How can we ensure consistency in digital education while leaving room for experimentation?

Mark Carrigan

There was a conversation with senior leadership at my departmental away day which made me think back to this interesting dialogue myself and JJ Sylvia had earlier on this year. I heard a clear explanation of the weight which my university places on consistency in the student experience, driven in part by an entirely understandable frustration on the part of students if they encounter differences in experience which can’t be adequately justified by staff. My intuitions are to be sympathetic to this imperative and I’ve noticed it myself in our own programme where we teach across a range of platforms within the same unit. This creates a challenge for students who are forced to operate in a multiplatform environment which is even more complex than the one in the wider university, with corresponding increases in the time spent learning new systems and synchronising between them. What JJ has written about articulately as the ‘infodemic‘ places a huge burden on students by expecting them to operate with a digital environment which hasn’t been optimised to meet their needs, to put it politely.

The problem is that if ‘consistency’ in student experience is the outcome then standardisation is the mechanism through which we achieve this. There are many ways in which standardisation can be pursued, from the training of new staff through to the issuing of best practice guidelines and the expansion of quality assurance. If we are talking narrowly about platforms for digital education then one of the most effective means for standardisation is to restrict the platforms which can be used and the options available to academics within those platforms. If we approach it through this lens it is clear that uniformity is the flip side of consistency which has the potential to flatten out the differences between disciplines, topics and student cohorts. If we take a university like my own which has over 40,000 students and almost 13,000 staff it’s clear there will be an enormous amount of diversity across students and staff. The risk is that standardisation eliminates our capacity to teach in a way which reflects the needs of the student and the material as opposed to simply reflecting what has been designated the pedagogical style of the institution.

There is a risk of straw man argument here and it’s clearly not the case that pursuing consistency in student experience inevitably leads to the flattening of difference. This is because consistency is one of many principles a university might pursue in its teaching and learning. Furthermore in a large university the agency of teachers and learners embedded across multiple levels of administration inevitably act as a bulwark against standardisation because actually existing practice reflects the current judgements and past activities within operational units. The key thing I’m trying to stress is that standardisation (the pursuit of consistency in student experience) is inevitably a process of centralisation and needs to be seen in terms of the organisational dynamics of centralisation and decentralisation which are at the heart of so much of how universities change or struggle to. I’ve been meaning for years to finish reading Margaret Archer’s Social Origins of Educational Systems in order to help me understand precisely these processes within educational organisations.

However I think here’s a further problem here concerning how we integrate new technologies into teaching and learning practice. By definition if we introduce a novel element into an existing process then the process itself takes on a novelty which it previously lacked. This raises the question of how we define ‘best practice’ in relation to situations characterised by novelty. How do we know what best practice is when we’ve only recently encountered these situations? In this sense there’s an unavoidable need for experimentation in the frontiers of digital education and I’m concerned that standardisation risks closing this down prematurely. How can the understandable impulse towards consistency be effectively allied with a recognition of experimentation? My intuition is that there’s a tendency to see these as principles which are inevitably in a state of institutional conflict with different groups fighting for experimentation and consistency in a never ending struggle. It could perhaps be framed in Emmanuel Lazega’s terms as the social struggle between collegiality and bureaucracy.

I find it hard not see this as an immensely counterproductive modus operandi which fails to grapple with the challenge of institutional learning under conditions of technological change. What are the strategies and tactics we could use to establish a more productive division of labour between these competing agendas? In part I suspect this is a problem I’ve long been fascinated by, albeit in the field of scholarship rather than teaching & learning, concerning how we have dialogues (or fail to) about practice. The aspect of social media for academics which I’ve never lost my excitement is the capacity it offers to facilitate professional dialogue which is faster, more open and more varied than that which would usually take place in closed professional networks or in the back pages of professional journals. This suggests a set of questions I would like to explore about how digital media (including podcasts and blogging alongside social media platforms) is being used for professional dialogue about teaching & learning, as well as how this could be used internally within organisations to support a more productive cease fire between consistency and experimentation.

Photo by Chris Montgomery on Unsplash


  1. Consistency with experimentation with digital education is easy to accomplish, using training, templates, and teams. Rather than standardisation enforced by locking down the LMS, instead staff can be introduced to a set of templates installed. Staff can change these if they want, but will be encouraged not to stray too far without a good reason. Staff can be trained to work in teams, both to teach, and carry out research, rather than one-off, ad-hoc experiments.


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