Quality and Higher Education.

The stated “mission and strategic goal” of my employer—University of Wales Trinity Saint David—is Transforming education, transforming lives. My default view on mission statements is to view them with some suspicion: however, I actually kind of like this one.

Can we transform education? Well, maybe… that’s actually a pretty tall order. However, it’s true that the practice of teaching and learning in the vast majority of Higher Education establishments is largely archaic and no longer fit-for-purpose: almost anything we can do to transform this has got to be A Good Thing. When our SA1 campus with its new-fangled teaching spaces has been built we’ll be in a better position to judge. Let’s just say the jury is out on this one, because the challenge is not going to be in building those new spaces but in fundamentally changing long-established and deeply engrained habits and practices. As Robert Pirsig has said:

If a factory is torn down but the rationality which produced it is left standing, then that rationality will simply produce another factory.

Can education transform lives? This one’s easier to answer: yes, it definitely and unequivocally can. I know this to be true from personal experience: a year’s study at City University completely and utterly changed me forever. It remains one of the most profound experiences of my life, and I remain eternally grateful…

But looking at this idea a little more critically, it’s obvious that just saying we’re “transforming lives” isn’t really good enough. Surely we need to say that we’re transforming them for the better? And, from there, go on to say what we actually mean by “better”. Happier? Ready for the workplace? More confident and mature? Perhaps all of these things…

The word that I am going to use as a unit of measure here is quality. Now quality is a concept that we all think we understand. I’m pretty sure that if I put a selection of objects out on a table somewhere—it wouldn’t matter what: cakes, or watches, shovels, underpants—we could all reliably pick out the high quality items from the poor. Quality, then, seems to reside in the objects around us. It is a property of things. But if we think about this a bit more, we can see that this is only actually true for a limited set of things. We do not, for example, say things like “oh, look at that high quality sunset”, or “look, there goes a high quality bee!” In fact, the only things we describe in terms of quality are those that are man-made. And the reason we describe an object as “high quality” is because someone—a designer, artist, craftsman, engineer—has invested that object with quality in the first place. Quality is something we make.

And the way we make quality is by engaging openly, honestly, calmly, and skilfully with our materials, whatever they may be. We have to pay attention to every detail. We must show infinite care. We must love what we do. It is our total commitment to the creative process that makes quality, that invests our animations, our games, our films, our music, with quality. In other words, quality is a function of the creator’s interaction with their materials.

We can take this train of thought further. Even if we do our very best and create a high quality product, that still isn’t enough. Before that quality manifests itself someone has to interact with it. So, yes, quality is embedded within man-made objects. But much more than that it is the fundamental descriptor for all human experience. Quality is the means by which we measure what is happening to us in the here-and-now. Quality is a function of interaction. It is the human measure of experience.

So what happens when we bring our new understanding of quality back to our mission statement, to transformed lives? Well, firstly, it implies that there should be a high quality interaction between the student and the university, particularly (obviously!) a high quality learning experience. Our job as educators, therefore, is to teach the student to engage openly, honestly, calmly, and skilfully with their materials, to pay attention to detail, to show infinite care, to show love for their subject. Then, secondly, it should follow that our transformed students go out into the world and make it a better place by investing everything they do with quality.

That is the goal. That is what we are here for.

[This is an edited version of a speech I gave at the School of Film and Digital Media end-of-year show in June 2015.]

 

Cité Internationale Universitaire de Paris

This chilly Easter weekend I’m in Paris for the 3rd Conference of the European Narratology Network. Here’s some pics of the venue: a lovely and extremely varied campus, if a little run-down in places:

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2_art_deco_staircase
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The delegates gather.

5_brian_richardson

Brian Richardson delivers the one of the first keynote speeches on Friday morning.

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Human sacrifices entirely absent!

9_walkway
10_campus
11_stairs

Exciting but brutal modernist workout. Derelict.

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13_heine_building
14_cambodia

Interesting juxtaposition: Bauhaus-style building with man-monkey statues.

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Raphaël Baroni delivers the final keynote on Saturday afternoon.

The conference? I delivered my paper yesterday and it went as well as could be expected. Overall? Let’s just say that I found the limits of my interest in narrative quite early on…

Gypsy Jazz – Puttin’ On The Ritz

From last November’s surround recording sessions at SMU:

Just recorded onto an iPhone 4S, topped and tailed, original audio track.

Quote of the Month

Students should be taken to the edge of the precipice beyond which knowledge does not exist.

Harold Innis

Sir Ken Robinson – Changing Educational Paradigms

Sir Ken Robinson is a sane, reasonable, and extremely funny man. You can find a previous post on him here. Today’s video is one of the excellent RSA Animate series: it covers some of the same ground as the TED talk but it’s developed further, and the visualization really adds something. Needless to say, in the current climate of student protests this is all too relevant.

These observations are not confined to schools; it is more-or-less the same at university. A couple of comments:

  1. The separation of subjects is embodied in the division of universities into faculties. In my experience, it is very difficult to work across these divisions.
  2. Universities are still organized like factories, and it may be that under the current political and financial climate this tendency will be exaggerated. As Sir Ken states, the opposite should be the case.
  3. The idea that there are multiple answers to most important questions—not one “right” answer—is crucial. In fact, as Jerome Bruner has suggested, it seems obvious that to know something well is to understand it from multiple points of view. Meaning is context-sensitive.
  4. Learning is primarily a social activity. Meaning is socially negotiated.

All together now: the process is the product.

[Via Gareth Whittock. Thanks.]

The Computational Turn

On Tuesday 9th March I attended the Computational Turn conference at Swansea University. Very good it was too, with the wide range of speakers packed into a single day all having a diverse set of approaches to the main theme. Some of the papers were very challenging, and—whilst not all were of particular interest to me—many shone light into areas I had barely perceived previously, let alone considered in any deliberate way. The highlights of the day were the day’s two keynote speakers: N. Katherine Hayles opening the conference and Lev Manovich closing it.

N. Katherine Hayles
N. Katherine Hayles

Hayles outlined the rationale for the “computational turn.” She began by asking how many books could we read in a lifetime. If we read one a day between the ages of 15 and 85, that turns out to be 25,550. Not many compared to the total number of books available. The question becomes, what if we could analyze a whole corpus of books—all the books ever written on WWII, say, or all the books written about Aristotle—using computers? What would this type of mass analysis reveal?

Of course the next question would have to be, an analysis on what basis? Computers can’t “read” in the same way humans can. They may be able to detect patterns in the data—frequency, repetition, structure—but that is a far cry from the type of hermeneutic interpretation that humans are so good at. Quoting Tim Lenoir, she suggests that we “forget meaning and follow the data streams.” Starting with meaning always embodies too many assumptions: if we start with the analytics we can work out what it all means later. She then went on to illustrate her thesis by showing the initial results of her computational analysis of Danielewski’s Only Revolutions.

The Q&A session ranged across a wide range of topics, all of which Hayles dealt with expertly:

  • Nigel Thrift’s “technological unconscious” was discussed, the observation that assumptions and limitations are embedded within the technologies we use which are largely unnoticed and unseen. (An idea that seems very close to McLuhan’s theories about media.)
  • There was talk of the “adaptive unconscious,” which posits a mind that is effectively a type of internal distributed network where the unconscious is not a Freudian dark place but an active participant in cognition and decision-making.
  • There was talk of the “Baldwin Effect,” an elaboration on evolutionary theory which suggests that specific inherited traits are emphasized by cultural behaviour.
  • Finally, Hayles talked of culture moving from a deep-attention mode (related to print) into a hyper-attention mode (related to electronic media).

All heady stuff. How some of these issues relates to the computational turn I’m not quite sure, but the whole session was never less than stimulating.

Lev Manovich
Lev Manovich

Lev Manovich’s talk was mainly concerned with his projects, all of which are related to visualizations of large bodies of visual data: one million Manga pages, all 3480 Time magazine covers, Vertov movies, the way saturation changes over time in modern painting. He also showed off the Cultural Analytics software his Software Studies initiative has been developing. Here’s one of his Manga visualizations (stolen from his CultureVis photostream):

Visualization of 50,000 Manga pages
Visualization of 50,000 Manga pages

The accompanying text reads:

X axis: Grey scale standard deviation (measured per page)
Y axis: Entropy (measured per page)

This visualization shows how cultural analytics approach allows us to map continuous style space of a cultural data set. In the current visualization, the pages which have more contrast appear on the right; the pages which have no grey tones but only black and white are on the bottom right; and the pages which have a full range of grey tone (and thus more “realism” ) on the top. Every page in the dataset is situated in the space defined by these extremes.

Here’s another example (from here) showing a subset of the Time magazine covers mapped out in the Cultural Analytics software:

Time Magazine covers
Time Magazine analytics

The accompanying text reads:

Exploring a set of 450 Time covers (sampled from the complete set of 4553 covers 1923-2009 by taking every 10th image). Mousing over points reveals larger images and metadata.

I’ve only really presented here the bookends of the Computational Turn conference. There was much else of value, some of which I intend to follow up in my own work. A special thanks must go to Dr. David Berry for organizing the conference, for attracting such marvellous speakers to Swansea, and for the invitation. Thanks also to Sian Rees for coordinating the event and for providing such a warm welcome.

Did You Know?

This should give my students something to think about:

So. What does it all mean? Well, for those currently studying in Higher Education it means things like these:

  1. The idea that your education will be finished when you leave University is patently daft. You will need to train and retrain yourself many times during your working life.
  2. You will almost undoubtedly changes jobs many times. You may also change careers more than once. The only constant will be change.
  3. Consequently, the most important skills you need to master are a) the ability to bootstrap yourself whenever necessary, and b) the ability to critically evaluate new information. The principle function of a university degree is to teach you how to do these two things. You need to learn how to learn.

End of lecture.

[Thanks to the G-Man for the video link.]