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Presented at Universities UK Marketing and Communications Conference, 2 December 2020 by Susan Kinnear, ChartPR
The crisis facing higher education in the UK comes not from the pandemic alone but from the current point we find ourselves in as developing technologies, the need to innovate teaching delivery models and new global public health and financial crises converge.
Convergence, and the effects of convergence, define the scale of recovery now required. A number of huge challenges for HEI’s have converged at the same time. These include:
- The necessity to now compete aggressively in the online education delivery market, especially with global megabrands from America, as a result of the impact of new, post Covid, delivery methodologies.
- At the same time the business model that many HEI’s have developed is arguably defunct. The current issues universities now face point to strategic failures such as over investment in capital assets. This, the unions argue, has converged with long-term underinvestment in staff, and has left universities struggling with the Covid related delivery challenges now before them.
- And coupled with these two huge threats is the requirement to revolutionise marketing and communications education at a tertiary level, as a result of the growing impact of data science on professional communications practice.
This crisis, therefore is not predicated on COVID-19 alone, or on the impact of Brexit; it is a crisis of convergence.
I would like to propose three key strategies that could help reposition marketing and communications teaching and help us grow out of this convergence crisis, rather than simply struggling to survive as it ravages our sector.
Much is made of the need to deploy new technologies to enhance the way we teach, but I believe we need to find a route out of this convergence crisis not through delivery innovation, but through curriculum innovation. Not through finding innovative ways to deliver what we already teach, but by innovating what we teach itself.
The market has changed. The skillset our graduates need has changed. The impact of artificial intelligence, machine learning and data science on marketing and communications is profound and it is no longer enough to regard data enabled strategic decision-making as a specialist or separate postgraduate stream of marketing and communications education. Indeed, we now know that marketing agencies are starting to hire computer science graduates rather than marketing graduates, to add what they perceive as enhanced data research rigour to their skills portfolio.
It is also a mistake to believe that artificial intelligence enabled communications can simply be taught as part of a digital marketing curriculum. The impact of AI on communications practice and decision making is far more profound than that. It is also developing at a much faster pace than digital marketing and communications teaching practice, and is, by comparison, relatively difficult for generalist marketing and communications academics to teach.
I will return to the topic of curriculum development and how we could facilitate better teaching of artificial intelligence later, but to start with I want to discuss the context in which that development could take place.
There is no point in academics throwing energy and resources into developing new courses in the deployment of data science for marketing and communications unless, and until, the frameworks in which they are able to validate such courses radically change. I have sat on many validation panels both in this country and in partner institutions abroad. In my experience, the frameworks academics must use to validate curriculum innovation and new courses in the majority of British universities are simply not sufficiently flexible to help us find a way out of the convergence crisis. This must change.
So my first strategy for recovery is the widescale adoption in UK universities of agile validation. We cannot continue to innovate if, as in many large universities, it takes up to three years from inception to launch for new, innovative courses. This is especially true of data and technology enabled marketing and communications, where the pace of change is doubly fast as public scrutiny of AI deployment is more intense than in other sectors due to its prevalence in social media. Three years is simply too long and steps need to be taken immediately to reduce this to 6 months.
New courses need to be structured to facilitate flexibility, and to incorporate optionality, that allows for the deployment and rapid change of AI related concepts and teaching. It is not however, upon academics, to create this change alone. This change needs to come from university senior management and from senior registrars and quality departments.
One of the best examples of how agile validation can enhance course development in the UK remains The Open University, and I would draw your attention to the excellent 2019 article on agility in higher education written by Matthew Moran, Head of Transformation at the Open University, for further information.
The second recovery strategy I would like to propose sits alongside this concept of agile validation. I will return to some of the challenges inherent in AI and data science teaching for my final strategy proposal, but at this stage I would like to stress that as universities innovate towards better, more industry focused, AI and data driven communications content, we need to recognise the highly demanding and specialised nature of teaching in this area.
The false split between research staff and, in some universities, scholarship or teaching staff, threatens our ability to deliver cutting-edge innovation. In order to overcome this, and the associated shortages many universities face in terms of suitably qualified, AI specialist lecturers, my second proposal is for a significant increase in partnership working between universities and specialist private teaching providers.
In all areas of data science driven communications, industry bodies and highly skilled practitioners provide professional training, guidance and advice that universities could capitalise upon. Bodies such as the International Association for the Measurement of Communication, or AMEC, and the Open Data Institute, as well as professional sector bodies such as the CIM and CIPR, all offer either qualifications and training or expert guides in areas such as communications measurement, deployment of data science and creation of algorithms to enhance communications practice. We need to harness this expertise rather than compete with it. And again this requires flexibility, adaptability, and agility from our internal quality teams, again led at a senior level.
Many of these bodies and institutions already have existing relationships with private training partners who deliver roll-on – roll-off courses online. They can often deliver content faster, more flexibly, and more attractively than universities can at the current time. Again, we need to learn how to work with both these specialist knowledge hubs, and the private education providers who facilitate them, to both enhance our own provision and plug the gaps we know exist in our own curriculum.
Again, this requires a mindset change. I sat on a validation panel earlier this year where a private education provider was attempting to validate a marketing course with a significant digital and data driven element. The conversation throughout the panel process was about ensuring that the partner met the same delivery quality as the University. The new degree had actually been written by both industry experts and a panel of marketing academics from other UK universities. What the private provider was offering included flexibility, adaptability, an excellent delivery platform, and cutting edge knowledge on data driven communication that the validating University would be hard pushed to deliver internally.
An ‘us and them’ attitude persists between institutions and partnership education providers. Again this needs to stop. There needs to be better recognition that private providers add a dimension to education that not every university is able to resource internally, especially within the area of artificial intelligence and data science for communication. We need both specialist industry bodies, and the benefits of accessing cutting-edge knowledge through private education initiatives for specific content, in order to remain relevant and innovative for our students.
This, again needs to be facilitated by flexibility and agility within the structure of curriculum delivery, and has little to do with technical or digital pedagogies. It requires that we recognise our weaknesses, leverage the abilities of private providers to breach our skills gaps, and buy ourselves time to upskill into critical areas where we know we must improve.
The last strategy that I would like to propose focuses on the difficulties associated with teaching artificial intelligence and data driven communication itself. Any of you who have ever attempted to teach digital communications, and especially communications evaluation, will know how difficult it is to access sufficiently large data sets, or live data that students can manipulate, in order to show them how the principles we are teaching work in practice.
The result is that many universities teach about the strategic decisions behind digital communications rather than the practice of digital communications itself. This frequently results in complaints from both students and employers that while students may well understand how to plan fantastic, imaginative, creative campaigns, they quite often don't know how to deploy the key practical elements that make these campaigns work. Many universities attempt to overcome this through digital takeovers, attempting to work with amenable live clients (which can be quite risky), or paying for specialised workshops with industry trainers who own closed platforms. But this issue is only amplified in terms of access to appropriate data sets for teaching AI and data enabled communication.
My third proposal is therefore that, via research projects and partnership with relevant professional data organisations, media and martec firms, and our own computer science departments, we look at developing Ai teaching tools and data sets that can be deployed in the online marketing and communications classroom. At the moment, even via Sage, there are only 8 datasets available for marketing communications teaching and these are designed for SPSS practice, not live communications practice. Access to digital channels and large, live data repositories would allow students to see how data can be deployed, and evaluation can be used to generate insight, not just in theory but in practice. This, again, requires partnership working, flexibility, vision and innovation, and most importantly, resource.
To summarise, we face a crisis of convergence and multiple threats from many channels, not purely from Covid 19.
There is the potential to innovate out of this crisis, but it requires a change not only in our curriculum but in our mindset. It cannot be achieved by marketing and communications academics alone and requires a new approach at the most senior level in many universities.
The three key strategies I therefore propose to drive us forward are:
- Agile validation to facilitate the development of a broader, more relevant and more flexible curriculum that includes artificial intelligence and data science for communications
- A step change in the way we approach partnership working, not just with industry bodies but with private education providers, in order to breach the skills gap that exists in many universities and to buy us time to upskill
- Close working and research investment in the development of large, live data sets that can be utilised in the classroom to enhance teaching practice and deliver the graduate competences our post Covid generation of students now need.
 https://www.infoq.com/news/2019/03/agile-higher-education/ accessed 1 December 2020
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