In other
words, cognitivists defend an approach that may be called ‘<a href="http://plato.stanford.edu/entries/folkpsych-theory/">folk
psychology</a>’. “In our everyday social interactions we
both predict and explain behavior, and our explanations are couched
in a mentalistic vocabulary which includes terms like ‘belief’
and ‘desire’.” The argument, in a nutshell, is that
the claims of folk psychology are literally true, that there is, for
example, an entity in the mind corresponding to the belief that
'Paris is the capital of France', and that this belief is, in fact,
what might loosely be called 'brain writing' - or, more precisely,
there is a one-to-one correspondence between a person's brain states
and the sentence itself.
I've never heard cognitivism compared to "folk psychology" before. I'm not totally convinced by this argument. Cognitivist methods do have some research support, after all. (Think multimedia learning, Clark & Mayer's "ELearning and the Science of Instruction.") But their methods could (at least sometimes) be right even if their explanation of the underlying mechanism is wrong.
We may contrast cognitivism, which is a
<i>causal</i> theory of mind, with connectionism, which is an
<a href="http://en.wikipedia.org/wiki/Emergentism"><i>emergentist</i></a>
theory of mind. This is not to say that <a href="http://en.wikipedia.org/wiki/Connectionism">connectionism</a>
(see <a href="http://plato.stanford.edu/entries/connectionism/">also</a>)
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: <i>the transfer of information does
not reduce to this physical substrate</i>. Contrary to the
communications-theoretical account, the new theory is a non-reductive
theory. The contents of communications, such as sentences, <i>are not
isomorphic</i> with some mental state.
From Wikipedia: "A property of a system is said to be emergent if it is more than the sum of the properties of the system's parts." If I understand Stephen's argument correctly, part of what he's saying here is that rather than knowledge being exactly what we perceive it to be (a sentence like "Paris is a city in France"), what's happening in our brains is more than that. When a teacher shares knowledge with a learner, it doesn't work like a copy machine where the teacher gives the learner a duplicate of the original and then both people have discrete copies of that knowledge.
For
example (and there are <i>many</i> we could choose from), consider
Randall O’Reilly on how the brain represents conceptual
structures, as described in <a href="http://psych.colorado.edu/%7Eoreilly/papers/OReillyIPap.pdf">Modeling
Integration and Dissociation in Brain and Cognitive Development</a>.
He explicitly rejects the ‘isomorphic’ view of mental
contents, and instead describes a network of distributed
representations. "Instead of viewing brain areas as being
specialized for specific representational content (e.g., color,
shape, location, etc), areas are specialized for specific
computational functions by virtue of having different neural
parameters...
I struggle a bit with the neurological arguments, but it does seem to make sense that the brain is divided by the different functions and not by the symbols we've created to communicate. And certainly when you look at brain scans of people doing different tasks, the activity isn't just in one area: multiple areas of the brain are involved in any complex task.
But I'm also cautious about the brain evidence because, frankly, I don't really understand it that well. I'm also aware of research about how people find arguments more convincing when they're shown with pictures of brain scans, even if it's the same text. I don't want to fall prey to that fallacy.
For example,
when I say, "What makes something a learning object is how we
<i>use</i> 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).<br><br>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 <i>function</i>.
These are better examples; this makes more sense to me. It does seems to support creating learning environments where content can be used multiple different ways, which fits with connectivism.
To illustrate this concept, I
have been asking people to think of the concept 'Paris'. If 'Paris'
were represented by a simple symbol set, we would all mean the same
thing when we say 'Paris'. But in fact, we each mean a collection of
different things and none of our collections is the same. Therefore,
in our own minds, the concept 'Paris' is a loose association of a
whole bunch of different things, and hence the concept 'Paris' exists
in no particular place in our minds, but rather, is scattered
throughout our minds.
Back to the cognitivist idea of the teacher as mental copy machine handing a student a duplicate copy of knowledge--this is the opposite of that. It's more like if 20 artists sit down to draw the same scene; there will be similarities and overlaps, but nobody's picture will be the same. This is, perhaps, part of why connectivism makes more sense when applied to learning complex topics. You don't need connectivism to explain memorizing the state capitals or multiplication tables; the idea of the mental copy machine is probably a functional enough explanation. But if you're trying to learn a big, gnarly topic, a model that works for regurgitating facts isn't enough.
<p><font face="Arial"><font style="font-size: 11pt;" size="2">As we
examine the emergentist theory of mind we can arrive at five major
implications of this approach for educational theorists:</font></font></p>
<p><font style="font-size: 11pt;" size="2"><font face="Arial">- first,
knowledge is <a href="http://www.ai.rug.nl/%7Elambert/projects/miami/taxonomy/node99.html">subsymbolic</a>.
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 '<a href="http://artsci.wustl.edu/%7Ephilos/MindDict/tacitknowledge.html">tacit
knowledge</a>' in '<a href="http://www.amazon.ca/Personal-Knowledge-Towards-Post-Critical-Philosophy/dp/product-description/0226672883">Personal
Knowledge</a>').</font></font></p>
<p><font style="font-size: 11pt;" size="2"><font face="Arial">- 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,
'<a href="http://www.cs.toronto.edu/%7Ehinton/distrep.html">Learning
Distributed Representations of Concepts</a>'.</font></font></p>
<p><font style="font-size: 11pt;" size="2"><font face="Arial">- 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, '<a href="http://www.guardian.co.uk/life/feature/story/0,13026,1517186,00.html">Where
Belief is Born</a>'</font></font></p>
<p><font style="font-size: 11pt;" size="2"><font face="Arial">- 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 <a href="http://en.wikipedia.org/wiki/Gestalt_psychology">Gestalt
tests</a> would be useless; we would all utter the same word when
shown the same picture.</font></font></p>
<p><font face="Arial"><font style="font-size: 11pt;" size="2">- 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.</font></font></p>
If this is
the case, then the concepts of <i>what it is to know</i> and <i>what
it is to teach</i> are very different from the traditional theories
that dominate distance education today. Because if learning is not
the transfer of mental contents – if there is, indeed, no such
mental content that exists to be transported – then we need to
ask, what is it that we are attempting to do when we attempt to teach
and learn.
I'm finding myself resisting some of these ideas, and I'm not quite sure why. Is it because it's so different from what I've been taught and assumed? Is it because I'm just too used to the folk psychology ideas and I need to unlearn them? I still feel like even cognitivism is a "good enough" explanation for some basic kinds of knowledge that do seem to operate as content transfer. Cognitivism isn't a perfect model, but a simple knowledge transfer model might be good enough for some areas. But maybe education has focused too much on the simple knowledge transfer because it's easy and we have an easy model to explan how it works--and education should be about a lot more than the kinds of learning that cognitivism explains well. The learning theories we believe must affect what we choose to teach, and not just how we choose to teach it.
<p><font face="Arial"><font style="font-size: 11pt;" size="2">we can identify the essential elements of
network semantics.</font></font></p>
<p><font face="Arial"><font style="font-size: 11pt;" size="2">First,
<i>context</i>, 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.</font></font></p>
<p><font face="Arial"><font style="font-size: 11pt;" size="2">Second,
<i>salience, </i>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. </font></font>
</p>
<p><font face="Arial"><font style="font-size: 11pt;" size="2">Third,
<i>emergence,</i> that is, the development of patterns in the
network. Emergence is a process of resonance or synchronicity, not
creation. We do not <i>create</i> 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 <i>salient</i> to a perceiver.</font></font></p>
<p><font face="Arial"><font style="font-size: 11pt;" size="2">Fourth,
<i>memory </i>is the persistence of patterns of connectivity, and in
particular, those patterns of connectivity that result from, and
result in, salient signals or perceptions.</font></font></p>
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 <i>nobody</i>
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 <i>distributed</i> across a network of
people, and the phenomenon of ‘flying a 747’ can exist at
all only because of the connections between the constituent members
of that network.
This is an example of complicated knowledge, I think, and not complex, but the idea of complicated knowledge being distributed makes sense.
<p>“<font style="font-size: 11pt;" size="2"><font face="Arial">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?”</font></font></p>
<p><font face="Arial"><font style="font-size: 11pt;" size="2">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.”</font></font></p>
Summary of e-learning 2.0, although so much of what is being developed is still about content consumption
The idea
behind the personal learning environment is that the management of
learning migrates from the institution to the learner.
Learning therefore evolves
from being a transfer of content and knowledge to the production of
content and knowledge.
I'm not sure if learning always has to be about the "production" of content by the learners; it could be about analyzing, summarizing, aggregating, tagging, etc. Am I really "producing content" with my comments on this article? I don't feel like I'm producing something new, but I definitely feel like this is e-learning 2.0. I'm building on Downes' work. But maybe my problem is with how I'm defining "content"; if "content" includes tagging and critiquing and commenting, then I am producing content now.
In
a distributed environment, however, the design is no longer defined
as a type of process. Rather, designers need to characterize the
<a href="http://www.cetis.ac.uk/content2/20050124115817">nature of
the <i>connections</i></a> between the constituent entities.
An interesting idea for instructional design. Usually a big part of what we do as instructional designer is think about the structure and order of learning objects. But if the learning objects are scattered in different places and nonsequential, then the support learners need isn't being told what order to follow: it's how the objects relate to each other.
In
effective networks, content and services are <i>disaggregated</i>.
Units of content should be as small as possible and content should
not be ‘bundled’. Instead, the organization and structure
of content and services is created by the receiver.
The problem from everyone who has tried reusable learning objects is that it's so hard to get objects that are really independent and free of context. I think this is a very difficult thing to actually achieve.
An
effective network is <i>desegregated</i>. For example, in network
learning, learning is not thought of as a Separate Domain. Hence,
there is no need for learning-specific tools and processes. Learning
is instead thought of as a part of living, of work, of play. The same
tools we use to perform day-to-day activities are the tools we use to
learn.
This is already happening to some extent. Blogs, wikis, and Twitter weren't designed as learning tools, but lots of people use them as such. A look at Jane Hart's top tools collection shows lots of tools used by learning professionals that weren't originally intended for learning.
Knowledge
is a network phenomenon. To 'know' something is to be organized in a
certain way, to exhibit patterns of connectivity. To 'learn' is to
acquire certain patterns.
If learning is about acquiring patterns, then the "to learn is to practice and reflect" would be ways of following and reinforcing those patterns. I suspect for this to really make sense that "pattern" has to be my individual pattern as a learner; my pattern isn't the same as Stephen's, even as I'm learning from him. But my pattern might be similar to Stephen's or overlap with his, or connect with his.
<tr><td><p align="CENTER"><font face="Arial"><font style="font-size: 11pt;" size="2"><b>Downes
Educational Theory</b></font></font></p>
</td>
</tr>
<tr>
<td>
<p><font face="Arial"><font style="font-size: 11pt;" size="2">A good
student learns by practice, practice and reflection.<br>A good
teacher teaches by demonstration and modeling.<br>The essence of
being a good teacher is to be the sort of person you want your
students to become.<br>The most important learning outcome is a
good and happy life.</font></font></p></td></tr>
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 <i>actual</i> instances of the
discipline being performed, where the practitioners of that
discipline are (hopefully with some awareness) <i>modeling </i>good
practice in that discipline. The student then, through a process of
interaction with the practitioners, will begin to <i>practice</i> by
replicating what has been modeled, with a process of <i>reflection</i>
(the computer geeks would say: <a href="http://www.seattlerobotics.org/encoder/nov98/neural.html">back
propagation</a>) providing guidance and correction.
This description is helpful, but I again don't see how demonstrating is different from modeling.
These
environments cut across disciplines. Students will not study algebra
beginning with the first principles and progressing through the
functions. They will learn the principles of algebra <a href="http://www.downes.ca/cgi-bin/page.cgi?post=41">as
needed</a>, progressing more deeply into the subject as the need for
new knowledge is provoked by the demands of the simulation. Learning
opportunities - either in the form of interaction with others, in the
form of online learning resources (formerly known as learning
objects), or in the form of interaction with mentors or instructors -
will be embedded in the learning environment, sometimes presenting
themselves spontaneously, sometimes presenting themselves on request.
This reinforces what Stephen said earlier about tools not being specific to learning; learning tools should be the tools we live and work and play with, integrated in our daily lives.
This
does not mean that a 'science' of learning is impossible. Rather, it
means that the science will be more like meteorology than like
(classical) physics. It will be a science based on modeling and
simulation, pattern recognition and interpretation, projection and
uncertainty.
This is in the postscript about the futility of traditional empirical research on learning. Maybe this is where I run into problems reconciling the cognitivist research I've read (which is all traditional "change one variable" research) with connectivism. This would also explain why some of the cognitivist research that works OK in a lab environment fails in real classrooms; a lab environment doesn't actually reflect the chaos of a classroom well enough. I've heard Stephen make this argument on a number of occasions, but I've always assumed that it meant any educational research would be worthless. That isn't what he's saying though; he's saying that educational research is a different type of research. Now it's finally making sense to me; of course educational research should be more like psychology, where we have trends and patterns but few absolutes.
