Wednesday, June 23, 2021

Eric Mazur- peer and online learning

Mazur is Professor of Physics at Harvard but perhaps better known as an educator. He has taken a data-driven approach to his teaching and moved from lecture to peer learning then online learning, in response to that data. He uses pre-and post tests to assess the methods he uses.

As an evangelist for peer learning and online learning, he is an academic who has taken his research mindset into his practical teaching. Spurred on by research, such as Freeman (2014), showing that active learning results in higher attainment than lectures, he has constantly striven to improve his teaching, all on the back of proven, measurable results.

Peer instruction

Explained in his book Peer Instruction (1997) Mazure xplained how he used clickers to improve his teaching for many years. Rather than deliver long lectures, without interruption, he stops at key points and asks diagnostic questions. These questions tend to be natural language questions that really test the underlying principles of physics, rather than the application of formulas. Teachers/lecturers can use them diagnostically to assess the overall state of knowledge of the class. This, as Dylan William states, needs the use of clever 'hinge’ or diagnostic questions, that really do test understanding, as opposed to recall. This is precisely what Mazur does at Harvard with stunning results in attainment. If the histogram shows that many of the class have not understood the point, he arranges them into groups so that peer-to-peer learning can take place, asks the question again, then moves on. The data he’s gathered suggests that this approach has led to significant increases in attainment and some universities have since adopted this approach. Note that it is the feedback process that is important. Mazur claims that coloured cards work just as well.

Mazur also found that seating in classes has pedagogic consequences. Move poor performing students to the front of the class and they improve attainment, yet the attainment of high performing students is unaffected when they are moved to the back but when moved to the corners, the whole class does better.

Online learning

Mazur described the Nobel winners who teach as “being no better than dinner party commentators when it comes to teaching and education”. He has spent decades pushing active learning and interactive learning and after he redesigned and shifted his course at Harvard completely online during the pandemic, claimed “I have never been able to offer a course of the quality that I’m offering now. I am convinced that there is no way I could do anything close to what I’m doing in person. Online teaching is better than in person.”. Minimizing synchronous elements he found that learners had better attainment gains and felt better supported than face-to-face students. He suggested that, after seeing the gains, it is “almost unethical” to return to classroom teaching.

His approach is to use lots of regular, but small assignments, make it acceptable for students to fail and redo work, rely on peer pressure for engagement and completion. Students work through a structured online textbook, posing and answering questions online for each other then groups do assignments and present, in their own time, as groups. It is this freeing learners from the tyranny of the clock that he sees as a vital ingredient in his methods. Another advantage of being online is that there is no front to back seating in rows. “When you teach online, every single student is sitting in the front row” says Mazur. This tradical shift from peer learning in lectures to peer learning online, marked a second major another shift in his thinking.


Mazur is a world-class physicist and teacher. Rare in Higher Education, he has applied his experimental and data-driven approach in physics to teaching and learning. This has resulted in a series of practical teaching methods that have been widely applied in Higher Education and other contexts.


Mazur, Eric (1997). Peer Instruction: A User's Manual, Prentice Hall. ISBN 0-13-565441-6

Freeman, S., Eddy, S.L., McDonough, M., Smith, M.K., Okoroafor, N., Jordt, H. and Wenderoth, M.P., 2014. Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), pp.8410-8415.

Teaching: Why an Active-Learning Evangelist Is Sold on Online Teaching

Tuesday, June 22, 2021

Little bits of transcendence?

Easy to get too esoteric over this Covid malarky but has something deep and oddly subversive occurred during Covid? Some sort of personal reassessment? It has for me.

Have people seen that work, even education, is not all that it used to be in our lives, that schools and offices, far from being rich social spaces, can be  unnatural and, at times, quite dull, even toxic? When forced to re-evaluate what we thought gave our lives meaning - education and work - have we come to experience joy in other things? Not the endless drudge of classrooms, uniforms, courses and exams but joy and sometimes learning in real places, with real people and real life? Was the car, bus, or train commute anything at best a daily dose of discomfort, at worst a serious threat to your mental health? Did you miss dressing for work, banal offices, in modern parlance your ‘leaders’ at work? Have you eaten better not being at work, seen more of your family? Has the whole offline-online balance of work, learning, eating and entertainment changed? It’s all more blended.

We had time to breathe, literally and metaphorically. Perhaps experience more beauty, the pure blue sky with birds not planes, deliberately suck in fresh air in streets without traffic, feel the earth beneath your feet, listen more, walk more, talk more, think more, read more, sleep more, cook more, watch more great drama. Even drop the things and people who turned out to be not what you thought they were. If I were honest, I haven’t really missed restaurants, early morning trains to London, office meetings or the canyons of Canary Wharf. I’ve travelled further, not in distance, to places I’d never seen, that were nearer and turned out to be dearer to me.

Now that it’s all threatening to surge back, I have a sense of dread. Then I speak to people who’ve had similar experiences - little bits of transcendence. They’ve had time to lift their heads and see all of this busyness and business for what it is - the illusion of progress. Things have changed and they’ve changed for the better.

Monkey’s mind plays pong - no hands. Frictionless interface. Here’s my musings on 10 possible, and probable, mind-blowing applications in LEARNING

This is the most fascinating video I’ve seen in 2021. It’s haunting me. Not because of what them monkey is doing, that’s astounding in itself, but what it points to in the future, that’s mind-busting.

What’s happening here? 

The monkey got  banana milk-shake rewards through a tube by learning to play ‘pong’ with a joystick. He got rather good. Remember that pong is a complex set of skills, where you have to anticipate the physics and movement of a bouncing ball off fixed and mobile objects. It requires good cognitive and motor skills. Then they pulled the joystick lead out of its socket and  simply read the data from a fine fibre array in the monkey’s brain using AI to translate those thoughts into action. They are literally using AI to mind-read. If this doesn’t blow your mind, what will?

So what are the potential consequences for learning?

1. Learn to move

Neurolink, who developed this technology, the robot that inserts the fibres, the fibre array and the AI that reads the brain signals see its immediate application in helping those with physical disabilities, missing, damaged or paralysed limbs. One can literally use your mind to operate robotic arms or legs. So we’re on the move.  But let’s also speculate on including those with dyslexia, dyspraxia, autism, ADHD. Could we also tackle issues of inattention, demotivation and persistence?

2. Learn by doing

Beyond the fantastically inclusive ideas that this enables disabled people to perform physical tasks in life, it also means they can ‘learn’ fine-tuned motor skills. You could play a musical instrument, conduct an orchestra, paint, play lots of sports, computer games and build things. You can learn to DO things. Note that learning to DO things can include doing things at any distance across the internet, even for able-bodied people, just by thinking them.

This is the instantiation of a famous experiment that took place 20 years ago by Pascuale-Leone (2001), where two groups practised piano, one on a real piano, the other just isn their head. Both groups resulted in similar brain changes as measured by FMRI.

Let’s take this a step further, we could ALL practice DOING things with AI software that formatively assess us, in realtime, provides coached feedback to improve our performance. This feedback can, of course be automated. Such coaching would be personalised, available at any time to anyone at any place, anytime. Behavioural skills could also be assessed. Want to know if I can do something. I need only think it and you need only interpret and assess it.

4. Thinking skills 

This may also apply to other cognitive skills such as speaking and writing. We are moving towards a world where we do not have to use a physical interface, keyboard, mouse or touch screen towards voice, already available in our cars and home on our phones, with voice activation and voice assistants, such as Google Assistant, Siri and Alexa. This will move towards invisible interfaces straight from brain to screen or machine. We all have a phonological loop. Close your eyes and say ‘I am Scottish’ in a Scottish accent. You can do this because you have an internal voice as a feature of working memory. That can be read and with AI translated into words on a screen or speech.

5. Tricky skills

Maths is an area of catastrophic failure. The vast majority never get beyond basic numeracy, even then, calculating an average or understanding a logarithmic scale on a graph is beyond many. You can’t progress in maths unless you master pre-requisite skills, as it has high dependencies on prior knowledge. Imagine a system that reads what you think, diagnoses the misconception, corrects that misconception through feedback, worked examples and so on, then asks you to try things within or just beyond your level of competence at that exact moment. Personalised, adaptive maths, globally scalable from the cloud, could massively accelerate and increase attainment and massively reduce early drop-out.

Let’s take another different skill, learning a second language, another area of catastrophic failure. All kids in the UK and US get years of language education, yet few could ask for a sandwich in that language, never mind hold a basic conversation. Imagine being able to try that language, privately, without public embarrassment and have your thoughts read, interpreted by AI and constructive correction and feedback, say correct pronunciation, position in sentence or endings. Personalised, adaptive, language learning, with immersive practice available, to everyone at any time, would revolutionise language learning.

6. Critical skills

Now imagine cognitive skills, where you can engage your mind in conversation with an expert, who can tune there reprises to your level of skill. Now, transformers that encode and/or decode natural language, such as GPT-3, BERT, BART,  MegatronLM, Turing-NLG, We saw an interesting example some years back when IBMs Watson debated with a national debating champion. These transformers can generate text and start to show how complex interactions with humans, drawing upon huge databases of existing text. Digital Einstein is a recent example of this. Imagine being able to just conjure any famous writer, scientist, artist, whoever, and start asking them questions, in your mind. Your questions will be interpreted and answers based on their archived works, used to provide meaningful answers.

7. Translations

Learning is often hindered by the lack of available teachers and resources in m minority languages. Text to speech and speech to text are limited to mainstream languages. Facebook has recently managed to get AI to learn how to translate any language just by exposing the software to speech in that language. This would free all of the above and liberate those who feel trapped an unsupported in their own first language. 

8. Write to minds

Now let’s take a final leap. And it is a big leap. Almost the above is about reading minds, what of we can write to minds. This is already there with neural ink and other invasive technologies, but not at the semantic or skill level. Imagine being able to pick up maths or a second language very quickly in days or weeks not months and years. Wouldn’t that be desirable? Hell yeah.

9. Mental health

Let’s take another leap and suppose that breakthroughs allow us to write to brains in a way that reduces or eliminates depression and other widespread disorders. Think for a moment about the vast reduction in human suffering that would follow. There is no doubt that education has been increasing mental health problems, inducing stress and in some case suicide. Almost every major institution has killed or helped kill young people. That’s sobering.

10. Anything, anywhere, anytime

You may be thinking, could ALL learning be liberated by such technology? The answer is probably yes. Time will tell and we don’t yet know how long it will take. One thing we do know is that these innovations are now coming thick and fast. Combined with other physical, global innovations, such as Starlink, a global network of low level satellites that give high speed, 5G, internet coverage to the whole world with no blindspots, and we have an educational network that excludes no one.


They used to say that information wants to be free. That’s done. Most information. Is free. We should now ask whether education wants to be free. Teaching is a means to an end, the relatively permanent change in the long-term memory of the leaner. If we can automate that process, make it frictionless, personalised available to anyone, at anytime, anywhere, we should. Learning longs to be free. Let’s not let our conceits hold that back.


Pascual‐Leone, A., 2001. The brain that plays music and is changed by it. Annals of the New York Academy of Sciences930(1), pp.315-329.

Thursday, June 17, 2021

Higher Education is exacerbating, not fixing problems, in teaching - much of it can be automated

The first step in finding a solution is to admit the true nature of the problem. In Higher Education we need to admit that the fundamental problem is that those that teach are not teachers but researchers. Lecturing is easy, teaching is hard and they often don’t have the motivation, skills or time to do what is necessary to teach effectively. Providing tons of Learning Design training and support is not the answer. On the whole this will increase time and costs. It’s time to take a more radical step, and rebalance the system back towards what it used to be.

The current model, the researcher who has to teach, is baked into the system. At its worst you have world class researchers who can’t teach at all. I knew a physicist who was taught by Peter Higgs (Nobel Prize Winner) and described him as “completely incapable of teaching at all”. At Harvard, Erc Mazur described the Nobel winners who teach as “being no better than dinner party commentators when it comes to teaching and education”. Mazur who has spent decades pushing active learning and interactive learning, after shifting his course at Harvard completely online during the pandemic claims...

I have never been able to offer a course of the quality that I’m offering now. I am convinced that there is no way I could do anything close to what I’m doing in person. Online teaching is better than in person.”

But let’s focus back on the core problem, which is the idea that every teacher has to design their own course from scratch. No other area of human endeavour adopts such an individualistic, and therefore costly and inefficient, approach to delivery. Research requires internal intellectual reflection, attention to detail and full analysis, long-form academic writing, with a skew of personality types towards introversion and not great communication skills. Teaching requires strong social skills, emotional intelligence, good communication skills, the ability to simplify content and provide constructive feedback.

Research is the foundation of Higher Education, so what does it say about this problem? Astin’s longitudinal study (1993) on 24,847 students at 309 different institutions, analysed the correlation between ‘faculty orientation towards research’ and ‘student/teaching orientation’. The two were strongly negatively correlated. The student orientation was also negatively related to compensation. “There is a significant institutional price to be paid, in terms of student development, for a very strong faculty emphasis on research” he concluded and recommended stronger leadership to rebalance the incentives and time spent on teaching v research. Note that this does not say that some researchers are not good teachers, nor that some teachers are not good researchers, only that, as Astin claims the research shows that there may be a large statistical mismatch. Note, that this is also not to decry either researchers or teachers, simply to point out that there is a negative correlation. 

Large studies from HEPI and HEA also show student dissatisfaction with the quality of teaching, with around a third reporting disappointment around the teaching, poor interaction, poor communication and interaction with faculty and far too little feedback. One could add to this the evidence that very large numbers of students fail to attend lectures, still the primary pedagogic method in Higher Education globally. Imagine the restaurant business reporting that 40% of their customers failed to turn up for their meals, even after they had paid for them in advance. That would be unthinkable.

How did this happen?

There has been a steady pendulum swing in Higher Education away from teaching towards other activities in terms of funding, status and rewards. Over 50 years ago Jenks and Riesman (1968) showed that salary shifts, promotion and funding suppressed teaching in favour of research. Non-research faculty numbers shrank and, as tracked by Massey & Zemsky (1990) teaching time was literally swapped with research and other professional activities. They described this as ‘output creep’. Boyer (2006) also tracked the year after year swing away from teaching towards research. It was exacerbated in the UK when a large number of Polytechnics were converted into research-based Universities.

The system is now geared towards teaching undergraduates to become post-graduates and faculty, not degree completion. In a profound sense, the research-driven, teaching agenda is punishing teaching and therefore the majority of students. It sometimes exhibits itself as blaming the students, seeing them as the problem. It is rarely discussed but poor teaching puts enormous pressure and stress on students, with high drop-out rates and increasing levels of mental health problems. I have seen plenty of heart-breaking evidence of this.

Breaking the link

Until we break this link, efforts to support faculty with professional learning design will simply plaster over the problem. Recording a bad lecture is a start but not any real solution. However, it may improve even a bad lecture. Yet some Universities still, in this digital age, do not record lectures (my son can testify to this). Yet the advantages are clear; Available 24/7, rewind if your attention drops, rewind if you didn’t understand, rewind if English is your second language, pause if you want to look something up, access to linked online resources on same device, pause to take good notes, fast forward (even 1.25 speed), if known or irrelevant, watch several times for increased retention, watch when in right attentive state for learning, watch if you have been ill, watch for revision as exam approaches, not wasting time travelling to and from lecture, academics can focus on tutoring and feedback, multiple uses in courses, MOOCs etc., data gathered on who, what, when and how long watched, can be subtitled for the deaf, can be translated and subtitled and can be delivered online at any scale, at almost no cost. What’s not to like?

In truth this digitising existing lectures and content into a VLE/LMS was a useful transitionary stage - digitise what you have. We now have to use better tech to improve teaching, not just mimic what we have. The current model, even with the help of Learning Designers, who do a brave and sterling job, is still a process full of resistance and friction. Until we break the ‘'all teachers must be researchers' link, it will continue to be a secondary consideration and learning design support will continue to be a finger in the dam.

Blended learning not Blended teaching

Let’s raise the stakes and bring in Blended Learning. Post-Covid there is a recognition that this should be the norm. Yet Higher Education has a very primitive view of Blended Learning, not as an optimal blend, but as a mixture of offline and online. I call this velcro design, online as an afterthought. This is not actually Blended Learning, it is Blended Teaching. True Blended Learning takes a deep analysis of the learners, types of learning, resources and constraints, then produces an optimal blend. This analysis can and should be automated. If you think that sounds utopian, read on.

Automating Learning Design

We’ve built such a system during the two years of Covid. It takes multiple INPUTS about your learners (distribution, first language, educational attainment, motivations etc), learning (contemporary taxonomy of learning), resources and constraints (available technology, human resources, teaching skills etc). It then uses a state-of-the-art analysis to produce an OUTPUT, which is an optimal blend. Note that this blend may not be a mixture of offline and online. It can be completely online or completely offline. It also produces scores, based on research on success criteria, from engagement to transfer.

More than this we’ve also been automating the production of online content. Using AI techniques that analyse your documents, powerpoints and videos, we can produce online learning in minutes not months. It also links out to external content automatically. This dramatically reduces the time and costs.

Another successful species of automation is adaptive learning, where adaptive courses deliver, not a linear course, but one where students vector through the course at different speeds, ensuring they stay at just the right level of challenge and do not suffer catastrophic failure. These courses are being delivered in the US and China, with proven increases in attainment and correlated falls in drop-out. Going back to my primary argument, why should every teacher invent a course from scratch, when low-cost-per-students online, adaptive courses already exist? The primary reason is, of course, culture. There is no real sharing culture in Higher Education teaching.

Beyond this, contemporary LXP (Learning Experience Platforms) can automate the delivery of learning, pushing and allowing students to pull learning as they progress. Learning is a process not an event. Yet event-based-learning, lectures, dollops of e-learning is what is usually delivered. 


Teaching is a means to an end, not an end in itself. Don’t see the automation of teaching as a threat but as something that frees us to do more research and produce better student results faster, with less mental pressure and at a lower cost. Higher Education should not accept low quality teaching or the description becomes an oxymoron. We now have the technology to hard bake proven, evidence-based pedagogy into the software, to do things human teachers could never do, on scale. We can use AI to personalise learning, deliver the right thing to the right person at the right time and deliver deliberate and spaced practice. My fear is that I see this happening in the US and China but not in the UK or Europe. We have the oldest and finest institutions in the world but that may now be holding us back on teaching. It is time we used smart software to deliver smart learning to make students smarter, faster and at a lower cost. That will increase equity more than any other initiative in Higher Education.


Astin, A. W. (1993). What matters in college?: Four critical years revisited. San Francisco: Jossey-Bass.

HEPI survey (2015)

Jencks, C., & Riesman, D. (1968). The academic revolution. Garden City, N.Y: Doubleday.

Massy, W. F., Zemsky, R., & State Higher Education Executive Officers (U.S.). (1990). The Dynamics of academic productivity: A seminar. Denver, Colo: State Higher Education Executive Officers.

Boyer, E. L., & Carnegie Foundation for the Advancement of Teaching. (1987). College: The undergraduate experience in America. New York: Harper & Row.

Saturday, April 17, 2021

Be heard and not seen… amazing rise of the podcast…

The rise of the podcast, let’s face it no one saw that coming. I knew it had arrived when I saw them being reviewed in the pages of the press. I, like hundreds of millions of others, am an addict. I blogged about users wanting to turn their cameras OFF during online learning. Podcasts provide ample evidence, that if it’s just talking heads, the camera of the teacher, lecturer or trainer can also, often be switched off. That’s why podcasts are so popular. Time and time again I hear people say they don't miss images and heads when ideas are being discussed and that they prefer the informality of a conversation or interview to a didactic presentation.

Purity of the podcast

The joy of a podcast is its purity. It doesn’t nag you with over-earnest graphic design. You’re alone with your thoughts and there’s space to think. Odd that it focuses you to focus by the absence of distracting images or talking heads. Not seeing their faces is a plus. It often adds little, can be distracting. It’s what they say that matters not what they look like. This fees the eyes and hands for other things, such as note taking. It’s a medium, not multimedia, that’s its strength. You have to make an effort, cognitive effort, to actively listen. It’s hard to be lazy when listening to a podcast, whereas you can sit back and let a video was over you. With audio you’re either in or out, there’s no half-way house. 


It a rebel medium, with lots of causes. As mainstream media becomes ever more homogenous, our attention has gone online and podcast are part of the counter-culture on the web. We had the YouTubers, now we have the podcasters, such as Joe Rogan and a massive array of funny, out-there podcasters breaking all the rules. Traditional media seems so formulaic, so hidebound, with a limited range of voices. Podcasts shatter that model. There’s no editor, little censorship. Swearing is not unusual, taboo subjects common. There is a sense of being on the edge, out there.


Podcasts may have had their precedents in radio but they are the child of a specific piece of technology. The portmanteau, podcast, comes from combining iPod and broadcast. It was coined by The Guardian columnist Ben Hammersley, in 2004 in a Guardian article. It’s ease of production and distribution, streamed or downloaded, means it can be used on almost any device, computer, smartphone or audio speaker.


Key to podcast culture is the ‘series’ with some sort of identity, the podcaster(s), theme or brand. On-demand streaming and downloading gave it legs. It’s a medium in itself and has spawned an entire global industry of platforms, sponsorship and audiences. They tend to be more personal, with a lead podcaster and interviewee(s), more informal that traditional broadcast media. Conversation is the aim, not a didactic talk. You’re talking with and to people not at them.


My favourite design principle for the design of learning (design in general) is Occam's Razor - the minimum number of entities to reach your goal... also useful in teaching and for learners. The podcast is an exemplar of this type of design thinking. Cognitively, give me things in the least cognitively loaded format. I’m happy with text if it’s just ideas, podcasts for just discussions, graphics if I need something illustrated visually, video for drama and its other genres. Don’t pack out screens or use media that is not matched to the learning content. Less is more.

Friday, April 16, 2021

AI revolutionises Higher Education in China Open. Huge project gets UNESCO Prize

After writing a book ‘AI for Learning’, I have given a lot of talks and podcasts on the need to use smart software to make people smarter. That means using the technology of the age – AI and data. In the book (p228), and in many of these talks, I explained how China is forging ahead with AI in this field. Unlike the West, China has focus, investment and a view that access to education needs to be cheaper, faster, smarter and with a massive increases in access. 

Meanwhile, we continue with a view that Higher Education needs to remain scarce and expensive, very expensive. We put more attention into AI and Ethics than real projects. Our Universities and colleges do little more than write reports on AI for learning. That’s a shame.

Meanwhile the Open University of China has been awarded a UNESCO Prize for its use of AI to empower rural learners. Their ‘One College Student Per Village’ is an ambitious and inspiring initiative that puts equitable access at the heart of their offer. This is all about improving access to education for the poor. Running since 2004, financed by the Chinese Ministry of Education to tackle access problems, it does far more than reach out with infrastructure. AI lies at the heart of their efforts to provide scalability.


In its efforts to provide quality learning experiences, the OUC set up over 500 cloud-based classrooms and smart classrooms in poorer areas in 31 provinces, municipalities and autonomous regions. The trick was to make the courses demand-led by asking what local people wanted and providing largely practical, vocational courses. They also built the courses to be accessible on mobile devices for farmers and those in rural professions and places. The numbers are impressive:

·      29 programmes (using AI) 

·      825,827 learners enrolled

·      529,321 graduated

·      1,500 OUC study centres

·      300 online courses

·      100,000 mini-lectures

·      all open to general public

AI for Learning

But the secret, sweet without the sour sauce, is AI. The learning is personalised using personal and aggregated data. This adaptive learning means that different students take different paths through the courses, a bit like the SatNav or GPS in your car, go off course, and it re-sequences content and provides feedback to get you back on course. I’ve been working with this for five years – believe me it works.

The really clever stuff is the use of AI to recognise text (text to speech), as well as semantic analysis of answers. This allows open input from students, as opposed to MCQs. I’ve also been working with this for some time in WildFire. This approach allows learners to answer or ask questions which are automatically recognised through semantic analysis, then feedback automatically provided by the system. This feedback (should really be called feedforward) is what oils the wheels of learning and provides real scalability. The immediate feedback with learning opportunities means that the system does not depend as much on human tutors. Semantic analysis of learner answers is something we’ve implemented. It is powerful and pedagogically superior to MCQs as it is more realistic, requires greater cognitive effort and can be more diagnostic for the purpose of feedback.

They also use AI for knowledge mapping, automatic content generation and smart chatbots for 24/7 online learner support. This use of AI for content creation is something we’ve been doing at WildFire. It reduces the cost of learning per student, as new content can be created quickly from documents, PowerPoints and videos. The point is to create content quickly, cheaply but using AI for semantic analysis and retrieval practice for high retention.

AI and assessment

The automation of assessment and essay marking is what allows them to scale sophisticated learning to so many people free from the tyranny of time, place and expensive human effort. Automating much of the assessment allows human tutors to focus on closing knowledge and skills gaps, rather than marking. As Li Ganged, a tutor at the OUC says, “Automated essay scoring is efficient in that I don’t have to mark these assignments myself but I can get a clear picture of where learners need help.” This idea of using AI to create assessments on all of your content at little extra cost is also something we've been doing using AI.


Another feature of this initiative, something our rigid system can’t really handle is the mix of programmes, degrees, diplomas and short courses. The focus on vocational training is also something we desperately need but underfund in favour of longer degree courses. We have to move beyond our sclerotic system of high cost content creation, high cost delivery and dependence on physical campuses. There’s too much at stake here for us to focus on education of the few at the expense of the many. Who would have thought that China is leading the way. They’ve just clocked up over 18% of economic growth but it’s not just about that, it’s the simple fact that they are leading the world in educational innovation.

Wednesday, April 14, 2021

Micro-videos; What are they? How to make them sticky? How to deliver? Good interface? How to make them?

What are they?

Micro-videos are short videos. How short? Often very short, 15 seconds on TIKTOK, Twitter at 2 mins max, up to 6 minutes or so at most. There is no absolute rule here but the research suggests that people duck out from learning video at around 6 minutes. The idea is to be short, to avoid cognitive overload, be hard-hitting, increase retention and deliver relevant learning. The learning world has picked up on YouTube, Twitch, Facebook, Twitter, Instagram and TikTok. It’s not hard to find good examples.

How to make ‘em stick?

You can try too hard here, with too much animation, sound effects and noise. This is learning. Learning is not a circus and we are not clowns. It is important to match the style to the content. Drama works for behavioural and attitudinal shift. In fact video’s primary strength is in motivation, attitudes and behaviour. It is not so hot on knowledge, and conceptual learning. The transience effect means you quickly forget detail on video, like a shooting star your 20 second attention span means the knowledge burns up behind you, and you forget. Think back to the last three series Box-set you watched. How much do you really remember? But you walk away with the ‘gist’ of things – impressions. Think of a micro-video, not as the primary learning event but the trigger or catalyst for further learning.

Some tricks to make ‘em stick? Here’s six starters…

Surprise with a question, counterintuitive point or dramatic statement. First impressions matter… don’t do a learning objective!

Take it slow. Learning needs attention and the mind needs time to digest ideas. Play it a little slower than they do in the movies.

Summarise at the end. Learning is a process and not an event so summarise points at the end.

Calls to action. Make them go off and DO something, then report back on what they did and what they found easy and difficult.

Leave them hanging… that’s what a good video TV series will do… want you to come back for more…

Follow up with some active learning using the narration from the video. We do this with WildFire, where AI creates the content.

In a sense, video is rarely ever enough. It needs to be supplemented by more active learning. It tends to give the illusion of learning.

How do you deliver them?

Most people will have a LMS/VLE. But this may be the most unsatisfactory method of delivery, as they are largely repositories, not designed for sophisticated delivery. That’s where an LXP (Learning eXperience Platform) scores better, where you can pull and push micro-videos to and from learners in the workflow. Learning I a process not an event. Emails can be just as powerful. I’ve seen some great examples of 90 second videos delivered by email, which is still a popular and powerful communications tool in organisations. Remember also, that YouTube and Vimeo are literally learning platforms, with tools for privacy, editing and transcription. Use them. You may also want to consider analytics. YouTube and Vimeo work, as will your LMS or LXP. Just decide what data you want up front and what you want to do with it. Dashboards don’t make decisions, you do.

What’s a good interface?

An interface that has emerged as dominant is the Netflix, YouTube, Vimeo, Prime interface, with its tiles, horizontal scrolling for more, vertical scrolling for themes, along with search, maybe a 'playlist' or 'what’s new'. This makes great use of limited screen real estate and, above all, is now familiar to almost everyone on the planet. Never underestimate the power of search, especially deep search, into the narration and detailed content of videos. Mobile’s different. Instagram and TikTok are the masters there.

How do you make them?

Take your smartphone and record. It’s really that simple. You can also record in Powerpoint with those slide images. For more complex stuff there’s tools like Vyond, Powtoon, Vlognow, Adobe Animate, Articulate replay, Storyblocks – a ton of them. Although I’m not a great fan of animated, cartoony stuff. I often think it would be better in a single image, like an infographic. There’s Captivate and other similar tools for capturing ‘how to’ software tasks. Remember some simple rules about framing. It’s all in the eyes, so go for close-ups in learning. If you’re showing how to do something, shoot first-person not third person i.e. put the learner in the shoes of the doer.


Micro-videos are coming of age, the result of their popularity on consumer devices, platforms and social media. But remember that learning is not entertainment. Learning micro-videos need to be made with learning in mind. If it’s edutainment you want, beware of too much ‘tainment' and not enough ‘edu’. That’s the big mistake. For a much deeper look at the research on video for learning click here.

AI: America innovates, China implements, EU regulates... where does that leave the UK?

America innovates, China implements, EU regulates 

The EU's AI obsession is regulation. That's fine and I have little criticism of the direction of such regulation, part from the usual bureaucracy. What I do find depressing is the dampening effect this has on actual effort. Thankfully our own little UK 'AI and Ethics' group was more like a Parish Council, a rather amateurish academic attempt to tell companies how to run their business by people who don't know much about business. It amounted to little more than a rather dull checklist. This is good news as it remains unknown and largely ignored. AI is not as good as you think it is and not as bad as you fear.

In AI for Learning in Higher Education, we have several world class companies in the UK. One has received a seven figure investment from a US University but has literally zero UK customers. Our effort in this area is largely third rate AI and Ethics commentators. In AI itself, however, we have a ton of talent.


Where does this leave the UK? We should diverge from the EU here. In fact, we already have, Deepmind and other AI companies in the UK looked to the US, not Europe or China for investment and markets. Similarly in my own field, AI for Learning. There is little UK-EU commercial or M&A activity. It is almost all UK-US. We need to stay innovative and look to those countries not obsessed by negativity around AI and Ethics to move forward.

The investment community in London and US is well connected and most fo the deals are on that axis. This has increased post-Brexit, with even more alignment. The EU is linguistically diverse and much messier in terms of marketing and implementation. few companies see the EU as their target market, preferring the much bigger US market, which more aligned linguistically, culturally and financially.


China has made the investment and is actually forging ahead with AI for Learning. I've written about this here. They have a strategic view, with huge government targets and investments, that is markedly different from the EU. We have already seen the emergence of large-scale projects in schools and Universities. On the other hand, their attitude towards social scoring and surveillance technology leaves them open to criticism.

Appendix - EU legislation

As I say, the proposed EU legislation is OK and has been leaked (probably deliberately). It is, as expected, bureaucratic with lots of quangos being set up and a typical piece of EU overkill. Some of it, however, is eminently sensible and Google and others have been asking for this for some time. This is the right level for such discussions, if it is aligned with other efforts from IEEE and so on.

To summarise:

1.     Yet another Board! European Artificial Intelligence Board (one for each of the EU27 countries, a representative of the Commission & European Data Protection Supervisor)

2.     Digital Hubs and Testing Facilities to be set up

3.     Member states need inspection bodies for assessment and certification (3rd parties for 5 years)

4.     High risk AI systems tested before release

5.     High risk is, for example, face recognition for physical safety decisions in healthcare, transport or energy

6.     Authorisation for use of biometric identification in public domain

7.     Rules on exploitation of data

8.     Manipulation of human behaviour (to people’s detriment)

9.     Prevents mass surveillance

10.  Disclosure for deep fakes

11.  Voice agents cannot pretend to be human

12.  Emotion recognition has to be made explicit to user

13.  Ban ranking social behaviour (as in China)

14.  Self-assessment requirements for AI used for the purpose of determining access or assigning persons to educational and vocational training institutions

15.  Fines on a GDPR scale

16.  Aim is to prevent abuses with sizeable fines up to 4% global revenue 

17.  Notable exceptions for military and safeguarding public security

18.  SMEs to get privileged access

19.  Exemptions for training data 

20.  Notable get outs for member states (national security worries)

Saturday, April 10, 2021

Monkey plays computer game by mind control, mediated by AI. Here’s 10 implications for learning…

Pager is a macaque. He will go down in history as one of the first sentient beings to play a computer game just by thinking. He was trained to play ‘pong’ using a joystick, which is fascinating in itself. Then when the joystick was unplugged, he played by THOUGHT ALONE.

I wrote about this in my book ‘AI for Learning’.

So what are the possibilities for learning?

1. Invisible interface

First there is the promise of the brain interacting with the world without speech or movement. Our fingers are slow input devices, even speech is slow. Imagine being able to conjure up answers, have a dialogue, practice a language and engage with learning experiences without the messiness of an interface. The invisible interface eliminates all of that packing away at screens and keyboards. 

When reading and writing from and to the brain, you don’t want to damage anything and you need precise control over a range of electric fields in both time and space, also delivering a wide range of currents to different parts of the brain. The device uses Bluetooth to and from your smartphone. Indeed, it is the mass production of smartphone chips and sensors that have made this breakthrough possible. The smartphone may in the end be merely a bridge to its own obsolescence.

Our current interfaces, keyboards, touchscreen, gestures and voice, could also be bypassed, giving much faster thought ‘to and from machine’ by tapping into the phonological loop. This is an altogether different form of interface, more akin to VR. Consciousness is a reconstructed representation of reality anyway and these new interfaces would be much more experiential as forms of consciousness, not just language. Note that Pager is not executing imagined speech but actions.

2. Reducing cognitive load

This invisible interface feature alone will save immense amounts of cognitive effort, thereby reducing cognitive load. This matters, as cognitive load, is a rate limiting step in learning. To give but one example, when we watch video to learn, we have a 20 second period of attention and can hold only 2-4 things in our head at any one time. This means that this learning experience is largely one of forgetting. We get the illusion of learning, we feel as though we’re learning but, like a shooting star, our memories burn up behind us. Unfortunately, this transience effect, severely limits what we learn from video. In fact, this problem of overload is common to most learning.

Eliminating the need to learn how to use an interface, recognise icons and manipulate things to increase the limited screen real-estate, means that much of our cognitive effort, the key to learning, is wasted. We’re so busy, scanning, clicking, scrolling and manipulating the interface, that it harms learning. UX design will disappear into understanding the psychology of learning not the ergonomics of screens.

3. Accelerated learning

Learning is a relatively permanent change to long-term memory. If we can use AI, as they do I this experiment, to read data from our minds, then good pedagogy can be applied. Immediate feedback to propel the learner forward. Feedback should be renamed feedforward, as its purpose is to accelerate learning. Fast, personalised feedback, can be provided on the basis of what we are thinking. All sorts of other AI and data-driven techniques, which I examine in my book ‘AI for Learning’, come into play – personalised, adaptive, deep search, chatbots, nudge learning, learning on the flow of work. This advance unlocks many other uses of AI for learning.

It doesn’t end there. This experiment shows something that we knew already, that mental rehearsal, leads to learning. Note that the Neuralink system captures what Pager learns, calibrates it using AI, then uses that to do what pager wants without any physical interface. They read Pager’s mind, literally intentions, in realtime to predict what Pager wants to do.

It’s the decoding of Pager’s brain signals that are being used here. This is not just about the fibre implants. It is the AI decoded data that does the smart work. You simply imagine something then the computer knows what you are thinking. These intentions can spark of actions anywhere on a network. For example, implants on the legs of paraplegics, allowing them to walk. More commonly, anyone could use a smartphone mentally, faster than anyone using it physically.

4. Insights on learning

At the very least this will give us insights into the way the brain works. We can ‘read’ the brain more precisely but also experiment to prove/disprove hypotheses on memory and learning. This will take a lot more than just reading ‘spikes’ (electrical impulses from one neuron to many) but it is a huge leap in terms of an affordable window into the brain. If we unlock memory formation, we have the key to efficient learning.

5. Read memories

Memories are of many types and complex, distributed phenomena in the brain. Musk talked eloquently about being able to read memories, that means they can be stored for later retrieval. Imagine having cherished memories stored to be later experienced, like your wedding photos, only as felt conscious events, like episodic memories. There are conceptual problems with this, as memory is a reconstructive event, but at least these reconstructions could be read for later retrieval. At the wilder end of speculation Musk imagined that you could ‘read’ your entire brain, with all of its memories, store this and implant in another device. 

6. Write memories

Reading memories is one thing. Imagine being able to ‘write’ memories to the brain. That is, essentially learning, especially if they bypass the limitations of working memory. If we can do this, we can accelerate learning. This would be a massive leap for our species. Learning is a slow and laborious process. It takes 20 years or more before we become functioning members of society, even then we forget much of what we were taught and learned. Our brains are seriously hindered by the limited bandwidth and processing power of our working memory. We are easily distracted, get demotivated, can’t upload, download and sleep for one third of our lives. Overcoming those blocks, by direct writing to the brain, would allow much faster learning. Could we eliminate great tranches of boring schooling? Such reading and writing of memories would, of course, be encrypted for privacy. You wouldn’t want your brain hacked!

7. Imagination

This is not just about memories. It is our faculty of the imagination that drives us as a species forward, whether in mathematics, AI and science but also in art and creativity. Think of the possibilities in music and other art forms, the opportunities around the creative process, where we can have imagination prostheses.

8. Consciousness

In my book I talk about the philosophical discussion around extended consciousness and cognition. Some think the internet and personal devices like smartphones have already extended cognition. The Neuralink team are keenly aware that they may have opened up a window on the mind that may ultimately solve the hard problem of consciousness, something that has puzzled us for thousands of years. If we can really identify correlates between what we think in consciousness and what is happening in the brain and can even simulate and create consciousness, we are well on the way to solving that problem.

9. End to suffering

But the real long-term win here, is the opportunity to limit suffering, pain, physical disabilities, autism, learning difficulties and many forms of mental illness. It may also be able to read electrical and chemical signals for other diseases, leading to their prevention. This is only the beginning, like the first transistor or telephone call. It is a scalable solution and as versions roll out with more channels, better interpretation using AI, in more areas of the brain, there are endless possibilities. This event was, for me, more important than man landing on the moon as it has its focus, not on grand gestures and political showmanship, but on reducing human suffering. That is a far more noble goal. It is about time we stopped obsessing with the ethics of AI, with endless dystopian navel gazing, to recognise that it has revolutionary possibilities in the reduction of suffering.

10. Neural interfaces are here

Musk showed three little piggies in pens, one without an implant, one that had an implant, now removed without any effects and one with an implant (they showed the signal live). Using a robot as surgeon the Neuralink tech can be inserted in an hour, without a general anaesthetic and you can be out of hospital the same day. The coin size device is inserted in the skull, beneath the skull. Its fibres are only 5 microns in diameter (a human hair is 100 microns) and it has ten times the channels of the Utah array, with a megabit bandwidth rate, to and from your smartphone. All channels are read and write.

From a pig in 2000 to playing a computer game in realtime in 2001. AI, robotics, physics, material science, medicine and biology collided in a Big Bang event, where we saw an affordable device that can be inserted into your brain to solve important spinal and brain problems. By problems they meant memory loss, hearing loss, blindness, paralysis, extreme pain, seizures, strokes and brain damage. They also included mental health issues such as depression, anxiety, insomnia and addiction. Ultimately, I have no doubt that this will lead to huge decrease in human suffering. God doesn’t seem to have solved the problem of human suffering, we as a species, through science are on the brink of doing it by and for ourselves.

Other companies are working on other neural interfaces. One promising line is a brain interface from a stent in a brain blood vessel, a stentrode. This is easily inserted, gets incorporated into tissue. 

Tech for good...