Intelligent Campus Community Event 23rd March 2018 – Sheffield

If you are working in the area of the Intelligent campus and are interested in work being undertaken in this space by others, then we would like to invite you to attend the first of our community events.

The community of practice gives people an opportunity to network, share practice, hear what various institutions are doing and what Jisc is doing in this space.

  • Smart City
  • Smart Campus
  • Wayfinding
  • Wi-Fi Heat Mapping
  • Mapping
  • Space Utilisation
  • Smart Buildings
  • RFID tracking
  • Wi-Fi tracking
  • Facial recognition
  • Chatbots
  • Robots
  • Artificial Intelligence

The first of these events is being hosted and  taking place at Sheffield Hallam University on the 23rd March 2018 from 10:00 to 4:00, and lunch will be provided.

Sheffield Hallam University Eric Mensforth Building

The focus of this community event will be the Intelligent Estate.

You will have the opportunity to discover more about the Jisc project that is being undertaken in the Intelligent Campus space as well as hear from others about their work in this exciting topic. There will be plenty of opportunities for discussion and networking.

Book now.

Join the mailing list

As the project moves through Alpha we will use the blog to update members and the community on progress.

We have also created a mailing list for people who are interested in the work we are undertaking, to find out more about the project, and how potentially to get involved in the alpha and beta phases.

The mailing list can also be the place to discuss issues that interested organisations are facing in the intelligent campus space.

We will also use the mailing list to tell people about forthcoming community events, other Jisc events such as Digifest, and other relevant events and workshops.

You can sign up to the mailing list using this link.

Hot wifi


There are numerous software applications that allow Universities and Colleges to create wifi heat maps. This is a very competitive market with many different free, freemium and charged for products.

The Jisc Technologies Wireless Advisory Service in 2015 recommended the Ekahau product for both mapping existing wifi networks and for planning new networks. Jisc Technologies published a guide on how to do this a few years ago.


As part of the intelligent campus discovery phase, the use of data from heat map software was seen as a potential data source for institutional analytics.

So why are heat maps useful for the intelligent campus?

We know they help IT teams understand where there are challenges and issues with wifi coverage, allowing them to move or add wireless routers to the network to ensure better coverage and experience for learners.

However could we use the data from these heat maps to see if we could make other interventions and improve the student experience?

It may not be just about adding wireless routers, if we know where wireless devices are being used extensively on a campus, could we ensure that those spaces are suitable and appropriate for learning? Conversely if there are learning spaces which we have already, but the heat maps show us they are been under-utilised, could we reflect on the reasons why?

Could we combine the wifi heat maps with maps of air quality, temperature and see if there are environmental issues that may have an impact of student wellbeing? Would we then turn off the wifi in order to nudge students to a different location on campus?


Currently a lot of universities and colleges have systems such as wifi heat mapping, these are often narrow systems with a focus on one aspect. Wifi heat maps are about improving the wifi. If we could bring the data from wifi heat maps and undertake analysis with data from other systems, could we have a better idea of how to improve the campus, enhance the student experience and a positive impact on learning?

What do you think?

Useful links

Location, location, education

Use Case: Location aware learning


Location, location, education

Location aware learning provides universities and colleges with a range of student learning opportunities which take advantage of their current physical location. Consequently, the potential for location aware learning to take place on the intelligent campus is being explored in a range of ways.

Starting with QR codes

Many mobile devices are already location aware but, even without this facility, location based learning is taking place on campuses through the use of the camera on mobile devices along with QR codes. Students can explore learning resources and opportunities across a campus relating to their field of studies, for example, architectural building features, museums objects, gallery artefacts, laboratories or gardens. QR codes are Quick Response Codes in the form of a matrix barcode providing a unique identifier that can be attached to a learning object. The Appalachian State University provides an example having created a learning resource in the Daniel Boone Native Garden using QR codes to identify native plants. (

mobile phone

Moving on to GPS

The technology available in most mobile devices, such as smartphones, can provide much more than QR scanning. They incorporate a range of location aware technologies. Most devices can use a number of these technologies to identify their location. These include:

  • Global Positioning System (GPS) – using satellite signals this technology is accurate to approximately 10 metres and is particularly effective outdoors
  • Mobile phone transmitter towers – generally good in cities
  • Wi-fi – coverage maybe limited but a high degree of accuracy
  • Device’s internal sensors, such as inertia, compass, accelerometer and gyroscope – no external input is required but this is only usable for a short time being very useful in locations such as tunnels
  • Bluetooth beacons –  the beacons need to be close together but they can provide very accurate location data

This all means that the technology is available for the intelligent campus to constantly locate an individual and for the individual to know their whereabouts. This could be useful for security and safety purposes providing reassurance to vulnerable or less able individuals, particularly at night.

Location awareness is a useful facility for a newcomer or visitor to the campus who requires a self-guided tour or directions to a building or room. Student attendance, for example, at lectures, could also be monitored using this location awareness.

Location learning

However, more ambitiously, location awareness could be taken advantage of in module teaching or when informal learning opportunities occur. Systems such as the Adobe Captivate 8, content development suite, utilise the GPS features in mobile devices to create location aware mobile learning modules, for example the Australia Zoo App ( As mentioned previously a wide range of opportunities exist on most campuses to take learning out of the classroom or lecture theatre using all of the campus resources, assets and facilities.

Beyond the campus

As the intelligent campus becomes more integrated with the smart city these learning opportunities will be taken beyond the campus to include the cities museums, galleries, businesses, transport system, utility infrastructure, government and public buildings. Indeed the whole city, in effect, becomes part of the campus. The University of Tampere in Finland is working with the city of Tampere to improve services through location awareness. This pilot includes the use of wi-fi and Bluetooth location techniques to provide indoor location coverage (


Augmented Reality check

A significant enhancement, both on the campus and across the city, to location based learning could be achieved through the development of augmented reality (AR).

The use of mobile and wearable technology will increase the functionality and popularity of AR combined with location awareness. Apps such as Lookaround (, developed at  the  International Institute of Information Technology, Bangalore, allows the provision of location based information on a mobile device using an AR interface. The Wikitude app is being use to explore and understand the Terracotta Warriors exhibition at the Franklin Institute (  ).

The location aware learning environment

At Athabasca University , Canada, Dr Qing Tan is investigating the “Mobile Virtual Campus (MVC)“ which could provide an interactive platform for online mobile learners using the location awareness and other built-in sensory components in mobile devices. Students will be able learn collaboratively and interactively either face-to-face, across the campus or elsewhere in the MVC at any time. Augmented Reality techniques and the location of learning objects, will allow learners to see the suitable learning contents superimposed upon these specific learning objects including interactive elements. ( )

And so…

If the technology hurdles are overcome, it is clear that location aware learning shares many of the ethical issues that are of concern with respect to other intelligent campus developments. Privacy, intrusiveness and security questions will need to be resolved if location aware learning is to take off.

Also, the range of people and departments from across the institution that will need to be involved which will add further difficulty to the management and administrative requirements.

However, location aware learning may well be considered an enhancement to the student experience for the Pokemon Go generation.

Intelligent spaces for learning

Use Case: Intelligent spaces for learning


Setting the scene

If a physical campus is to exist in the future it’s likely that learning spaces of some kind will be at its core. All colleges and universities will aim to improve the student learning experience and outcomes through high quality learning spaces. Such spaces are also an aid to both student recruitment and retention. They may resemble the traditional lecture theatre, albeit enhanced by technology, or highly adaptive, open learning areas that can be adapted to many styles of learning

It is probable that the requirements and development of learning spaces will continue to change over the coming years, possibly in unpredictable ways, and so flexible spaces will need to be able to accommodate these changes. Walls, floors and ceilings that are mobile and not structural, highly controllable lighting, temperature and humidity levels, and moveable seating and furniture. All these elements will be part of the future learning space on the intelligent campus.

The need for spaces to provide for a variety of learning styles and pedagogies, from formal lectures through group projects and collaborative learning involving activity and movement, through blended learning, using online resources and activities to individual, personalised learning where quiet spaces allow solitary concentration.

Who’s there?

While the physical space will remain an important part of the learning experience on the intelligent campus, it does not mean that all of those participating in the experience are present. Guest lecturers, teachers and experts might well be interacting with the students remotely. Similarly students who are not there in person through illness, disability or remoteness can take a full part. The growing use of lecture capture facilities that many learning spaces already provide could be extended through interactive walls incorporating screens, speakers, microphones and cameras, even robotic arms in teaching labs.

The use of these types of interactive walls is being used in spaces such as the Decision Theatre1 at Newcastle University. This facility provides a 2m x 5m HD interactive, 3D, video and data visualisation screen. Users can share data from their mobile devices and work collaboratively together on problem solving and explore simulated scenarios.

The Internet of Things and Learning Spaces

Technology, such as the Internet of Things (IoT), will allow everything about the learning space to be monitored. For example, when students spend long days in a room, rising levels of CO2 can lead to sleepiness and poor concentration. Sensors will automatically lead to windows opening, also this data will be collected and recorded as part of the campus management system.

Sirkka Freigang’s article “IoT in education – designing smart learning environments2  discusses many of the issues around the “smart” learning spaces that the intelligent campus will provide. She focuses on the need for flexibility and says: “This allows for hybrid learning approaches that switch between formal and informal settings, independent and class learning, varying learning times and places, and analog and digital learning formats. “

She feels that the learning spaces should adapt themselves to learners’ needs by “taking information from the environment, processing it, and using it to initiate appropriate steps”

In a recent article Arkessa (experts in the IoT) looked at learning space projects in Europe, the USA and China. The article explores the use of smart boards connected to student tablets allowing collaboration and presentation sharing. They also discuss the Bosch Quantified Art installations – “artwork containing smart sensors which monitor CO2 levels, room temperature, pressure and humidity and which trigger colour changes in the picture to alert students to fluctuations in their environment. A picture of Einstein whose skin is turning a peculiar shade of green indicates that the classroom is no longer at the right ambience for optimum learning.”

The intelligent campus will also extend the learning space outside the classroom or lecture hall. The IoT means that a vast number of devices and items will provide data to students. The Educaus article “The Internet of Things”3 speculates that data streams will be available “from sensors embedded in objects including library resources, whiteboard writing surfaces, game boards, and robots. The components that collect and relay data being used in makerspaces, laboratories, and projects undertaken by students….” Also “Off campus, students can visit historical locations or study urban environments where information is transmitted from nearby sensors. Tagged plant markers in a public herb garden, for example, could send data to a student’s phone, relating common and scientific names, date of planting, culinary uses, medicinal uses, mature size, and country of origin.”

Classroom by James Clay CC BY NC 2.0

Other resources

Other use cases developed in this series have discussed and explored a range of issues that impact on, and form part of, the new learning spaces the intelligent campus. These include student attendance monitoring during timetabled teaching and learning sessions and the monitoring of the movement of people around the campus during very busy periods, for example when a number of popular lecture sessions take place at the same time.

Many of the new and exciting ideas that are emerging in developing the learning spaces on the intelligent campus are investigated in the excellent Jisc article “Eight inspirational learning spaces”3 by Prof. Andrew Harrison.

UCISA have also produced a very useful resource “The UK Higher Education Learning Space Toolkit”4 . This publication provides a wealth of detailed information about the creation of new learning spaces.


The development of learning spaces will inevitably move in directions that cannot be predicted. It may be that the virtual, or digital, learning environment will become much more integrated into the campus learning space, particularly in relation to the use of augmented reality, blending the physical with the virtual. The constant and enthusiastic use of social media by students could provide opportunities for more collaborative learning between those in the learning space that those outside. Connected devices will allow tutors and lecturers to receive constant real-time formative feedback from students that could guide the direction of the teaching that is taking place, as well as contributing to learning analytics.

Indeed it really does feel as if we are that the beginning of a major shift in how learning spaces are used with a plethora of options and ideas available to those willing to be bold in taking advantage of them.

1 Decision Theatre at Newcastle University –

2 IoT in education by designing smart learning environments – Sirkka Freigang –

3 The Internet of Things, Educaus –

4 Eight inspirational learning spaces – Prof. Andrew Harrison –

5 The UK Higher Education Learning Space Toolkit –


How are we doing?

Use case: Capturing Feedback on the Intelligent Campus


What’s the Issue?

As universities and colleges feel the pressure to provide an enhanced student experience the intelligent campus will play an increasingly important role. An acknowledged “great campus” will be a major asset. Students and staff can all help in developing a better campus, identifying issues and successes, contributing ideas and improving the experience. This input can be collected, at least in part, through a range of feedback mechanisms.

Universities and colleges already collect considerable quantities of feedback, whether it is formative and summative feedback, relating to academic work and learning, or with respect to their overall experience through the National Student Survey (NNS).

However, as the intelligent campus becomes an important reality for many institutions there will be a need to find how its users feel about the campus environment and to find the answers to questions about:

  • The quality of the facilities, the buildings, campus environment etc.
  • The availability of facilities. Is it too crowded? When are the busy times? Are they in the right place?
  • Is the campus user friendly and enjoyable?
  • Do they feel safe on campus? Is the lighting adequate? Is help readily available?
  • Are there problems and issues that need addressing?
  • What do they like about the campus and what could be improved?
  • What was their experience today, or even right now, on campus?

Perhaps questions about wider issues such as its integration with the city and its services could be addressed as well.

Indeed, feedback can be used to manage and run the campus better. For example, identifying crowded periods and encouraging or timetabling for use during quieter times.

How to address the issues

The happy sheet

The traditional questionnaire is still the first port of call for most feedback, unfortunately, there are limitations to this method with generally low levels of participation, little flexibility, processing overheads and lack of immediacy. However, the questionnaire is being given a helping hand through technology that improves the processing and interpretation of free text survey results. The days of a pile of forms that sit waiting for the time consuming, manual processing may have gone, thanks to online, mobile feedback surveys but results still need to be collated and analysed. Systems such as Keatext are employing AI to interpret natural language content and machine learning to analyse feedback and enhance its value.

Make me smile

A very quick, if slightly superficial, feedback method that many of us will have seen and used in places such as airport security are the smiley buttons. Simply press one of 4 or 5 options – each has a smiley style face with varying degrees of happiness (or unhappiness). These units can be placed at various locations on a campus such as outside lecture theatres or libraries asking how the student feels about their experience of that location. Companies such as HAPPYorNOT provide a range of these types of terminal along with software to provide real-time analysis of the data collected. Systems like these can also give early warning of a looming problem. Large numbers of very negative responses in a short period could lead to an appropriate urgent response.

The power of the touch

The introduction of touch screens on campus can provide a much more sophisticated feedback mechanism across the campus. Again, placed in strategic locations, campus users are able to provide instant feedback. The feedback requested could vary depending upon the time of day or activities taking place.

  • First thing in the morning feedback on morning commute could feed into transport and travel policy,
  • On the hour information about the congestion across the campus can be collected
  • Alternatively real-time feedback on the quality of their last lecture could be gathered
  • The lunchtime waiting times and queuing arrangements could also be commented upon
  • In the evening campus users thoughts on campus lighting and safety can be collected
  • On open days screens can be configured to receive feedback from prospective students and their parents

Mobile feedback

Similarly much of this type of feedback can be collected via apps on mobile devices. A wealth of feedback applications are available for smartphones and tablets examples include the Instant Student Feedback (ISF) app developed at Uppsala University in Sweden which can provide the institution with instant feedback on the student experience.

Being sociable

Social media is increasingly being used in the retail world to elicit feedback from customers and a number of universities are following suite in terms of monitoring social media outlets such as Facebook, Twitter, Snapchat and Instagram. The intelligent campus could proactively encourage this though the use of hashtags and QR codes. These could be promoted across the campus and related to a particular building or location. For example, users of the university Learning Resource Centre might be asked to provide feedback using “#UniversityLRC”. There may be dangers in this type of public feedback with its lack of structure and possible Trip Advisor type ratings but it will provide another valuable feedback channel as long as resources are made available to monitor and respond to the feedback generated.

We know what you’re thinking

Another method of collecting feedback that is being investigated is the use of emotion recognition technology. This could have the potential to provide unconscious feedback, making it more difficult to be manipulated by those with an agenda. While this development is at the early adopter stage it could be envisaged that images of campus users will be used to establish their state of mind and how they feel about their current experience on the campus. Companies like Crowdemotion are piloting systems to track and interpret emotions such as happiness, surprise, puzzlement, fear, sadness and rejection.

The “Internet of Things” is watching

Subtle, unconscious feedback could also be gathered through the internet of things, including the use of wearable devices, sensors, cameras, vending machines, doors, lifts and mobile devices to track:

  • movements around the campus,
  • the choices made by campus users
  • what events and activities they attend,
  • where they spend time
  • what they purchase

All of this data will provide feedback about how the campus is being used, by whom and how often. Using this data the campus will learn what is successful, and do more of it, along with what is unsuccessful, and do less of it.

And so…

A key message is that the opportunities to collect feedback are many and increasing across the intelligent campus. Any university or college that is committed to the development and improvement of the student experience will recognise that the overall campus environment is a significant part of this experience. However resources will need to be made available to interpret and learn from feedback, and for the appropriate changes to take place as a result.

Finding your way

Use Case: Finding your way around campus


What’s the issue?

Universities can be complex physical spaces, with a myriad of different types of buildings, added in different decades and spread across a wide area, sometimes multiple campuses. Some buildings may be old with obscure access points and navigating adjoining buildings can be confusing. For come city universities sometimes the buildings are even close to or integrated with other shops and services or transport facilities, with the public crossing the campus en route to other destinations. In rural settings, campuses can be spread out with natural features interspersed and clusters of academic or residential buildings.

What specific challenges are there?

Once you are familiar with the campus it may become easier to find your way, but for visitors, prospective students and their parents on open days or graduation, or new staff, it can be confusing and time consuming to locate your destination and an effective route to it. There is a danger that such visitors leave frustrated and get a poor initial impression of the institution.

In addition, campus users with mobility difficulties or other special access needs can be particularly challenged by the campus layout.

Universities have invested considerable time and effort in providing good signage, but buildings change name, departments relocate and new facilities are added, impacting on multiple signs and making it difficult to avoid redundant signage and out of date labelling.
Also dynamic information about activities and events in a building can avoid the need for temporary (paper) notices on doors that look unprofessional and often remain after the event.

What could be done?

Digital maps and records of buildings and facilities could be combined with knowledge of the user’s location to provide information on routes, timings and facilities. Their mobile device could tell students (and staff) where the closest free work station, desk, or collaboration area is as they move around campus.


The navigation information could be tailored to the individual needs of the user, for example their mode of transport, special access requirements, or personal interests and preferences such as what type of food they like. Retail outlets could notify nearby campus users of their services or special daily offers, allow them to order items and collect on their way past. Information from their calendar and lecture timetable on their device could be combined with estimated timings to enable users to know whether they have time to stop or would be late for their lecture. In the event of missing a lecture, students could be notified of missed recordings and materials.

What wayfinding examples exist?

Google maps is familiar tool to many, identifying routes for different types of transport (including cycling and walking), estimating duration times and identifying bottlenecks and delay points. This information isn’t exact and typically not sufficiently detailed for on campus use.

Wayfinding solutions using smart devices have been used in other contexts such as car parking, where real-time information about parking spaces is used to control digital signage and a diagram showing the allocated parking space is shown to the user.

Gatwick Airport, way too early in the morning

Airports are introducing wayfinding solutions to provide information on key aspects such as waiting times at queues. At Miami airport a contextually aware app tailors the user experience based on their location. This includes retrieving flight schedules, navigating to the airport (integrating with Google Maps and other services) and identifying shops and restaurants near to them within the airport.

As satellites are unreliable indoors, at Gatwick airport a beacon based positioning system enables an augmented reality wayfinding tool. This allows passengers to see directions on their mobile device, overlaid on the camera view, to navigate to check in areas, departure gates and baggage belts.

Are there any examples in universities?

University of Technology Sydney created a digital ecosystem of physical and digital signs. Updating names and locations in the digital directory is immediately reflected on screens and signage.  Connective technologies are embedded inside each signage “totem” using NFCs and QR codes to allow mobile phones to interpret the information. Additional features include security and emergency devices, and directions provided via speech in multiple languages. In addition to improving navigation, it has led to reductions in cost and effort of maintenance .

What about ethical and other issues?

In many cases no personal data is collected to provide wayfinding services. The apps combine knowledge of the user’s position with generic digital map and route information transmitted. Generic information on ‘people densities’ can be used for example for queue measurement, streamlining flows of people and reducing congestion.

With additional consent, users could receive more personalised notifications for example if you are late for a lecture, estimate how far away you are and use this information to advise the lecturer whether to start without you or wait.

Who needs to be involved?

Existing campus services such as facilities management and IT would need to coordinate to maximise the benefits, but also those interested in supporting students with special needs, language, and the running of special events such as open days and graduation.

Nudge me

Use Case: The nudging campus


What is Nudging?

Wikipedia says:Nudge theory (or nudge) is a concept in behavioural science, political theory and economics which proposes positive reinforcement and indirect suggestions to try to achieve non-forced compliance to influence the motives, incentives and decision making….”

Nudging aims to influence individuals or groups to act or behave in a certain way using gentle encouragement. It should make the behavioural change simple and easy, with little effort from the individual concerned.

Nudge theory came to prominence through the book “Nudge: Improving Decisions About Health, Wealth, and Happiness”  by Thaler and Sunstein in 2008.

Possibly the best known example of the nudge is the fly etched on urinals at Schiphol airport resulting in lower cleaning costs. Its potential was seen across society with the British government even setting up its own “Nudge Unit”.

Nudging on the campus

A number of universities have been investigating the use of “nudge” to improve their campuses and enhance the student experience. In some cases these techniques are being used but are perhaps not always recognised as nudging. While a clear nudge example might be green footsteps, painted on the pavement, leading to a litter bin, a less obvious case would simply be positioning the bin in the right place and clearly marking the type of litter it should be used for. However, technology can help to nudge the individual and make it fun to use the bin. For example, turning a litter bin an arcade game – by depositing litter though different slots, as they light up, users achieve a game score.

Nudging techniques also overlap with a number of other use cases such as; finding your way around the campus, attendance and wellbeing. It can be used in almost all aspects of campus life. Although many examples are not related to technology, or are particularly innovative, they simply require a slightly different way of thinking, for example, relating to health, campus food outlets putting healthy options at eye level with less healthy options low down on shelving. In fact the area of healthy eating has numerous nudging examples:

  • using smaller plates for meals to reduce portion size and waste,
  • a default of salad rather than chips with a main course,
  • calorie counters and traffic light symbols on menus,
  • putting a high price unhealthy option next to a lower cost healthy choice on the menu

Using technology to nudge

Some of the healthy eating nudges can also involve the use of technology such as:

  • dietary requirements or allergies stored in a smartphone access the menus at eating places across the campus providing healthy or allergy appropriate options
  • the menu calorie counters and traffics lights could be provided via a mobile app

When looking at other technology based nudging, “the internet of things” is likely to play a role. As thousands of objects across the campus connect with the network, including vehicles, room sensors, lights, wearable devices etc. there will be increasing opportunities for campus users to be nudged, particularly by their mobile devices.

We are already familiar with our activity being monitored by wearable devices and may well find that in tracking our movements we will be nudged, by messages and flags on our device, to take routes that make us work a bit harder – using the stairs, walking via an open space and so on. Also data on likely drive times, can be used to nudge campus commuters to take public transport or cycle.

Providing enjoyable or rewarding feedback can also nudge campus user’s behaviour. One well know example is the piano stairs. A set of steps next to an escalator were refurbished to replicate piano keys visually and to play notes when stepped on. This resulted in more people using the stairs rather than the escalator.

The improved use of analytics and student record systems is allowing a number of universities in the United States to identify “at risk” students, who might drop out, and nudge them, using messaging, to remind them of the help and support that is available. In the UK a experiment used text based nudging to improve student attendance and attainment at Leicester College and Manchester College1. This wealth of data about students could include financial information and identify students who are likely to be in financial difficulty. They could then be nudged towards special offers in food stores, free forms of entertainment and low cost meals.

Nudge overload

There has been some discussion of nudge overload since it has become more widely recognised as a useful technique. Constant automated nudging, through the internet of things and use of analytics, could be considered nagging, particularly to those who have become aware of it and possibly resentful of what could be seen as manipulation. Nudging, at its best, is subtle and occasional, not badgering.

When to nudge

Although the definition of nudging has become very wide, with many marketing techniques being aligned with nudge theory, there are many benefits for universities and colleges that employ nudging intelligently across their campuses. The key will be to look for opportunities that are created by changes in behaviour, and particularly when the traditional approach is to give a negative message – “don’t drop litter”, “don’t miss your assignment deadline”, “you need to do more exercise” etc.

The availability of knowledge about the personal needs of campus users, through analytics and the internet of things, along with improved understanding of behavioural change, means that nudging has the potential become a huge asset to the intelligent campus.


1 Using text reminders to increase attendance and attainment (

Building analytics

Use Case: Building analytics


What’s the issue?

Universities have large numbers of buildings of varying ages and conditions, spread over a wide geographic area, sometimes multiple campuses. Managing energy, waste and resources efficiently is important for a number of reasons, including financial pressures, environmental principles and regulations, and improved working conditions for campus users.

What could be done?

A wide variety of environmental conditions can be monitored by sensors connected via the IoT to central control systems and mobile devices. These include temperature, lighting, noise and air quality. In addition, use of other resources such as water, waste and recycling

Collecting data from buildings on the current conditions and energy usage, systems can analyse needs and adjust based on a variety of factors including:

  • the occupancy of buildings and rooms
    • eg turning off heating and lighting when not in use or adjusting to account for the numbers in the room
  • the weather conditions and use of the room
    • perhaps dynamic temperature that takes account of people coming in from the cold and then warming up as they adjust, or getting colder as they sit still
  • predicting the conditions for the following day
    • setting heating and lighting accordingly
    • informing room users of the conditions, or even what part of the room is warmer or colder
  • the real time cost of energy supply
    • taking advantage of cheaper energy at different times
  • feedback from people currently within the building
    • individually reporting their comfort level or reporting problems via mobile devices
  • sensors reporting waste levels from bins to optimise collection


Another key issue is maintenance – that by understanding a more comprehensive picture of usage, the systems and infrastructure that provide heating, lighting etc can be optimally used to minimise or even predict failures and breakdowns and to provide maintenance more efficiently. Staff responsible for such systems can receive data in real time on mobile devices, respond to warnings and alerts, and more quickly be on hand to provide assistance. Routine maintenance can be prioritised to best avoid future problems by detecting the current state of systems.

Building analytics also has the potential to provide reductions in time and effort spent on building maintenance and the associated processes with live reporting and alerts.

What examples are there?

At Georgia Tech University, over 400 smart meters monitor 200 buildings. Their Smart Energy Campus Program works as as a “living laboratory” collecting data from energy utility systems across campus. They can quickly identify areas of unusually high energy usage, and provide a response, reducing time and resources. A visualisation of the campus energy usage gives them a view of consumption via a dashboard. Using thermal network and electric grid modeling, researchers aim to better understand energy usage as well as assess potential upgrades to energy systems and technology.

In Ontario, Canada, McMaster University is collecting real-time data from 60 campus buildings to optimise energy consumption. They also use dynamic-pricing data to forecast and simulate future usage.

Bristol University’s diverse buildings range from the 17th to 21st century and they use a building management system that includes alerts to the security office out of hours to monitor critical systems. A particular goal was to reduce carbon emissions, but the systems also include fire monitoring, and sustainability monitoring.

wind turbine

Looking further to the future

A variety of potential applications arise that are beginning to be explored in research using the data being collected from building analytics. These include:

  • predicting future energy consumption including the impact of increasing battery recharging such as mobile devices and electric vehicles and the provision of charging points
  • changes in energy sources such as the campus generating it’s own electricity from solar, and potentially selling it back to the national grid
  • the impact of weather patterns on the availability of different energy resources and smoothing demand
  • taking advantage of price variation of energy at different times

Scientists at Newcastle University’s Smart Grid Lab simulate power distribution for future scenarios. These include a severe weather power cut and increasing numbers of electric vehicles. Concepts they explore in the living laboratory including real-time notifications to users of consumption, peak demands and pricing, and problems within energy supply. They are developing a model of a “micro-grid” that provides local resilience to wider disruption.

What about ethical and other issues?

Environmental data typically doesn’t include personal data, although individual feedback on conditions could be identifiable. If health-related conditions or special needs were to be taken into account, to adjust environmental conditions, then this data is more sensitive and need appropriate management. There is a wider issue regarding the challenges of responding to individual preferences and the difficulty of getting the balance right across a group of users. Another difficulty is the lack of standards across legacy sensors and the huge challenge of maintaining and upgrading many hundreds of sensors across campus.

Who needs to be involved?

For most of the applications mentioned, the estates and facilities services are the most heavily involved, with appropriate collaboration with IT and networking services. In some examples data from timetabling and other student support services may be necessary to offer the more integrated solutions, and external data from weather forecasting or energy usage more widely.

Adaptive learning

Use Case: Adaptive learning


What’s the issue?

With the increasing use of e-learning and blended learning, there is a growing tension between the potentially interactive physical classroom experience and the largely static, homogeneous content provided through systems such as virtual learning environments. In addition, increasing student numbers, with widening diversity, pose challenges for teaching staff attempting to provide a differentiated, contextualised and personalised learning experience to a large audience.

How can intelligent campus help?

A combination of mobile devices and classroom technology, learner data and institutional systems can integrate to understand learners’ needs and assist them to effective outcomes. A multitude of data about learners already exists, not just their activity within the VLE, but their location on campus (or outside), interactions with the library, demographic data from student information systems, their historical patterns of learning and even their ambitions or goals for the future.

Individuals’ mobile devices along with classroom technology could interact with this wealth of data available about the student and their learning to provide an enhanced experience. Personal devices and apps may also hold information on individual preferences and learning styles to enable differentiated interactions.

What could be done?

  • Digital textbooks providing hints, explanations and practice questions, linked to the class content on the VLE. Monitoring common problems could feed into course design.
  • Headsets supporting virtual or augmented reality views of content and places around the lab, campus or at sites of educational interest.
  • Adaptive devices providing personalised interfaces (including accessible tools) to meet learning preferences, and by monitoring progress adding new or supplementary content to match the needs for individual pace and style.
  • Virtual field trips (or augmented reality physical field trips) including location-awareness to give students prompts and contextual content, allowing personalised interaction with the environment.
  • Science experiments conducted remotely through the internet, with devices connected to laboratory equipment to take measurements and set analysis tasks.

Other applications use real time natural language processing to spot intentions or emotions in conversations with peers or tutors, for example using chat-based apps on smartphones. Intelligent “conversational” agents can provide tailored feedback to students on performance or respond to common questions with personalised information understanding their context, including their geo-location on campus and proximity to learning resources and facilities.

Are there any current examples?

There are few examples in practice of IoT devices integrating into adaptive learning approaches. In most cases, where adaptive learning is being used, it relies solely on institutional systems and learner analytics. These could be adapted to add integration with mobile devices.

Bolton College is providing a personalised pathway through VLE content by differentiating students according the data about them such as how they performed in previous tutorials, leading to more stretch and challenge if they do well. The college have also implemented an online digital assistant that can respond to natural language questions, in some cases removing the need for the student to access other systems such as the VLE.

At Edinburgh University, a tool using algorithmic machine intelligence has been used and quizzes designed linking to learning pathways. Elsewhere they have piloted use of a twitterbot to answer simple questions for example about deadlines. Cluster analysis and segmentation has also been applied on a wide range of historical data to identify learning trajectories and map these to current students.


What about ethical and other issues?

Security of data is crucial, with these applications involving wide ranging data including personal circumstances of students and, potentially, financial data. For example a student may use a chatbot to query how much money they are still owing on their course.

Algorithms may be making decisions and providing feedback on behalf of tutors, including on sensitive topics. Even if teaching staff have been involved in programming the behaviours and responses, sometimes an automated system may not produce an output that a human would agree was “appropriate”. It is likely that careful moderation would be needed, at least until the technology was sufficiently mature.

Who needs to be involved?

Having teachers involved in the development alongside data and systems specialists allows for a more integrated approach utilising the technology within the pedagogic context. Skill sets including instructional designers and algorithm specialists may be needed. Having a robust policy at an institutional level will help manage the ethical issues more effectively.