Internet of Things (IoT) workshop – Friday 15th February 2019

We would like to invite colleagues from higher and further education to our Internet of Things Workshop in London on the 15th February 2019.

This workshop offers an introduction to LPWAN and LoRaWAN technology, in collaboration with Digital Catapult.

Those who have undertaken work in the intelligent campus space will realise the potential benefits of using IoT networks in connecting sensors, vehicles equipment, and other connected devices to a network.

The workshop has two purposes:

  • To learn more about LPWAN and LoRaWAN and to meet SMEs working with the technology
  • To help shape a Jisc and Digital Catapult initiative which will lend gateways to a small group of universities and organise SMEs to respond to IoT challenges that the universities set

The event takes place at Digital Catapult, 101 Euston Road, London, NW1 2RA – Friday 15th February 2019.

Book now

Time for a story

It was raining and Leda was off to her University for the day. Her phone had already sent her notification to leave for campus early as there was a lot of traffic on the roads and the buses were being delayed. She got to the bus stop earlier than usual and within a few minutes the bus arrived. On the bus, on her phone using the University App, she looked over her schedule for the day. There were lectures, a seminar and she also had a window to get to the library to find those additional books for the essay she needed to hand in next month. She was hoping to catch up with some friends over coffee. There were some notifications in the app, the seminar room had been changed, there was a high chance that the library would be busy today. Leda looked out of the window of the bus at the rain. Today was going to be a good day.

The bus arrived at the campus and Leda got off, she checked her app and started to walk to her first lecture. As she passed one of the campus coffee shops she was sent a notification that three of her friends from the course were in there, so she checked the time, she had the time, popped in and found her friends. Her app let her know that she had enough loyalty points for a free coffee, well why not, Leda thought to herself, she could check if there were any additional resources for the lectures today.

coffee

As Leda drank her coffee, she reflected on why she had chosen thus university. One of the things that had attracted her was the positive reviews and feedback that had come from existing and previous students on the whole student experience. This positive view of the university had resulted in her putting in an application. She was reminded though of one of the induction sessions where the University had taken the time to discuss the whole concept of the gathering of data, the processing of that data, the what interventions were possible and the importance of consent at all three stages. She did worry about this and wondered if all appropriate mechanisms and security was in place to protect her personal data. As she finished off her coffee, she did think was all this data gathering really necessary?

Leda’s phone buzzed, she needed to be at her lecture in ten minutes, however the room was different to the one she was usually in. Leda didn’t concern herself with this, as she knew that the phone would direct her to the room quickly and efficiently. What was so great about this, Leda thought to herself, was that the sessions she attended were always in the right kind space. Sometimes her lecturer wanted to do group work and the usual lecture theatre wasn’t appropriate, so having that in a more suitable room allowed her and her friends to focus on the learning.

As Leda walked around the campus she noticed that there was a lot of devices attached to ceilings and walls. She recognised the CCTV style cameras, though some looked more like speed cameras with some kind of sensor. She had also seen devices with lights in the classrooms and the lecture theatres. Leda made her way to her next session, she used the Wayfinding app on her phone as she knew due to building work on the campus, her usual route was closed. The app would give her the fastest route to get there. As she walked into her seminar room she touched her RFID enabled smartphone to the touchpad by the door. This registered her attendance, but the app recognising her location, started to download the resources for the seminar to her phone and registered her device for the polling and audience response system. Leda found the process much more transparent than being given a clicker. She liked being able to use a single device, her phone for all her smart campus interactions, rather than using a range of devices, cards and equipment to do so.

When Leda had started her degree programme she had been concerned about how data on her was being gathered, processed and acted upon. It was apparent from the start that her journey through the university, both academically and physically would be tracked. She was happy though that the University had published a guide for students on the ethical use of data. She was aware of what data she had to provide and other data about her for which she had a choice on whether it was collected or not. Leda with her friends had been looking at the open algorithms the University used and had been playing with some of them to see if there were any interesting insights into the way her and her friends interacted with the university systems and the campus.

Though Leda had concerns about her personal privacy with all the data gathering happening on campus, her and her friends had noticed a reduction in crime and vandalism. When incidents happened on campus, reaction time from the campus security officers was really fast they could get to the right place much quicker. Leda did think it was all a bit Big Brother, but did feel safer.

Leda was sitting in the library reading through the book she had borrowed, her phone buzzed with a notification, her bus home was due shortly and if she left now, she would be able to catch it. Leda really liked this as though there was a bus timetable, the realities of traffic and weather meant that the buses weren’t always on time. The bus company used GPS to identify the exact location of their buses and this data could then be used by the university app to help learners catch their buses on time. One of the reasons Leda liked this was that it was raining and it saved having to stand in the rain for too long. As Leda sat down in the bus, her phone buzzed again, as she had walked from the library to the bus stop, the phone had downloaded an interesting podcast related to the lecture she had been to ready for her to listen on the journey home.

As Leda settled down for the evening, she reflected on her day. What kind of day would have it been without her phone, without it connected to the different services on campus, the way it worked in a smart or even intelligent way. It was making her whole experience better, she could focus on her studies and spend a lot less time trying to find rooms. The university called it the intelligent campus, in Leda’s view it was more than that, it was a campus that improved the whole student experience. Well for her it did.

The Intelligent Learning Space

Photo by Philippe Bout on Unsplash

So what do we mean by a learning space and how is an intelligent learning space different?

Though the main thrust of the Jisc Intelligent Campus project is looking at how we can extend learning analytics to include and incorporate physical data, there is also space to discuss peripheral and related issues to the work. One aspect of this is the development and design of learning spaces as well as the use of data gathered from the use of learning spaces.

Generally most learning spaces are static spaces designed to allow for particular kinds of learning. Some have an element of flexibility allowing for different kinds of learning activity.

Often the pedagogy is shoe-horned into the space that is available and even if more appropriate spaces are available on campus, often they are unavailable for that particular slot or cohort.

Photo by Nathan Dumlao on Unsplash

A smart learning space would taken into account historical usage of the room and how people felt that the space either contributed or hindered the learning taking place there. You can imagine how users of the room could add to a dataset about the activities taking place in the room and how they felt it went.

You would think that data from the timetable could allow for this automatically, but timetabling data tells us about the cohort, the course they are on and the academic leading the session, most timetabling software doesn’t have the granular activity data in it.

The course module information may have the plans of the activity data within it, but may not have the room data from the timetable, nor may it have cohort details. You could easily imagine that some cohorts may be quite happy with undertaking group activities in a lecture theatre space, but there may be other cohorts of students who would work more effectively if the space was better at facilitating the proposed learning activity.

Likewise when it comes to adding feedback about the session, where does that live? What dataset contains that data?

Then there are environmental conditions such as heat, temperature, humidity, CO2 levels, which can also impact on the learning process.

So an actual smart learning space would be able to access data about the session from multiple sources and build a picture of what kinds of learning spaces work best for different kinds of learning activities, taking into account factors such as cohort, environmental conditions, the academic leading the session and so on…

These datasets could also be used to inform future space planning and new builds, but smart learning spaces are only the beginning. Taking a smart space and making it intelligent is an obvious next step.

An intelligent learning space would take this data, and then start to make suggestions based on the data. It would identify possible issues with the learning plan and make recommendations to either change the learning activities planned, or recommend a more appropriate space. An intelligent learning space would adjust the environmental conditions to suit the activities planned for that spaces, rather than users of the space having to manually adjust the conditions when it becomes too cold, too hot, too bright, stuffy, etc….

classroom

Making the timetabling software intelligent, well dynamic, could mean that rooms are not allocated to cohorts of students for a set amount of time, but rooms are allocated based on pedagogical need and student need and done as and when needed.

One of the key issues with all this is to collect and store the data somewhere, a centralised hub would be critical and that is something Jisc have built for the analytics service and would be used for the future Intelligent Campus service.

Intelligent Campus Community Event – City, University of London – 17th January 2019

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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 one 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
  • Learning Spaces

The third of these events is being hosted and  taking place at City, University of London on the 17th January 2019 from 10:00 to 4:00, and lunch will be provided.

Please put this date in your diary, you can book onto the event using this link

https://www.eventsforce.net/jiscevents/434/register

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.

Join the project mailing list

Photo by Rafaela Biazi on Unsplash

As the project moves through the various project phases we will use the blog to update members and the community on progress. We are also using the blog to post drafts of documents for comment and review.

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 different phases of the project.

The mailing list can also be the place to discuss issues related to the Intelligent Campus space such as library spaces, learning spaces, the Internet of Things, Wayfinding, WiFi tracking and heat mapping.

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.

Le campus intelligent et l’expérience étudiante

48em ADBU Congrès

It was with a little trepidation that I stood on the stage at the 48em ADBU Congrès to deliver a keynote on the intelligent campus and the student experience. The audience were all French library professionals attending the Congress.

I delivered my presentation in English, and for those who needed it a translation service was available. The presentation covered the background to the Intelligent Campus project and it builds on the existing Jisc analytics service. I briefly covered the service and what it enabled for universities and colleges using the service. I also spoke about how the service can provide data and visualisations to students to improve their own performance.

I described the plan for the technical infrastructure behind the intelligent campus and how the data hub can be used to deliver data to different presentation layers. These presentation layers covered a range of possibilities.

48em ADBU Congrès

Talking about tracking students and gathering other data about student brings the legal and ethical issues to the fore. It is important to think about these issues before moving ahead with analytics. We also considered the technical challenges, can we actually measure some of the things that would provide an useful insight. Are these insights even valid? It was this last point that was picked up in following discussions and presentations at the Congress. Do certain kinds of activities actually help students to achieve and succeed? More research in this space is needed.

Many of the questions at the end of the presentation were similar to questions we’ve had at events in the UK.

Overall my keynote provided an insight into the work Jisc is undertaking in the Intelligent Campus space and how far we have come in the realm of learning analytics.

Location-Aware Applications

This is a guest blog post by Andrew Cormack, chief regulatory adviser, Jisc technologies looking at some of the issues that arise when using location-aware applications.

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Wouldn’t it be great if, when passing the library, your mobile phone reminded you of the books you meant to borrow? Wouldn’t it be scary if your tutor knew everywhere you had been in the past week? Your phone’s ability to determine its own location – whether by GPS or by knowing which access points are within range – creates opportunities for highly beneficial applications, as well as highly intrusive ones. When designing, implementing and choosing location-aware applications several indicators can warn you which of those you may be looking at…

Opt-in vs Invisible?

The first distinction is between an application that the individual user enables, versus one that notes the location of any device within range. Both require clear and accurate descriptions of all the information they access and what it is used for. Clearly it is much easier to provide that as part of an active download/enable process than when an individual simply wanders into a monitored space – just one reason why both law and our instincts regard the former as much more acceptable than the latter.

On-device vs On-server

Another significant difference is where the location information is processed. Applications that run within the device (e.g. the “you’re near the library” example above) are likely to cause fewer concerns than ones that require location to be reported to a central service. Even on-device applications still need to be careful to minimise processing of location data; but central services that know the locations of many devices/people are likely to be expected to provide more safeguards and explanation.

Point vs Track

Applications that involve recording a sequence of locations are likely to be perceived as more intrusive than those that simply record presence. Indeed European legislators are currently debating whether tracking applications that are not Opt-in should be banned. However there are many applications that only need to process a single, current location (again, see the library example) or, indeed, merely the number of devices present in an area (for example to identify where additional wifi coverage might be beneficial!). Since the same technology is used for all these options, applications should include, and describe, safeguards to ensure the broader functionality is not, in fact, used. If you are using technology to count the number of people in location, make sure you describe what prevents the same sensor being used to listen, watch or track them.

Update on the Intelligent Campus hackathon

Following the hackathon, we have published a blog post (on the main Jisc website) on the completed hackathon.

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Students working on hackathon pitch by Paul Bailey CC BY-NC-ND

Student ideas become a reality following two app development challenges – We only asked for prototypes – but two student teams in our development competition gave us app store-ready solutions to tackle common campus annoyances.

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?

microphone

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?