A new series of updates about emerging trends in learning technology and digital literacy
Big news from LSE tonight (I think, anyway). I was in a meeting with colleagues in which I’m pretty sure I learned that we will be selling the campus in its entirety because we have no further use for the buildings.
I say “pretty sure” because my kid was on Fortnite and the WiFi kept dropping out.
But the gist of it was: everyone is so completely comfortable with working, teaching, learning, researching and so forth at home that we really don’t need this massive investment in bricks and mortar, so why not sell it?
Word is, Mike Ashley is interested in turning the Old Building into a Sports Direct and Tim Martin has made a very reasonable offer for the Three Tuns.
Someone with a cat avatar in MS Teams said:
LSE has a grand tradition, stretching back at least two weeks, of making bold decisions and acting on them swiftly. We are proud to be the first of the Russell Group universities to follow the Open University into the online space. Stop hitting your sister with that. Don’t make me come upstairs. One… TWOOOOO
The advantages are legion, and even I can think of at least one, despite having been woken by my children at 04:30 because someone thought it would be fun to put the clocks forward in the middle of a pandemic.
No you shut up.
As I was saying: the “academic hour” is at an end. No longer will I have to scoot across campus from Clement House to 32 Lincoln’s Inn Fields and show up late for a meeting. I can simply drop out of my eleven o’clock and spend the following ten minutes saying: “Can you hear me? No? What about now? Ugh! I hate this computer. Wait, I have a headset somewhere…”
This post is based on a presentation I gave at this afternoon’s M25 Learning Technology Group meeting at King’s College London.
I like to think
(it has to be!)
of a cybernetic ecology
where we are free of our labors
and joined back to nature,
returned to our mammal
brothers and sisters,
and all watched over
by machines of loving grace.
This is a lyric expression of something that’s come to be known as Technological Utopianism. This isn’t merely the preserve of beatniks and hippies; Bertrand Russell wrote, in his 1932 essay In Praise of Idleness, “four hours’ work a day should entitle a man to the necessities and elementary comforts of life, and that the rest of his time should be his to use as he might see fit,” because:
Leisure is essential to civilization, and in former times leisure for the few was rendered possible only by the labors of the many. But their labors were valuable, not because work is good, but because leisure is good. And with modern technique it would be possible to distribute leisure justly without injury to civilization.
And John Maynard Keynes wrote, in his 1930 essay Economic Possibilities for our Grandchildren, that within 100 years the “economic problem” would be solved. In 2030 we would all be working “three-hour shifts or a fifteen-hour week” and:
For the first time since his creation man will be faced with his real, his permanent problem-how to use his freedom from pressing economic cares, how to occupy the leisure, which science and compound interest will have won for him, to live wisely and agreeably and well.
Keynes’s grandchildren are eleven years from this horizon, and (needless to say) things haven’t quite worked out that way. Why not?The Musée des Arts et Métiers in Paris is a temple to scientific progress. In its galleries you’ll find hundreds of machines, including a lovely example of the Jacquard loom. Looking at the machine, you’ll see some punched cards which, as the mechanism moves, raise and lower different warp threads, producing patterned textiles. The cards can be re-ordered to create different patterns.
Those punched cards may look familiar to computer users of a certain age; they are practically identical to those once used to program computers. The comparison is not lost on the curators of the museum; the exhibition leads finally to a room containing a Cray 2 supercomputer. Nor was it lost on Charles Babbage, who understood punched cards could be used to program his Analytical Engine.
The textile industry gave Britain its first full-blown industrial relations crisis: the outbreak of machine breaking by the Luddites. The Luddites were not, contrary to popular opinion, opposed to technology per se; textile workers had been using stocking frames since Tudor times, but in a highly-regulated industry. The Luddites’ machine breaking was instead a response to the use of machinery in “a fraudulent and deceitful manner,” particularly by unskilled apprentices working without the supervision of master craftsmen. In Eric Hobsbawm’s memorable phrase, the Luddites were conducting “collective bargaining by riot“.
The textile workers’ expertise, hitherto distributed among men and machines in a cottage industry, became concentrated in machines housed in factories owned by capitalists.
Did the textile workers share in the profits that followed? Did they reduce their hours to fifteen a week? Of course not:
[Wages] could be compressed by direct wage-cutting, by the substitution of cheaper machine-tenders for dearer skilled workers, and by the competition of the machine. This last reduced the average weekly wage of the handloom weaver from 33s. in 1795 to 4s. 1½d. in 1829.
Eric Hobsbawm, The Age of Revolution: Europe 1789-1848
In thirty four years, their wages were reduced to one eighth.
This is not a phenomenon confined to the pages of history books. A similar battle is playing out right now, on the streets of London, between black cab drivers and Uber.
In order to become a cabbie, you need “the knowledge,” earned by learning 80 runs across the city, getting at least 60% on two written exams, and passing three oral exams. This can take between three and five years to accomplish. By contrast, becoming an Uber driver in London requires that you have a TfL Private Hire license, and I estimate this to take a minimum of eight weeks. The process includes what TfL calls a “topographical skills assessment“, which (being brutally honest) ensures you are able to read a map.
It is very difficult to find out the earnings of black cab drivers or Uber drivers, because they are self-employed and not required to reveal their earnings to impertinent systems administrators. But the New York Times estimates Uber fares to be about 30% cheaper than black cabs, and Uber extracts a fee upwards of 25% from its drivers. As Daniel Markovits, author of The Meritocracy Trap, points out, a cabbie can earn enough to own a home, provide for his family, and go on holiday. The precarious finances of the Uber driver, on the other hand, are legendary.
Naturally all this enrages the black cab drivers; as with the looms in the dark satanic mills of the 19th century, their previously distributed expertise is becoming concentrated in the machines of capitalists.
But this time, there are no looms to smash. Uber has developed not one machine learning algorithm, but so many that their engineers have created a bespoke machine learning service, so the teams of engineers working on the myriad components of the Uber service can automate them more easily.
So they want to replace you with a machine. Is it any good?
Models used in forecasting have a property called “skill”, which measures how good they are at what they’re intended to do. I’d like you to consider a specific example which, while detailed, is readable enough for a non-technical audience. Amazon Web Services will rent you a machine learning service, which you can bend to your requirements. In this example, Denis Batalov shows how you can use Amazon Machine Learning to predict customer churn from a mobile phone service.
For mobile providers, obtaining new customers is costly. Those special offers you see advertised are loss leaders designed to lure you into signing a contract. They will absorb the loss because they assume you are too lazy to switch providers at the end of your contract, at which point they can milk you for profit. Those customers who do leave are said to “churn”.
The set uses comparatively few data points, for example how long the customer has had the service, how much they use their phone, how much the service costs and how many times they’ve called Customer Services. The goal of the exercise is to identify those customers most likely to churn, and to stage an automated intervention, buttering them up with free minutes, a new handset, etc.
The algorithm is trained first on data where it can see the outcome. Customer x with the following attributes remained a customer, but customer y with these attributes decided to leave. Then, to test its skill, it is shown the data without seeing the outcome. More successful models are selected for evolution, and the remainder are culled. This continues until the returns are diminished to the extent that there’s no more tweaking to be done.
As you can see, 14.5% of customers in the set “churned”. Can the machine identify those who will stay, and those who will leave? Well, it can identify 86% of them. But this is, in practical terms, the same as having no model at all, or (to put it another way) having a model which assumes all customers are loyal but is wrong about 14.5% of them.
However, since losing customers is expensive, and offering butter-ups is (comparatively) cheap, you can tweak the model so that it is more wrong than having no model at all, and yet saves the company $22.15 per customer. Scaled up, this is big money (and a big bonus for the ML developer).
What has this to do with Learning Technology? I’ve seen ML models not very different to the above, deployed in a VLE and using very few data points, making predictions about whether a student will pass or fail a particular module, or whether a student will drop out or remain enrolled. The problems come in the “costs” we attach to the quadrants in the truth table, and in concentrating expertise in a machine at the expense of our distributed expertise as individual educators.
Seen it all before
Any forecasting discipline also suffers the problem of bias. In human actors, we hope it is unconscious. But in machine learning, it is built in, because we are training our AIs on historical data.
In 2017, Amazon announced it had shuttered an experimental programme to train an AI for recruitment. Scouring LinkedIn or sifting a pile of applications is time-consuming (and thus costly), repetitive, boring, and tiring. These characteristics belong to tasks which IT professionals immediately select for automation. But, just as when training a model to predict customer churn, historical data are required, with all the perils inherent therein. Amazon’s AI was chucking women’s applications on the discard pile, because the company had, in the past, consistently favoured male applicants over female ones.
Again, what has this to do with learning technology, or even with IT in HE? I’ve found institutions which were at least toying with the idea of using machine learning to sift admissions applications. What will that do to our efforts to widen participation? Instead of artificial intelligence, we will have automated ignorance.
When you combine bias-as-code with the kinds of de-skilling discussed in the cases of the textile workers and taxi drivers, you have a potent recipe for problematic decision making. The most recent example is the “sexist AI” behind Apple Card, which assessed a married couple who, on the face of it, presented identical credit risks. It offered the husband 20x the credit limit it offered his wife. Apple’s customer services people threw their hands up: “It’s just the algorithm”. Even The Woz waded in, observing “It’s hard to get to a human for a correction though. It’s big tech in 2019.” Once again, we see expertise, previously distributed among financial advisers, concentrated in a machine.
Weird, inscrutable logic
Even if Apple had retained the human capability to consider an appeal against the AI’s decision, it wouldn’t have been able to explain that decision, because ML algorithms do not admit of human scrutiny: software that is evolved is unreadable.
As a systems administrator, I like code that can be audited. When something aberrant happens, I like to be able to see if there’s a logic problem. But in discussions with non-systems (i.e. normal) people, I’ve come to agree that it’s acceptable, in some circumstances, to audit a system only knowing its inputs and its outputs. An example is the pocket calculator. You can ask it to solve 5 x 5, 10 – 8, etc, and compare it with your own working. Eventually you come to trust the system and ask it to solve the square root of pi, and because it’s been right about everything until now, you believe that it’s right about this.
But as we’ve seen, ML is being asked to solve more complex problems than the root of pi. It’s being asked to make predictions and decisions, with multiple inputs that it may or may not be using to draw its inferences, some of which could be wildly inappropriate. There are, after all, a lot of spurious correlations in the world.
So I finish on an appeal: if your institution is ever considering the use of AI to admit applicants, or mark students’ work, or predict their likely success, press as hard as you can for the institution to retain a human in the process. Because if the past is any guide — and it surely is, because that’s the basis on which we’re training our machines — if you don’t, there won’t be anyone left to hear an appeal.
Next Tuesday LTI will be hosting a presentation by Chrissi Nerantzi (Principal Lecturer in Academic CPD, expert in creative, innovative learning, teaching and assessment from Manchester Metropolitan University) on Playful and Creative Learning. A good opportunity to reflect on what playfulness and creativity mean in an educational context and explore ways in which we can promote it in our practice.
Definition: What is playful learning?
In a blog post by JISC titled “Learning to play, playing to learn: the rise of playful learning in higher education“, Chrissi gives an explanation of what playful leraning means to her:
“Playful learning is using play activities to immerse ourselves and learn, either on our own or with others in a space we feel safe. In playful learning it’s ok to make mistakes when experimenting with new ideas, when challenging ourselves and others and doing things we normally wouldn’t do – which can lead us to surprising discoveries.
Playful learning can happen anywhere.”
Play and Its Connection to Creativity
The “Creativity for Learning in Higher Education” open course, based on the Manchester Metropolitan University’s PgCert and MA in Higher Education in which Chrissi is involved, offers colleagues with an interest in creative teaching and learning to explore three areas that foster more creativity in their practice and their students’ learning experience. One of which is play and games.
As Resnick (2017) puts it,
“Creativity doesn’t come from laughter and fun: It comes from experimenting, taking risks, and testing the boundaries.”
When it comes to experimenting, games are a very powerful learning tool. Games are by definition a space where the rule of the real world do not apply, thus providing a safe space to take risks and experiment with various choices, strategies and outcomes.
Moseley and Whitton (2015) define games as“a safe space in which participants have freedom to make mistakes, learn from failure, play with fantasy and identity, have control over decisions and outcomes”
Interested in finding out more?
Check out Chrissi’s various projects around playful and creative learning:
- #creativeHE Google + community site
- Creativity for Learning in Higher Education open course from MMU
- Head to our dedicated web pages to find out more about Game-Based Learning and related projects run and supported at the LSE.
- Apply for our IGNITE and SPARK grants to support your game-based learning project for your own course or at programme and departmental level.
- Join Chrissi’s session on Tuesday 6th February or LTI’s practical session on Designing Games for Learning on 26th February
Resnick, M. (2017). Lifelong Kindergarten: Cultivating Creativity through Projects, Passions, Peers, and Play. MIT Press.
Moseley, A. and Whitton, N. (2015). Using Games to Enhance the Student Experience. Higher Education Academy
In the 2016-2017 academic year, staff at LTI undertook an evaluation of the use of new LSE informal learning spaces. The findings and lessons learnt can be found in our final report. Here are the highlights.
As part of a School-wide objective to provide students with more informal learning spaces across campus, “forgotten” spaces were redeveloped and opened for the 2016-17 academic year. Staff at LTI led the design of 6 spaces – one at each landing of Clement House’s back stairwell- along with Estates, the Teaching and Learning Centre and AV services.
While each space was designed to fulfil a specific function, such as collaborative work or quiet study, they were also intended to be flexible so that students could own and shape them.
This work was also an opportunity for LTI to experiment with new configurations and technology to apply a variety of modular spaces for LSE’s future buildings. LTI’s report investigates the effective use made by students of the six spaces, and whether they match the design intentions. It also provided a context to understand how they fit into the overall experience of students with informal learning spaces at the School.
In spite of the fact that the effective use of the spaces did not always match the original design intentions, the spaces were welcomed by both students and staff and saw high levels of occupancy.
As far as use is concerned, students seemed to favour individual use of the spaces, even on those floors fitted with collaborative furniture. This was found to align with the most common approach to teaching and learning adopted at the School and also reflected in assessment, namely quiet study and individual working. It would be interesting to reassess the use of those and similar spaces once other modes of teaching and assessment are adopted as a result of the School-wide initiative to diversify assessment from next year.
With regards to the spaces themselves. students appreciated the calm and relaxed feel to the spaces and the range of equipment available to them. Areas for improvement include noise levels (especially between classes) and a lack of work space (such as tables or chairs).
More information about the spaces, findings and our analysis can be found in the full report: An Evaluation of Clement House Informal Learning Spaces.
LTI is currently working on the redevelopment of other informal spaces, as well as three rooms in various areas of the campus (more details to follow soon)
Findings from this evaluation and our previous new teaching spaces evaluation will inform the design of these spaces and the future ones.
We would love to hear your feedback, please use the comments below or email LTI to share your thoughts!
The past couple of years an increasing number of LSE academics started integrating blogging in their courses. This took various forms, from using the blog feature on Moodle as an added activity to creating individual blogs for students as part of a course summative assessment. One thing that all these projects shared is the rationale for using blogs in an educational context: encouraging student engagement, making learning more student-centred and diversifying assessment with the view to making it more relevant to the course and developing students’ transferable skills.
A good example of such initiative is Anthropology’s Dr Walker AN300 student blogs project. Dr Walker applied for a SPARK Grant last year in order to support his project to “develop the use of student blogs as one component of the summative assessment for AN300 Advanced Theory of Social Anthropology”.
Below is a summary of the project and its outcomes, with quotes form Dr Walker’s application and project report, whose full version can be found on his project page.
What was done
AN300 is an intensive reading course focusing on full-length books rather than journal articles. There are three ‘cycles’ per term, each devoted to a different book […] Each student was required to produce his/her own blog. […]Students were expected to make one post each week for the first two weeks of each book cycle (12 posts for the course overall). Every third week was dedicated to commenting on the posts of others. The final mark consisted of the average of each student’s best eight posts.[…]The posts were assessed weekly by a GTA who was also in charge of providing feedback.
Students also attended a session on writing for blog run by LTI at the beginning of their course.
Developing students’ academic and life skills
The aim of this project was to encourage students to develop their own original ideas and critical responses to key texts in social anthropology, as well as to cultivate their capacity to respond thoughtfully and diplomatically to the ideas of others. Making regular blog entries was also meant to encourage students to keep abreast of the required readings for each week, partly in order to positively impact the overall quality of class discussions. The project was also intended to cultivate students’ digital literacy, providing them with training in an increasingly widespread form of disseminating information.
[The course format] is sometimes described as an advanced reading group. This makes it ill suited to exams as a mode of assessment. The blogs, by contrast, allowed students to develop their own ideas about the books they were reading as they went along.
Students appreciated the opportunity the blogs provided […] to work in a medium other than an essay or exam.
In general, the trial can be considered a success. […] The posts that resulted were often highly original and creative. Students appreciated the opportunity the blogs provided to be more experimental with their ideas and arguments, and less formal in their writing. […] Having to write a post prior to class gave students an opportunity to critically reflect on the readings, and to bring to the class ideas they had developed in their blogs
Clarity was identified as a key area for improvement in the project, as its absence seems to have caused some frustration among students. The main aspects that were identified as critical were clear guidance and expectations, grading criteria and feedback on the blog posts.
It is also worth noting that it was the first year blogs were tried in the department. The fact that students were not (yet) familiar with this type of activity made it even more important to provide them with extra guidance.
You can find a detailed evaluation report on the project’s dedicated web pages. The report includes guidance given to students at the start of term along with marking criteria, and examples of student posts and comments.
This project was funded by LTI’s SPARK Grant. More info on similar teaching innovation projects and how to apply on our website.
Last academic year, two PhD students teaching in the Department of International Relations embarked on a journey to make their course more engaging to students. They applied for an LTI SPARK! Grant to support the development of Powerpoint-based simulation games.
Related outcomes and resources on our website
Currently available IR simulations for teaching purposes are often high-cost/high-tech and especially time-intensive: even if they do not require custom-made software packages with difficult interfaces and expensive licensing fees, they are almost without exception targeted at course-long or at least day-long activities that demand extensive preparation of both teachers and students, with book-length manuals, intricate rules, integrated assessment tools, and specific secondary literature. This is irrelevant for most of the undergraduate teaching practice, especially in introductory courses that often treat specific concepts only once in a 50-minutes class. But this should not mean that undergraduate students simply never get the chance to profit from interactive gaming and simulations.
Why simulations? The pedagogy behind the technology
The project is based in the pedagogy of experiential learning, student ownership and self-directed learning, and the use of gaming activities and simulations in the classroom.
Simulations and interactive gaming solutions have long been known to enhance understanding both of specific empirical examples as well as, more importantly, theoretical linkages because they make students experience, rather than only hear about, factors and variables involved in such different topics as foreign policy decision-making, diplomacy, great power dynamics or identity formation.
Students do not simply passively receive the PowerPoint (as in a standard presentation), but play it, change its outcome (within given options), determine what the next slide will show, and are thus actively involved in what they learn. This is thought to encourage deeper learning.
It is not the outcome of the simulation that matters, but the process of its coming-about. Just as in real-world foreign policy or diplomacy, there is not necessarily a correct path to take or a right decision to find – instead, by playing the simulation, students engage in discussion and compromise, take into account a multitude of different factors, realize own mistakes, and get a feeling for the complexity of decision-making in multiple settings.
There is no need to change the course design, overhaul the entire teaching approach, or experiment wildly outside what is currently known and available. Instead, our project aims at diversifying teaching where possible to integrate student-centered, activity-based teaching and learning. It does so by bringing out the true potential of already available teacher skills and learning technologies.
We do this by employing PowerPoint, specifically in-built features such as hyperlinks, interactive pathways, or audio or video integration that can be used interactively rather than passively.
Integration into the course
By necessity, simulations do not stand alone: they are accompanied by a set of theoretical structures and debates in which students talk and theorize about their experiences during the gaming activity
Each of our simulation classes consisted of an introductory stage of about 5 minutes, a simulation stage with multiple discussion periods interspersed (moderated variously by the class teacher or by the students themselves, depending on class dynamics) of about 20-30 minutes, and a discussion stage to tease out theoretical insights of about 20-30 minutes.
“Andreas and Gustav came up with a formula that gave students ownership of their own decisions and helped them to apply their knowledge to difficult real world dilemmas. Students were able to experience the consequences of both the cautious and risk taking approach and the many nuances and customs that apply to foreign policy decisions.”
Sarah Leach, Senior Learning Technologist on the project
Overall, results indicate a positive impact on student learning: students on average perceived simulations were enjoyable, allowed for stimulating discussion in the classroom and an experience of expertise and immersion into the topic of the class.
Not only did the simulations add an important additional method to diversify the learning experience and complement more “traditional” instruction styles, they also led to greater overall participation rates in class (compared to more conventional class types, as assessed by teachers, observers, and the students themselves), allowed students to bring in own previous experience and learn from their peers, and try out learned theoretical concepts in class.
They gave students a language to talk about new and often highly abstract concepts, and allowed for smooth and often in-depth reflection and discussion. The simulations also proved entertaining and supported positive group dynamics in class, such as self-moderated discussion and quick exchanges between students without teacher interference.
The teacher’s views
They allowed us as teachers to transition more easily towards roles of moderator and facilitator, as students interact with the simulation and with each other without input or instruction from the teacher.
Students worry that the simulations somehow divert from the “actual” material they are supposed to learn from the course, which means additional effort has to be put into developing desired learning outcomes and appropriate theoretical teaching materials.
“Andreas and Gustav have demonstrated that engaging students with technology doesn’t have to be daunting or cutting edge, a simple tweak can dramatically change the learning experience for students. To make this step even easier, they have written a ‘how to’ guide for any teachers who want to create simulations for small class teaching. The guide covers every aspect from defining the learning objectives and creating the slides through to teaching plans and evaluation. It’s a great resource.”
If you are interested in using technology to support teaching, learning and assessment like Andreas and Gustav, then please get in touch with LTI to discuss your ideas. Take a look at LTI’s SPARK! Grants for more information.
In February I was lucky enough to attend the ‘RemixPlay’ event at Coventry University. Hosted in the amazing ‘Disruptive Media Lab’ the day featured some really interesting speakers (Ian Livingstone (CBE), Bernie DeKoven, Professor Nicola Whitton and Dr Sebastian Deterding). There are already some great write-ups about the event which I won’t replicate here, instead see the blog post by Daryl Peel from University of Southampton and The Flying Raccon’s write up of Remix Play.
For me the conference highlighted the positive aspects of play and I left thinking that we should do more to invite ‘Playfulness’ in Higher Education. Creating a playful environment/community encourages exploration, collaboration, creativity and gives people agency to try things out and have the freedom to fail, all key conditions for learning. There is an abundance of literature on learning through play and it’s importance see ‘Play, Playfulness, Creativity and Innovation’ by Patrick Bateson, Bernard Suits book ‘The Grasshopper – Games, life and Utopia’ and the ‘How We Get To Next’ reading list on the Power of Play especially the video’s at the end.
Some nice examples of a playful environment given by speakers at the event:
As Jordan Shapiro et. al. note in Mind/Shift Guide to Digital Games + Learning (Joan Ganz Cooney Center/KQED, 2014)
“Play is exploration. It involves imagination. It means investigating the world of the game and feeling the frustration, flow, and excitement that goes along with playing it.”
Games designed to enable learning are becoming more popular in Higher Education. Games are a more structured version of ‘play’ and allow players to problem-solve and often involve collaboration and peer learning. Although they often involve rules and winners, games give autonomy to the players and provide a safe environment to fail and to try and test things out. They are often about making decisions and then seeing the consequences and receiving feedback on your actions. As Professor Nicola Whitton stressed, students need low-impact opportunities to experience failure (micro failures); it’s how they get feedback, learn and improve.
Games at LSE
As part of an LTI grant, I have been working with colleagues in LTI on the LSE100 course to create a board game which was played in classes this term. One of the key difficulties when designing the game was to get the balance between play and content right. Too much content, and it’s not a game anymore, it’s a lecture and it’s not fun. Too much concentration on the game, and the learning outcomes are not as obvious and it’s harder for students to make the links between the concepts that you are trying to illustrate. We are now evaluating the game collecting and collating feedback from students and staff, so look out for updates on this shortly.
LTI has awarded several grants to projects involving games, including ‘Capture the Market’ board game mentioned above and an Ethnographic point and click video game, more info and resources can be found on our website.
If you are interested in exploring the use of games in education, we are running a workshop on ‘Designing quick and effective games for learning’ with Alex Moseley on Wednesday 26 April. Alex has been involved with games in education for 8 years and has lots of experience with designing games for learning. You can read an interview with Alex on this blog and you can book a place on the workshop on Eventbrite.
Applications for LTI spark grants are now open http://lti.lse.ac.uk/lti-grants/ with the deadline of Friday 5 May. If you are interested in finding out more, check out the LTI website and contact us to discuss your idea.
The 2017 (Higher Education) horizon report was released a week ago by the New Media Consortium (nmc). It reflects on what the global HE sector is doing with and about (educational) technologies, how it deals with key trends and how it faces critical challenges. Most interestingly, it reflects on these trends and challenges and forecasts which technologies will be taken up by the sector in the short, medium and long term.
It is one report to which it really pays to pay attention, and is short enough to be read in a lunch hour. For a shorter read you might look at their summary 10 talking points. Or you can stay with this blog post and have a look at my summary of this year’s trends, challenges and technologies below. I explain some of the terminology used in my summary of the 2015 Horizon report. Technology concepts are explained or linked to below.
Trends, challenges, technologies:
Key trends in the sector drive technology adoption, and in the short term these are: Blended Learning Designs and Collaborative Learning.
In the mid-term, the sector is driven by Growing focus on measuring learning and Redesigning Learning Spaces.
In the long term, the sector is driven by Advancing Cultures of Innovation and Deeper Learning Approaches.
The sector faces plenty challenges, some that we know, understand and are able to meet ‘easily‘, because we have been facing them for a while now: Improving Digital Literacy and Integrating Formal and Informal Learning.
The Achievement Gap, which “reflects a disparity in the enrollment and academic performance between student groups, defined by socioeconomic status, race, ethnicity, or gender”, and its ‘complement’ challenge to Advance Digital Equity present more of a headache and are a difficult demand on the sector as a whole.
The report suggests as wicked challenges, those that are “complex to even define, much less address”: Managing Knowledge Obsolescence, and Rethinking the Roles of Educators.
The former refers to the rapid rate of technologies cropping up and (possibly) vanishing again, while the latter refers to how teachers are to cope with that and the shift towards proper student-centred learning. The latter was mentioned as a key trend for the first time in the 2010 report and continued to appear until 2013, after which it was dropped, presumably as something that had happened. That this year it is highlighted as a wicked challenge suggests that a) it has become a much more pressing issue and b) educators continue to struggle with adapting to the changes in the Higher Education sector, and/ or the 21st Century as such.
Technologies take-up projection:
In about one year: Adaptive Learning Technologies (Think of it as mimicking the luxury of personal tutoring which reacts to individual students’ progress through their learning as it happens); Mobile Learning (harnessing the awesome computing power that almost all of us have in our phones these days).
In about 2 to 3 years: The Internet of Things (your fridge tells your phone to tell you to buy milk; moodle tells your students’ Applewatch to remind them to eat porridge and finish their dissertation); Next-Generation LMS (Moodle, but a bit slicker? So Moodle with a make-over…).
In about 4 to 5 years: Artificial Intelligence (intuitive computer tutors; HAL); Natural User Interfaces (“speech recognition, touchscreen interfaces, gesture recognition, eye-tracking, haptics, and brain computer interface”).
How good at predicting are Horizon reports? In a follow-up post I will offer an overview of ten years of Horizon report predictions.
LTI are pleased to host Professor Robert Allison Vice-Chancellor & President of Loughborough University for our first NetworkED of 2017. Professor Allison will be discussing the role that technology and innovation have played in the success of Loughborough in becoming one of the leading universities in the UK, and the challenges he sees in the ‘uncertain futures’ for HE over the next 5 years.
The talk will be held on Wednesday March 1st at 2.30pm and will be held in KSW G.01. Book your free ticket for this event here http://bit.ly/2lgAlSy
Ahead of his NetworkED seminar next week we had a quick Q&A with Professor Allison.
Q. You have been the Vice-Chancellor and President of Loughborough University since September 2012, how has Loughborough responded to the significant changes that have occurred in Higher Education during that time?
Loughborough has responded by maintaining a degree of agility, allowing us to respond promptly to the expectations of our students and through working in partnership with them as stakeholders in the University, not as customers or consumers.
Q. Loughborough University has been successful in numerous university rankings during this time including being awarded 1st for student experience in the TES 2016 survey, what are the key principles at the heart of that success?
The most important factor in our success has been seeing our students as co-creators of their education and wider student experience and, through this, giving them a tangible link to the success and future of the University.
Q. What role have technology and innovation played in the enhancing the student learning and teaching experience at Loughborough?
In some areas the role of technology has been significant, in others not relevant at all.
Q. The theme of the 2017 NetworkED seminars is ‘Uncertain Futures’, what do you feel will be the most potentially disruptive (or transformative) issue facing Higher Education institutions in the next 5 years?
For those that cannot attend the seminar will be recorded and added to the LTI Youtube channel.
You may also be interested in attending our upcoming NetworkED seminars:
Liz Sprout from Google education on Wednesday 10th May
Andy Moss from Pearson Education on Wednesday 14th June.