Tuesday, April 29, 2014

Response to Dron

I don't want to restate the theses of connectivism as I understand it but it may help readers of Jon Dron's to identify where his exegesis leads him into misunderstanding.

Here's his paper, which you may need to read first.

Let me first and foremost be clear about my objectives in my work. Dron writes, "But I'm not so sure that, as presented here, it is a learning theory at all." Honestly, I don't care whether it's a learning theory. I also don't care whether it is original to me, whether it borrows from someone else's work, or any of the usual academic trappings.

I care precisely and only about the following:
- whether I can describe what learning is and explains why learning occurs.
- whether I can use this knowledge to help people learn and make their lives better

Dron then uses that classic form of criticism, "if it is [a theory] - it is very hard to tell as it gets a bit fuzzy at precisely the point at which it seems to become one [a theory] -  then it is one that appears either inconsistent or very likely wrong." We'll discuss this.

Dron  also seeks to establish a wedge between what I describe and what George Siemens described. He writes, "this is my attempt to make sense of what Stephen means and, at the end of it, to explain why connectivism (small 'c'), as George Siemens has explained it, is such a good idea not just despite but because it is not a coherent learning theory." We'll discuss this too.


Let's let Dron introduce connectivism:

The Connectivist account of individual learning, in which the nervous system is understood as a neural network with emergent properties and behaviours resulting from its connections that we describe as 'learning', is certainly compelling. In fact, it is so compelling that it is accepted by most proponents of almost every theory of learning without blinking an eyelid and without any contradiction.
I wish that were true. There is one trivial sense in which every theorist agrees that learning is based on a neural network as described - they agree because they have to. Cut open a human brain and that's what you see. So, trivially, everyone has to agree with that theory, because, manifestly, that is what we all see.

But network theories of learning have long been contrasted by what may be described here under the heading of the 'physical symbol system' hypothesis. This is the idea that we think, literally, in words and rules and principles. When you say something like "Freddy is learning about such-and-such by forming a generalization," you are implicitly appealing to the physical symbol system hypothesis, because you are suggesting that Freddy is learning by making a model or representation in which certain principles govern explanations and predictions.

I've spent a lifetime arguing against this proposition, so I know that what I am arguing is not accepted by every theory of learning. It follows then that the first line of Dron's criticism is a caricature of connectivism as I understand it. It's not just that neurons produce knowledge. Everyone knew that. It's how they produce knowledge that's important. And that is the core of the theory.

A theory which Dron tells us has already been invented:

We even have a word for it that has been around a great deal longer than Connectivism: connectionism.  There may be a few that believe in incorporeal souls or that, more plausibly, seek quantum explanations of consciousness but, even for these, a connectionist account is recognized as possibly incomplete but certainly true of how we think and learn at some level. 

Oh, if only I had known about it. But wait. I did. I wrote about it at length in 1990 in my long essay The Network Phenomenon: Empiricism and the New Connectionism. This thesis has been available on my website for five years now. Additionally, the word 'connectionism' appears hundreds of times in my work. I am very open about my debt to connectionism and what I am drawing from it.

The four learning mechanisms I described in my 'Connectionism as Learning Theory' paper are all to be found in Rumelhart and McClelland's Parallel Distributed Processing, the influential two-volume tome of connectionism and the basis for the industry that has sprung up around these ideas since.

There is certainly a large camp of writers who do what Dron does: they say, "Oh yes, connectionism must be true at some level," but then go back and start talking about beliefs and intentions and representations and all that. People like, say, Jerry Fodor, say, or (from a different perspective) Daniel Dennett. I am not one of those. I am much more like Paul Churchland or Steven Stich. I don't think we can just dismiss connectionism by saying that it's true 'at some level' - I think that connectionist mechanisms are literally how we learn.

And I would say that almost every published author in education today falls into the 'true at some level' camp. In education, most writers - including Dron, and even to a degree Siemens - give lip service to the idea that learning is a network phenomenon, but don't apply that understanding to their actual theorizing about learning. This is where connectivism goes beyond connectionism: it asserts that learning is a network phenomenon, and then  proceeds to apply that understanding to things like learning design and pedagogy (subjects about which the connectionists are largely silent).

So this is just a misrepresentation of what I say then: "Stephen asserts Connectivism's distinctiveness by extending that concept into our other networks, broadly lumped together as social networks." It is true that I believe network learning also applies to social networks. I also think it applies to networks of crickets, as described by Duncan J. Watts. But that is not why I think connectivism is distinct from connectionism (I honestly don't know whether Rumelhart, for example, would have said connectionism applies to networks of crickets - but I imagine that if he thought about it for a while he would agree that it does).

Let me review, for those who are just skimming this post:

   Connectionism - the theory describing how networks learn

   Connectivism - the theory applying that understanding to education

Whether you say one or another is or is not a theory interests me not in the least. But I would assert that (a) each is a distinct understanding of learning that can be distinguished from other approaches that genuinely are called theories, and (b) neither is widely adopted (much less consensus opinion) in learning technology, or education generally, today.


I wish Dron would actually go into some of my work and extract the position he attempting to criticize rather than trying to make it up on the fly. He writes:
If Connectivism is about saying that our individual intelligence or capabilities to function as social beings cannot meaningfully exist nor be meaningfully described without considering the people and objects with which we interact then it is again hard to disagree.
Have I ever said this? Or anything like this?

He continues, 
We have a label for it: socially distributed cognition, a widely accepted and venerable family of models and theories that delves into the idea very deeply. This does not constitute a new or distinctive theory of learning either. 
Again, I do not care whether someone has previously come up with the same idea. That said, socially distributed cognition is not a description of what I am describing. Let's quote a bit from the Wikipedia reference Dron offers us:

Distributed cognition is a psychological theory that knowledge lies not only within the individual, but also in the individual's social and physical environment... In a sense, it expresses cognition as the process of information that occurs from interaction with symbols in the world. It considers and labels all phenomena responsible for this processing as ecological elements of a cognitive ecosystem. The ecosystem is the environment in which ecological elements assemble and interact in respect to a specific cognitive process. Cognition is then shaped by the transduction of information across extended and embodied modalities, the representations formed as result of their interactions and the attentive distribution of those representations toward a cognitive goal.
You can see pretty clearly why I don't mean anything like distributed cognition. Distributed cognition is an instance of the physical symbol system hypothesis.  

We can see where Dron becomes misled - he characterizes connectivism as merely "considering the people and objects with which we interact." But connectionism (and hence connectivism) is based on a much deeper understanding than that: they assert that knowledge is the set of connections between entities (and not the content of the signals being exchanged between them).

This results in what we call 'distributed representation' - see Geoffrey Hinton's introduction to the concept. In a nutshell (as Hinton says):

     - Each concept is represented by many neurons
     - Each neuron participates in the representation of many concepts

Nothing to do with symbols (or for that matter, rules nor generalizations not principles). I would argue (and have argued) that it's not even a type of representation at all, that the identification of 'concepts' in the neural network is an after-the-fact 3rd party interpretation of what is going on in a network, and not what is actually going on in a network.

I've tried to explain this all before, at length, in my essay Principles of Distributed Representation, from 2005.


Another case in which Dron makes up his own account of connectivism on the fly, rather than referring to what was actually written:
if we are simply looking at first-order connections between individuals and the objects and people they interact with (the most basic hub and spoke model) then there seems no point in talking about networks at all in this context because none of the interesting things about neural networks have any meaning or relevance in a hub and spoke model.
Of course, we are not we are simply looking at first-order connections between individuals and the objects and people they interact with. I've never suggested we were. I've criticized the hub-and-spoke model on a number of occasions. In my essay Fairness and Democracy in  Education, for example, I write, "The shape of the network that forms as a result of preferential attraction is the now-familiar hub-and-spoke network.... The problem with the hub-and-spoke network is that it is less stable."

At a certain point, the complaints that my argument is 'fuzzy' have a lot more to do with the critic not reading them than they do with the arguments actually being fuzzy.
The whole discussion about first-order networks seems to be setting up some sort of straw man. I'm not sure what Dron is arguing against here (certainly not me!) but it seems very important to him:
For Connectivism to make any sense as a distinctive learning theory, there must be learning in networks beyond our own first-order connections with them  - something important about the emergent behaviours of the networks themselves. 
So, OK, the word 'emergent' is another one of those words you can find hundreds of times in my writing dating back to the 1990s. Here's something from 2005 called Emergent Learning: Social Networks and Learning Networks that explains some of my thoughts on the idea.
This is not exactly the same as 'collective intelligence', but let's let Dron introduce the idea:
This brings us into the very well-trodden field of collective intelligence, that looks at how the interactions of large groups of agents leads to emergence of behaviours and learning at a group/network level.
I mention these particular writings because they use very similar terminology and concepts to the six (I think - going on memory here) used by Stephen to characterize what makes social networks tick. 
In the field of 'collective intelligence' Dron cites specifically "Howard Bloom's Global Brain, or pretty much anything by Scott E. Page." Today's readers may be more familiar with Surowiecki The Wisdom of Crowds.
But emergent learning is not the same as collective intelligence. The latter (to again use the same Wikipedia reference Dron cited) includes "It may involve consensus, social capital and formalisms such as voting systems, social media and other means of quantifying mass activity." But none of these is a type of emergentism; indeed, the idea of voting and quantifying run directly contradictory to emergentism. I think Surowiecki is pretty clear about this; I haven't read the other two authors.
Dron should know this - it was the basis of my work on Groups and Networks - which was followed (but not cited) by his own paper on 'collectives, networks and groups' in social software.

I don't forward six criteria, I forward four, and yes, there is definitely overlap with what people in the field of collective intelligence say. The principles (by now familiar to anyone who has read my work) are: autonomy, diversity, interactivity, and openness. 

Dron, who can't recall them, or even how many there are, writes,
It's well worth studying but it is not something that Connectivism can claim as its own territory unless there is something more to it. That something appears to lie in its treatment of networks as a fundamental unifying principle. 
I'm not sure I even want to get into how patently absurd this comment is - nobody can claim things like 'diversity' as 'its own territory'. 

For what it's worth, I first offered a version of them in Learning Networks: Theory and Practice, again in 2005 (a very productive year for me) and finalized the list of four by adapting a presentation from Charles Vest in Snowmass, Colorado. I've never claimed any of them as my own - I may have been the first to identify all four as a unit, but even this might not be the case (I've seen diversity and autonomy emphasized a lot, but not so much openness and interactivity).

No matter. I think where I advanced is in the following: first, explaining why these principles are important in terms of network principles (and not 3rd party observer principles or folk psychology) as a response against cascade phenomena (see, for example, Cascades and Connectivity, or Community Blogging), and second, using these four principles as design principles for learning technology and learning design generally. The concept of the MOOC is based on these four principles.

But again: it doesn't matter whether I own this, was first to talk about any of this. It doesn't matter whether I 'own' it. It matters only whether or not it is right.

So, is it right? Dron says it isn't.
...the crux of the issue: That Connectivism provides a unified model of how networks (including people's brains and their social networks) learn. This starts to look like the basis of a theory and seems more distinctive than any of the components so far. However, I think it is based on a spurious bit of reasoning and cannot ever work but, because it is a bit fuzzily portrayed...
If you really want to represent it that way (remember, I don't really go in for models and theories and such) then, yes, I am arguing that there is a unified model of how networks learn.

But understand: it's 'unified' in the way mathematics is unified. The fact that the same principles apply to counting does and counting sheep doesn't mean that I think that dogs are the same as sheep, nor does it mean I am conflating dogs and sheep, nor that I have described something fundamental about the essential nature of dogs and sheep.

Moreover, to make the point the other way: it doesn't follow (nor should it follow) that there are special properties inherent to dogs only, or sheep only, that makes us count them a special way. Yes, dogs are special and unique creatures and we should all cherish them, but we still count them the same way, one by one, and dogs aren't any the less dogs for that.

Again, I've made this sort of point before. In An Introduction to Connective Knowledge I begin with the observation that we has two ways of talking about things in the past - by talking about qualities, which leads to syllogistic reasoning, and by quantities, which leads to mathematical reasoning, and that we can now talk about a new form of connective reasoning, which underlies our understanding of things in the same way the previous two do. You can blame me for audacity, but it should be clear I'm not talking about a 'unified theory' the way Newtonian Laws or the Theory of Relativity are unified theories.

With those caveats, I don't confess to fuzziness, but let's examine Dron's understanding of the argument. he writes,
There are some topological similarities between brains and our social networks (including the mediating objects within them) but there are exactly the same kinds of topological similarity in the spread of disease, mob dynamics and the formation of traffic jams. There therefore has to be more substance to this idea than topological similarity. 

See, again, what he is doing here is not looking at what I actually say, but rather, what he thinks "has to be" in the argument.

This is actually a fairly common form of argument against connectionism, and against associationist theories of knowledge in general. Chomsky called it Plato's problem - the idea that the mechanisms in question are impoverished, that they are not sufficient to produce the phenomena in question. Dron's version isn't quite so sophisticated: he says the similarities found between brains and social networks are also found in mobs, therefore, these similarities can't explain what brains do. He doesn't tell us how the 'topological similarities' are impoverished; he just implies that they are.

Indeed, the argument here is based on innuendo rather than assertion. The phrase 'topological similarities' is just another way of saying 'surface features'. It's like he's arguing, 'tomatos are red, and strawberries are red, but so are holly berries, so we can't use the red colour to explain why some are safe to eat (unlike holly berries, which are poisonous). It sounds like a good argument, but it really requires that the 'topological similarities' in question be identical. If we're just talking about similarities, then there's lots of room where the differences can explain why one thing and not the other.

And in fact, we get exactly that sort of essential difference between learning networks and mobs. And we get it in precisely the way described by the theory. Human brains and social networks learn, while mobs do not, because human brains and social networks are more resistant to cascade phenomena than mobs. And this is because human networks and social networks are in important ways more diverse and more interactive than mobs. They are interactive in a way that mobs are not. They are defined by differences in opinion, objective and perspective, where mobs are not.

Dron goes on to describe the type of similarities I have in mind:
This is where things get sticky because, as Stephen is the first to admit, brains are different. However, he appears (this is the point at which it gets fuzzy for reasons I describe below, so I apologize if I misrepresent this) to wish to apply the same kind of principles that relate to neural networks, which have broadly uniform nodes, directed edges, constant distribution and qualitatively identical connections, invoking ideas that relate to neural networks like back-propagation, Hebbian rules and Boltzmann distributions as though they apply equally and similarly to the discontinuous, messy, asymmetrical, diverse, complicated world of social networks. 
Yes, just in the same way I would count simple things using the same mathematics I would use to count complicated things.

But note the argument here: neurons are simple, social entities (being mostly humans) are complex, and yet (says Dron) I want to apply the same sorts of principles that relate to neural networks that I do to social networks.

Dron demands that I be more precise, but it is his own formulation that creates the fuzz. I don't simply 'apply the same kind of principles' (as though they were some kind of ointment, I guess). Rather, I am saying that similar principles describe how connections form between entities (or to use the terminology being employed by Dron, which is derived from graph theory, similar principles describe how edges are created between nodes). 

Well I've offered four such theories, which I call properly 'learning theories', because they are theories describing how these connections are formed. The four (for those who have forgotten them) are complimentarity (aka Hebbian associationism), contiguity, back-propagation, and Boltzmann settling mechanisms. The precise physical mechanism via which these principles operate may vary, but the principles underlying them may be the same. This should be surprising to anyone; planets and billiard balls are very different, but they are still governed by things like inertia and momentum. It turns out that what Dron calls 'topological similarities' are actually deeper identities. Discount the innuendo, and you don't have an argument at all.

But whatever. It is a matter of empirical fact whether or not neurons and humans associate and form connections with each other according to the same underlying principles. The isomorphism uniting graph theory, social network theory, neurophysiology and computational neural network theory is strong prima facie evidence that it is an empirical fact. Certainly the sameness of these connective principles can be observed. The determination of whether it amounts to an underlying logic will take empirical science years, maybe generations, to determine.

Where Dron suggests I'm fuzzy, I think I'm pretty precise (certainly, it seems to me I've offered a level of precision far exceeding anything constructivism, say, has to offer - because, really, what can you say about that black box called 'making meaning'. But I digress).

He writes, "His assertion seems to be that they are not exactly the same, but that they are part of the same class of explanations and, importantly, that learning happens within them in broadly related ways. But what does this actually mean?" What it actually means is what I've just asserted above.


Remember, above, where Dron said 'neurons are simple, social entities (being mostly humans) are complex.' Well, now he's going to backtrack on that:
Brains have levels of emergent and structural organization that are tightly hooked into our bodies, with evolved intentionality to help us stay alive, look after children, eat, mate, seek comfort, avoid danger, learn to use tools etc. 
And at the risk of getting mystical, he continues, 
They have a purpose and that purpose is us (though, evolutionarily speaking, they may equally have a group-selection role too as we are a eusocial species). Technically speaking, they are directed networks. They are inherently contained, otherwise we die. 
Technically speaking, I would respond, they are not directed networks. They are self-organizing networks. Neurons aren't created with a purpose; they adapt and change according to the circumstances they find themselves in. This is well understood. The visual cortex, for example, doesn't have the purpose of seeing; sew the eyes shut (as researchers did with cats) and the very same neurons will be employed in some other task.

That does not mean there are no innate properties to brains, neural organization, and bodies in general. If course there is. But the innate principles are simple - as simple as they could possibly be - because the evolutionary advantage expressed in brains is the capacity to learn. Most of the rest -  " stay alive, look after children, eat, mate, seek comfort, avoid danger, learn to use tools etc." - is learned behaviour. The behaviours we observe in animals - the nursing instinct, say - are very simple and non-intentional, which is why you can fool birds with black dots and why cats grow up thinking a human being is its mother. They are responding to cues, not goals, objectives, concepts, or any of the rest of it. A sea-slug can reproduce, but can't even form a coherent thought resembling our idea of mating and parenthood.
They are inherently contained, otherwise we die.

What nonsense. Without being taught, we die. Tarzan is a myth (and even he was raised by chimps).

Why don't we die? Because we have neural nets that adapt very quickly to new information and learn from example (and are especially nimble when young). What makes these nets so good that way? Because the learning principles they physically instantiate create dynamic yet stable networks - that is, networks in which the nodes are autonomous, where they are diverse, there they interact as a coherent whole (and not an incoherent mass), and where they are open both to signals from each other, and to input (and output) with the external world. Take away any of these conditions, and that's why they die. Not because of some mysterious (and causally inexplicable) 'nurturing instinct' or whatever.

Dron continues, 
Moreover, the things that make brains work are a specific kind of neural connection between the same types of entity. If a neuron could decide to behave differently from other neurons it would not be a good thing at all. Even a simple change in behaviour ('today I think I will reduce the strength of my signals' or 'I wonder what it would be like if I responded when things are quiet rather than when I get stimulation' or 'I'm going to talk back') would quickly degenerate into chaos and no thought at all if more than a few errant neurons began to diversify. Crucially, knowledge and learning in a neural network exists entirely within its configuration of connections, not in its individual neurons.
Well I guess that if humans turned into toads, that would be hard on social networks too.

But in fact, neurons are very diverse. And if you want to take the embodiment argument to its natural conclusion, the many different types of things that make up a human body are very diverse. And individually, each of them differs from the other in many ways - including things like internal structure, activation potential, and the rest. 

And so... still, social networks might be more diverse than human bodies. Probably they are. What would follow from that? Dron goes on at length, and I'll elide here:
Our social networks, including the mediating objects we create, are diverse, plural, parallel, reaching whatever emergent patterns they fall into by many different processes....  Suffice to say, the differences between social and neural networks go more than skin deep while their similarities lurk mainly on the surface.
We can accept that social networks are distinct from neural networks. It does not follow that a different logic must be used to describe social networks and neural networks, nor does it follow that a common logic cannot have explanatory power.

Planets are much more complex than basketballs. But put the two of them in space, and the same principles can be used to describe (and predict) their respective motions. Yes, they are (if you will) both superficially bodies in space with a certain mass and momentum. But that, it turns out, is all that matters. The same is the case, I argue, with neural networks and social networks.

What's important at this juncture is the following: I have a story that explains how and why both humans and societies, though very different, can both learn and know. Moreover, this theory can be used to make interesting and useful predictions (such as: societies organized using parliaments rather than mobs will be more stable and will last longer; such as: too much extraneous neural noise, such as a loud buzzing sound, will make it difficult to learn; such as: increasing social resistance through immunization protects society against disease; and on and on and on). By contrast, Dron's sort of explanation is this: "Some of the nodes are intentional agents, with different agendas, and not all are nice." Well, what do we learn from that? How widely applicable is this knowledge? How does this even qualify as a theory?


Indeed, in the end, it is the utility of networks that Dron focuses on:
... This all helps us to make effective use of social networks for learning, to find strengths and limitations in them and to design or influence systems that make use of collectives to exhibit crowd wisdom in support of individual learning.
In additon, there is (to Dron) something magical about brains:
However, though sharing some similar dynamics and topology, brains do something pretty cool that the spread of memes, the movements of pedestrians on sidewalks, the formation of ecosystems, the flocking of birds, the nest building of termites and social connections between people do not: they think.
Wait a second. Wait a second. What do you mean, "They think?" What is this magical this that is not all the network phenomena described before, the having of experiences, the creating of associations, the cascading of neural networks, etc?

One wants Dron to read his Gilbert Ryle. "A foreigner learns what are the functions of the bowlers, the batsmen, the fielders, the umpires and the scorers. He then says 'But there is no one left on the field to contribute the famous element of team-spirit.'" What is this thing, 'thinking', that humans do that societies do not?

I talked about this in The MOOC of One. Dron wants to import some combination of functionalism and subjective experience into his explanation of learning. My response is that it is neither necessary nor sufficient to do so, and ultimately involves the invocation of magical entities to do the explaining - some sort of cosmological teleology, a 'will to live' (as alluded to above), or at the very least, a fear of flying.

Read this, and see what I mean:
This is because they are utterly different networks organized in utterly different ways performing utterly different functions. To suggest they are similar is perfectly reasonable but it is no more or less relevant than saying that the fact that salt and sugar are similar because they are composed of electrons, protons and neutrons. In a great many important ways beyond this similarity they are alike, and it is indeed a little too easy to mistake one for the other, but you would not normally want to substitute one for the other in a recipe.
His explanation for human learning is like explaining the difference between sugar and salt by saying the function of sugar is to sweeten. But we don't explain the differences between sugar and salt by appealing to their inherent nature, or their function, or some other mysterious force. We look at how they are the same underneath. The behaviours of sugar and salt are both explained by molecular chemistry (and so is DNA, even though it is much more complex) just in the same way the learning of humans and societies can be explained by identifying underlying principles. Indeed, it's the very fact that sugar and salt composed of electrons, protons and neutrons that explains why they react the way they do. I don't see how Dron doesn't get this.
Dron wants to say I don't get this:
Stephen goes to some lengths to disavow that notion that that social networks and neural networks operate in the same manner. But this is why it seems fuzzy to me because he also appears to be claiming fairly unequivocally that they do.
By now this should be pretty clear, right?

Two humans will form a connection between them in a manner very different from the way two neurons form a connection between them. Humans connect on a macro scale, neurons connect on a micro scale. Human connections are more complex and have more variability. So they're different.

But we can say the same thing about them. We can say 'a change of state in one neuron can result in a change of state in the second neuron'. And in the same way, 'a change of state in one human can result in a change of state in the second human'. The nature of these states is different; in a neuron, it might be a difference in the concentration of potassium ions, in a human it might be the acquisition of a social disease (or an idea). The physical instantiation of the connection can be different, but the fact of the connection can be the same.

I don't see a fuzzyness there, no more than I see a fuzziness in the idea that we can count neurons and we can count humans, even through the physical instances we are counting are very different.

And yes, I say that these connections are the learning. In humans, neurons are just the tools we use to make connections. In societies, humans are just the tools we (they?) use to make connections. 

Just so:
... these are 'actual' learning theories and that learning is the formation of connections in a network....  social networks learn in ways that can be explained or at least described by things like back-propagation, contiguity, etc, in much the same way as we describe neural networks.  So, as far as I can make out, Stephen is telling us that a social network is both not at all like a brain and very much like one. 
Yes. Exactly.

Such an apparent contradiction can only be true, without the Moon being made of green cheese, if and only if these claims relate to two epistemologically fundamentally different entities, which is exactly the problem that he has with other theories of learning.

I don't get this sentence at all. First of all, there's no contradiction, not even remotely one (unless you things that counting dogs and counting sheep is also a contradiction). But more, why would my claims have to related to two epistemologically fundamentally different entities?

Maybe he just has a fundamentally incorrect understanding of what 'social learning' means in this context:
Unless, of course, he is talking about ways that social networks can exhibit collective intelligence, in which case I am fully on board (and wrote a book, a PhD and numerous papers about it)
Oh, well, hallelujah, maybe he does agree with me except he thinks it's collective intelligence, about which I have already written above.
but that's another fundamentally different kind of entity, not directly about knowledge in a social network, and there are many other processes involved of which those relating to neural networks and their kin are but a very small if significant subset.  

So after all this the problem is the distinction between "knowledge in a social network" and "knowledge in a social network"? 

No, his problem is that I think  "knowledge in a social network" - that is, the knowledge humans have - and "knowledge in a social network" - that is, the knowledge networks have - are formed through the same processes of associative learning (and not through teleology, black boxes, or 'thinking'). And he fundamentally disagrees with this.

I don't know what his QED says but I think by now we've pretty much established that it is incoherent:

Therefore, either this is wrong, or this is not one theory but at a number of existing theories lumped together with only a common theme of networks to very loosely bind them or, as David Wiley suggests, it could be that it is just very incomplete. If so, it is much too incomplete to be described as  a learning theory, even if it does press-gang a bona fide learning theory (connectionism) into its service. I welcome correction if I am mistaken about this.

Well we come back to what I said at the top of the post:

   Connectionism - the theory describing how networks learn

   Connectivism - the theory applying that understanding to education

I've never made this a secret, and if Dron thinks this is press-ganging a bona fide learning theory, so be it. I honestly don't care. If the whole point of Dron's post is to say I've contributed nothing to the field, who cares?


Maybe the service Dron's post does is to drive a wedge between George Siemens's connectivism and my own. I actually think we're working different aspects of the same theory, but if people really need to identify why George is so great and I'm not, then this work may be useful.

So what does Dron think of Siemens's connectivism:
it is a situated set of principles, observations, perspectives and suggestions about how to learn, given the conditions that are made possible through the read-write web. It's thus a theory (using the term a little loosely but, I think, accurately) of how to learn, given a particular set of conditions, not a theory of learning.  
I guess my first response is to ask whether Dron skimmed Siemens too. Here's an excerpt from his Connectivism paper:
Learning is a process that occurs within nebulous environments of shifting core elements – not entirely under the control of the individual. Learning (defined as actionable knowledge) can reside outside of ourselves (within an organization or a database), is focused on connecting specialized information sets, and the connections that enable us to learn more are more important than our current state of knowing.
That doesn't sound like a set of principles, observations, perspectives and suggestions about how to learn. It sounds like a theory of learning to me.

I think his idea may have shifted through the years, but what's interesting about Siemens's connectivism is the idea that, if learning occurs in a network, that this network need not be constrained by the person. I agree. And we could talk a lot about the sort of things external to a person that can be a part of a person's learning network. And I think we both agree that there is a sense in which a society can have its own knowledge over and above what any individual can have - we've discussed this idea many times. But none of this is a set of principles, observations, perspectives and suggestions about how to learn. So maybe Dron skipped to the end of the paper... oh wait. Nope. Not there at all.

Both George and I believe that novel and important conclusions about how to learn follow from our theory(ies). But both of our approaches are based in something important (and maybe novel, but who knows) understanding of how learning happens. We built things like MOOCs together based on these principles. The impact our MOOCs had on the world suggests we were right about something - and probably something pretty fundamental. More than just tips and tricks, at any rate.

I'll let George deal with the rest, with whether Dron's characterization of his form of connectivism is accurate, about whether it was all actually to be found earlier in Dron's own work, or in Bateson, Hofstadter and Illich (none of these forms any particular influence in my own work, though I am a posteriori sympathetic with Illich).

I will say, in closing, this: there is no movement, and there is no high priest. Those concepts themselves are an incorrect understanding of the organization of society, at least, as I understand it. We are not unified around a single idea, following a single voice, marching to the same tune or singing Hosanna! together. These things belong to a world where we thought people were replaceable parts in a machine, not autonomous and diverse entities interacting in an open-ended (but endlessly interesting) firmament of experience and imagination.

To that end, Robert Bateman, who has influenced me:
I can't conceive of anything being more varied and rich and handsome than the planet earth. And its crowning beauty is the natural world. I want to soak it up, understand it as well as I can, and to absorb it.... and then I would like to put it together and express it in my painting. This is the way I want to dedicate my life.  
Is it a theory? Its it owned by me? Is this more complex than that? Silly questions.

Monday, April 21, 2014

Connectivism as Learning Theory

I think the students in the Building Online Collaborative Environments Course has an almost impossible task. Here is their effort to prove that connectivism is a learning theory.
"Connectivism has a direct impact on education and teaching as it works as a learning theory. Connectivism asserts that learning in the 21st century has changed because of technology, and therefore, the way in which we learn has changed, too.

"Not too long ago, school was a place where students memorized vocabulary and facts. They sat in desks, read from a textbook, and completed worksheets. Now, memorization is not as prevalent because students can just “Google it” if they need to know something."

Though this is not very accurate, in fairness it was an impossible task because of the readings they were assigned (Verhagen’s criticism of connectivism and Siemens’ response to Verhagen) and because the context appears to be the application of learning theories in the classroom.

Verhagen's criticism is an early and not particularly well-informed criticism, which Siemens does a reasonable job refuting. But if the sort of perspective of connectivism that you're given is one where 'you look up answers through your network instead of remembering them' then your understanding of connectivism will be significantly limited.

What is a Learning Theory

So in this post, let me clear, first, about what a theory actually is, and then let me outline the ways in which connectivism can be thought of as a learning theory.

To start then: theories explain. They're not handbooks or best-practices manuals. They're not taxonomies, in which a domain of enquiry is split into types, steps or stages. Theories answer why-questions. They identify underlying causes, influencing factors, and in some cases, laws of nature.

Explaining why learning occurs has two parts: first, describing what learning is, and second, describing how it happens (or what causes it to happen). Both parts are important. Theories may be as deeply divided about what something is as they are in how it happens.

A learning theory, therefore, describes what learning is and explains why learning occurs. It is not a teaching manual or a set of pedagogical best practices. You don't 'apply connectivism in the classroom' (though you might apply an understanding of connectivism in the classroom).

What is Learning?

According to connectivism, learning is the formation of connections in a network. The learning theory, therefore, in the first instance, explains how connections are formed in a network.

But think for a moment about how this contrasts with the theories of learning offered by other theories. For example:
  • in behaviourism, learning is the creation of a habitual response in particular circumstances (or as Gilbert Ryle would say, to learn is to acquire a disposition).
  • in instructivism, learning is the successful transfer of knowledge from one person (typically a teacher) to another person (typically a student).
  • in constructivism, learning is the creation and application of mental models or representations of the world.
As you can see, these are very different stories about what learning is. This is why it's diffiocult to compare theories of learning. The vocabularies are different, and they are talking about different things. Thomas Kuhn called this the incommensurability of theories.

As you can see, connectivism says that learning is something very different from what is described in other theories. This is one reason we say connectivism is a learning theory: the vocabulary of learning it employs is in some ways importantly incommensurate with that of other theories.

When I say of connectivism that 'learning is the formation of connections in a network' I mean this quite literally. The sort of connections I refer to are between entities (or, more formally, 'nodes'). They are not (for example) conceptual connections in a concept map. A connection is not a logical relation. It is something quite distinct.

In particular, I define a connection as follows (other accounts may vary): "A connection exists between two entities when a change of state in one entity can cause or result in a change of state in the second entity."

Why is this important? Because it captures the idea that connections are something that we can observe and measure (they're not a black box), and because it captures the idea that networks are not merely structures, but also that they enable (what might be called) signalling between entities.

How Does Learning Occur?

The question of how learning occurs is therefore the question of how connections are formed between entities in a network. There is a deep and rich literature on this topic, under the heading of (not surprisingly) 'learning theory', though most of it is published outside the domain of education. The first chapter available here provides a good overview.

The literature describes either actual networks of neurons ('neural networks', such as human or animal brains) or simulations of these networks ('artificial neural networks'), which are created using computers. In both cases, these networks 'learn' by automatically adjusting the set of connections between individual neurons or nodes.

This is a very different model of learning from that proposed by other learning theories.
  • In behaviourism, learning takes place through operant conditioning, where the learner is presented with rewards and consequences.
  • In instructivism, the transfer of knowledge takes place through memorization and rote. This is essentially a process of presentation and testing.
  • In constructivism, there is no single theory describing how the construction of models and representations happens - the theory is essentially the proposition that, given the right circumstances, construction will occur.

To be fair, a long discussion here would be required to talk about constructivist accounts of model or representation formation. This is a weakness of constructivist theories - there's no particular means to determine which constructivist theory is actually correct.

And this points to an underlying weakness of all three approaches: they all involves, ultimately, some sort of black box beyond which no further explanation can be provided. How does reward stimulate behaviour? How is transferred information stored in the brain? What is a model and how is it created?

In my talks I've presented four major categories of learning theory which describe, specifically and without black boxes, how connections are formed between entities in a network:
  • Hebbian rules - 'what fires together wires together' - neurons that frequently share the same state then to form connections between each other
  • Contiguity - neurons that are located near to each other tend to form connections, creatinhg a clustering effect
  • Back Propagation - signals sent in reverse direction through a network, aka 'feedback', modify connections created by forward propagated signals
  • Boltzmann - networks seek to attain the lowest level of kinetic energy 
The actual physical descriptions of these theories vary from network to network - in human neurons, it's a set of electrical-chemical reactions, in social networks, it's communications between individual people, on computer networks it's variable values sent to logical objects.

These are the actual learning theories. Connectivism essentially collects these theories together into a single package as a mechanism for explaining how connections are formed in a network.

Building on the Theory

These are the foundations of connectivism as a learning theory.

As you can see, it has nothing to do with 'looking up the answer on Google' or any of the surface characteristics commonly associated with it.

A connectivist view of the world is very different from one found in other theories.

For example, to the question what is knowledge a connectivist will talk about the capacity of a network to recognize phenomena based on partial information, a common property of neural networks.

Connectivism proposes therefore what might be called 'direct knowledge', following the work of people such as J.J. Gibson. This is very different from what might be called 'indirect knowledge', which is based on the creation of models or representations using an internal (and possible innate) language or logic.

Consequently, a connectivist account of literacy will be very different from that found in other theories. These theories are essentially language-based and are concerned with the coding and decoding of information in such a language. Major principles will revolve around syntax (aka grammar) and meaning and truth (aka semantics).

A connectivist account of literacy reinterprets both syntax and semantics, looking well beyond rules and meaning. In my 'Speaking in LOLcats' presentation, I propose a six-element connectivist account of literacy, one that also includes elements of cognition, context and change.

Additionally, the question of how we evaluate learning in connectivism is very different. Rather than focus on rote response, or on manipulations inside a model, a connectivist model of evaluation involves the recognition of expertise by other participants inside the network.

In connectivism, the principles of quality educational design are based on the properties of networks that effectively respond to, and recognize, phenomena in the environment.In various works, I have identified these as autonomy, diversity, openness, and interactivity. These are very different from standard accounts of quality.

With each of these aspects of connectivism being identified and developed, it becomes increasingly apparent that a connectivist sees learning very differently from those who follow other theories.

They see a person learning as a self-managed and autonomous seeker of opportunities to create, interact and have new experiences, where learning is not the accumulation of more and more facts or memories, but the ongoing development of a richer and richer neural tapestry.

They understand that the essential purpose of education and teaching is not to produce some set of core knowledge in a person, but rather to create the conditions in which a person can become an accomplished and motivated learner in their own right.

Tuesday, April 15, 2014

OLDaily Over the Years

Still messing around with statistics. Here's the graph of the production of posts on OLDaily over the years since the first posts in 1995:

Until 1999 the only posts produced are my articles. Subsequently, as I began the newsletter, the posts include the now-familiar links to resources in OLDaily. The red line represents the total, the blue line represents the daily tally. Notice the flat line where I took a hiatus in 2006. You can also see a bump in 2001. This was from a short period where I experimented with post-creation using the harvester.

Here's the same blue line with a somewhat larger y-axis:

Here again you can see my harvester experiments in 2001 (reaching a peak of 162 posts created on September 22, 2001). But even then, most of my posts were created manually, and sometimes I created a large number of them. I actually remember the very full days back in 2001, and in particular one huge newsletter I created from a cybercafé in Cronulla, Australia. Most of the links from that newsletter no longer exist.

Here's the graph of the persons who have signed up on OLDaily over the years. As you can see, my email subscriptions started in 2001 (that's why when I'm asked when I started, I give one of two dates, either 1995, or 2001).

This chart needs a little explanation. There are 1480 people without a start date. This reflects a period between 2006 and 2008 when the person-record creation date was not being logged. That's why you have the flat line during that period. So the chart should start in the lower left at '0', and should reflect a gain of 1480 new subscriptions during that flat period.

It's interesting to note that while the growth in sign-ups has never been exponential, it has never really slowed down, either.

Subscriptions are a different story. People come, people go, and a lot of people cancel their subscription and move to RSS. Here's what my subscriptions look like over the years:

This one's a bit harder to figure out. There's no good reason for the jump in subscriptions in 2007 (except that maybe I came back from my hiatus in 2006 writing slightly longer and opinionated posts - though the same data suggest that people soon tired of the new format).

I don't have good page view history through the years, mostly because my log files have always rotated and I haven't been diligent about keeping statistics. But here are stats for 2012 showing 2.2 million page reads and 774K visits, and here are stats for 2013 showing 5 million page reads and 1.13 million visits.

Meanwhile, in case you're curious, the states for this blog, Half an Hour, as volunteered by Google are as follows:

I'm not sure whether we learn anything from these results, other than that persistence pays, but it's interesting to observe them just the same.

Monday, April 14, 2014

Measuring MOOC Media

Here's the information I sent Steve Kolowich for his Chronicle article on the (possible?) decline of MOOC mentions in the media:

My data measures news articles from selected (and reasonably representative) sources (including Google News) and counts instances of the term ‘MOOC’ in title or descriptions. It is essentially the number of items published each day in the MOOC.ca newsletter http://www.mooc.ca/news.htm (filtered to remove duplicate listings).

Based on this data, I would say that MOOC coverage has not flagged significantly in the last few weeks or months.

I’ve revised the algorithm a bit to make it more accurate, and also the list of feeds.

New chart:

List of Feeds: (The bulk of results come from the Google news feed)
Can be found here: http://www.mooc.ca/feeds.htm

@Ignatia Webs (mobile;eLearning;mobile learning;education;mLearning;mooc;conferences;research;social media;presentation;informal learning;book)
Abject (edubloggers)
AddGab (category)
bavatuesdays (edubloggers)
Brainstorm in Progress (buddhism;Rory;Zemanta;Plurk;China;Homer;visuallearning;community;Social justice;csubioeacademy;analytics;Interaction;Brigham Yo)
Center4Edupunx (edubloggers)
Computing Education Blog (edubloggers)
Connectivism | Scoop.it (edubloggers)
Coordination Régionale de la FC Universitaire (category)
coursera - Google News (edubloggers)
Dave's Educational Blog (edubloggers)
daybydaylinux (category)
D’Arcy Norman dot net (edubloggers)
Digital Humanities Now (edubloggers)
e-Literate (edubloggers)
elearnspace (edubloggers)
Hack Education (edubloggers)
Hybrid Pedagogy (edubloggers)
Inside Higher Ed | News (edubloggers)
Leading From the Inside Out (category)
mooc - Google News (edubloggers)
My old blog (In Spanish) (category)
My site (category)
Notes on MOOC Lectures (category)
Official WizIQ Teach Blog (edubloggers)
Open Culture (category)
open thinking (edubloggers)
osvaldo rodriguez (#lak12;research.;education;research;elearning.;distance education;#oped12;#change11;connectivism)
Pontydysgu - Bridge to Learning (edubloggers)
Recent content (edubloggers)
Stephen's Web ~ OLDaily (edubloggers)
Teaching and Learning Institute (edubloggers)
The corridor of uncertainty (student recruitment;tools;multitasking;books;collaboration;free;digital divide;MOOC;privacy;art;peer learning;mobility;safety;l)
the theoryblog (edubloggers)
Tony Bates (edubloggers)
Unizor (category)
Unizor - Creative Mind through Art of Mathematics (category)
unmaestrocreativo (category)

Filtered as follows:
SELECT link_crdate,link_title,link_link FROM link WHERE (link_title LIKE '%MOOC%' OR link_description LIKE '%MOOC%'

Update - April 15, 2004 - From Twitter

Google's record of search volumes would suggest a growing public interest in MOOCs

Friday, April 11, 2014

MOOCs for Development - Day 2

The Challenge of MOOCs Panel

Stephen Downes

Please see my presentation and audio here: http://www.downes.ca/presentation/339

N.V. Varghese

- view from developing countries
    - largest expansion of the system in this century
    - did not rely on public resources at all - shows willingness to pay
    - GER (gross educational? resources) disparity worldwide
    - OECD countries universalized higher ed, but developing countries still in an elite system
    - social demand far outstrips brick-and-mortar solutions

- can MOOCs address this?
    - enormous potential
    - Tsinghua (#1 in BRICs) created a consortium of leading universities to teach Mandarin
    - IIT in India relies on MOOCs for skills in IT sector
    - 330 million in India will have Internet in 2015

    - technology and infrastructure
    - language constraint - courses are in English
Who benefits?
    - mostly the elite - already have degrees (80%)
    - they are proficient in English, they are employed, they're not looking for a degree

So - MOOCs serve privileged students, not a reliable way to increase equivalent access to higher education
    - private institutions and commercial interest in MOOCs
    - are the MOOCs taking all the money?
    - MOOCs give them a way to feel like they are contributing even if they aren't
    - disparities in access are getting narrowed, but disparities in achievement are not
    - argument that MOOCs are widening the disparities
    - propose partnering with existing institutions as an initial step to make them more
        widespread in developing countries

Russell Beale - The MOOCs Challenge - FutureLearn / U of Birmingham
    - inspire learning for life by telling stories, celebrating progress
    - nothing has more potential than MOOCs - range of views from sceptics to advocates
    - challenges:

        - MOOC 1.0 to MOOC 2.0
            - not just putting materials online
            - MOOCs 2.0 - more social, learning for life (SD- note that this was part of original vision)
            - activity feed, social network ethos
            - based on empowering not just learners but also educators

        - pedagogy of the massive
            - basically a social constructivist approach
            - we want educators to engage with learners
            - go beyond what we currently know - invent new pedagogical approaches
            - we don't think completion is a sensible metric
            - peer review, peer assessment, that will work on a massive scale
            - (yet!) - exams, statements of participation

        - mobile first
            - responsive design - apps for specific things, aware of bandwidth       

        - delightful user interface
            - we are competing with people causally watching TV, watching cat videos
            - this might not be the same in the developing world
            - still, UI is important - but this is very hard to do
            - can do without it, but makes it more engaging

        - insightful analytics
            - transactional analytics - who viewed what
            - interactions - how did people work through the people
            - conversational - who did they talk with, what did they say

    - "unlike Stephen, we do have courses and we do have course structure, and students seem to like that"
        - long lectures don't work
        - shorter videos are more engaging
            - eg by identifying when and where and how people view videos
            - eg. we can show people like the ebb and flow of the course
        - various other statistics from FutureLearn
            - eg. 34% are social learners - contributing to discussions
        - we can understand the learning design (eg., paragraph from Halmlet)
            - 23% would write their contribution only after reading the other discussions first
        - in general the feedback is good - 90% would recommend FutureLearn to other people

- we could have both - we could have he massive courses, plus we could have the community-based courses
    - but what is happening now? there is this fear that the big boys will drive everybody out
- for Russell Beale - are your users also young, male, highly educated
- if your model stopped unless you reached the developing world what would you do
    - you have to have equal inputs to achieve equal outputs
- MOOCs may enlarge the gap within developing countries
    - many people at the bottom do not have the access to connect
    - MOOCs - not just going to be superprofessors - if open for personalized teaching could be good
        - but remember MOOCs 1.0 are just eBooks
        - when MOOCs are combined with local prrofessors the learning can happen locally
- celebrating the movement from knowledge transfer to knowledge exchange
    - what can technology to to support deeper learning and mastery
- issue of language, culture, local anchoring of education
    - education was once nation-building, today it is future-building
    - isn't this calling for a new kind of partnership - between local learning forces & universities
        (SD - Triad Model)
- have MOOCs been crowdsourcing funding?

- SD - elites?
    - view of the elite swamps view of the smalls - eg. origin of MOOCs, eg. MOOCs 2.0
    - also - based on bad data - the votes of the people who already use the system
    - question of sustainability - the largest *must* create income which pushes toward a revenue model
- SD - on private participation
    - on one hand, we hear people in developing world talk about how much of their system is based
        on private enterprise
    - on the other hand, they talk about how the educational system favours only the elites

- Russell - interesting that MOOCs being promoted higher education - but concerns that they will
    displace HE are misplaced
        - the people building courses for the millions are not the people we will get the impact from
        - need a complex web of materials
        - disseminate down and outwords

- moocs will make the elites in various countries more alike, but will increase disparity
- agree with

Expanding Inclusion
Masennya Dikotla - South Africa
Why current strategies are insufficient
    - they do not meet the needs of marginalized children and youth
        - most programs function outside the mainstream, creating a second rate citizenry
        - most exclusions are based on excuses from elite groups - eg. 'we will not give you this computer system because you don't have electricity, or you don't know what to do with it, or you will just steal them' - and so they just wait
What Can be Done?
    - curriculum should meet the needs of a wide range of different learner
        - these need to be legislated
        - teaching and learning should be in first language of the learner
    - schools should accommodate all children, no matter their intellectual, social, linguistic or other conditions

Case Study - Bridges to the Future Initiative


Minghua Li - China
    - initiatuve to provide education to migrant workers in the factories sponsored by ministry of education
    - looking at how MOOCs
    - students - working in facories like foxcom (?)

    Networks of learning clusters:
        - problems: physical accessibility problems - transportation, long working days, living conditions,
        - approach - network of learning centres in close walking distance
            - to establish community college edu
            - local social sypport for learning - social learning incubator

    MOOCs come...
        - classes from all over the world
        - but do they develop the kind of courses specifically targeted to these workers
        - MOOCs can play a part - the open market concept which aims to break the monopoly on education
            - broadcast of learning not enough, we need local support - mentors, facilitators, even teachers
            - eg. 'MOOCs Inside' courses (like 'Intel Inside')   
        - also - these migrant workers also need eductational credentials

    Two-market picture of MOOCs
        - one parket it the degree market
        - a parallel independent course market
            - how do they work together? Core courses for degree + additional courses from independent
        - how do we determine MOOCs meet standards? missing point - an institution to accredit independ courses


    - currently working with a group of US community colleges to form an alliance to develop individual
        courses just to meet the standards for associate degrees

Barbara Moser-Mercer - InZone, Universite de Geneve
    - Higher Education in Emergencies - education as a humanitarian response
    - where we started - education something we impose on them as something we think they need
        - changed to blended course - we go onsite into conflict zones - in our own learning environment
        - we also need knowledge - that's what MOOCs can offer
        - but do MOOCs hold up in the fragile states we work in

    - Education in fragile states
        - contributes to political stability
        - primary & secondary education, virtually no tertiary
        - convention on refugees - Article 22

    - UNHCR - higher commission for refugees
        - has developed a new education strategy - goal of 100% access to higher edcuation
    - study of MOOCs
        - principle - you can't just do a project and extract data for your norther project
        - the recipients have to receive a benefit
        - hence, the drop-out rate isn't acceptable
            - the principles of humanitarian law say you have to get them to the finish line

    - challenges
        - tech - negotiated special deal with Coursera to download all the materials to USB keys
            - but they keys would be used as a last resort
            - most access to info from mobile phones
            - forums - basically inaccessible, too chaotic, to much data to download
        - the geography of thought
            - how people think in different cultures
                - not as a barrier, but how to leverage the learning of differnt cultures
        - the students need skills & MOOCs aren't good for that
            - but to learn skills you need knowledge, and MOOCs can help with that

Driss Ouaouicha - Al Akhawayn University, Morocco
    - anglophone university in Morocco (francophone and arabic environment)
    - weakness - mismatch between education and needs of industry
    - advantage - widespread access to mobile (& therefore internet - 52% use of internet)
    - recent conference - recommendations
        - create a National e-learning centre at the university with ministry support
        - train the trainer approach
        - use open educational resources
        - success factors: HR, technology, partnership

    - As Russell said today, the MOOCs in higher ed are an accident
        - we need to address public and secondary - esp. dropouts
        - 'second opportunity' school
        - 28% of the population are illiterate
    - we may be overestimating the power of the MOOC
        - UK person - 'a MOOC is like a book' - it's not going to solve all your problems
    - cooperation is extremely important - gap between north and south

Copyright and IP Panel

Edward Rock -  UPenn Law Professor
    Coursera / university partnership
    - question - why are you doing this - my leagl background not unimportant
        - issues around ownership of IP and copyright
    - partnerships with Coursera...
        - are non-exclusive
        - IP stays with university, content licensed to Coursera
    - necessary to negotiate ownership up front because
        - ownership may have stayed with faculty, or may have been work-for-hirre - it was unclear
        - we set up a structure to govern those questions up front
        - had to be balanced with responsibilities to paying students
        - used 'internal grants' model for most courses - around $50K / course
            - stipend, assistants, copyright, and videotaping
            - think of copyright as a publishing venture not a teaching venture
    - ownership rights, control rights, cash flow rights
        - content belongs to the faculty member, expression belongs to the university
            - faculty member licenses content to university, university licenses the videotapes
                to the faculty member - eg. university has a veto if faculty want to use a different
            - university has the right to say whether the course is offered / reoffered
            - the university could offeer the course over the objection of the faculty
                - because it needs to be economically sustainable
                - need to be able to capitalize if it makes money
                - by 3rd run, could run by itself - faculty member would get 30% share of revenue
            - university is in the position of publisher, movie studio, etc
    - course doesn't go into development without the agreement
        - but nobody is forced to develop courses through this process

Candace Reimer - Google - Learning & Development Organization
    - cloud-based open source platforms
    - Peter Norvig is on our team - shared inspiring stories
    - offered initial open online course - 157K - we saw lots of dialogue, we had TAs around the world
    - decided based on this to open-source platform - CourseBuilder
        - runs on AppEngine, ongoing deveelopments in internationalization, analytics, assessment   
    - when EdX announced open source engine, Google looked at collaborating
    Discussion of what OSS is - Google OSS these are cloud-based, though
        - anyone can be an author
        - they have full control of the course and the materials
        - they also own the relationship with the student, own the data, own the brand
        - allow course design, customized features, reasearch and community

        - is the authorship open or closed
        - do you want hosted or unhosted

Maureen McClure - Uni Pittsburgh
    - who owns development? MOOCs in a wicked world
    - how can we start thinking about these issues in a way that fits development?
        - issues become very complex in a hurry (so we need expensive lawyers)
        - who owns development? the authors? the investors? the affected?
        - authorship can address moral rights (in perpetuity)

    - question: elite education+ cooperative extension = strategy for development?
    - elite education - experts/authors as doctors, solving the 'knowns'
    - cooperative - we're neighbours, articipation and buy-in necessary

    - the Global Generation
        - development is a generational; responsibility to protect the future's sustainability
        - like radio, TV, MOOCs can impact millions
        - don't want to show up with tech and not address core issues

    - elite education is...
        - radically convenient, no loose ends
        - licensing, export controls, etc., all managed for you
        - they tend to track telecom & engineering schools
        - support national certification efforts
        - can negotiate permanent access to OERs

    - cooperative extension models...
        - stay close to local
        - promote OERs
        - non-forma;l and co-created - sidestep copyright issues
        - can generate a national voice (eg FutureLearn - choice of national cultural institutions)

    - Contexts matter...
        - critical thinking for both employbility and governance
        - more focus on international credit for mobility
        - UNESCO can help address international copyrights
        - Explore 'American Corner' or 'British Council' models
        - do not succumb to conference fever
        - is tech culture an invasive species
        - Putnam Bowling Alone
    - two generations of MOOCs
        - democratization of content
        - second generation - cMOOCs - democratization of platforms

    - licensing - any time a lot of money is at stake its a huge political issues
    - licensing and silos?
        - Coursera not open source,
            - universities could make content open course but all of us have chosen not to
            - on the other hand - the iTunes threat - hence the need to keep control over the IP
            - sensitivity to platform dependence - esp. eg. Stanford using multi platforms
            - UPenn not keen to have professors teach at other institutions
        - Google - not trying to break down silos
            - no-one has the answers right now - interested in lots of answers right now
            - area where there can be a lot of discussion
            - Q - what about G+ being locked down? No response - "I can't talk about those aspects"
        - issue of the right to earn money vs an obligation to protect the next generations