I am currently involved in a large-scale project on digitalization. The aim of the project is to instil practices relating to the manipulation of data, coding, algorithmic thinking and creativity throughout the curriculum in the University. While this appears, on the one hand, an attempt to reignite the "everyone should code" kind of stuff, something is clearly happening with the technology which is necessitating a reconfiguration of the activities of education with the activities of society.
As with many big structural changes to education, there are already quite a few signs of changes in practice in the University: many courses in the humanities and sciences are using programming techniques, often in R and Python. My university has established faculty-based "data centres" - rather like centres for supporting e-learning - which provide services in analysis and visualisation. However, across the sector, there is as yet little coherence in approaches. It is rather like the situation with teachers using the web in the late 90s where enthusiastic and forward-thinking teachers would put their content on websites or serve them from institutional machines. The arrival of VLEs codified these practices and coordinated expectations of staff, students and managers. This is what is likely to happen again, but instead of codifying the means of disseminating content, it will codify programming practices across different aspects of the curriculum.
There is a further implication of this, however, which has to do with the nature of disciplines and their separation from one another. One of the reasons why digitalization has such a hold in education at the moment is the dominance of digitalization in industry across all sectors. Where sectors might have distinguished themselves according to expectations formed around concepts, products and markets, increasingly we are seeing coordination of industrial activities around practices and processes. This has been slowly happening for the last 20 years or so, but is evidenced by the way that industries are realigning themselves by synergising practices and technologies across different fields of activity. Think of Amazon. This has been coupled with increasing "institutional isomorphism" in the management of institutions across the board. This has produced many problems in institutional organisation - partly because the old identities of institutions have been torn-up and new identities imposed which, although they exploit the new technologies available, almost always reinforce and amplify the hierarchies and inequalities of the old institution.
With this in mind, this next phase of digitalization is going to be very interesting. The old hierarchies of the university are established around academic departments and subjects. These are basically codified around concepts which, within academia, operate to define and redefine themselves in contradistinction to one another. This is not to say that interdisciplinarity isn't something that's emerged: obviously we have things like biochemistry or quantum computing, but even within these new fields which appear interdisciplinary, the codification around concepts is the central mechanism which provides coherence. Look, for example, at how academic communities fracture and form tribes: not just the mutual antipathy between psychology and sociology, but between "code biology" and "biosemiotics", heterodox vs classical economics, etc. A lot of this kind of division has to do not just with disciplinary identity, but personal identity. Concepts are tools for amplifying the ego (am I not doing it here?), and the principal mechanism for this process has been the way we conduct scientific communication.
Digitalization means that increasingly we are going to see research and learning coordinated around practices with tools. This is a more fundamental change to what is loosely called the "knowledge economy". It won't be enough to simply name a concept. We will need to show how what is represented by a concept actually works. Argument will be increasingly embellished with concrete examples, some in code, and all of which presented in a way in which mechanisms can be communicated, experimented with, new data applied to, refined, and continually tested. More importantly, because these practices become common, and because practices supersede concepts in scientific inquiry, the traditional distinctions between disciplines will be harder to defend. This will produce organisational difficulties for traditional institutions in which disciplines will perceive threats in the digitalization process and seek to defend themselves.
Another threat may come in the form of what might be called the "status machine" of the university. Concepts don't only codify a discipline, they codify the status of those who rise to positions where they can declare concepts (what Searle, who is not alone in pointing out this mechanism, calls "deontic power"). While new practices are codified in a similar way, practices are only powerful if they are adopted widely, and in being adopted, they are continually adjusted. Eventually we don't care about the concept or who thought of it, but about being part of the game which is developing and upholding a common set of practices. The operating system Linux is a good example: nobody really cares about who invented it; but we do care about using and developing it. We can start to make a list of similar practices which fit this model: computer languages, online programming environments, visualisation tools, etc.
But the university is a "status machine": its business ultimately lies in selling certificates and through codifying status. So if it comes to be about practice rather than status, what does the University then do? New forms of personal status codification are emerging. The online machine learning competition site Kaggle, for example, provides opportunities to do real and meaningful data analytic activities. Winning a Kaggle competition is an important marker of personal status carrying more meaning than a degree certificate because it demonstrates what someone can actually do, with references to things that were actually done. But Kaggle does not lock its status mechanisms behind the expensive close-system of an institution: it is open and free, funded by the fact that the fruits of intellectual labour become the property of Kaggle (and by extension, Google). Intellectual activity given to the platform is exchanged for status enhancement. It is in many ways an extension of the Web2.0 business model with some important differences.
What happens in Kaggle educationally is particularly interesting. Kaggle teaches simply by providing a network of people all engaging in the same activities and addressing the same, or related, problems. There is no curriculum. There are emerging areas of special interest, techniques, etc. But nothing codified by a committee. And it exists in the ecosystem of the web which includes not only what Kaggle does, but what StackOverflow does, or anything else that can be found on Google. Human activity contributes to this knowledge base, which in turn develops practices and techniques. Learners are effectively enlisted as apprentices in this process. Experts, meanwhile, will go on to exploit their knowledge in new startups, or other industrial projects, often continually engaging with Kaggle as a way of keeping themselves up-to-date.
The University Professor, meanwhile, has both become increasingly managerial, and increasingly status-hungry as they seek the deontic power to declare concepts, or make managerial things happen ("we should restructure our University" is a common professorial refrain!), but increasingly (and partly because there are so many of the bastards now), nobody is really interested - apart from those who will lose their jobs as a result of professor x. We just end up with a lot of mini-Trumps. Deontic power doesn't work if nobody believes you, and it doesn't do any good if, even if they listen to you, they merely repeat the conceptualisations you claim (but with different understandings). The academic publishing game has become very much about saying more and more about less and less, where each professorial utterance merely adds to a confusing noise that only benefits publishers.
Kaggle shows us that we don't need professors. There will always be "elders" or "experts" who have more skin in the game and know how to use the tools well, or to apply deeper thinking. But it is not about leading through trying to coin some attractive neologism. It is about leading through practice and example.
Here we come to the root of the organisational challenge in the modern university. Their layers of management are not full of people leading by example with deep skill in the use of digital tools. They are full of people who postured with concepts. And yet, these are the people who have to change as the next wave of digitalization sweeps over us. I suspect it's not going to be an easy ride.
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