Monday 21 December 2020

The Technological Meaningfulness of Academic Work in the 21st Century

I was speaking to an academic colleague the other day who was trying to work out why her quizzes weren't displaying properly in the VLE. She'd spent many hours entering them and editing them, only to become frustrated that they didn't appear to display as she wished. We worked on the problem together and sorted it out (this is very much my preferred course of action in these circumstances), but I asked her how she felt as she was entering all this data. Of course, it's incredibly boring and tedious. I remarked that one of the effects of technology in education is the amount of low-level repetitive work it creates for people who feel they are meant to engage in high level intellectual work.   

A similar situation arose with a member of professional service staff who was trying to get some specific data from the system that the system would not provide through the interface. Basically, we wrote a little program together (our shared Python coding environments are brilliant for this kind of thing), and again, solved the problem together. Here the alternative repetitive low-level work was by proxy, as a different system would be used that demanded far more tedious work than would be required from a bit of computational thinking and practice. Finally, I have been working with our medical school, the vets and the dentists in trying to synergise their educational approaches (they basically share a similar technical and pedagogical approach). Doing these things with people (not doing it to them) has become important to me, just as the process of working together to create synergies in activity. It's about bringing wholeness to the relationship between technical and intellectual work.

More broadly these situations have led me to reflect on the nature of academic work, and the relationship between high level and low level work. The distinctions are difficult: artistic work, for example, often features much repetition - indeed, it is characterised by this. But where that feels fulfilling and in a certain sense "high level" because it leads to self-expression, the tedious button-clicking of an interface definitely feels menial and low-level. So what's the difference?

I think looking at individual tasks in isolation is not helpful. Bridget Riley adding rows to one of her geometric paintings, or Lowry painting matchstick figures, is not a task that is disconnected from the overall artistic aims and ambition: it is entirely consistent with it.  The work has to be taken as a whole, and taken as a whole, the artist's "menial" tasks are consistent and coherent with the whole. So what does modern academic work  look like as a whole? 

I think this is at the root of what's happened to academia since the advent of technology (and possibly a bit before). The ideal of the academic institution is not individual academic labourers pushing out publications to raise their H-index, bidding for grants, etc, but the community of scholars - teachers and students - in the college working together for the wellbeing of each other and the pursuit of truth. When everyone works as one, the mechanism which selects what must be done is understood by all, and each responds in the knowledge that whatever task has to be performed - whether repetitive or deep and intellectual - is done because it is required for the common good. 

The point about the college is that it is a coherent whole. So what did technology do? It carved up the function of each individual and instead of it being a convivial activity, it became specialised and professionalised, given to a particular individual who could basically perform this function on behalf of everyone else. It's precisely what Illich says in "Tools for conviviality" about the difference between the conviviality of the shovel, and the lack of conviviality in the JCB. 

What is a convivial approach to technology in universities? It is certainly about doing things together, not doing things to people, or even for people. What's been so interesting with the current crop of technologies in institutions is that the skills for accessing and manipulating their data have become common: very often these are simple programming skills, and these skills, even if they are not known by everyone, can be communicated and their experience shared across a community. 

The technical essence of the Personal Learning Environment was a common toolset with which universal skills which could be shared. What we didn't talk about so much was the fact that because these skills were common and universal, effective convivial activity could be organised. (I do remember Oleg saying that the PLE was a way forwards to Illich's ideas of conviviality, but I couldn't see it clearly at the time). 

Today academic work is disaggregated, individualised, compartmentalised and specific functions are separated off with no connection to a common purpose. Moreover, the disaggregation is reinforced by tools which position themselves in "market segments" when in fact the whole thing is manipulating the same data. The one blessing that we have is that access to the core data underneath each of these specific functions has become easier through the APIs. Increasingly, I think we will see a single "back-end" for these systems - probably (so it seems at the moment) coordinated by Microsoft. 

But a deeper issue lies in the fact that our disciplines themselves have become separated from the technological and institutional context within which they organise themselves. Discplines tend not to think of themselves technologically or institutionally - and yet almost all disciplines these days see at their frontiers issues of technology, uncertainty, institutional organisation and data. In fact for many disciplines, if they were to examine the institutional and technological context of their own educational technology, they would find real-life and tangible examples of the very things they concern themselves with in an intellectual way. For example, medicine is increasingly going to become dominated by the kind of AI tools which sit behind many of the interfaces they use to organise and discuss their content. The same goes for Law (think Turnitin), Maths (learning analytics, convolutional neural networks), Psychology (AI), biology (bio-sensors, imaging), and so on.  

Change requires a new meta-language of subject orientation with technology. Institutionally, maybe this can be organised through inter-disciplinary collaboration and engagement, where deep questions about disciplinary knowledge and technical engagement can be asked. All disciplines demand that certain competency criteria are met. But there are always many ways to do this. And doing it together, where the disruptions of technology can be felt in the very fabric of disciplines themselves, may provide a powerful way back to the kind of wholeness and integrity of academic life that the college once had, but within a new digital context.  The way forwards is synergy.

Friday 18 December 2020

Bio-drama and new ways of teaching

For a few years now I've been exploring with John Torday how the many profound aporia we live with (what we seem to accept as "wicked problems" - climate change, inequality, homelessness, educational problems, health, geopolitics, etc) result from some gap in our understanding of how human consciousness came to be: to put it simply, it is Bateson's "gap between the way people think and the way nature works". In excluding the possibility of a deeper and more coherent narrative, we have grown to believe that our profound problems cannot not exist. But I am now asking a question that was once asked by Jiddhu Krishnamurti to David Bohm - is it possible for humans to have no "problems" at all?

The root of most of our human problems lies in the way that our egos separate themselves from their natural origins - their origins in cellular, biological, evolutionary processes. From mechanisms of cellular self-organisation and cooperation (without which there would be no advanced biological forms at all), we establish an idea of self which is more often competitive, defensive or combative. Our social structures are designed on the model established by the nature-divorced ego, and encourage this divorce from nature. Only when natural disasters occur - not just things like Covid, but earthquakes, floods, etc - that we are reminded of our origins in nature, and cooperation comes more to the fore (we are often surprised when it does, and praise the "heroics" of individuals doing what they were biologically programmed to do). We intellectually know that the split between ego and environment will kill us - but we seem powerless to intervene. 

Artists have always understood this split. Shakespearean tragedies show how the ego is torn apart by exposing the fundamental rift between culture and nature, but how Shakespeare does this is the thing. The play is a form in time, conceived in the mind of the playwright to unfold its moments of tension and tragedy in the lived experience of all the audience and players. This time of unfolding has a structure which is tied up with the structure of the drama, and this is the playwright's art - in concieving of the unity of the diachronic and synchronic aspects of drama as a whole.  

This uniting of the diachronic structure of life and its synchronic structure is something that also can be seen in cellular evolution. The principal mechanism by which it occurs is endosymbiosis - the absorption by the cell of aspects of its environment. As the endosymbiosis process unfolds, then obviously the structure of the cell reveals its history - rather like the rings in the trunks of trees, or ice cores represent a history book. Diachronic and synchronic are united. 

If this is the stuff that we are made of - if each of us, even at birth, are all "history stuff" - then the power of Shakespeare makes sense. In its unfolding temporal structure it recapitulates a much deeper temporal unfolding which unites each of us to each other through our cells. It's not that King Lear's tragedy awakens specific agonies in us with regard to our encounters with politics or power; it is that in the lived experience of seeing this unfolding dramatic structure, we see "through" each other - in Alfred Schutz's words, we "tune-in" to the inner life of each other. The power of the play is the power of deep connection and mutual recognition - all the more potent for it being so old and yet so fresh.

What we lack in our educational structures is the ability to make a similar kind of connection between teachers and learners. But such a connection can be made, I think. But like a Shakespeare play, it must be constructed so that this can happen. If there is any utility in the concept of "learning design", then it is this intended purpose - that something is designed or constructed such that learners and teachers can can perceive their shared biology and connection. 

How can you teach maths like that? or biology or physics? or medicine or brain surgery? Perhaps a better question to ask is "What stops us teaching maths, etc, like that?". And the answer to that is the nature-divorced ego implicit in our social structures of education. And yet, division and multiplication, cell boundaries, or the symmetries of quantum mechanics are all tied up in the processes which unify the diachronic and synchronic. We just lack a clear way of showing how - we lack a narrative and structure which reveals it. I think it's right, for example - as Louis Kauffman has argued (and more recently Steve Watson) that the symbolic notation that we use as the basis of teaching maths takes us in the wrong direction if we wish to identify wholeness. There may well be better "iconic" ways of approaching mathematical thought - as Lewis Carroll or Charles Sanders Peirce knew.

I'm increasingly interested in exploring what novel iconic and dramatic approaches to learning might reveal, and how they might be organised with technology. For example, knots are a powerful metaphor which extend from mathematics to psychology and organisation theory. And there's wonderful software to explore them (see The KnotPlot Site

It's something which I think does unite some of the better critical pedagogic thinking (Freire, Boal, Shor, etc) with deeper understanding of the relationship between mind and nature. 

Thursday 10 December 2020

Networks and Biology: Wiring ourselves into a bad theory

The one thing that can be said about networks is that they are easy to draw. Anyone who's done "join the dots", or who has looked at a map, or studied physiology or neuroanatomy understands networks in their essence: a set of points joined together with lines. The join-the-dots pattern permeates the natural world like a kind of fractal motif. But what we see and what things actually are, are not the same. How would we know if networks actually exist?  

In order to know whether a network is real, we would have to be able to establish some kind of correlation between our observations of the network's structure (which is "the network"), its behaviour, and any changes we might make to that structure. Obviously, if the network is human-made, then the relationship between an electronic  network's structure, how it behaves, and predictable outcomes in the light of changes to it would seem to be straight-forward. But in complex artificial networks, such as those defined by machine learning models, predictability in the light of network change is elusive. We are strangely unbothered by this, because we see the same type of unpredictability in natural networks. 

We may see in the brain an array of enormously complicated "networks", but beyond some very crude interventions which zap entire sections of "the network", there is little predictability in the effects of these interventions. So when we see little predictability in the AI webs our consciousness has made, we are inclined to imagine ourselves in the image of God, and satisfy ourselves that if fuzziness is good enough for our understanding of nature, it is good enough for our understanding of artificial intelligence. 

But this fuzziness should ring scientific alarm bells. Networks do not just spring from nothing. They emerge in nature from biological processes. To put it more directly, networks emerge from the dynamics of cells. Neurons are cells. Nerves are made from cells. Tree roots, fungal fibres and bacterial colonies are made from cells. The cell is the thing. The network is an epiphenomenon arising from the cell's behaviour.

This point is important when we think about our technology. If we designed our technology from the metaphor of the cell, rather than the metaphor of the network, we would have very different technology. And I am increasingly convinced that if we understood our existing networks with their mystical properties (like machine learning) from the perspective of cells, then their behaviour would be much less mysterious to us.

The main thing a cell must do is maintain a boundary between itself and its environment. It must maintain its internal environment and maintain balance with an ambiguous external environment, and it requires energy to perform these functions. It is through performing these functions that the cell establishes relations with other cells, from which the physical characteristics of a "network" might be seen to emerge. 

However, this mechanism must drive the cell through the processes of self-organisation with its environment. Networks are driven through a process whereby the cell seeks stability in its organisation relative to its environment. This can be achieved through absorbing features of the environment so as to adapt itself and organise itself into increasingly complex life forms - a process Lynne Margulis called "endosymbiosis". Increasingly complex life forms in turn provide the cell with increased adaptability in the face of environmental challenge. These processes of endogenisation and adaptation are the basis for the epigenetic mechanisms which are exciting increasing interest in current empirical biology.

But endogenisation and adaptation mean that history and time is embedded in the structure of the cell, and in the networks it forms. Biological networks - like neural networks - are more like scar tissue, or the scree lines fomed by geological events than they are simple nodes and arcs. At each stage of organisation, the cell must maintain homeostasis and balance with its environment; at each stage it tends towards the conditions of its initial formation - conditions which are historically embedded in its own structure.

This is the "network" science we need. It is not a science of networks at all, but of dynamic processes of maintaining boundaries at all levels of organisation, from the brain and the liver through to consciousness, communication, technology and education. Behind the rigid visualisations of network dynamics on Facebook, and the scree lines and scar tissue of individual biographies, and biological history. 

Looked at this way, the way we think about our networks of human communication are grotesque distortions of nature produced by a bad theory. Instead of cooperating and organising themselves, the bruised egos of individual nodes compete against one another, each node seeing to be the loudest or the best, or "clusters" of damaged souls reinforce pathological and explosive boundaries in politics. 

The basic point is that the homologue of the cell's  boundary wall is not the person's skin; it is a dialogue's boundary. At a human level, we organise ourselves through communication - that's where our boundaries are formed. However, when locked-in to the network technologies of social media, the boundary walls are reinforced against the environment - there is little endogenisation, and hence little growth and development. It is the homologue of the cancer cell. 

There is an urgent question in technical design: whether it is possible to create a dialogical technology which can reproduce the organisational processes of the cell, including its endogenisation of the environment, and its maintenance of self-organisation against an ambiguous environment. To do this requires a much less mystical view of nature and of things like machine learning. Such a view can be found if we jetisson our obsession with the network, and instead think about the commonalities between how we maintain our communicative boundaries, and how a cell does it.