The Intelligent University in the Smart City
The smart campus is already here. Universities across the UK are already using technology, sensors and data analysis capability to prompt simple, automated actions such as adjusting heating, for example. The smart campus goes further than such simple actions and allows us to gather data about the spaces and, importantly, act on that data. We can turn down heating in rooms which aren’t being used, and the system will take into account the actual external temperature and not just the time of year. We can use electronic entry systems, such as swipe cards, to ensure the security of the rooms, but also to measure room occupancy. Similar occupancy data can be gleaned from campus wide wireless neteworks. We can also ensure that the lighting, heating and CO2 levels are within defined parameters. We often have insights into specific aspects of the smart campus, environmental conditions or room utilisation but these insights can be limited in what they can tell us. Most of the time, it isn’t all joined up and so has limited scope in terms of what we can learn and how we can use the knowledge to enhance and improve the student experience.
It’s a similar story with the smart city. Cities across the world are looking at how they can use technology to make transport and transit systems work for the people in that city. The Wikipedia definition of a smart city is “an urban development vision to integrate information and communication technology (ICT) and internet of things (IoT) technology in a secure fashion to manage a city’s assets”. Data from systems and sensors across a city can tell us some stories, about the use of transport, congestion, parking, pollution, health care, crime and a range of other functions and aspects of the modern city. Like the smart campus these insights and stories can be limited in scope, and often only give us part of the story.
Newcastle University worked with its local authority to develop a Cooperative Intelligent Transport System (CITS) leading to a “smart corridor”. This will mean that buses will be controlled by digital technology. This will allow buses to “talk to” traffic lights, maybe holding green lights for a short time or redirect drivers past congestion, improving journeys and reducing delays. Many of these smart city initiatives work in isolation, where real benefit comes is when the data from these systems is integrated and new possibilities emerge. It’s not always about systems talking to each other – if a system can utilise the data from another then maybe better informed decisions can be made.
There is a vision of universities moving from a smart campus, to a smarter campus, to the intelligent campus. An intelligent campus will take data from a range of sources, not just the physical aspects of the campus and how it is being used, but also the data from digital systems such as cohort information, attendance records, the virtual learning environment, the library, student records, even Electronic Point of Sales and online services. This joined up approach can provide insights into the student experience that we would otherwise miss. These insights can inform and support decision-making by individuals across the campus, including students, academic and professional service staff.
An intelligent campus using predictive analytics means that rather than responding to problems, universities and colleges are proactive and make appropriate interventions earlier to avoid those problems happening in the first place, based on a range of live data sources. It’s a similar story within the smart city, where using a range of data sources and analytics could enable informed decision making removing the need to react to problems by ensuring they don’t happen or are mitigated as much as possible.
So how does the intelligent campus slot into the smart city? The reality is in many cities the campus and the city are not distinct spaces, and for many people they will move between city and campus across the day. If a university with an intelligent campus does not integrate or work with the smart city then they won’t have the full picture and in some cases could be at odds with each other. Bringing in the full picture, all the data, a better understanding can be drawn from the experiences of the students and the city population at large.
The collection and interpretation of data from a wide variety of sources understandably raises some concerns about the appropriateness of data collection and usage. One of the challenges that does need to be seriously considered are the ethical issues of using a range of sensors, tracking technologies, even CCTV that could be used to track not just people’s movements, but could actually be linked to individuals.
Some of the examples of data used in intelligent campus or smart city activities might not be thought of as personal. However, with the combination of different types of data from different sources, it becomes potentially easier to identify individuals, for example precise location and user behaviour. Anonymised data once aggregated can lead to better understanding of user behaviour and the management of facilities, but also potentially reduce privacy.
Even in an age where sharing of data on an app is commonplace, and sometimes scant attention is paid by users to the extent of this sharing, the fears and concerns of individuals should not be underestimated nor dismissed. This is something that we at Jisc have had to grapple with when developing our Learning Analytics service, which we recently launched, along with a code of practice to address ethical questions.
Could this become a reality? Learning analytics services rely on a hub where academic and engagement data is collected, stored and processed. What if we, as a sector, could extend the learning data hub to enable data to be gathered in from physical places? Could we also bring in data from public systems, such as transit information, traffic, pollution levels, health care, entertainment information and provide a full enhanced student experience? Well that’s a dream that may well be possible in the near future.
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Jisc. (2015). Code of practice for learning analytics. [online] Available at: https://www.jisc.ac.uk/guides/code-of-practice-for-learning-analytics [Accessed 13 Feb 2024].
Clay, J. (2024). What makes an intelligent campus? [online} Available at: https://elearningstuff.net/2024/02/12/what-makes-an-intelligent-campus/ [Accessed 13 Feb 2024].
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