wallpaper dinding

The fundamental thing is the sheer  inauthenticity of it. The forced enthusiasm extolling the 'coolness' of science or mathematics is peculiar and rather unnerving. But if I was to speak against these kids' enthusiasm for science (if it really was genuine and I was misjudging it) I would be fearful of committing a kind of heresy. Mine would be a reactionary or purist complaint, not in tune with the young generation.

Well, stuff that - I am going to say something! The real problem with this is not that it is or isn't genuine (it isn't!), but that it escapes the principal ingredient of any branch of knowledge, which is love of the subject. It confuses exuberance for passion for a subject. These kids do not express a passion for mathematics or science; they express their own exuberance as performers acting a script that they don't appear to understand.

But the way it is done, the enthusiasm, the enforced 'rightness' or 'right-on-ness' of it all makes thoughtful critique difficult. It drowns out thoughtfulness in the name of extolling the virtues of science - a discipline which requires thoughtfulness more than any other human quality. Why? Where's the sense in that? Is this simply in the name of corporatism or political expediency? We should ask who serves to benefit from this kind of message, and then we should worry about the power that they wield not only on the education system, but on the growth and freedom of knowledge in our society.

But then we could ask "why have the STEM agenda in the first place?" Clearly governments are anxious to produce capable scientists and technicians. But, in another irony of the education system, teaching science and mathematics is not always the best way to get great scientists. Classics, languages, music, drama, literature and philosophy have between them had a highly successful track-record in producing technical and scientific minds. (This is interesting: http://dana.org/news/artseducationinthenews/detail.aspx?id=21838). What matters, more than anything else, is a love of knowledge and an unquenchable search for meaning in a confusing universe.

Cybernetics, perhaps more than any other science, has had a deep connection to the arts. Norbert Wiener studied mathematics and philosophy with Bertrand Russell and G.H. Hardy; Stafford Beer similarly studied philosophy at UCL; Bateson started in Biology, but ended up an anthropologist; Niklas Luhmann originally studied Law; Von Foerster started as a physicist, but was equally at home in the arts. The list goes on.

Why does this escape those that insist on the STEM agenda? There are of course well-meaning scientists who love their subject and believe kids should have the opportunity of experiencing it. But then there are politicians who watch their backs and try to ensure that their educational decisions are defensible. Imagine a politician supporting a position of prioritising the arts for the sake of producing better scientists! How would that be defensible? (yet it might make more sense!)

My answer to this is that the arts should become more genuinely scientific and critical. In fact, since the STEM agenda has a narrow view of science as a set of technical skilled performances (rather than a critical inquiry about nature), the arts are well-placed to be the home of true science. I think that one of the most important outcomes from critical realist research is that science is not scientific enough: it has lost the ability to inspect its own assumptions and methods. The arts, on the other hand, could fill this void because they require much deeper authenticity and honesty in engaging with what are on the whole bio-psychosocial phenomena. 

Visual analytics is the fast emerging field where data is presented graphically and interactively for individuals to explore. The problem that it addresses is simple and fundamental: too much data; too little meaning. The visual analytics approach aims to provide users with interactive tools for allowing them to explore the data in a variety of ways identifying what appears to be meaningful, and what appears to be superfluous. There has been a lot of work going on in this area, including a large EU project called Vismaster (see http://www.visual-analytics.eu/)

I've complained in this blog about the learning analytics/visualisation movement's obsession with pretty pictures (see http://dailyimprovisation.blogspot.co.uk/2012/01/knowledge-and-performance-art-of.html and http://dailyimprovisation.blogspot.co.uk/2012/02/rethinking-impact.html), and I have argued that the principal objective of any kind of analytics is decision and control. Too frequently this objective is lost as we become hypnotised by the pictures. The fundamental question is always "what does it mean?"; not only "what does it mean?" to me, but "what does it mean?" to us collectively (as we try to steer our institutions, learners, etc).

Recently I have also tried to get a grip on 'meaning'... or (I guess) the meaning of meaning. I am quite convinced by the arguments presented by Leydesdorff, following von Foerster's ideas of eigenform and Krippendorff's ideas around 'content analysis' that meaning has to do with expectations or anticipations. For Leydesdorff, meaning is "the structuring of expectations" (see http://www.leydesdorff.net/giddensluhmann/structuration.pdf). I like that. The meaningful moments when I look at a beautiful sunset seem to me precisely moments where my anticipation and expectation is transformed; somehow the gears crunch together in a new way. The same is the case with the meaningful moments in music: at the climax, the expectations shift, the elements come together. This is probably similar (although a bit more precise) to Koestler's bisociation model (see http://en.wikipedia.org/wiki/The_Act_of_Creation).

But there is a problem here and it has to do with abstraction. I love to think abstractly (this blog is a testament to that!!), but the dream of abstract thinkers is somehow to put the world in a formula or a model - or a 'visualisation'. To have the answer. Yet whilst we are (I am) driven to think like this (intellectual progress depends on it), it's a false hope. Experience happens over real time whilst models and formulae abstract time. The time of experience is not the same as the time of formulae or models. Their time is the time of clocks. The time of experience, however, is more fluid. Time ebbs and flows, sometimes (at deeply meaningful moments) standing still.

The meaningfulness of me writing this now is meaningful now. In the future, all I have is a vestige of what might have been meaningful at some point. But yet, there is a vestige of meaning in the document I produce, and as I browse my archives, my experience might recapture some essence of the meaningfulness that was experienced at the time of writing. This is a kind of hermeneutic analysis, and it has  been practiced for centuries.

But to come back to the problem which visualisation tries to address: too much information, too little meaning; No decision and control. If abstractions can't deliver the goods, then what might?

This is where I find the 'visual analytics' work most interesting. Because in visual analytics, what is created are experiences for interactively engaging with the data. So we have an original experience in time; we have a visualisation of the data from that experience which is also experienced in time. This means that users exploring the data explore their meanings of the data through interaction with it, and relate those meanings to the meanings of original experiences.

But this visualisation process could go deeper and more recursive. What about visualising the visualising of the data? or visualising the visualising of the visualising of the data? That's fascinating me because it starts to look like a von Foerster 'eigenform'. It also conforms with the fractal mathematical models of Dubois that desribe anticipatory systems.

But most interestingly this 'recursive visualisation' deals with the problem of abstraction by always having time and process as the fundamental underpinning of a meaning-making process of exploration. So time isn't stuffed into an equation or a pretty picture; it remains part of the process. In this way, analysing data and looking for meaning starts to look more like analysing music.

I've often wondered why I find composing so difficult. I always wanted to be a composer (much more than a learning technologist!). But I struggled to get notes down. It was only much later in my education (after studying music at University) that I realised that getting notes down was the most difficult thing, and the principal objective of any composer. [I wish someone had told me this at the time!]. I have spent years analysing the problem in the hope that one day I will find a solution. This angst and struggle has not (I believe) been in vain, since many ideas which have been useful in other areas (for example, learning technology!) have emerged from it. But still, I haven't really cracked it.

Composing is weird for me because I struggle to understand how writing notes is different from writing words. I don't struggle with writing words. Or at least, when I am working on a paper, I know where I am in the process and can coordinate myself accordingly. I have a set of techniques which are now well-developed for getting words down, getting rid of them, honing arguments, etc. This seems to work for me (even if it doesn't always please reviewers).

But my composing process goes something like this...

1. I feel like doing it (I get inspired)
2. I begin to notate the musical idea that excited me (either on paper or on the computer)
3. I end up with 3 or 4 bars of music which I am happy with
4. and then I stare at the blank staves which are left unfilled and think "what do I do about that?"
5. If I have enough energy, I will battle on to try and get something down in these gaps, although at some point I get tired and give up
6. My intention is to return to the composition... 
7. If I do return to it, I may add bits and pieces for the next week or so
8. But gradually it fades from my consciousness and nothing materialises