Libraries at the heart of the institution

As part of a wider Jisc consultation on libraries with key stakeholders I was invited to present some background to the Intelligent Campus

Digital horizon: The intelligent campus

During this session James will provide you with an overview of Jisc’s intelligent campus project, our direction of travel and why.

This was based on previous presentations I have given in this space.

It did raise the notion of the intelligent library and the potential of data and analytics to enhance the library user experience.

As one of the delegates remarked on the Twitter, this can be frightening as much as it can be fascinating.

Hey Siri, what’s my day like today? Alexa what’s my next lesson? Okay Google, are my library books available?


Voice assistants are becoming not just more widespread, but also much more useful.

Alexa was announced by Amazon in November 2014 alongside the Echo devices, which act as connected speakers and hubs for voice controlled devices. The Echo devices act as connected hubs complete with speakers and in some cases small screens.

Photo by Rahul Chakraborty on Unsplash

Cortana from Microsoft was demonstrated in April 2013, and was released as part of Windows 10 in 2015. In May 2017, Microsoft in collaboration with Harman Kardon announced INVOKE, a voice-activated speaker featuring Cortana.

Bixby from Samsung was announced in March 2017. Unlike other voice assistants Samsung are going to build Bixby into a range of consumers goods such as refrigerators and TVs which they manufacture.

Google has their Google Home which was announced in May 2016 and released in the UK the following year. Google Home speakers enable users to speak voice commands to interact with services through Google’s intelligent personal assistant called Google Assistant.

Photo by Charles Deluvio 🇵🇭🇨🇦 on Unsplash

And of course Siri from Apple. Siri was originally released as a stand-alone application for the iOS operating system in February 2010, but after a buy out from Apple was released as part of the operating system in October 2011. It wasn’t until 2018 that Apple released their own connected speaker hub with the HomePod in February of that year.

Many of these voice assistants started their journey on mobile devices, but over the last few years we have seen connected voice controlled hubs appearing on the market.

An online poll in May 2017 found the most widely used in the US were Apple’s Siri (34%), Google Assistant (19%), Amazon Alexa (6%), and Microsoft Cortana (4%).

Though we might think we want to see how we can embed these into the classroom or education, they are not aimed at this market, they are consumer devices aimed at individuals. Our students are certainly the type of consumers who may purchases these devices and they will want to be able to connect them to the university or college services they use.

chat bot

All the voice assistants require some kind of link to information and in some cases data.

If I ask Alexa to play a particular song, she delves not just into my personal music collection on the Amazon Music app but also what is available through my Prime subscription. If the song isn’t available I could either subscribe to Amazon Music streaming service, or purchase the song.The Alexa ecosystem is built around my Amazon account and the services available to me as a Prime subscriber.

With Google Home I have connected my free Spotify account to it. This is one of the key features of these devices that you can connect services you already subscribe to, so you can control them via voice. Of course the reason I have a free Spotify account is that Google Home would much prefer I was connected to Google Music, and it certainly won’t let me connect to either my home iTunes library (where virtually all my music is) nor to Amazon Music. So when I ask Google Home to play a particular music track, she gets annoyed and says that she can’t as that is only available on Spotify Premium.

This is one of the challenges of these devices that they are quite reliant on subscriptions to other services. Apple’s HomePod only really works if you have an Apple Music subscription.

When it comes to connecting services to voice assistants then are two key challenges, can you get the right data out to the right people, and similarly can you do this for the range of voice assistants available especially when you remember that there is no de facto standard for voice assistants.

It would be useful to know and understand what sorts of questions would be asked of these assistants. There are the known problems, such as where is my next lesson? What books would be useful for this topic? When is my tutor free for a quick chat on assignment? Do I need to come into college today? Even simple questions could result in a complicated route to multiple online systems. Imagine asking the question, where and when is my next lecture, what resources are available and are there any relevant books in the library on this subject? The module design or course information system (or more likely this is a dumb document) would have the information on what would be next. Timetabling systems would be able to inform the learner which space and when the lesson was. Imagine the extra layer of last minute changes to the information because of staff sickness, or building work resulting in a room change. As for what resources are available, this may be on the VLE or another platform. As for additional resources then this could be on the library systems. How would the voice assistant know what to do with this information, could it push the links to a mobile device? Add in a social platform, say a closed Facebook group, or a collaborative tool such as Slack, then you start to see how a simple question about what am I doing next and where is it, becomes rather complicated.

There is though something to be said to ensuring services work with voice assistants, as the same data and information could also be used with chatbot interfaces (ie textual assistants) and with campus bound services such as kiosks or web portals. Get the data right then it’s simple a matter of ensuring the interface to either voice, text or screen is working. Learning analytics services, such as the one we are developing at Jisc, rely on a hub where academic and engagement data is collected, stored and processed. Could we use a similar data structure to build the back end system for chatbots, kiosks and voice assistants?

Could we Siri? Could we?

Looking forward to working with our Intelligent Campus hackathon winning teams next week

See this post from Paul Bailey to find out who got through selection and what they are going to be doing next week.

We’ll keep you updated after the hackathon completes but I think its going to be an interesting week at Aston.


The Challenge of the Intelligent Library


There has been plenty of hype over artificial intelligence and the internet of things. Is it time to put aside the cynicism that this kind of hype generates and look seriously at how we can take advantage of these emerging technologies to improve the student experience and build an intelligent library?

The internet of things makes it possible for us to gather real-time data about the environment and usage of our library spaces.  It is easy to imagine using this data to ensure the library is managed effectively, but could we go further and monitor environmental conditions in the library, or even, using facial recognition software, student reactions as they use the library so that we can continually refine the learning experience?

The background to the Jisc Intelligent Campus project was the basis behind my recent keynote at the CILIP UKeiG Meeting in London on the 26th June 2018.

I discussed firstly the concept of the Intelligent Campus which describes what we at Jisc understand by the term and how it is different to the ideas of a smart campus or smart buildings. I then talked about the Intelligent Library.

creepy library

I also covered the issues in this area, which includes of course not just GDPR and data protection, but also the huge ethical issues that arise when tracking users of the library in not only in what they use and borrow but also their physical movements around the library. I also brought up the technical and validity challenges in using data and analytics and the importance of understanding the narratives behind any data story.

There were some great questions for the audience and a lot of interest in this topic, and the conversation continued over lunch.

Write a case study or two

working on a laptop

Case studies are a useful tool to describe how others across the sector are working on the concept of the intelligent campus. Though not always instantly transferable from one situation to another, they can be used to see what experiences, problems and issues others have faced and importantly how they overcame them.

We would like to invite you to write case studies, which may then be published here on the blog. In order to give you a starting point, we have created a template that asks a series of questions.

Overall the expected length of the case study would be 500-1500 words.

This template is designed to encourage you reflect on the projects or activities you have undertaken in the intelligent campus space and then to share the outcomes with other practitioners.

Case study title

Institution name

Background [Give brief details of institution, type of campus, buildings or learning spaces, in which the activity/ies or project took place]

Intended outcome(s) [Describe the objective(s) behind the the activity/ies or projec outlined here]

The challenge [Identify the issues that required attention or which prompted you to re-assess your previous experiences]

Established practice [Identify features of the experience previously in use – this may include any aspects which were subsequently amended]

The digital advantage [Describe the benefits of the the activity/ies or project, as experienced by the institution as a whole]

Key points for effective practice [Briefly identify the most important points in the case study for other organisations – these may include risks as well as benefits]

Conclusions and recommendations [A summary of how and why the the activity/ies or project outlined here has been effective]

Additional information [Use this optional section to add related materials or content e.g. a strategy, a plan or a set of data, or to supply your email address]

Please send completed case studies to

Get involved with our intelligent campus hackathon


As part of our edtech launchpad programme we are looking for student teams from universities and colleges across the UK to join us in building the campus of the future.

The aim of the hackathon is for student teams to design, develop and build “something” that would benefit students in the world that is the intelligent campus.  We are looking for some creative students to spend a week in August working to build and test tools that can enhance the student experience and make the campus more intelligent.

There’s up to £1,000 in prizes up for grabs, we’ll provide you with equipment and cover your accommodation and expenses.  .

Venue: Conference Aston, Birmingham

Timings: 12:00 Monday 6 August to 13:00 Friday 10 August

You’ll will need to bring laptops and the ideal team size is 2-4 people.

for more information contact

Entry form

Come along and join us – lets create something exciting.

Strategic approaches to the intelligent campus

Strategic approaches to learning analytics in UK higher educationThe opening paragraph in the Jisc publication Strategic approaches to learning analytics in UK higher education says:

Learning analytics cannot yet be considered a mature field in UK higher education. However, the use of data about learners and their learning to address areas such as attrition and curriculum enhancement is increasingly being investigated through projects at an institutional level. 

This will resonate with many individuals in UK higher education who are investigating the use of data in the intelligent campus space.

The document takes you through the many different approaches to learning analytics and this landscape is similar to the landscape of the intelligent campus.

There is no single driver behind the intelligent campus, for some it’s about improving and enhancing the student experiences, for others it’s about making effective use of the estate and learning spaces. There are demands for efficiencies in space utilisation, and reduction of costs of energy, water, maintenance and waste collection. There are a diverse range of reasons and strategies that results in interest in the intelligent campus space.

The document is an interesting read on the current learning analytics landscape and is a format we may look at late in the intelligent campus project.

Download the document from the Jisc repository.

Learning from intelligent tourism

Cinque Terre

In this news piece, Tourism pressures: Five places tackling too many visitors – BBC News,  there is an interested use of technology by empowering visitors with knowledge of how busy the paths in the cliffside towns of Cinque Terre.

Tourists cannot get enough of the five brightly painted cliffside towns in northern Italy known as Cinque Terre. The area, which has about 5,000 residents, became a national park in 1999 and now receives more than two million tourists per year. People come to hike the scenic paths that link the towns and the terrace vineyards. Over the years, the walkways have fallen into disrepair from erosion and overuse.

Faced with this problem, the park authorities came up with a technological solution for the tourists. 

Lately, park authorities have been trialling an app which tourists can download to see the number of people on the routes in real time. When a red warning sign shows, a path is overcrowded and visitors can then make up their minds if they want to join the throngs. In the future, they may trial virtual waiting lists.

One of the discussions that has come out of the recent Intelligent Campus community events has been about empowering students with knowledge through data, to enable them to make informed decisions that will enhance their experience.

It is easy to see how providing students with information on how busy parts of the campus is then they can (like the tourists) make up their minds if they want to join the throngs in the library, the catering facilities or the computer labs.

chat bot

You could do this three ways.

  • Use historical data to inform the students.
  • Use live data to inform the students.
  • Use predicted data, based on historical and live data to provide future information to students.

How you collect data is a different question, you could use software such as Lone Rooftop to measure occupancy using wifi and the devices learners are carrying. Some universities are using infra-red technology to measure occupancy (with less impact on gathering data about individual users).

Removing frustrations, making timely and appropriate interventions are just some of the ways you can enhance the student experience. Providing them with accurate insight into where they want to go and what is happening there goes some way to do this.

Intelligent Campus Data Requirements


We’re working on ways to improve the student experience by capturing and analysing the many kinds of data that can be collected across university and college campuses. This research is developing alongside our effective learning analytics project and our work to build a learning analytics service.

At the core of the learning analytics service is the learning data hub where academic and engagement data is collected, stored and processed.

We’ll extend the learning data hub to enable data to be gathered in from physical places (movement trackers, heat and CO2 sensors, for example) and from systems that record and monitor space and equipment usage, timetabling and other activities.

Our vision for the intelligent campus is that we will work with existing services rather than compete against them. We will establish a standardised data hub for the intelligent campus. This data hub will make it easier for institutions to collect data from their various software and devices and will allow vendors to build tools that analyse data from a range of software and devices to deliver tools and insights to students, teachers and institutional managers.

This is the same model we have used for learning analytics and after some initial scepticism learning analytics vendors have embraced the concept as it reduces their cost of sale to institutions and allows them to access richer data more easily. Similarly institutions see the value in the learning data hub approach since it takes the pain away from extracting data from institutional systems and helps them avoid vendor lock in as standardising the data means it is easy to switch all their existing data to a new provider. The diagram below illustrates our proposed approach.

Intelligent Campus Data Requirements

The Jisc learning data hub sits in the middle layer of the diagram and works with existing and new vendor products in the top and bottom layers. The top layer relates to the use cases we have identified in earlier blog posts.

The other significant differentiator is the link to our learning analytics data. Most existing products focus purely on the smart campus idea of making the estate more efficient and responsive to user needs. By using this data alongside our learning analytics data, we can explore how the use of the campus relates to learning progress and outcomes and use these insights to make improvements.