The HEFi19 conference looks to the 4th industrial revolution and how the development of artificial intelligence and automation continues to take advantage of big data to change not only higher education but also graduate employment.
What implications does this have for learning and teaching in higher education? The options are endless and how these current and new technologies are implemented are decisions which are being made across the sector. In my own research, I am particularly interested in the field of design and design theory for learning. You might ask, is design a science or an art? A quantitative science based on numbers and statistics, or in contrast, based on a creative, intuitive, qualitative process. This thinking can be applied to how we go about the design of our programmes, modules, lectures, seminars and resources.
Underpinning the rise of the 4th industrial revolution is the proliferation of big data and the ability to process this data to inform us about our behavioural trends as well as modelling, to predict future behaviour. This includes how we interact with the web and social media but also how we interact with ‘things’. The Internet of Things (IoT) puts data collection in everyday objects and can tell us information from where the closest car parking space is in a busy city, to reminding us when our fridge at home is running low on milk.
Already we can mine huge amounts of data regarding learning and teaching:
- How many times, for how long and when a video (lecture) recording is watched
- Which online pages have been most visited and when
- How often and when books have been taken out of the library
- The number of online resources such as journals and ebooks which have been downloaded and when
Analysis of data such as this is revolutionising design practices in many fields to give ‘users’ a truly personalised experience. The field of User Experience Design is one that is harnessing and using much of this type of data. We are now seeing the emergence of Experience Design as a field in its own right, moving from industrial design of ‘things’ (objects) to products, services, information and organisations.
Returning to Design Theory, and a positivist movement of Design Science in the mid-20th century advocated that artefacts are designed using quantitative data. Here you may see design as taking analytic data from website visits and clicks and creating the ‘optimal’ website based on data. In learning and teaching this can be translated into the field of Learning Analytics. Carnegie Mellon University are trialling their OpenSimon Toolkit which collects data on learning activities. On the flip side we have the work of Donald Schön who studied professional practice and how designers went about their work in a more implicit and idiosyncratic way. Designers may come up with designs seemingly without an evidence base but based on experience, built up over years of practice. This in the field of design has been termed ‘designerly ways of knowing’ by Nigel Cross. Cross enthusiastically called for design to be part of education and the missing piece between science and the arts.
The data driven approach versus the more intuitive ‘gut feel’ is not an uncommon one in business and sport. Data is used in a variety of contexts to evaluate and improve. Sport widely use a huge amount of data to analyse performance, from Team Sky collecting huge amounts of cyclists’ performance and health data to the selection of baseball players. The Oakland Athletics Baseball team most famously used data analysis to draft their 2002 team and against the odds won the American League West. The general manager who masterminded this approach, Billy Beane, was played by Brad Pitt in the film, Moneyball. In golf, Bryson DeChambo is known as ‘the professor’ for his collection and analysis of data. In cricket, data collection can provide objective data rather than what may be conceived as opinion.
There are those that are not convinced by such data and its analysis. The more traditional football pundit seemingly doesn’t want to look at expected goal statistics. In business, a similar debate can be seen where some are using data to make decisions and automate in contrast with those that feel that experience and intuition are what really matters for business leaders and decisions.
So, how in practical terms can this help learning and teaching at the University of Birmingham? As designers of programmes, modules and learning resources should data rule our thinking or should we follow our experienced intuitive ‘gut feel’?
Or is there an opportunity for both to inform our design decisions?