Effective networks are decentralized.
  - 2.
    Effective networks are distributed. Network entities reside in
    different physical locations. This reduces the risk of network
    failure.
  - 3.
    Effective networks disintermediated. That is, they eliminate
    ‘mediation’, the barrier between source and receiver.
    Examples of disintermediation include the bypassing of editors,
    replacing peer review prior to publication with recommender
    systems subsequent to publication
  - 4. 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.
  - This was the idea behind learning objects; the
    learning object was sometimes defined as the ‘smallest
    possible unit of instruction’.
  - 5. In an
    effective network, content and services are dis-integrated.
    That is to say, entities in a network are not ‘components’
    of one another.
  - 6. An
    effective network is democratic. Entities in a network are
    autonomous; they have the freedom to negotiate connections with other
    entities, and they have the freedom to send and receive information.
  - 7. An
    effective network is dynamic. A network is a fluid, changing
    entity, because without change, growth and adaptation are not
    possible.
  - 8. 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.
  - Given the destructive nature of cascade phenomena,
    it would make more sense to leave entities in the network unconnected
    (much like Newton escaped
    the plague by isolating himself). Terminating all the connections
    would prevent cascade phenomena. However, it would also prevent any
    possibility of human knowledge, any possibility of a knowing society.
  - Perception itself consists of selective
    filtering and interpretation (pattern detection!). The mind
    supplies sensations that are not there. Even a cautiously aware and
    reflective perceiver can be misled.
  - Quantitative
    knowledge, the cathedral of the twentieth century,
  - What
    we count is as important as how we count, and on this, quantitative
    reasoning is silent
  - We can measure grades, but are grades the
    measure of learning?
  - We can measure economic growth, but is an
    increase in the circulation of money a measure of progress?
  - We can
    easily mislead ourselves with statistics, as Huff
    shows, and in more esoteric realms, such as probability, our
    intuitions can be exactly wrong.
  - It is important to
    recognize that a structure of connections is, at its heart,
    artificial,
    an interpretation of any reality there may be, and
    moreover, that our observations of emergent phenomena themselves as
    fragile and questionable as observations and measurements - these
    days, maybe more so, because we do not have a sound science of
    network semantics.
  - is constructed in such a way
    that no single impulse is able to overwhelm the network. A perception
    must be filtered
    through layers of intermediate (and (anthropomorphically) sceptical)
    neurons before forming a part of a concept.
  - the human mind
  - The
    mechanism for attaining the reliability of connective knowledge is
    fundamentally the same as that of attaining reliability in other
    areas; the promotion of diversity
  - This leads
    to the statement of the semantic condition:
  - First,
    diversity. Did the process involve the widest possible
    spectrum of points of view? Did people who interpret the matter one
    way, and from one set of background assumptions, interact with people
    who approach the matter from a different perspective?
  - Second,
    and related, autonomy. Were the individual knowers
    contributing to the interaction of their own accord, according to
    their own knowledge, values and decisions, or were they acting at the
    behest of some external agency seeking to magnify a certain point of
    view through quantity rather than reason and reflection?
  - Third,
    interactivity, or connectedness. Is the knowledge being
    produced the product of an interaction between the members, or is it
    a (mere) aggregation of the members' perspectives? A different
    type of knowledge is produced one way as opposed to the other.
  - Fourth,
    and again related, openness. Is there a mechanism that allows
    a given perspective to be entered into the system, to be heard and
    interacted with by others?
  - 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.
  - A learning activity is, in
    essence, a conversation
  - This conversation forms a rich tapestry of resources, dynamic
    and interconnected, created not only by experts but by all members of
    the community, including learners.
  - the course content (if any) ought to be subservient to
    the discussion, that the community is the primary unit of learning,
    and that the instruction and the learning resources are secondary,
    arising out of, and only because of, the community.
  - What needs
    to be understood is that learning environments are
    multi-disciplinary. That is, environments are not constructed in
    order to teach geometry or to teach philosophy. A learning
    environment is an emulation of some 'real world' application or
    discipline: managing a city, building a house, flying an airplane,
    setting a budget, solving a crime, for example. In the process of
    undertaking any of these activities, learning from a large number of
    disciplines is required.
  - “Educators play the same sort of role in society as
    journalists. They are aggregators, assimilators, analysts and
    advisors. They are middle links in an ecosystem, or as John
    Hiler puts it, parasites on information produced by others. And
    they are being impacted by alternative forms of learning in much the
    same way, for much the same reasons.”
  - Postscript:
    The Non-Causal Theory of Knowledge
  - Calls for "evidence that show this claim is true"
    and "studies to substantiate this claim" are, like most
    Positivist
    and Positivist-inspired theories, reductive
    in nature; that is why, for example, we expect to find something like
    a reductive entity, 'the message', 'the information', 'the learning',
    and the like.
  - They are also aggregationist; the presumption, for
    example, is that knowledge is cumulative, that it can be assembled
    through a series of transactions, or in more advanced theories,
    'constructed' following a series of cues and prompts.
  - Saying that
    there are ‘thoughts’ and ‘beliefs’ that
    somehow reduce to physical instantiations of, well, something
    (a word, a brain state…) is a mistake. These concepts are
    relics of an age when we thought the mental came in neat little
    atomistic packages, just like the physical. They are an unfounded
    application of concepts like 'objects' and 'causation' to phenomena
    that defy such explanation; they are, in other words, relics of 'folk
    psychology'.
  - emergent phenomena are
    not causal phenomena. That is (say) the picture of Richard Nixon does
    not 'cause' you to think of the disgraced former president. They
    require a perceiver, someone to recognize the pattern being
    displayed in the medium.
  - perception (and
    language, etc), unlike strict causation, is context-sensitive.
  - traditional research methodology, and
    for that matter, traditional methods of testing and evaluation, as
    employed widely in the field of e-learning, will not be
    successful (are high school grades a predictor of college success?
    Are LSAT scores? Are college grades a predictor of life success?).
  - Environments with numerous mutually
    dependent variables are known collectively as chaotic
    systems. Virtually all networks are chaotic systems. Classic
    examples of chaotic systems are the weather system and the ecology.
    In both cases, it is not possible to determine the long-term impact
    of a single variable. In both cases, trivial differences in initial
    conditions can result in significant long-term differences (the
    butterfly
    effect).
  - 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.
  - Learning
    theorists will no longer be able to study learning from the detached
    pose of the empirical scientist. The days of the controlled study
    involving 24 students ought to end. Theorists will have to, like
    students, immerse themselves in their field, to encounter and engage
    in a myriad of connections, to immerse themselves, as McLuhan would
    say, as though in a warm bath. But it’s a new world in here,
    and the water’s fine.
08 Jun 07

Cristian Cabezas
educacion edublogs
15 Apr 07

Wolfgang Franz
blog imported
02 Mar 07

patarakin
theory learning learning2.0 connectivism downes
22 Jan 07

José Mota
October 16, 2006
PDF - http://it.coe.uga.edu/itforum/paper92/DownesPaper92.pdf
DOC - http://www.downes.ca/files/lnck.doc

downes ple tese elearning articles
14 Nov 06

Mike Hetherington
connectivism downes imported:del.icio.us networks neurons research
11 Nov 06

Ole C Brudvik
connected downes learnin network semantics
- We begin with the
  nature of a network itself. In any network, there will be three major
  elements:
  
  –        Entities, that is, the things that are connected that
  send and receive signals
  
  –        Connections, that is, the link or channel between
  entities (may be represented as physical or virtual)
  
  –        Signals, that is, the message sent between entities.
  Note that meaning is not inherent in signal and must be interpreted by
  the receiver
  
  In an environment of
  this description, then, networks may vary according to a certain set of
  properties:
  
  –        Density, or how many other entities each entity is
  connected to
  
  –        Speed, or how quickly a message moves to an entity (can
  be measured in time or ‘hops’)
  
  –        Flow, or how much information an entity processes, which
  includes messages sent and received in addition to transfers of messages for
  other entities
  
  –        Plasticity, or, how frequently connections created,
  abandoned
  
  –        Degree of connectedness – is a function of density,
  speed, flow and plasticity
  
  Given this description
  of networks, we can identify the essential elements of network
  semantics.
- Network Semantics
  and Connective Learning
  
  If we accept that
  something like the network theory of learning is true, then we are faced with a
  knowledge and learning environment very different from what we are used to. In
  the strictest sense, there is no semantics in network learning, because there is
  no meaning in network learning (and hence, the constructivist practice of
  ‘making
  meaning’ is literally meaningless).
  
  Traditionally, what a
  sentence ‘means’ is the (truth of falsity of) the state of the world it
  represents. However, on a network theory of knowledge, there is no such state of
  the world to which this meaning can be affixed. This is not because there is no
  such state of the world. The world could most certainly exist, and there is no
  contradiction in saying that a person’s neural states are caused by world
  events. However, it does mean that there is no particular state of
  the world that corresponds with (is isomorphic to) a particular mental state.
  This is because the mental state is embedded in a sea of context and
  presuppositions that are completely opaque to the state of the
  world.
  
  How, then, do we
  express ourselves? How do we distinguish between true and false – what, indeed,
  does it even mean to say that something is true and false? The answer to
  these questions is going to be different for each of us. They will be embedded
  in a network of assumptions and beliefs about the nature of meaning, truth and
  falsity. In order to get at a response, therefore, it will be necessary to
  outline what may only loosely be called ‘network semantics’
- 26 more annotations...
- - 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.
  - Connective semantics is
    therefore derived from what might be called connectivist ‘pragmatics’,
    that is, that actual use of networks in practice. In our particular
    circumstance we would examine how networks are used to support learning. The
    methodology employed is to look at multiple examples and to determine what
    patterns may be discerned. These patterns cannot be directly communicated. But
    instances of these patterns may be communicated, thus allowing readers to (more
    or less) ‘get the idea’.
  - 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.
  - Or, another story: if
    knowledge is a network phenomenon, then, is it necessary for all the elements of
    a bit of knowledge to be stored in one’s own mind? Karen Stephenson writes, “I store my knowledge in
    my friends.” This assertion constitutes an explicit recognition that what we
    ‘know’ is embedded in our network of connections to each other, to resources, to
    the world. Siemens writes,
    “Self-organization on a personal level is a micro-process of the larger
    self-organizing knowledge constructs created within corporate or institutional
    environments. The capacity to form connections between sources of information,
    and thereby create useful information patterns, is required to learn in our
    knowledge economy.”
  - This approach to
    learning has been captured under the heading of ‘connectivism’.
    In his paper of the same name, George Siemens articulates the major
    theses:
    
    Learning and knowledge rests in diversity of opinions.
    
    Learning is a process of connecting specialized nodes or information
    sources.
    
    Learning may reside in non-human appliances.
    
    Capacity to know more is more critical than what is currently
    known
    
    Nurturing and maintaining connections is needed to facilitate
    continual learning.
    
    Ability to see connections between fields, ideas, and concepts is a core
    skill.
    
    Currency (accurate, up-to-date knowledge) is the intent of all
    connectivist learning activities.
    
    Decision-making is in
    itself a learning process. Choosing what to learn and the meaning of incoming
    information is seen through the lens of a shifting reality. While there is a
    right answer now, it may be wrong tomorrow due to alterations in the information
    climate affecting the decision.
  - Is this the definitive
    statement of network learning? Probably not. But it is developed in the classic
    mold of network learning, through a process of immersion into the network and
    recognition of salient patterns. What sort of network? The following list is
    typical of what might be called ‘network’ practices online (I won’t draw these
    out in detail because there are dozens of papers and presentations that do
    this):
    
    Practice: Content
    Authoring and Delivery
    
    –        Numerous content authoring
    systems on the web…
    
    –        Weblogs – Blogger, Wordpress,
    LiveJournal, Moveable Type, more
    
    –        Content Management Systems –
    Drupal, PostNuke, Plone, Scoop, and many more…
    
    –        Audio – Audacity – and
    audioblogs.com – and Podcasting
    
    –        Digital imagery and video – and
    let’s not forget Flickr
    
    –        Collaborative authoring –
    Writely, Hula, the wiki
    
    Practice: Organize,
    Syndicate Sequence, Deliver
    
    –        Aggregation of content metadata
    – RSS and Atom, OPML, FOAF, even DC and LOM
    
    –        Aggregators – NewsGator,
    Bloglines – Edu_RSS
    
    –        Aggregation services –
    Technorati, Blogdex, PubSub
    
    –        More coming – the Semantic
    Social Network
    
    Practice: Identity and
    Authorization
    
    –        A raft of centralized (or
    Federated) approaches – from Microsoft Passport to Liberty to
    Shibboleth
    
    –        Also various locking and
    encryption systems
    
    –        But nobody wants
    these
    
    –        Distributed DRM – Creative
    Commons, ODRL…
    
    –        Distributed Identification
    management – Sxip, LID…
    
    Practice: Chatting,
    Phoning, Conferencing
    
    –        Bulletin board systems and chat
    rooms, usually attached to the aforementioned content management systems such as
    Drupal, Plone, PostNuke, Scoop
    
    –        Your students use this, even if
    you don’t: ICQ, AIM, YIM, and some even use MSN Messenger
    
    –        Audioconferencing? Skype…Or
    NetworkEducationWare…
    
    –        Videoconferencing? Built into
    AIM… and Skype
  - “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.”
  - In the days since this
    shift was recognized a growing community of educators and developers has been
    gathering around a model of online learning typified by this diagram authored by Scott
    Wilson (and remixed by various others since then):
    
    Figure 1: Future
    VLE
    
    http://www.cetis.ac.uk/members/scott/blogview?entry=20050125170206
  - In an environment such
    as this, the nature of design changes. In a typical computer program, the design
    will be specified with an algorithm or flowchart. Software will be described as
    performing a specific process, with specified (and often controlled) inputs and
    outputs. 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.
  - We compensate for these
    weaknesses by recognizing that a single point of view is insufficient; we
    distribute what constitutes an 'observation' through a process of description
    and verification. If one person says he saw a zombie, we take such a claim
    sceptically; if a hundred people say they saw zombies, we take it more
    seriously, and if a process is described whereby anyone who is interested can
    see a zombie for themselves, the observation is accepted. In other words, the
    veracity of our observations is not guaranteed by the observation, but by an
    observational methodology.
  - What are the core principles
    that will characterize such a description? The internet itself illustrates a
    sound set of principles, grounded by two major characteristics: simple services
    with realistic scope. “Simple service or simple devices with realistic scope are
    usually able to offer a superior user experience compared to a complex,
    multi–purpose service or device.” Or as David Weinberger describes the network:
    small pieces, loosely joined.
  - But it should be
    self-evident that mere organization is not the only determinate of what
    constitutes, if you will, 'good' knowledge as opposed to 'bad' (or 'false')
    knowledge. Consider public knowledge. People form themselves into communities,
    develop common language and social bonds, and then proceed to invade Europe or
    commit mass suicide. Nor is personal knowledge any reliable counterbalance to
    this. People are as inclined to internalize the dysfunctional as the utile, the
    self-destructive as the empowering. Some types of knowledge (that is, some ways
    of being organized, whether socially or personally) are destructive and
    unstable.
    
    These are examples of
    cascade
    phenomena. In social sciences the same phenomenon might be referred to as
    the bandwagon
    effect. Such
    phenomena exist in the natural world as well. The sweep of the plague
    through medieval society, the failure of one hydro plant after another, the
    bubbles in the stock market. Cascade phenomena occur when some event or property
    sweeps through the network. Cascade phenomena are in one sense difficult to
    explain and in another sense deceptively simple.
    
    The sense in which they
    are simple to explain is mathematical. If a signal has more than an even chance
    of being propagated from one entity in the network to the next, and if the
    network is fully connected, then the signal will eventually propagate to every
    entity in the network. The speed at which this process occurs is a property of
    the connectivity of the network. In (certain) random and scale free networks,
    including hierarchal
    networks, it takes very few connections to jump from one side of the network
    to the other. Cascade phenomena sweep through densely connected networks very
    rapidly.
    
    The sense in which they
    are hard to explain is related to the question of why they exist at all. Given
    the destructive nature of cascade phenomena, it would make more sense to leave
    entities in the network unconnected (much like Newton escaped
    the plague by isolating himself). Terminating all the connections would
    prevent cascade phenomena. However, it would also prevent any possibility of
    human knowledge, any possibility of a knowing society.
  - 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. This is as
    true for a community as it is for an individual.
  - 1. Effective networks
    are decentralized. Centralized networks have a characteristic ‘star’
    shape, where some entities have many connections while the vast majority have
    few. This is typical of, say a broadcast network or the method of a teacher in a
    classroom. Decentralized networks, by contrast, form a mesh. The weight of
    connections and the flow of information is distributed. This balanced load
    results in a more stable network, with no single point of
    failure.
    
    2. Effective networks
    are distributed. Network entities reside in different physical locations.
    This reduces the risk of network failure. It also reduces need for major
    infrastructure, such as powerful servers, large bandwidth, and massive storage.
    Examples of distributed networks include peer-to-peer networks, such as Kazaa, Gnutella and content syndication networks,
    such as RSS. The
    emphasis of such systems is on sharing, not copying; local copies, if they
    exist, are temporary.
    
    3. Effective networks
    disintermediated. That is, they eliminate ‘mediation’, the barrier
    between source and receiver. Examples of disintermediation include the bypassing
    of editors, replacing peer review prior to publication with recommender systems
    subsequent to publication. Or of the replacement of traditional news media and
    broadcasters with networks of news
    bloggers. And, crucially, the removal of the intermediate teacher that
    stands between knowledge and the student. The idea is to, where possible,
    provide direct access to information and services. The purpose of mediation, if
    any, is to manage flow, not information, to reduce the volume of information,
    not the type of information.
    
    4. 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
    . This allows the integration of new
    information and services with the old, of popular news and services with those
    in an individual’s particular niche interests. This was the idea behind learning
    objects; the learning object was sometimes defined as the ‘smallest possible
    unit of instruction’. The assembly of learning objects into pre-packaged
    ‘courses’ defeats this, however, obviating any advantage the disaggregating of
    content may have provided.
    
    5. In an effective
    network, content and services are dis-integrated. That is to say,
    entities in a network are not ‘components’ of one another. For example, plug-ins
    or required software to be avoided. What this means in practice is that the
    structure of the message is logically distinct from the type of entity sending
    or receiving it. The message is coded in a common ‘language’ where the code is
    open, not proprietary. So no particular software or device is needed to receive
    the code. This is the idea of standards, but where standards evolve rather than
    being created, and where they are adopted by agreement, not
    requirement.
  - 6. An effective network
    is democratic. Entities in a network are autonomous; they have the
    freedom to negotiate connections with other entities, and they have the freedom
    to send and receive information. Diversity in a network is an asset, as it
    confers flexibility and adaptation. It also allows the network as a whole to
    represent more than just the part. Control of the entities in a network,
    therefore, should be impossible. Indeed, in an effective network, even where
    control seems desirable, it is not practical. This condition
    
    –
    
    which may be
    thought of as the semantic condition – is what distinguishes networks
    from groups (see below).
    
    7. An effective network
    is dynamic. A network is a fluid, changing entity, because without
    change, growth and adaptation are not possible. This is sometimes described as
    the ‘plasticity’
    of a network. It is through this process of change that new knowledge is
    discovered, where the creation of connections is a core
    function.
    
    8. 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. Viewed more broadly, this condition amounts to seeing the
    network as infrastructure. Computing, communicating and learning are not
    something we ‘go some place to do’. Instead, we think of network resources as
    similar to a utility, like electricity, like water, like telephones. The network
    is everywhere.
    
    It should be noted that
    though some indication of the justification for these methodological principles
    has been offered in the list above, along with some examples, this list is in
    essence descriptive. In other words, what is claimed here is that
    successful networks in fact adhere to these principles. The why of
    this is the subject of the next few sections.
  - In practice, these
    principles may be realized in the following design principles. It is worth
    noting at this juncture that these principles are intended to describe not only
    networks but also network learning, to show how network learning differs from
    traditional learning. The idea is that each principle confers an advantage over
    non-network systems, and that the set, therefore, may be used as a means of
    evaluating new technology. This is a tentative set of principles, based
    on observation and pattern recognition. It is not a definitive list, and indeed,
    it is likely that there cannot be a definitive list.
  - . Perception itself
    consists of selective
    filtering and interpretation (pattern detection!)
    . The
    mind supplies sensations that are not
    there. Even a cautiously aware and reflective perceiver can be
    misled.
  - "A
    distributed
    representation is one in which meaning is not captured by a
    single symbolic unit, but rather arises from the interaction of a set
    of units, normally in a network of some sort."
    
    As noted
    in the same article, "The concept of distributed representation
    is a product of joint developments in the neurosciences and in
    connectionist work on recognition tasks (Churchland
    and Sejnowski 1992). Fundamentally, a distributed representation
    is one in which meaning is not captured by a single symbolic unit,
    but rather arises from the interaction of a set of units, normally in
    a network of some sort."
    
    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.
    
    Now what the article is saying is that
    human brains are like computers - but not like the computers
    as described above, with symbols and programs and all that, but like
    computers when they are connected together in a network.
    
    "The
    brain as a whole operates more like a social network than a digital
    computer... the computer-like features of the prefrontal cortex
    broaden the social networks, helping the brain become more flexible
    in processing novel and symbolic information." Understanding
    'where the car is parked' is like understanding how one kind of
    function applies on the brain's distributed representation, while
    understanding 'the best place to park the car' is like how a
    different function applies to the same distributed
    representation.
    
    The analogy with the network of computers is a
    good one (and people who develop social network software are
    sometimes operating with these concepts of neural mechanisms
    specifically in mind). The actual social network itself - a set of
    distributed and interlinked entities, usually people, as represented
    by websites or pages - constitutes a type of distributed
    representation. A 'meme' - like, say, the Friday
    Five - is distributed across that network; it exists in no
    particular place.
    
    Specific mental operations, therefore, are
    like thinking of functions applied to this social network. For
    example, if I were to want to find 'the most popular bloggers' I
    would need to apply a set of functions to that network. I would need
    to represent each entity as a 'linking' entity. I would need to
    cluster types of links (to eliminate self-referential links and
    spam). I would then need to apply my function (now my own view
    here, and possibly O'Reilly's, though I don't read it specifically in
    his article, is that to apply a function is to create additional
    neural layers that act as specialized filters - this would
    contrast with, say, Technorati, which polls each individual entity
    and then applies an algorithm to it).
    
    This theory,
    stated simply, is that human thought amounts to patterns of
    interactions in neural networks. More precisely, patterns of input
    phenomena - such as sensory perceptions - cause or create patterns of
    connections between neurons in the brain. These connections are
    associative - that is, connections between two neurons form when the
    two neurons are active at the same time, and weaken when they are
    inactive or active at different times. See, for example, Donald
    Hebb's 'The Organization
    of Behavior', which outlines what has come to be called 'Hebbian
    associationism'.
  - 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.
    
    This set
    of features constitutes a mechanism for evaluating whether a
    cognitivist theory or a connectivist theory is likely to be true. In
    my own mind (and in my own writing, as this was the subject of my
    first published paper, ‘Why Equi Fails’), the mechanism
    can be summed in one empirical test: context sensitivity.
  - If, as
    asserted above, what counts as knowledge of even basic things like
    the meanings of words and the cause of events is sensitive to
    context, then it seems clear that such knowledge is not a stand-along
    symbolic representation of that knowledge, since
    representations would not be, could not be, context sensitive.
    Rather, what is happening is that each person is experiencing a
    mental state that is at best seen as an approximation of what
    it is that is being said in words or experienced in nature, an
    approximation that is framed and indeed comprehensible only from
    which the rich set of world views, previous experiences and frames in
    which it embedded.
  - 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.
  - Postscript:
    The Non-Causal Theory of Knowledge
    
    In recent
    years we have heard a great deal about evidence based educational
    policy. It is an appealing demand: the idea that educational policy
    and pedagogy ought to be informed by theory that is empirically
    supported. Such demands are typical of causal theories; following the
    methodology outlined theorists like Carl
    Hempel, they require an assessment of initial conditions, an
    intervention, and a measurement of observed difference, as predicted
    by a (causal) generalization.
    
    In the
    earlier theory, there is a direct causal connection between states of
    affairs in the communicating entities; it is, therefore, a causal
    theory. But in the latter theory, there is no direct causal
    connection; it is what would be called (in the parlance of the new
    theory) an emergentist theory (that is, it is based on emergence, not
    causality). Calls for "evidence that show this claim is true"
    and "studies to substantiate this claim" are, like most
    Positivist
    and Positivist-inspired theories, reductive
    in nature; that is why, for example, we expect to find something like
    a reductive entity, 'the message', 'the information', 'the learning',
    and the like. They are also aggregationist; the presumption, for
    example, is that knowledge is cumulative, that it can be assembled
    through a series of transactions, or in more advanced theories,
    'constructed' following a series of cues and prompts.
  - But what
    happens, first of all, if the entities we are ‘measuring’
    don’t exist?
    
    Even if
    there are mental states, it may still be that our descriptions of
    them nonetheless commit some sort of category error. Saying that
    there are ‘thoughts’ and ‘beliefs’ that
    somehow reduce to physical instantiations of, well, something
    (a word, a brain state…) is a mistake. These concepts are
    relics of an age when we thought the mental came in neat little
    atomistic packages, just like the physical. They are an unfounded
    application of concepts like 'objects' and 'causation' to phenomena
    that defy such explanation; they are, in other words, relics of 'folk
    psychology'. Saying 'someone has a belief' is like saying that 'the
    Sun is rising' - it is literally untrue, and depends on a mistaken
    world view.
  - But even
    more significantly, what happens if we cannot ‘measure’
    the phenomena in question at all?
    
    On the
    network theory knowledge and learning are emergent phenomena, and it
    is necessary to highlight a critical point: emergent phenomena are
    not causal phenomena. That is (say) the picture of Richard Nixon does
    not 'cause' you to think of the disgraced former president. They
    require a perceiver, someone to recognize the pattern being
    displayed in the medium. And this recognition depends on a relevant
    (but not strictly defined) similarity between one's own mental state
    and the pattern being perceived. That's why perception (and
    language, etc), unlike strict causation, is context-sensitive.
    
    And there
    is no means for a student to 'cause' (strictly speaking) recognition
    on the part of, say, an examiner, that he or she 'knows that Paris is
    the capital of France'. What is essential (and, I might add,
    ineliminable) is that the complex of this person's behaviours be
    recognized as displaying that knowledge. As Wittgenstein says, we
    recognize that a person believes the ice is safe by the manner in
    which he walks on the ice. And because this demonstration is
    non-causal, it depends on the mental state of the examiner, and
    worse, because (quite literally) we see what we want to see, the
    prior disposition of the examiner.
    
    If this is
    the case, the very ideas of ‘evidence’ and ‘proof’
    are turned on their heads. "Modeling
    the brain is not like a lot of science where you can go from one
    step to the next in a chain of reasoning, because you need to take
    into account so many levels of analysis... O'Reilly likens the
    process to weather modeling."
    
    This is a very important
    point, because it shows that traditional research methodology, and
    for that matter, traditional methods of testing and evaluation, as
    employed widely in the field of e-learning, will not be
    successful (are high school grades a predictor of college success?
    Are LSAT scores? Are college grades a predictor of life success?).
    This becomes even more relevant with the recent emphasis on
    'evidence-based' methodology, such as the Campbell
    Collaboration. This methodology, like much of the same type,
    recommends double-blind tests measuring the impacted of individual
    variables in controlled environments. The PISA
    samples are an example of this process in action.
    
    The
    problem with this methodology is that if the brain (and hence
    learning) operates as described by O'Reilly (and there is ample
    evidence that it does) then concepts such as 'learning' are best
    understood as functions applied to a distributed representation, and
    hence, will operate in environments of numerous mutually dependent
    variables (the value of one variable impacts the value of a second,
    which impacts the value of a third, which in turn impacts the value
    of the first, and so on).
  - As I argue in papers like Public
    Policy, Research and Online Learning and Understanding
    PISA the traditional methodology fails in such environments.
    Holding one variable constant, for example, impacts the
    variable you are trying to measure. This is because you are not
    merely screening the impact of the second variable, you are screening
    the impact of the first variable on itself (as transferred through
    the second variable). This means you are incorrectly measuring
    the first variable.
    
    Environments with numerous mutually
    dependent variables are known collectively as chaotic
    systems. Virtually all networks are chaotic systems. Classic
    examples of chaotic systems are the weather system and the ecology.
    In both cases, it is not possible to determine the long-term impact
    of a single variable. In both cases, trivial differences in initial
    conditions can result in significant long-term differences (the
    butterfly
    effect).
    
    This was a significant difference between
    computation and neural networks. In computation (and computational
    methodology, including traditional causal science) we look for
    specific and predictable results. Make intervention X and get result
    Y. Neural network (and social network) theory does not offer this.
    Make intervention X today and get result Y. Make intervention X
    tomorrow (even on the same subject) and get result Z.
    
    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. One would think at first blush that this is nothing
    like computer science. But as the article takes pains to explain, it
    is like computer science - so long as we are studying networks
    of computers, like social networks.
    
    Learning
    theorists will no longer be able to study learning from the detached
    pose of the empirical scientist. The days of the controlled study
    involving 24 students ought to end. Theorists will have to, like
    students, immerse themselves in their field, to encounter and engage
    in a myriad of connections, to immerse themselves, as McLuhan would
    say, as though in a warm bath. But it’s a new world in here,
    and the water’s fine.
13 Oct 06

Susan Manning
connectivism
11 Oct 06

Werner Trotter
e-learning downes connectivity networks Collaborative Cooperative

Public Stiky Notes

Christy Tucker on 2009-10-06

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.
Christy Tucker on 2009-10-06

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.
Christy Tucker on 2009-10-06

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.
Christy Tucker on 2009-10-06

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.
Christy Tucker on 2009-10-06

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.
Christy Tucker on 2009-10-06

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.
Gillian Watson on 2009-10-08

I think what he is getting at is that meaning is infered from the perceiver based on context and salience vs a meaning that is constructed by the perceiver alone. So it is not that I construct meaning for something, meaning is created based on the interaction of variables and my perception.
Christy Tucker on 2009-10-06

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.
Christy Tucker on 2009-10-06

This is an example of complicated knowledge, I think, and not complex, but the idea of complicated knowledge being distributed makes sense.
Christy Tucker on 2009-10-07

Summary of e-learning 2.0, although so much of what is being developed is still about content consumption
Christy Tucker on 2009-10-07

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.
Christy Tucker on 2009-10-07

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.
Christy Tucker on 2009-10-07

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.
Christy Tucker on 2009-10-08

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.
Christy Tucker on 2009-10-08

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.
Christy Tucker on 2009-10-08

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.
Christy Tucker on 2009-10-08

This description is helpful, but I again don't see how demonstrating is different from modeling.
Christy Tucker on 2009-10-08

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.
Christy Tucker on 2009-10-08

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.

Page Comments

Pat Parslow on 2008-09-13

presumably information transfer can alse be when the assessment of the total possible states of affairs is increased (e.g. someone 'opens your eyes' to a new possibility)
In fact, all that is really required is that there is a change in the assessment of possible states - you may become more convinced that one possibility is more likely than others, without ruling any out - this is still a result of a transfer of information (and happens to fit quite well with Bayesian analysis - not that that is a requirement!)
Pat Parslow on 2008-09-13

Knowledge is always generated by the receipt of a signal, even if only the trivial "I received a signal".
Communication is (almost) a misnomer for the one-way event - "meaning" has to be negotiated, especially with the multiplicity of different meanings for some words, and the innate capactiy of communication channels to corrupt signals (including attentional problems in the receiver causing mis-understanding).
Pat Parslow on 2008-09-13

well... related to the teachers - given the wealth of experiences in each individual's past, it is highly unlikely that the actual state will be the same - it will just have sufficient in common to be able to consider the subject matter as having been learnt.
The differences, I would suggest, are what can spark an imaginative leap to new knowledge, as the idea from the "teacher" triggers other memories in the "student"
Pat Parslow on 2008-09-13

I think that is a non sequitur - the predicate does not imply the result. It would also be more definitive to say that the concept is distributed throughout our brains - location in minds is hard to define.
Pat Parslow on 2008-09-13

barring errors in the system
Pat Parslow on 2008-09-13

except that folk psychology is fundamentally connectivist in nature, and capable of holding more than one (potentially contradictory) point of view simultaneously due to its distributed nature.
Pat Parslow on 2008-09-13

the context sensitivity is obvious, in my view, whereas I suggest that a private language is feasible - if someone is born deaf and blind and mute, do they really have no language until taught to touch-sign?
Pat Parslow on 2008-09-13

Indeed all sense phenomena must be processed by the brain, which is 'trained' throughout life by the experiences it has already had, so in each case, the observation will be unique to the individual.
Pat Parslow on 2008-09-14

It also depends on experiences shortly after the fact, with post-event suggestion playing a large role in our subjective view of events.
Pat Parslow on 2008-09-14

And quite possibly from mood to mood
Pat Parslow on 2008-09-14

or the questioner
Pat Parslow on 2008-09-14

stand-alone
Pat Parslow on 2008-09-14

The "knowledge" could still be symbolic (though I strongly doubt that it is) because the necessary interpretation of that symbolic knowledge would still be subject to the subjectivity born out of a lifetimes experience.
Pat Parslow on 2008-09-14

mental content still exists in this model, in the form of distributed states - it is not isomorphic, however. The isomorphic representation can be seen in the codified signal - language - but this relies on processing at both the sender and receivers end - so the question is still valid.
Pat Parslow on 2008-09-14

I do not understand that bit. The meaning is present, but it is formed through the process of negotiation through feedback loops between, and within, nodes in the network.
Pat Parslow on 2008-09-14

I would argue that - there is a personal (subjective) state, and there is a probabilistic consensual state.
Pat Parslow on 2008-09-14

a reference to semiotics might be handy here
Pat Parslow on 2008-09-14

Surely the network is affected by the ability of entities to retain information too - whether they have absolute memory limits, dynamic limits (short term/long term) and the dynamics of recall within the entities - also whether there is any processing of the information within an entity, or whether it merely acts as a relay
Pat Parslow on 2008-09-14

is this the salience to the network or the entity? If the entity, are these connections within the network, or within the entity itself?
Pat Parslow on 2008-09-14

That sounds a bit dualistic - interpretation by what?
Pat Parslow on 2008-09-14

Knowledge is justified belief.
Belief is holding a concept without a justification (but may still be correct)
Faith is holding a belief without any need or desire for justification.
Superstition is holding a belief despite evidence that it is untrue.
Pat Parslow on 2008-09-14

what
Pat Parslow on 2008-09-14

say
Pat Parslow on 2008-09-14

for a period of time - the knowledge (organisation) can be lost. Interestingly, re-finding Waldo becomes easier, probably because the brain is trained to look in certain ways, at certain patterns, and to follow an internal narrative of exploration practices.
Pat Parslow on 2008-09-14

Learning as a noun, which, by reference to the first point, is presumably different to knowledge. I am unclear about the definitions here
Pat Parslow on 2008-09-14

This is more contextual than the other points - in a stable, non-growth oriented society, this need not be true.
Pat Parslow on 2008-09-14

This is the ability to learn, in my opinion
Pat Parslow on 2008-09-14

It is probably a mistake to define what other people's intentions are. This need not be true of connectivist learning
Pat Parslow on 2008-09-14

Not universally true
Pat Parslow on 2008-09-14

That is also true of the preceding model
Pat Parslow on 2008-09-14

for all entities in the network...
Pat Parslow on 2008-09-14

Assuming that the signal is not modified - an issue here is also positive feedback loops - entities/nodes on the network can amplify a signal if it has sufficient salience for them, and this makes the propagation more succesful
Pat Parslow on 2008-09-14

consensual reality
Pat Parslow on 2008-09-14

Or bad practice which you are cognitively equipped to recognise as such - this too is a useful learning strategy.
Pat Parslow on 2008-09-14

That is 'receiving training' rather than learning (receiving education) I think
Pat Parslow on 2008-09-14

not necessarily what we 'want' to see - but what we have been 'trained' to see by life experience to date.
Pat Parslow on 2008-09-14

Despite the plethora of comments I have made, I would say this is a cogent and useful discussion of the issues (and one with which I broadly agree in most regards)
Pat Parslow on 2008-09-14

Oh bother, those annotations were supposed to be anchored to paragraphs and snippets

http://it.coe.uga.edu/itforum/paper92/paper92.html - The Diigo Meta page

Christy Tucker's personal annotations on this page

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