Category Archives: stuff

Keeping you awake?

Across the country there are a real variety of university campuses. No two campuses are alike, but all have similar challenges that the Estates team have to work with. There was an interesting article from Wonkhe a few weeks back on what keeps your estates manager awake at night? from the incoming AUDE chair.

Estates Directors, by and large, are significant net spenders of university income. While we may also run aspects of our institution’s income-generating commercial services – conferencing and retail for instance – and we know our university built environment can be key in attracting research income, staff and students too, on the whole we sit on the expenditure side of the balance sheet, with buildings second only to people in terms of operating costs.

It noted the challenge that costs are rising, and budgets are being cut, and the challenges that this budgetary nightmare brings to the Estates team. There are things they can cut, things they can spend less on, and then there are the statutory requirements that have to be met and paid for. In addition the way in which campuses are been used are changing.

The issue of both students and staff using the campus differently now, post covid, and their hybrid use of space for studying and working. We know that space designed for the way we would use those spaces pre-covid, aren’t necessarily now the kinds of spaces that we need post-covid. Easy to say, actually quite challenging to think about and design spaces that meet these new needs. What are those new kinds of spaces and how would we know?

Most universities I have discussed this with reinforce the importance of the university space, a place for people to come together for education and research. There is an expectation that staff and students will be physically travelling to and using the university buildings and spaces.

Even so with hybrid working and studying the norm these days, spaces have to be flexible to allow for in-person working and studying, as well as allowing for online interactions to take place on campus as well. Just because a meeting or a lecture is online this doesn’t automatically mean that the person participating is going to be off campus. Are there spaces on the campus that can be used for these online activities.

Another challenge isn’t just space, but also time. You can already see that more people are coming into the office for two or three days a week, and those days are usually in the middle of the week. The challenge that anyone has in managing space is how do you provide the capacity needed for two or three days, knowing that for the rest of the week it will be underutilised. How do you incentivise people to spread their in-person working (and studying) patterns across the week, to ensure space is being used efficiently.

With people working at home for part of the week, what I am seeing in our own spaces, and hearing about on university campuses, and also seeing in the office work environment; is that without some kind of intervention, people are creating their own working patterns based on the patterns that benefits them individually and aren’t necessarily the most efficient mechanism for space utilisation across a working space. There is a default to mid-week in-person working, resulting in less utilisation on the extremities of the week. Should spaces be closed one day a week to allow for this?

Might it be more efficient to spread utilisation across the week, and then reduce the size of the space required? How then do you encourage and incentivise people to work on the less popular days of the week?

Whatever decisions are made by estates teams in relation to the campus, it is understandable why it might be keeping them awake at night.

Personalisation, just some thoughts

man on windowsill looking over a city
Image by Pexels from Pixabay

I have been looking at what we mean by personalisation in higher education. What I have discovered is that there isn’t really any clear idea or definition of what we mean by personalisation and across the sector there are varied views and opinions about what is personalisation, what can be personalised, and importantly why we would do this.

In this blog post I am not trying to come up with a definition of personalisation, nor am I proposing what it definitively means for higher education. No, this post is much more some of my recent thinking on the issue and what personalisation could mean for higher education.

In many ways the sector already has personalisation, the student journey for each student, though common in many areas, is pretty much unique to each individual student.

There are reasons why universities might want to be more explicit in how they personalise the student journey. These may include equity, inclusion, adaptation, enhancement, efficiency, and other reasons. Obviously though you might want to allow all of the student journey to be personalised, the reality is that a fully personalised student journey isn’t practical or affordable.

Reviewing the student journey, there will be touchpoints, where the student interacts and engages with the university. Across those touchpoints the university will need to decide which touchpoints:

  • Don’t need to be personalised.
  • Must be personalised
  • Should be personalised
  • Could be personalised

This will then allow those touchpoints to be prioritised.

In addition the level of personalisation would need to be considered, what changes would have to be made and the subsequent impact of those changes. What are the variables across the touchpoints and what does personalisation look and feel like for students for all the touchpoints.

I am interested in how data, digital, and technology can help and support this process. Certainly from a data and analytics perspective, the ability to record those touchpoints and the changes that arise from personalisation.

From my perspective, the next stage will be is, what is the role of an organisation like Jisc in the process of personalisation.

Transformation and all that

graphic
Image by Gerd Altmann from Pixabay

As we discuss and talk about digital transformation, it becomes apparent very quickly that digital transformation is not about digital causing transformation. It’s not as though if you invest in digital and online technologies that therefore you will be (magically) transformed.

When discussed digital transformation, it is probably best explored as transformation which is enabled by digital technologies.

I have written before about this, two years ago I published a blog post, called Thinking about digital transformation which I used to discuss and reflect on my then thinking about digital transformation.

Well, I have been thinking about what we understand mean by digital transformation and in some discussions, I have been using different kinds of explanations to explore what I see and understand digital transformation is.

In the post I draw out that merely making something digital, doesn’t mean you have transformation. The example I used was about the authorisation of leave.

calendar
Image by tigerlily713 from Pixabay

When you start to think about this digitalised process, using a bespoke system, over spreadsheets or pieces of paper, you may think of this as transformative. However, when you did deeper, there is still that same old authorisation process there.

I concluded…

The digitalisation of the HR system only becomes transformative when you actually look at and transform the processes and the thinking behind those processes. You need to transform the process; the digital HR system enables that transformation. Simply digitalising your HR system results in less benefits than if you transform the organisation and use digital technologies to support that process of transformation.

Here we are two years later and re-reading the blog post, much of what I wrote still stands up. In some cases the technology has moved forward already.

I wrote…

Now looking further forward, could you use artificial intelligence (AI) to learn from leave request, rejections and authorisations to have a better idea of when there are potential pinch points…

There was this Gartner article on AI in HR which was published last year.

AI will have an effect on the work conducted by the HR function, across the employee life cycle. This impact includes HR operations and service delivery, recruiting, learning and development, and talent management.  In a first step, AI will lead to new sets of employee expectations about how employees interact with HR and HR technologies. Over time, this shift will lead to rethinking the purpose and structure of individual HR roles and teams.

meeting
Image by StartupStockPhotos from Pixabay

I also mentioned the cultural challenges that existed.

When I first started asking this question a few years back, I was quite surprised by the resistance to the idea of a system or an individual self-authorising leave, and it got to the point where often the discussion would just fall down. Culturally people (okay managers) were struggling with the concept that they no longer had the power to authorise leave or not.

These are still very much here. I recently attended UUK’s Survive or thrive? Grasping the financial sustainability challenge event and the cultural challenges were echoed there as well.

We need to apply all the innovation, creativity and business acumen across the sector and beyond and grasp the nettle to find solutions to the big questions.

In one session it was clear that the technological or digital solutions were there already, what was holding back the transformation was the cultural and people issues.

In the past we may have wanted to transform, but we didn’t have the necessary tools to make that happen. Today we have the tools, the question is, do we have the will?

In the city there’s a thousand faces, all shining bright

Image by Pexels from Pixabay
Image by Pexels from Pixabay

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.

London
Image by Free-Photos from Pixabay

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.

Image by rawpixel from Pixabay
Image by rawpixel from Pixabay

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.

CCTV
Image by Stafford GREEN from Pixabay

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.

laptop
Image by fancycrave1 from Pixabay

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.

References

Clay, J. (2018). Guide to the intelligent campus. 1st ed. [ebook] Bristol: Jisc. Available at: http://repository.jisc.ac.uk/6882/ [Accessed 13 Feb 2024].

Clay, J. (2017). In the city there’s a thousand faces, all shining bright | Intelligent campus. [online] Intelligentcampus.jiscinvolve.org. Available at: https://intelligentcampus.jiscinvolve.org/wp/2017/06/12/in-the-city-theres-a-thousand-faces-all-shining-bright/ [Accessed 13 Feb 2024].

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].

Education Technology. (2018). What makes an intelligent campus?. [offline] Available at: https://edtechnology.co.uk/Article/what-makes-an-intelligent-campus [Accessed 10 Sep. 2018].

En.wikipedia.org. (2018). Smart city. [online] Available at: https://en.wikipedia.org/wiki/Smart_city [Accessed 13 Feb 2024].

What makes an intelligent campus?

campus
Image by 小亭 江 from Pixabay

I often, well sometimes, I got asked what is the difference between a smart campus, and an intelligent campus.

A dumb campus is merely a series of spaces and buildings. For example the heating comes on at 8am, off at 5pm, and is only switched on between November and March, regardless of the external temperature.

A smart campus, uses data from the spaces and buildings to make decisions. For example, a thermostat controls the heating, as the room warms up, the heating turns off.

An intelligent campus, uses data from across the organisation to make decisions and make predictions. For example, a team is out on an away day, so the intelligent campus, switches off the heating and lighting on that floor for that day.

Now onto the detail…

The smart campus is already here; the technology, sensors and data analysis capability are all available, but 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.

What we need now is the intelligent campus, where data from the physical, digital, and online environments can be combined and analysed, opening up vast possibilities for more effective use of learning and non-learning spaces.

But before we head down that road, it is useful to reflect on what we mean by a campus.

Universities across the UK are a diverse mix of traditional campus-based institutions, city-based universities where the estate is mixed in between retail, office and housing, and multi-site universities, with geographically spread campuses.

But they all have many features in common. There are formal teaching spaces, including classrooms, lecture theatres and labs. There are informal learning spaces, where learners can learn when they want to, including computer labs and the library.

There are also a whole host of social spaces such as cafeterias and dining halls, sports facilities, concert halls, common rooms, and student accommodation. Then there is the work space you need to run the institution, from the finance office to the server rooms, and there may also be green spaces, parking areas and business parks.

The non-smart or “dumb” campus is one where information on how physical infrastructure and space is being used, and what it is being used for, is rarely noted, and even more rarely acted upon. Any room utilisation surveys will be carried out by people with clipboards noting the number of people in each room, but probably not considering their activities. It would be really hard to work out how many people were in the building, even more challenging when there are multiple entrances and exits. In the non-smart campus heating comes on at 8am, and is only on between November and March, regardless of the external temperature. There are physical switches for the lighting system. Timetabling will dictate which cohort has to be where and when, and what subject they are doing. But often that is planned a year ahead and won’t take account of the kinds of teaching and learning that may be undertaken in any given slot, or whether the cohort changes size.

The non-smart campus can be inefficient, costly, and impact negatively on the student experience.

In many ways the smart campus is already here. Most new buildings built on the university campus will have an element of smartness about them.

There is some technology already in place which can start us on the road to making better-informed decisions about how best to use space, sensors, for example. We all know when lighting is linked to a movement sensor because everything goes dark when we sit still for too long, promoting much frantic arm-waving to turn the lights back on. The data from these sensors can tell us a lot about occupancy, as well as saving electricity costs.

But 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. We can also ensure that the lighting, heating and CO2 levels are within defined parameters.

Timetabling and curriculum planning, gives us an insight into when students could be on campus (not saying they will miss lectures, but you and I know this happens), adding in attendance data (if used) can enhance that picture. Bring in retail date from the coffee places and the shops. Data on bus tickets and car parking will also be an useful indicator of visitors to the campus.

Throw in library data, PC bookings and book issues.

Then there is network data, how busy is the network, how many devices are connected. Top end wireless networks can also help with occupancy based on connections to different wireless endpoints.

Even energy costs will add some useful insights into the use of the campus.

So what is the difference between a smart campus and an intelligent campus?

There are some key considerations when we come to looking at what makes an intelligent campus – the first is the aggregation and analysis of different kinds of data.

One of the noticeable attributes of a smart campus is that data is often siloed and isolated, and analysis and decision-making is based on a single data set.

An intelligent campus will take data from a range of sources, not just the physical aspects of the campus and how they are being used, but also the data from digital systems such as attendance records, the virtual learning environment, the library, student records, electronic point-of-sale 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. By using live and dynamic data, decisions can be made that are based on the current state of the campus. Here are some examples:

A student using an app on their smartphone can decide very quickly if they should visit the library that day as the app is indicating that the library is very busy, but it also informs the student that the noise levels in the coffee shop on the other side of campus are low, making it a possible option for study.

A lecturer decides on various group activities in a classroom, the room system having checked the module information and activity parameters, and set the heating and lighting to the optimal level for the activity, while also considering the preferences of the particular cohort of students.

A member of the finance team finds out from an app on their computer, based on data from previous visits, that the cafeteria is running low on the vegetarian special that day, so they can decide to leave now for lunch, so as not to miss their favourite meal.

The next level of the intelligent campus is to move beyond using this live data to add a machine learning or artificial intelligence element to the analysis, providing further insights. Bringing in historical data, feedback, and evaluative data, we can start to add a level of predictive analytics that will allow students and staff to make informed decisions on what is likely to happen.

Predicting when the library will be most busy and noisy, for example, will allow students to choose whether to come to campus before or after peak times, and will reduce the probability that the library will be too full for comfort at any one time.

An intelligent timetabling system could start to reflect on the sort of activity likely during a particular study module, not just on the number of students taking part, so the group activity could be shifted to an optimal room, rather than sticking rigidly to the same space.

By analysing when and how rooms are used, organisations will be able to make smarter, more effective use of learning spaces and other facilities across campus and to improve curriculum design and delivery.

But could this dream 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? How would we collect that data and what smart campus technologies would we need to have?

Making timely interventions to ensure that the best available spaces are being used for each session will enable students to learn more effectively and ensure that the organisation is running efficiently. Insights into utilisation of space, services, and resources, will enable universities to enhance the student experience.

Though much is being done in this space, the work has been focused on the relationship between the physical estate and the IT infrastructure coming together. The reality is that a university campus is awash with data from many different systems. A truly smart campus needs to bring that altogether, and an intelligent campus will enable deeper and more useful insights. Longer-term the possibilities for the intelligent campus are practically limitless.

e-Learning Stuff: Top Ten Blog Posts 2023

coffee
Image by David Schwarzenberg from Pixabay

This year I have written 89 posts on the blog. There were 92 posts in 2022, 113 blog posts in 2021. In 2020 I had written 94 blog posts. In 2019 I had written 52 blog posts which was up from 2018 when I only wrote 17 blog posts.

I decided when I got my new role in March 2019 that I would publish a weekly blog post about my week. I did this all across 2023 as well which added to the number of posts. I did once get asked if these week notes were popular, not really, but they are much more for me than for others. However, for the first time in the five years I have been doing week notes, one of them has made the top ten.. Interesting how so many old posts (more than ten years old) are in the top ten. Probably means I need to write better and more interesting blog posts.

The post at number ten asked the question, Hey Siri, are you real?

A really old post from 2008 was the ninth most popular post. Full Resolution Video on the PSP. Do people still use the PSP?

The blog post at number eight in the top ten is an old post from my series on how to use a VLE.  100 ways to use a VLE – #89 Embedding a Comic Strip. This one is still popular and is about embedding comic trips from online services into the VLE.

The seventh most popular post was from 2008, and asked the question, Can I legally download a movie trailer? One of the many copyright articles that I posted some years back. Things have changed since then, one of which is better connectivity which would allow you to stream content direct into a classroom, as for the legal issues well that’s something I am a little behind on the times though in that space.

At number six was a post on freakish occurrences, “million-to-one chances happen nine times out of ten”. One of my favourite quotes from Terry Pratchett is that “million-to-one chances happen nine times out of ten”. When something awful happens, or freakish, we hear news reporters say “it was a million-to-one chance that this would happen”.

Fifth place was Ten ways to use QR Codes which was not a post about ten ways to use QR codes.

The post at number four was a week note from 2019, Student Journey – Weeknote #08 – 26th April 2019.

The third most popular post was from 2009 and asked To Retweet or not to Retweet which was a post about retweeting on the Twitter.

The post at number two was from 2015, I can do that… What does “embrace technology” mean? was from the FE Area Reviews.

The most popular post in 2023 is one of the all time popular posts, The iPad Pedagogy Wheel. Published in 2013, this was number one for many years.. I re-posted the iPad Pedagogy Wheel as I was getting asked a fair bit, “how can I use this nice shiny iPad that you have given me to support teaching and learning?”. It’s a really simple nice graphic that explores the different apps available and where they fit within Bloom’s Taxonomy. What I like about it is that you can start where you like, if you have an iPad app you like you can see how it fits into the pedagogy. Or you can work out which iPads apps fit into a pedagogical problem.

Time for another story

The sun is setting over a sports arena. In the background is some stadium covered tiered seating. In the foreground are some trees and a wire fence.
Image by 小亭 江 from Pixabay

In the world of the intelligent campus I wrote a student story to illustrate and add a personal touch of what the intelligent campus could mean for the student experience.

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.

Dom Pates did a similar thing using ChatGPT to write a story.

As the day wore on, Marcel attended a variety of classes and workshops, each designed to challenge and inspire him. The smart campus was constantly adapting to his preferences and needs, and he felt like he was learning more in a day than he had in a week at his old university.

I have recently been using Bard for various things, so I thought I would get it to write me a story, using the same prompt that Dom used.

Write a short story in the style of speculative fiction about a university student who spends a day on a smart or intelligent campus some time in the future. Ensure that the campus is designed to address issues around sustainability and data privacy, yet is responsive to the student in a personalised (but not creepy) way. Ensure that the student is studying a course for a future-focused job, and that the campus is actually a desirable and ethically-designed environment. Make the short story go through different stages of the day, and comes to approximately 1,500 words in total. Include an ethical quandary that the central character faces during the course of their day.

This was the result.

The year is 2042. University student Anya wakes up in her dorm room, which is located in a smart campus that is designed to address issues around sustainability and data privacy. The campus uses solar panels to generate electricity, and it has a rainwater collection system that provides water for irrigation and flushing toilets. The campus also has a sophisticated data privacy system that protects students’ personal information.

Anya gets out of bed and walks over to her desk. Her computer screen lights up as she approaches, and a personalized greeting appears. The computer knows Anya’s name, her major, and her class schedule. It also knows her preferences for learning style and study environment.

Anya opens her textbook and starts reading. The computer automatically highlights the important passages and provides links to additional resources. Anya can also ask the computer questions about the material, and the computer will provide answers or generate practice problems.

After a few hours of studying, Anya takes a break. She walks over to the window and looks out at the campus. The buildings are all connected by a network of walkways and bike paths. There are solar-powered charging stations for electric vehicles, and there are even a few self-driving cars that students can use to get around.

Anya decides to go for a walk. She walks through the quad, which is filled with students studying, chatting, and playing games. She walks past the library, the student union, and the gym. She even stops by the campus coffee shop for a cup of coffee.

As Anya walks, she thinks about her future. She is studying to be a data scientist, and she knows that she will be working with a lot of personal data. She is concerned about the ethical implications of her work, and she wants to make sure that she uses data in a responsible way.

Anya arrives at her next class, which is on artificial intelligence. The professor is talking about the potential for AI to be used for good or evil. Anya thinks about the ethical quandary she is facing, and she wonders how she will use her skills to make a positive impact on the world.

After class, Anya goes back to her dorm room. She logs onto her computer and starts working on a project for her data science class. She is using a dataset of social media posts to track the spread of misinformation. She wants to find out how to identify and counter misinformation before it can cause harm.

Anya works on her project for several hours. She is exhausted, but she is also excited about the potential of her work. She knows that she is making a difference, and she is confident that she is using her skills in a way that is ethical and responsible.

Anya goes to bed, knowing that she has made a good use of her day. She is confident that she is on the right track to a future-focused job that will allow her to use her skills to make a positive impact on the world.

I think this is an interesting interpretation of the prompt. I also think that much of what is mentioned or discussed is already here, or will be here in the next five years. What will the campus of 2042 look like, I think it will be very different to this story.

It was original I tell you, it was…

laptop user
Photo by Christin Hume on Unsplash

A story of a personal perspective and recollection.

With the imminent release of AI detection tools within Turnitin, I am reminded of an incident over ten years ago after we introduced a plagiarism checker tool into the college I worked at. I was responsible for a lot of the initial training in the tool. For each training session I would create three pieces of content to be put into the tool to check for originality. One was a straight copy of something from the web, usually a blog post of mine, or wikipedia. The second was an original piece would contain (and correctly) quote third party content. For the third piece I would always create a new original piece of content.

So, there I was delivering the training and I put the first piece into the plagiarism checker tool. It straight away identified that this was copied from the web, and showed the original source.

The second piece went in, again it identified there was non-original content in the submission. However I used this piece to demonstrate the limitations of the tool, as the academic would need to check the submission themselves. They would then see that no plagiarism had taken place.

I always had to create a new piece of content for the third (original) submission so that it would be identified as original.

However one time I did this, the plagiarism checker tool, identified the third original submission has having been copied. I was astounded, as I knew I had only written it that morning.

Upon further investigation I found out what had happened. The originality report indicated that my original piece of work had been “copied” from a university website. Well I hadn’t done that I had written it that morning.

Hmmm….

Doing some more Google searching, what I found out, was that the university did indeed have some content on their website. They had in fact “lifted” it from an article I had written a few years previously.

So what had happened was that. Back in the 2000s I had written an original piece of content. The university had taken and used that content.

I in the 2010s had then written an original piece of content, well so original that it was very similar the content I had written years earlier. Obviously I based my new original writing on something I had forgotten I had written about before. 

Putting this “new” content into the plagiarism checker tool resulted in the “new” work been seen as a copy of the earlier work. The plagiarism checker tool only checked originality, so didn’t know (or realise) that the university had copied me. The plagiarism checker tool doesn’t tell you the source.

The key lesson here though was that the plagiarism checker tool was insufficient on its own. It only told part of the narrative. Further investigation was needed and further checking was required to get to the actual truth, and not the perceived truth of the plagiarism checker tool.

What does this mean? Well if your plagiarism checker tool has AI detection in, then you will need to recognise that whatever the plagiarism checker tool tells you, this isn’t the end of the story, it is only the beginning.

The other thing I learnt was that I needed to be more creative in my writing going forward…

Time for a story

Back in 2018 I wrote a story about the Intelligent Campus. Sections of the story were part of the Guide to the Intelligent Campus. I posted the whole story to the project blog, but am re-posting here as a starting point for new and different stories.

Image by Pexels from Pixabay
Image by Pexels from Pixabay

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
Image by David Schwarzenberg from Pixabay

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.

CCTV
Image by Stafford GREEN from Pixabay

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.

campus
Image by 小亭 江 from Pixabay

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.

I am planning to update the story to reflect changes in both society and technology.

Spaces and Wellbeing

Group working
Image by StockSnap from Pixabay

Could we use space utilisation data to support wellbeing?

As students frequent and move about the campus, the spaces in which they study, learn and relax can have an impact on their wellbeing.

Student wellbeing is a key priority for the Higher Education (HE) sector. The Stepchange framework, created by Universities UK, calls on all universities to make wellbeing a strategic priority which is “foundational to all aspects of university life, for all students and all staff.”  We know, as discussed in a recent Jisc blog post, that good data governance provides the foundation to build new wellbeing support systems that can respond to the needs of students – helping more people more quickly while maximising the use of available resources.

As well as the usual suspects that universities can use to collect engagement data, such as the VLE, library systems and access to learning spaces, could universities use space utilisation data to, enhance and improve the spaces (formal and informal) on campus to deliver a better student experience and support wellbeing?

Could we use data from how spaces and when spaces are being used to deliver a better student experience and maximise student wellbeing.

Space utilisation

 Currently universities will use manual and automated methods to measure space utilisation. Often this data is used to for self-assessment reports and proposals for expansion. Few are analysing that data in real time and presenting the information to students.

We know that measuring usage of space, tables and desks can be fraught with ethical concerns. It is critical when measuring space usage that the university is transparent about what it is doing, how it is doing it and why.

group
Photo by Annie Spratt on Unsplash

The importance of space

 You can imagine the scenario when a student who is facing challenges on their course, and decides to visit the campus, expecting to find space to study, but the library is busy, the study areas are noisy and even the café is closed. This disappointment can lead to annoyance. This small negative experience could potentially impact on the wellbeing of the student. They are probably not alone, as other students (and staff) are equally frustrated and disappointed. If they had known about the current (and predicted future) utilisation of that space, they may have made different plans. They could have left earlier, or arrived later.

Using data on spaces to support wellbeing

Analysing the data on space utilisation could provide a valuable insight into ensuring that when students need space to study that space is available, and can support wellbeing.

Universities could use the data to ensure that when space is unavailable, for example for cleaning, so that this is done at the best possible time, for the minimal impact on student wellbeing.

 Space isn’t the answer

Of course, when it comes to improving student wellbeing, just having data about the spaces students use is most certainly not going to be enough. Data on how students interact with online systems and services, the resources they engage with, all provide a wealth of engagement data. We know that engagement is one measure that universities can look at to understand if there is a story behind a student’s dis-engagement with the university and work to improve that student’s wellbeing.

As Jisc’s Andrew Cormack and Jim Keane said in their recent blog post on data governance,

If their new university does not use data intelligently to improve their day-to-day experience, students could be disappointed, which reflects badly on the institution.  

Universities should reflect on all the data they collect, and decide what the data can tell them about the student experience, and importantly what interventions they need to make to positively impact on student wellbeing. Running out of coffee isn’t the end of the world, but combine many small negative impacts on the student experience, students will not be happy and wellbeing could suffer as a result.

Read Jisc’s framework and code of practice for data-supported wellbeing – which outlines how to promote ethical, effective, and legally compliant processes that help HE organisations manage risk and resources.