Blogs in Education

Submission for a forthcoming STRIDE handbook for The Indira Gandhi National Open University (IGNOU). See related handbooks here.


What is a Blog?

A blog is a personal website that contains content organized like a journal or a diary. Each entry is dated, and the entries are displayed on the web page in reverse chronological order, so that the most recent entry is posted at the top. Readers catch up with blogs by starting at the top and reading down until they encounter material they’re already read.

Though blogs are typically thought of as personal journals, there is no limit to what may be covered in a blog. It is common for people to write blogs to describe their work, their hobbies, their pets, social and political issues, or news and current events. And while blogs are typically the work of one individual, blogs combining contributions of several people, ‘group blogs’, are also popular.

While the earliest blogs were created by hand, blogging becam widely popular with the advent of blog authoring tools. Among the earliest of these were Userland and LiveJournal. Today, most bloggers use either Google’s popular Blogger service or WordPress. These services allow users to create new blogs and blog posts by means of simple online forms; the writer does not need to know any programming or formatting. As a result, blog aggregation services such as Technorati have reported that tens of millions of blogs have been created.

Blogs are connected to each other to form what is commonly known as the ‘blogosphere’. The most common form of connection is form blogs to link to each to each other. Blog authors may also post a list of blogs they frequently read; this list is known as a ‘blogroll’. Blogs may also be read through special readers, known as ‘RSS readers’, which aggregate blog summaries produced by blog software. Readers use RSS readers to ‘subscribe’ to a blog. Popular web-based RSS readers include Google Reader and Bloglines.

While blogs once dominated the personal publishing landscape, they now form one part in a much more diverse landscape. Many people who formerly write blogs are using social networking sites such as MySpace or Facebook. Others use ‘microblogging’ services such as Twitter. And blogs, which began as text-based services, have branched into audio blogs (also known as ‘podcasts’) and video blogs (‘vlogs’). Authors typically upload a wide range of multimedia content such as art to sites like Deviantart, videos to hosting services such as YouTube, slide shows and PDFs to SlideShare and photos to sites like Flickr.

Why Use Blogs In Education

Blogs are widely popular in education, as evidenced by the 400 thousand educational blogs hosted by edublogs. Teachers have been using them to support teaching and learning since 2005. Through years of practice, a common understanding has formed around the benefits of the use of blogs in education.

Because blogs are connected, they can foster the development of a learning community. Authors can share opinions with each other and support each other with commentary and answers to questions. For example, the University of Calgary uses blogs to create learning communities.

Additionally, blogs give students ownership over their own learning and an authentic voice, allowing them to articulate their needs and inform their own learning. Blogs have been shown to contribute to identity-formation in students. (Bortree, D.S., 2005).

Further, blogging gives students a genuine and potentially worldwide audience for their work. Having such an audience can result in feedback and and greatly increase student motivation to do their best work. Students also have each other as their potential audience, enabling each of them to take on a leadership role at different times through the course of their learning.

Moreover, blogging helps students see their work in different subjects as interconnected and helps them organize their own learning. Working with the teacher and informed by blogs authored by experts in the field, students can conduct a collective enquiry into a particular topic or subject matter creating their own interpretation of the material.

Blogs teach a variety of skills in addition to the particular subject under discussion. Regular blogging fosters the development of writing and research skills. Blogging also supports digital literacy as the student learns to critically assess and evaluate various online resources.

How To Use Blogging In Learning

Begin simply. Most uses of blogs in the classroom began with the instructor using blogs to post class information such as lists of readings and assignment deadlines. This fosters in the teacher a familiarity with the technology and with students a habit of regularly checking the online resource.

Lead by example. Before requiring students to blog, instructors should lead by example, creating their own blogs and adding links to interesting resources and commentary on class topics. This not only produces a useful source of supplemental information for students, it creates a pattern and sets expectations for when students begin their own blogging.

Read. Students should begin their entry into blogging by reading other blogs. Teachers should use this practice not only to demonstrate how other people use blogs to support learning but also to foster critical thinking and reading skills. Teaching how to respond to blog posts is as important as creating blog posts.

Create a context. Like the author facing a blank sheet of paper, a blogger will be perplexed unless given something specific to write about. Have students blog about a current issue, about a specific peice of writing, or some question that comes up in the course.

Encourage interaction. Blogging should not be a solo activity. Encourage bloggers to read each other’s works and to comment on them. Encouraging students to set up an RSS reader with each other’s blogs will make reading and commenting a lot easier. Teachers, also, should subscribe to student blogs and offer comments, again setting an example of the expected practice.

Respect ownership. A student blog becomes important because it is a manifestation of his or her own work. However, to have this value, a student’s ownership of a blog must be genuine. While reasonable limits or codes of practice need to be respected, student bloggers should have the widest latitude possible for personal expression and opinion.

Address issues immediately. The most significant danger to students online is posed by other students. In particular, bullying (or ragging) is a significant problem. It is important to spot instances of bullying as soon as they occur and to take steps to prevent further incidents. Teachers should educate themselves as online bullying can be invisible and hard to address.












Reference

Bortree, D.S. (2005). Presentation of self on the Web: an ethnographic study of teenage girls’ weblogs. Education, Communication & Information, 5(1), 25-39 [Paywall - http://taylorandfrancis.metapress.com/(2cddl0eerkzfyf45pssrryqw)/app/home/contribution.asp?referrer=parent&backto=issue,3,8;journal,3,13;linkingpublicationresults,1:107512,1 ]

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Relativism and Science

I don't want this to be a long post, but Rose sent me this, which people should watch. Then I'll add some comments.



OK, I hope you enjoyed that.

So the question I want to address is, "How can you be a relativist?" Because, as the video says, there is knowledge. You don't walk out the second floor window, and we can extend our lifespans with medicine, and all that. It's not all a big mystery, and it's not all "anything goes."

Quite right. There is knowledge, and it's not all "anything goes." But it does not follow that relativism is false.

Listen to the video carefully. As it says, science is the willingness to change our views based on the evidence. If you can show us how homeopathy works, and how it works, says the author, I'll go running down the street shouting "It's a miracle."

To the scientists, to the empiricist, to the reasonable person, beliefs are based, through reason, on the evidence. It is precisely the mark of the unreasonable, of the unscientific, that they will not change their beliefs even in the face of evidence, that their 'knowledge' is constant, unchanging.

One should ask, rather, how can someone be an empiricist, how can someone base their beliefs and knowledge on the evidence, and not be a relativist? If one's own belefs and knowledge might change from time to time, why is it not reasonable to suppose that others' might as well?

Indeed, it is pretty evident that each of us has his or her own distinct set of experiences. A very different body of evidence on which to base their knowledge and beliefs. It would be a miracle were it to turn out that everyone's knowledge and beliefs were the same!

Yes, it may be argued that there is an element of commonality to people's knowledge, that the world is the same world for everyone, and that we go to great effort, through scientific method and repeatable, testable, experimentation, to ensure that people have the same experiences, and thus, the same knowledge and beliefs.

That's quite so, and for some big gross things - like the influence of gravity - it appears that we may be able to achieve some constancy, by carefully regulating our experiences to achieve precisely this result. But as the world is constantly changing, and as each of us is limited to our own direct limited point of view, our capacity to achieve constancy is limited, and always subject to question from the periphery, from personal experience.

Because people have different points of view, because people have different experiences, they come to mean slightly different things by their words, to develop slightly different principles of reason, to develop slightly different pictures of reality. Multiply this over a lifetime, and over seven billion people, and you have the recipe for relativism.

Our differences in knowledge and belief - our legitimate differences in knowledge and belief - lie precisely at those fault lines where our personal experiences differ. A person borne in injustice will come to have a different view of fairness than one born in a society of equality and right. A child raised in starvation will have a different view of food than one raised in plenty. Our beliefs - even our scientific beliefs - are ineliminably subject to our personal experiences. And that's a good thing, because in this is our capacity, as individuals, and as a society, to learn.

This view should not be confused with the view that it's all a big mystery and that "anything goes."

First of all, from the fact that your knowledge differs from another's, it does not follow that you cannot criticize, or have no grounds for criticizing, another's knowledge. One appeals to one's own experiences, one's own reason, and invites the other to consider similar experiences, to follow a similar reasoning, to explore and to experiment. Because we all have different experiences, our individual experience becomes a basis on which to criticize others' points of view.

And second, this does not grant any sort of license to someone who asserts some proposition on the basis of no experience, no reasoning, whatsoever. Such a person has produced exactly the opposite of knowledge, some set of statements that will not be revised, not even in the face of contradictory experience.

The fact that we each have different experiences, and hence, different knowledge, does not free us from the constraint of basing our knowledge on experience and reasoning, whatever they may be for each of us. That each of us has a slightly different basis for knowledge or belief does not legitimize the employment fo no basis for knowledge or belief.

Relativism is the open-eyed recognition that knowledge and truth are empirically bound, and hence contingent and subject to change, not the uncritical acceptance of any proposition, no matter how poorly formed and supported.

Finally: one may ask, isn't this basis in 'experience' and 'reason' itself a common, non-relative article of knowledge? Yes, one could say such a thing - but swuch a sentence remains true only if it is not examined in any sort of detail. As we push the parameters, as we come to ask for a definition of 'knowledge', 'experience' and 'reason' we find that these concepts, and the logic that relates them, vary from person to person.

The proposition "knowledge is based in experience" itself varies slightly from person to person, and its experession in a language represents an abstraction of the actual belief, as instantiated in different people, but not the belief itself. Each person wears his or her experience differently, and part of the challenge (and the fascination) of life and interaction is to understand this.

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TNP: 20 Years On

In 1989 I was reaching the peak of my career. My PhD coursework was complete and behind me, I was gainfully employed (if underpaid) teaching logic and philosophy for the U of A and Athabasca University, I was elected for my first term as president of the Graduate Students' Association, and I was riding a wave of personal and political popularity.

More importantly for me, I was finally understanding the problems that had drawn me to formal learning in the first place. Though I had started university simply because it was a requirement for advancement in the work world, over the years I had been drawn increasingly to political activism and philosophical exploration. In 1989, the pieces came together. I watched the rise of 'people power' around the world. I had seen Francisco Varela speak on AIDS and immunology at the University of Alberta hospital. I began to see how networks, whether of individuals of cells, could take shape, form patterns, act with purpose. And how this would reshape how we understood the world.

In 1990 I attended, along with a number of the other graduate students at the University of Alberta, the Connectionism conference at Simon Fraser (downtown), combining it with a National Graduate Council meeting and a week-long vacation in New Westminster I spent reinterpreting the Tao Te Ching. That summer I sat at the very top of the hill at the Edmonton Folk Festival and in a frenzy of writing, completed the first draft of what would eventually become The Network Phenomenon: Empiricism and the New Connectionism. In the fall of that year, I presented it to my doctoral committee as a proposal for the work I wished to do to complete my PhD.

It has been almost 20 years, and I thought I had put it behind me, but recently I see that my former supervisor, and chair of that committee, is now one of the people blogging on a philosophy website.

Now you can read the proposal for yourself - that's why I put it online. Having just retyped it (I'll use OCR for my other work, but I wanted to revisit this paper personally) I can see that it is an overly ambitious work covering a wide swath of theory and evidence. As a proposal, it also lacks a lot of the depth and research that one would want of a completed dissertation. Yet, still, 20 years later, the paper strikes me as genuine, original, and important. A dissertation based on this work - or even just a chapter of this work, which might have been more appropriate - would have been a worthwhile contribution to the field.

The committee didn't see it that way. Led by the chair, they engaged in an attack on the basic premises of the work, of the idea of associationist forms of reasoning and connectionist models of cognition. The idea that cognition could be non-propositional, the idea that proof would proceed by metaphor and similarity, rather than form and validity, they rejected as ridiculous. For good measure they offered the opinion that even if the work were worthwhile, it would be well beyond my capability. The committee felt that my PhD would be better spent in an investigation of mental content - something I had denied in the paper even existed! - rather than this fool's errand.

I submitted a dissertation proposal based on mental content a couple of weeks later, a 30-page overview of the field they were quite enthused about. But my heart had fallen out of the project. I wrote for myself a long diatribe attacking a book the committee was enthusiastically recommending, Jerry Fodor's Psychosemantics, called "Trash Fodor" (when I find it, I'll post it). I thought the book represented the epitome of the inanity of the cognitivist approach. I gradually turned my back on the program and on philosophy in general. I retreated to my little cabin in northern Alberta, taught logic, and worked on my computer.

I have never forgotten - or stopped believing - the work I presented in that paper. About five years later I began writing again - you can see it as the beginning of the work on Stephen's Web - and began rebuilding my understanding of learning, inference and discovery. My work continued to be informed by my understanding of connectionism, people power, the Tao, and related concepts. The structure of content networks, the organization of metadata, and my description of connective knowledge, all are based on this basic foundation.

I struggle every day with the question of whether my work is genuine, original and important, whether, indeed, it is even academically and scientifically sound. I look at the work of others - like Varela's, for example - and I am daunted and humbled. But such work, too, is rare. And what I leave behind is so different in format and method and in style and structure a comparison is probably impossible. The best I can do is to work as honestly and as openly as possible, consistent in my pratcice and my principles.

So when I realized how angry I was, even these many years later, I concluded that the best - and only - response would be to put the material into the open, and let people decide for themselves. Because there's a certain sense in which I feel I have missed out. And I'm sure some people will find it trivial and others obscure, some will find it too dense and others too simplistic, some will see in it a naive foray into amateur epistemology while others will see it as part of a wider discipline. Some will think I should have been able to complete my PhD, while others will question whether I have any academic merit at all. And I - well, I will see it as mine. As me.

Being angry was cathartic, because it made me see what I've had to come through, and I'm over it now.

You know, in life, you have certain kinds of regrets. One kind of regret revolves around the opportunities you never had - what if I had had better schools, better teachers, better jobs, better finances. What if I had been treated fairly here, rewarded justly there, shown this in that place. Things I could never be, places I could never go. These are regrets over things I cannot control. But the other kind of regret - ah. The regret of a man who was not true to himself, who did not give his all, who held himself back or conformed for the sake of advancement, of the man who stopped seeking because he was told what to believe: these are the regrets I could not bear to feel.

I guess I had a choice, back in 1990, about which kind of regret I would feel 20 years later. I do not, for an instant, think I made the wrong choice.

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TNP 11. Projects and Investigations

The Network Phenomenon: Empiricism and the New Connectionism
Stephen Downes, 1990
(The whole document in MS-Word)


TNP Part X Previous Post


XI Projects and Investigations

A. Computational Difficulties

Let me now conclude this paper by outlining a number of areas of further investigation which ought to be pursued in order to accomplish the fullest and most useful presentation. These areas divide into two distinct categories: conceptual difficulties and computational difficulties. Let me outline some computational difficulties first.

By computational difficulties I mean aspects of the implementation of connectionist theories on computers. A number of concerns can be raised by viewing connectionism within a philosophical framework and some additional features are required. What I would like to do here is actually build a connectionist system using the C programming language and intended for application on an IBM XT compatible or clone. The large number of options, for example, different learning rules, will be incorporated as options on my own system. This system will fill a void on the market: an easy to use connectionist system which costs less than $1,000.

Having developed a connectionist system (which I'll call SDPDP) I want first to look at network variability in aconnectionist system. First of all, I want to construct nets in which different options may be employed in different parts of the same net at the same time. For example, in a PDP net [83] either every unit employs a stochastic on-off activation or every unit is activated in degrees. But in some systems, we want to be able to have units of both varities. In addition to variable structure, I want to incorporate some mechanisms of network plasticity. For in human systems, not only the connections, but the units themselves grow in response to input, especially in early life. Finally, I want to consider what I call "dimensions". For we want it to be the case that such things as religious conversions and scientific revolutions are possible. This requires that a network be able to construct [alternative] pairs of stable representations at the same time, which may alternate in priority. [84] Each of these alternative representations I cann a "dimension".

Another computational problem which I wish to consider concerns learning schedules and annealing. Currently, PDP systems employ a system which is very similar to that employed in physics. But, first, it is not clear that an annealing equation which is suitable for thermodynamics is suitable for human brains. I would like to investigate grounds for choosing one, rather than another, annealing equation. Second, it is clear to me that the annealing schedule employed is inadequate. In my view, temperature increases and decreases ought to be cyclic, as for example paterns of increased brain activity when we sleep. In addition, temperature ought to be sensitive to input, so that we can rapidly process conflicting input.

Finally, there is the hardware itself to think about. Human hardware is much smaller and more complex [than] contemporary computer technology. Perhaps we will not be able to build actual neurons, however, it seems reasonable that, now that we know exactly what we are looking for, we can make some plausible suggestions regarding how to build a computer neuron. I think that it would be best if many of the features currently represented by parameters, for example, threshold or rest values, can be implemented physically.


B. Conceptual Questions

By conceptual questions, I mean investigations into some of the things which connectionism can tell us about epistemology and the philosophy of mind. For, if the arguments concerning rules and categories are sufficiently strong, then we will want to reevaluate such concepts as knowledge and belief. For example, I would like to say that an item of knowledge is a stable pattern of activation, a pattern which tends not to change given varying input. If this is the case, then I may want to say with Feldman that "you do not have a store of knowledge, you are your knowledge." [85] In such a case, then, it becomes necessary to explore what we are and what part of us it is which is our knowledge.

In addition, I want to consider questions concerning theoretical and physical parallelism which arise. For example, through this paper I have used the terms "neuron" and "unit" roughly equivalently. I have also talked of the advisability of using this or that learning equation according to whether or not humans actually employ (or instantiate) the equation. We need to ask, first, whether or not we should design systems in parallel with human neural structure, and if so, what they would look like, and even further, how we would determine what they would look like.

As another investigation, I want to make some remarks about the nature of knowledge (as opposed to the definition of knowledge). For, if knowledge consists of stable patterns of activation then we cannot think in terms of knowledge as being sentences which have a given propositional content. It is unclear whether we can assign propositional content to patterns of activation. If that difficulty does arise, then we may want to consider some other relation between that which would serve as content (for example, representations of events in the real world) and patterns of activation. Here, perhaps, one could follow Armstrong and oldman and assert that there is a causal connection (and distinguish between appropriate and inappropriate causes). In order to successfuly defend this approach, it is necessary to give a fulla ccount of how we learn about causes.

Yet another investigation concerns consciousness. I have suggested above that there are conscious and unconscious regions of the brain. My belief is that those regions which are conscious are those which correspond to the activation of sensory input areas. In other words, my hearing someone speak a sentence and my thinking in a sentence is an activation of the same set of neurons (or an overlapping set). This solves the problem of how we can have a "stream of consciousness [86] in a non-linear network. But a much more detiled story is required here.

Finally, it is worth posing the question of whether connectionism is a type of scientific revolution, in the Kuhnian sense, or whether it is not. Some philosophers, for example Stich and Johnson-Laird, have expressed the opinion that it is not. In my own view, since so many traditional concepts must be overturned, then it is a scientific revolution. Haveing said that, however, I must ask whether or not we are working within an eliminativist paradign, as suggested by, say, Churcland, or not. In my view, there is still a role for words such as "knowledge" and "belief". If I believe this, then first I must explain this role, and then show how this role makes sense within the new paradigm.


C. Other Projects

When I began by asserting that connectionism vindicates empiricism, I embarked on a philosophical enterprise. What followed has been primarily technical and non-philosophical. I would like to return to a connectionist treatment of some philosophical issues.

For example, some contemporary [87] adocate a form of nominalism. While the philosophical debates concerning realism and nominalism are periphrial to this project, it is still the case that connectionism, if successful, should shed some light in this direction. I assume that it would support a form of nominalism, but this should be more fully explained.

Another project of a philosophical nature concerns the foundationalism-coherence debate. If we employ relevant similarity instead of truth-presenvation as a means o evaluating inference then the traditional concept of justification, if it must not be abandoned altogether, must be radically altered. This sheds a completely new light on the traditional problem and is worth investigating.


TNP: 20 Years On


[83] Rumelhart and MacClelland, Explorations.

[84] For example, we may switch back and forth between views of a Necker Cube.

[85] J.A. feldman, "A Connectionist Model of Visual Memory", in Hinton and Anderson (eds.), Parallel Models of Associative Memory, p. 51.

[86] See William James, The Principles of Psychology, p. 279.

[87] Like Nelson Goodman.

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TNP 10. Summary

The Network Phenomenon: Empiricism and the New Connectionism
Stephen Downes, 1990
(The whole document in MS-Word)


TNP Part IX Previous Post


Part X Summary

This concludes the presentation of the theory of learning and cognition which I wish to present. Before describing some of the further avenues of investigation I wish to pursue, let me summarize what I have asserted to this point in this paper.

I began by proposing a new theory of learning, connectionism, and described some prima facie objections to the theory. In order to respond to those objections, I argued that we need to reconsider some paradigms concerning rules and categories. Then I developed connectionism as an alternative theory of rules and categories. On this new theory, any concept is represented as a pattern of activations in a network of interconnected units. A category, on this view, is represented by a unit which can be actiuvated, and the members of the category are the concepts whose connections activate the unit which represents the category. Connectionist networks not only store categories in this manner, they can learn them on their own. In order to develop this idea further, I examined a number of objections to the concept of distributed representation. To meet these objections, I described how patterns are developed from perceptual input and described and defended the "picture" theory of representation.

I then turned to considering detailed objections to associationism and connectionism. These divided into problems concerning distributed representation, problems concerning perception, and problems concerning associative mechanisms in general. In order to defend against problems of distributed representation, three types of patterns of connectivity were identified and the concept of similarity was defined in terms of activation vectors. In order to develop a theory of perception, I defined perception as input activations and two types of perception, conscious and real perception, were identified. This successfully explained theory-ladeness and the development of three-dimensional representations without the requirement of a priori or innate knowledge. Finally, a number of arguments against associationism were considered. In order to show that associationist and connectionist systems can perform higher level cognitive functions, I argued that a two-stage process is employed. First, prototypical representations are constructed, and then second, these are used to support inference by analogy or metaphor. This is in turn supported by the observation that such process [can] be viewed as operations. Finally, I considered the problem of the evaluation of models and inferences in connectionist systems, and argued that we should employ the concept of relevant similarity.


TNP Part XI Previous Post

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More on New Knowledge

Responding to Tony Bates, Bates and Downes on new knowledge: Round 3

You say > However, I don’t believe the distinction between ‘academic’ knowledge and ‘applied’ knowledge is particularly useful.

Here we agree.

You say > What is useful is a distinction between academic and non-academic knowledge, as measured by the values or propositions that underpin each kind of knowledge.

Here we disagree.

First, I'm not sure you can made the distinction stick.

Second, even if you make the distinction stick, then so much the worse for academic knowledge, because the values or propositions that underpin academic method are unsound.

You say academic method > AIMS for deep understanding, general principles, empirically-based theories, timelessness, etc

Yes. But it shouldn't. That's my point.

You say > Academic knowledge is not perfect, but does have value because of the standards it requires.

This is a statement deserving of more discussion, because I think that either academics have lost track of the standards, being devoted to process over rigor, or that the standards adhered are in fact no guarantor of worthwhile results.

You say > I also agree with Stephen that knowledge is not just ’stuff’, as Jane Gilbert puts it, but is dynamic. However, I also believe that knowledge is also not just ‘flow’.

It is neither 'stuff' nor 'flow', in my view. I explicitly reject both views in my post and in the comment that follows.

As I wrote:

"The central tenet of emergence theory is that even if stuff flows from entity to entity, that stuff is not knowledge; knowledge, rather, is something that 'emerges' from the activity of the system as a whole.

"This network - and subnets with the network (aka 'patterns of connectivity') - may be depicted as knowledge...

"A second way of representing knowledge, and one that I embrace in addition to the first for a variety of reasons, is that patterns of connectivity can be recognized or interpreted as salient by a perceiver."

The reason why this depiction is important is that knowledge, on this view, is *not* "deep understanding, general principles, empirically-based theories, timelessness, etc."

So whatever it is that academic method is aiming for, it is not knowledge.

This is a key point of contention between us:

You write > at some point each person does settle, if only for a brief time, on what they think knowledge to be. At this point it does become ’stuff’ or content. I still contend then that ’stuff’ or content does matter, though recognising that what we do with the stuff is even more important.

I disagree with.

I do describe (following o0thers) 'settling mechanisms' in the brain. We can say that we 'settle'. We can hypothesize, at least, a (thermodynamically) stable state of connections and activations in the brain.

But the 'entities' in such a system (if we can call them that) that constitute 'knowledge' do NOT have the properties of 'stuff' or 'content'. This is the key and fundamental point of my argument:

Not 'stuff' - not discrete, not localized, not atomic
Not 'content' - not semantical, not propositional, not symbolic

And that's my problem with academic method. It seeks out specifically propositions - symbolic or semantical - that are discrete, localized and atomic. Things that are _candidates_ for deep understanding, general principles, empirically-based theories, timelessness.

I think that maybe if we can untangle the vocabulary we might come to agreement on this. After all,

You say > this is likely to result in a shift in knowledge that may be very important, and it is in this area where I think Stephen and I may have some agreement.

This encourages me.

Skipping ahead quite a bit...

You write > My concern about much of the discussion of the ‘new’ knowledge is that it seems to depend on what I might call majority voting - it is the number of hits that matter, not the quality of the content.

Quite so.

Voting - and counting generally - record only the mass of a thing. They require some sort of identity (in order to identify that which is being counted).

This is distinct from the type of knowlecdge I have been trying to describe, which depends not on the quantity of things assembled, but on the way those things are interconnected.

This is what I have tried to clarify with the distinction between 'groups' and 'networks'. http://www.downes.ca/post/42521

The properties found in the group are (to my way of seeing) just those embraced by what we have been calling the academic method. If you look at the diagram http://www.flickr.com/photos/stephen_downes/252157734/ you see typical academic values: unity (of purpose, of workers, of science), coordination, closed systems, distributive (expert-based) knowledge.

Knowledge based on networks is not based on counting - not on votes, on surveys, on mass, on category or type, etc. because knowledge is not the sort of thing that can be counted, not the sort of thing that can be generalized (as a mass).

The objection to voting *is* an objection to academic method.

The new knowledge is precisely *not* knowledge by counting, knowledge by popularity.

But it's not knowledge by experts ether. Because if we say that knowledge is based on experts and expertise, then we are saying that knowledge is the 'stuff' that's in people's heads that goes from place to place. Which - again - it isn't.

Now it is reasonable to disagree with my position on knowledge, but it's important to recognize that 'network knowledge' isn't based on counting or popularity - no matter how much this is emphasized by the (popular) media.

Finally,

> Lastly, Stephen was puzzled as to why I felt a blog was not the best way to discuss this issue. What I feel the topic needs is more space and time, and a critique from philosophers would also add to the discussion, I am sure, because I do not have specialist knowledge or training in epistemology. I would like to have had more time to review other writers on this topic, and more space to elaborate my views. I feel that I could do a better job that way.

Well - take all the time and space you need. Neither are in short supply on blogs.

Indeed - and this is one thing I like - you can go back over again, return to the same point again, attack it from various angles - a whole range of things you can't really strive for in any other forum.

> It was not because I needed the discussion to be academically reviewed in the way that journals are reviewed

Good. because if we were restricted by reviewers, we could never be having this discussion. Which would be a pity.

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TNP 9. Connectionism and Justification

The Network Phenomenon: Empiricism and the New Connectionism
Stephen Downes, 1990
(The whole document in MS-Word)


TNP Part VIII Previous Post


IX. Connectionism and Justification

A. When Some Connctions Are Better Than Others

An objection exactly analogous to the objection to operationalism may be brought against connectionism in general. In connectionist systems, anything may be connected with anything else. However, it is clear that there must be some subset of the set of all possible connections such that the connections in this subset are better than the other connections. For example, among the types of connection which are possible, there is a subset of connections which corresponds to logical inference. [74] We want to distinguish these logical connections from those connections which are (for lack o a better term) merely accidental. However, there is no means, from within a strictly connectionist framework, of establishing this distinction. Therefore, connections must be evaluated according to constraints over and above any given connectionist system.

One weakness of the objection just stated is that there is no clea agreement regarding what constitutes the proper constraints for such an evaluation. Suppose, for example, we are attempting to parse a sentence in order to determine its meaning. According to some philosophers, for example, Fodor, this task may be accomplished with reference to grammar, that is, rules and structure. Others, for example Winograd, argue that semantical consideraions need sometims to be taken into account. It is also reasonable to argue that the meaning of a sentence can only be determined with respect to pragmatic, or context-dependent, constraints.

Similarily, in the philosophy of science, there is no clear agreement regarding what constitutes a good scientific theory. Some philosophers, for example van Fraassen, argue that theories ought to be evaluated according to their empirical adequac. Others, such as Hooker, argue that "epistemic virtues" such as simplicity and coherence are what guides the evaluation of a theory. According to many philosophers, most prominent among them being Popper, a scientific theory ought to be testable, bt this does not stop some theorists, for example van Daniken, from porposing untestable theories. And finally, some philosophers follow Feyerabend and assert that there are no standards of goodness for scientific theories.

These examples may appear to be out of place on the ground that, in the formal disciplines, there are clear standards for the evaluation of operations. In logic, we have the constraint of truth-preservation, specifically, an inference is valid if and only if it preserves truth, and is invalid otherwise. In mathematical equations, similarily, an operation is correct if and only if it preserves equivalence, and incorrec otherwise. Therefore,if a connectionist system cannot distinguish between, say, truth-preserving and non-truth preserving operations, then the system must be guided by some set of constraints over and above itself, that is to sa specifically, it must be guided by innate constraints. There are several examples in the literatire of this sort of consideration. Fodor [75] criticizes the "picture" theory of representation on similar grounds, and Holland (et.al.) [76] build such constraints into their system of inductive inference.

The idea here is that in any representation, there will be representational content. Representational content may be more or less representative of what it represents. For example, if the representation is propositional in form, then the proposition will be either true or false according to whether whatever is asserted by the proposition is in fact the case. the criticism, therefore, of connectonist systems is that there is no means of evalating connections such that it can be determined that their representational content corresponds, or does not correspond, to whatever happens to be the case.[77]

If I were to use the general response to ojections outlined above, then if this were an item of knowledge, I would deny it, and if it were a skill or capacity, I would explain how it can be accomplished using an associationist (connectionist) mechanism. owever, ruth does not appea to fall under wither category, and hence, needs a special discussion of its own.


B. Truth

Let me examine the concept of truth more closely. The standard, naive definition of truth is correspondence with reality, for example, a proposition P is true if and only if P. This definiion of truth is inadequate because there are many propositions which are true, for example, predictions and other subjunctive conditionals, or statements about possibility, to which by definition nothing in the world corresponds. A better definition of truth is provided by Tarski: P is true if and only if it corresponds with a model of the world.

But this is a different definition of truth than the definition of truth which is considered to apply in formal inferences, for in this case, we are talking about truth-preservation and not truth per se. A logical inference is valid strictly according to its form; the world is not a factor to be taken into consideration. Thus, the claim that logical inferences are truth-preserving by itself has nothing to do with the nature of the world or models of the world. An additional link - between truth-preservation and correspondance - must be established indepe3ndently. For, without such a link, truth-preservation by itself is no virtue. It must be shown that truth-preservation is a good means of constructing inference about the world or about models of the world.

For a certain set of inerences, we can concede that this is the case. Take, for example, an inference about points on a journey. If x arrived at A before B and x arrived at B before C, then the rules of truth-preservation tell us that x arrived at A before C. This inference is confirmed by observation. It is however by no means clear that the rules of truth=preservation always apply when we are talking about the world. First, there is no reason to believe that these laws actually apply to the real world or even t models of the real world (unless the models are governed by an a priori stupilation that they must adhere to such rules, in which case holding up the model as an example is a fancy way of begging the question). [78] And second, it is clear that we want to make many [other] inferences aout the world or models of the world, for example, inductive inferences, for which the rule of truth preservation [is] of little or no use. Therefore, in at least some cases, something other than the rule of truth preseration must be employed in order to evaluate our inference.

This is an important criticism of the objection to connectionism and associationism. In response to the objection that connectionist systems cannot provide an evaluation of this or that representation, the response is that traditional systems fare no better, or at least, are only a very slight improvement.


C. Relevant Similarity

Opposed to the concept of truth as our standard of evaluation, I wish to propose the standard of "relevant similarity". This standard has a number o advantages. First, it works, in the sense that successful inferences can be distinguished from unsuccessful inferences using relevant similarity. Second, in order to employ relevant similarity, no innate or a priori constraints are required. We know this because systems which naturally employ relevant similarity, connectionist and associationist systems, require no innate or a priori constraints. And third, the standard of relevant similarity is exremely powerful. For example, inferences ma be evaluated drecly according to relevant similarity, for example, the sample of a generalization must be relevantly similar to the whole. Or at another level, an inference may be evaluated according to whether or not its form (that is, some abstraction of the inference) is relevantly similar to previously successful inferences. Let me sketch these in a bit more detail.

Consider the typical industive inference. The premises consist of a set of instances of some phenomenon or state of affairs, for example, "A1 is a B", "A2 is a B", etc. The conclusion is either a generalization of these observations, for example, "All A are B:, or a prediction about the next instance, for example, "An+1 is a B". Standard textbooks [79] list two major fallacies which can occur in such inferences: hasty generalization, in which too few instances are observed, and unrepresentative sample, in which observations are biased in some way. Both of these fallacies can be explained with reference to relevant similarity. An industive argument works because the premises and the conclusion all describe similar phenomena, so, if the phenomena described are not sufficiently simila, the inference fails. An inrepresentative sample is significantly different from the [sample described in the] conclusion, thus, the inference fails. In a hast generalization, we have not seen enough samples to be sure we have established similarity, hence, the inference fails.

Connectionist systems using relevant similarity for the evaluation of industive inferences avoid many of the problems which plague standard work in induction. [80] For example, one may ask why we use one particular set of premises, and not so0me other set of premises. The answer is naturally provided by the clustering mechanism described above. Another problem is the question of how many instances are required before we are able to say we have sufficient grounds to draw a conclusion. This answer is given by the activation value of the abstraction from a given cluster. If that abstraction has a sufficiently high activation value compared to other evaluations, then the inference works. Otherwise, it does not. There is no clear-cut numerical answer to these questions: it will always be relative to the structure of the net as a whole. What connectionism provides, and what traditional theories do not provide, is a mechanism for determining the answer in particular cases, rather than a mechanism which determines one answer for all cases.

I have already mentioned a few cases of the second sort of evaluation, that is, an evaluation which asserts that an inference is successul if its form is sufficiently to some previously correct or successful inference. So, for example, a person learns modus ponens by being shown examples similar to "If I am in Edmonton then I am in Alberta..." and learns not to deny the antecedent in the same way.

As I mentioned above, a connectionist system will attempt to employ relevant similarity on its own. It does this because such a system tends to adjust connection weights and unit activation untl it reaches a stable or "rest" position. The exact ature o this rest position depends to some degree on how the system is constructed: change the leaning rule and you change the rest position. However, in all cases, the settled state will be one in which all and only those units who's vectors are similar to the input activation will themselves be activated (or as nealy so as possible [81]). I have illustrated how we might develop a rule of transitivity which is useful on journeys from place to place. For example, Lakoff suggests that we develop the concept of cause by analogy with human actions.


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[74] These are described in Rumelhart and McClelland, Parallel Distributed Processing.

[75] In Ned Block, Imagery.

[76] In Holland, Holayk, Nesbitt and Thagard, Induction: Processes of Inference, Learning and Discovery.

[77] Here I am assuming a correspondance definition of truth. Other definitions are available, see, for example, Rescher.

[78] This is very similar to the point made about scientific theories, above, for if a scientific theory is a model of the world, then, as noted, there are innumerable possible ways of building such models.

[79] For example, Jerry Cedarblom and David Paulsen.

[80] See henry Kyburg, "Recent work in the Problem of Induction."

[81] See Rumelhart and Mac Clelland on satisfying multiple simultaneous constraints in Explorations in Parallel Distributed processing, ch. 3.

[82] Philip Kitcher, The Nature of Mathematical Knowledge.

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