Monday, March 19, 2018

AI has and will change language learning forever

Just before the dawn of the internet I worked with the CEO of a major CD language learning company. His business model was fascinating, 
I don’t sell language learning, I sell the false promise…. My customers are ‘false starters’ mostly middle-class people who think they’ll learn a language in a few months before they go to Italy, Spain or France on their holidays… they never do.” He explained that the whole market was based on this model. The BBC packages at the time were the worst, he explained, “They’d send a film team to France for a month or so, come back and write a book around it…. it is literally impossible to learn a language from their materials”. I stayed out of that market. But times they are a changin’….
The technology moved on from CDs, along came the internet, and we saw the first big effect on learning languages – mainly English. The abundance of music, films, sport in all media on the web, allowed ready access to content, allowing contact, practice and immersion. Huge numbers have learnt languages without direct instruction. But learning a second language remains one of the most difficult things one can do in life and direct instruction still has a place. The problem with online instruction is that the technology was still too flat, text based and restricted to simple drill and practice. The content was too linear, often dull and struggled when it came to the spoken word, practice and immersion. Technology is now influencing not only what languages we learn but how and even why we learn languages. Some argue that the Anglo-saxon domination of the internet has accelerated the expansion of English as a global language. Machine translation raises the interesting possibility in that it may lead to less people learning new languages, if frictionless, real-time translation is available. But the most obvious and immediate impact will be on the practical teaching and learning of languages, where smart technology is already having a global impact.
Of one thing we can be sure; AI brings a new paradigm to language learning. Natural Language Processing (NLP) has brought entity analysis, sentiment analysis, classification and machine translation. In addition we have text to speech and speech to text, now revolutionising interfaces. At the same time algorithmic techniques and machine learning brought adaptive, personalised and spaced learning. Even image recognition is being brought into identification and assessment. These technologies are being blended to produce sophisticated language learning and the possibility of learning a language without human instruction. One has to look across the whole learning journey to see how this is potentially possible.
Machine learning
To see how far AI has come in languages, Machine Translation is a good starting point . Google Translate can handle over 100 languages and us used over half a billion times a day. Launched in 2006 it used Statistical Machine Translation to match strings by probability against strings in another language, basically pattern matching. But in late 2016 it switched to Neural Machine Translation, making it much more successful and contextual. It is available as a browser extension and on Google Home and Google’s Pixel Buds. The ear ‘Buds’ can translate 40 languages in real time. To be fair, like Skype’s real time translation, it’s far from fluid and perfect but the direction of travel is clear – it will get better and better. 
Learning journey
So what about learning a language? Most successful language learning models take the learner on a learning journey from simple basics to practice then production. This progression normally starts with structural basics on the alphabet, vocabulary and grammar. Practice usually starts with limited and controlled practice and moves towards more open and free practice. Finally, there is generative production and use of the language. In addition to the actual learning there are also pedagogic issues such as motivation (a particular problem in language learning) and assessment. AI has a role to play across the whole of this learning journey.
Drill and practice
My first ever computer-based learning programme was teaching the Russian alphabet, which I built using the Commodore 64 graphic characters.  You saw a character and had to type in the corresponding English sound (as a letter or letters). I then programmed a behavioural drill and practice vocabulary programme. Randomisation was a feature, stratified with progress dependent on scores. This was typical of most early computer assisted language learning programmes.
Adaptive learning
Basic drill and practice is still a feature of most adaptive systems, such as Duolingo, with 200 million registered users, where structured topics are introduced, alongside basic grammar but adaptive algorithmic techniques track your progress and take into consideration, your forgetting curve, short-term success rate and effort. Adaptive systems can blend individual with aggregate data to optimise progress for the learner, depending on need. Every new learning event can be uniquely presented to that learner thus personalising the learning, an important form of optimisation in language learning, give the distribution of ability.
Spaced practice
Spaced practice, where the learners use retrieval techniques in a structured reinforcement pattern to push knowledge and skills from working to long term memory is a good starting point for the consolidation of acquired knowledge and skills. Anki is a free package that uses the algorithmic control of spaced practice to determine the learning path.
Controlled practice, to varying degrees, can also be delivered using chatbots. There are many species of chatbots from learning engagement, teaching, mentorbots, and practice bots. Chat has overtaken social media on mobiles and is clearly the preferred interface. We seem to have a natural affinity to chat interfaces and in some cases, with wellbeing bots, even the anonymity of the machine has been shown to be an advantage. They have been successfully used in educational and corporate training environments. They offer a dialogue interface, so are eminently suitable for language learning, with flexibility around the recognition of replies by the learner and, of course, speech. They have huge potential and when embodied in consumer, home devices can bring language learning into to the home.
Open practice
But active immersion is also now possible with home devices. You can switch your Amazon Echo to respond in German. Consumer technology, such as Alexa, Google Home and others will offer cheap, free and increasingly sophisticated language learning in your home. Ask it a question in English and it will reply in German. This is a bit like having a German person in your own home 24/7.
The internet provides a wide and deep set of resources in most major languages. There’s an endless amount of content in your target language, in all media – text, audio and video - movies, box sets, music videos, Youtube, Wikipedia, whatever. Here other immersive technologies come into play, such as VR and AR. These are not AI technologies but AI techniques can be used within these environments to provide immersion, attention and context for language learning. In a current project (WildFire) we have successfully integrated speech input within VR, which not only allows you to navigate through the learning using just your voice but also input open response input and so on.
Both Babbel and Doulingo offer paid English assessment testing. Face and digital recognition allow unique identification of candidates for assessment. Keyboard typing patterns can be recognised, along with adaptive assessment, which adapts to the candidate’s ability level, are all being used. Online assessment is now here, which increases accessibility and progress in language learning.
AI, with its rapid advances, specifically in technologies that aid language learning, may turn out to be the most significant technology in this field to date. The technology provides behind the scenes language processing that allows machine translation, speech recognition and many other services to be used across the learning journey to keep learners moving forward, optimising and personalising delivery. It has already accelerated the digitisation, disintermediation, decentralisation and democratisation of language learning.  Yet we must be careful in attributing too much efficacy to AI. Its translation ability is nowhere near as good as human translation, speech recognition still a bit ropey and with other services, such as chatbots you need to be a bit forgiving. Nevertheless, it is constantly improving and on current rates of progress, it seems likely that it will have a major impact in language learning.

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Thursday, March 15, 2018

Should you listen to music while studying? No... here's why

There are those who extoll the Mozart Effect, I know of one who extolled the virtues of playing Mozart to her children when they were very young and when they were learning. This, she claimed, had been proved scientifically to improve IQ and their ability to retain knowledge. Remarkably, she extended her claim to the foetus.
This baloney was sparked off by a paper in Nature by Rauscher, Shaw and Ky (1993), which showed a small improvement in spatial reasoning score (very specific), the effect lasted no longer than 15 minutes, then disappeared. The theory also disappeared, as several follow up studies could not replicate the effect. Rauscher herself, disclaimed the idea, saying that they had made no claim linking the playing of Mozart to intelligence. Chabris and Steele in a meta-studies paper in 1999 put the nail in the coffin by showing that such effects are merely the result of short-term and temporary ‘enjoyment arousal'.
But education can never resist a fad and there's always someone in education who can't let a bandwagon pass  in this case Don Campbell, who published The Mozart Effect (1997) and The Mozart Effect for Children. These books are, quite simply, bogus. His claims bear no resemblance to the actual research and, if you have this idea floating around in your brain, it’s largely down to him trade-marking the effect, then publishing these books, that were then taken up by lazy ill-informed journalists. This is how it ended up in the minds of so many parents and teachers. It was even funded and applied in some states in the US, notably Georgia and Florida.
Music in general
On the general proposition, that listening to music helps one learn, we have to be as equally careful. There is a large and complex literature on this subject, testing the effect of music on various cognitive phenomena and there is some evidence that it improves mood, even motivation, but one must be careful when it comes to actual learning.
In this interesting study, silence is used as a control, along with the two major components in popular music - music and lyrics. Perham and Currie (2014) created four groups:
Music without lyrics
Music with lyrics they liked
Music with lyrics they did not like
The sample (30) was small, and I'd like to see this replicated with a larger group but the results were interesting:
Revising in silence was signifiantky better than revising  while listening to music that had lyrics, liked or disliked
No difference between 'silence' and 'music with no lyrics'
Revising while listening to 'music without lyrics' was better than revising to 'music with lyrics'
Students who revised in 'silence' were the best at predicting the results

It's to do with the overloading of working memory, especially with spoken words. One quick experiment you can do with your kids, or students, is to take a random page from a book on a subject they are unfamiliar with. Now tell them to read it in silence. Now choose another page and ask them to read it while repeating the word ‘boing-boing’ over and over. They will be unable to meaningfully learn from the text. The reason is the overloading of working memory, the phonological loop to be exact. Music takes up valuable bandwidth, therefore inhibits learning.

It may be devilishly difficult to convince your offspring that music is bad when they’re studying but when faced with a 60% differential it may be worth telling them about this study. There is lots of bad advice around study techniques that focus on superficial, low retention study methods and ignore attention, effort, retrieval and deliberate practice. No doubt some wag will tell us that music is good for those with an auditory learning style... that's also bullshit.

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Monday, March 05, 2018

Why is online learning ‘all fur coat and no knickers’? We design to forget.

Online learning has gone down the ‘all fur coat and no knickers’ route. It’s more presentation than pedagogy, more look and feel than learning. Rather than focus on what makes learning a success in terms of understanding, retention and recall, it allows the learner to skate across the surface of a thin layer of crisply designed but thin ice. It often creates the illusion of learning by illustrative graphics/animation that, as Mayer often showed, actually inhibit rather than help retention. That old adage, which is as good a summary of learning theory as any, that ‘less is more’, has been abandoned by a glut of over-engineered graphics, animation and effects. We design for forgetting.

Rather than taking our lead from the most successful online services the world has ever seen, such as Google, which has the simplest, most successful and most alluring interface ever seen, we wallow in an agency-model that delivers a diet of over-designed cartoons, stock images, animation, badges, gamification and every other damn distraction we can think of. ‘Keep It Simple Stupid’ has been replaced by ‘Keep It Stupid Stupid’. Google focus on back-end functionality to deliver a superb service, not front-end visuals. So should we.
Take the world’s most successful retailer, Amazon, with 44% of all online sales. They are obsessed with customer behaviour and simplification – not aesthetic design. Their website could be described as quite ugly, but it’s a masterpiece of cognitive simplification and the design of process and success, not aesthetic ‘look and feel’. They are successful because aesthetic design isn’t the point – selling and buying is the point. Similarly in learning – teaching and learning is the point. Like Google, they focus on back-end functionality to deliver a superb service, and do not rely on front-end visuals.
Social media
One could hardly describe Facebook and Twitter as relying on their designed interface or images for success. There are no Facebook or twitter images, there is no animation, only a core, scrolling timeline that draws you in and a simple interface that gets you typing stuff in. They understand that the goal is interaction, not spoon feeding, that the software behind the skin is where the real power lies. They understand that less is more.
Successful learning design
So how should we design for success in learning? First up, we need to focus on the outcome – successful retention and recall. This is our equivalent of Google's ‘finding the right thing' so that we click on it’ or Amazon’s ‘offering us the right thing so we buy it’ and Facebook/Twitter’s ‘interaction with others’. This comes down to a few simple principles:
1. Effortful design
Forget the graphic/text/graphic/text/MCQ model for one moment and think about the simple fact that the learner really does need to make the cognitive effort to learn. You have to make them think and act. The online learning industry is obsessed with the MCQ and their awkward cousins, the T/F, drag and drop and so on. Multiple choice questions are light touch, give the answer anyway and are poor on retention. That is because they are weak in terms of effort. You are not making the learner recall the answer from their own brains, rather, they are choosing from a list. It's an act of recognition. These interactions bear no similarity to how people actually use what they learn in real life. You have to know stuff, recall stuff, not pick stuff from lists or drag words from one place to another. If you don’t you’re designing for forgetting. So move to open input.
2. Simplicity
Google, Amazon, Twitter, Facebook, Netflix and every other online service, allows you to scroll down the page. They have largely abandoned the online learning, fixed-page model. Most online learning vendors have scrolling on their own websites but when it comes to learning design they default back to some old-school, fixed-page turning model. Sure you need to chunk material down but electronic page turning through coffee-book designed pages, is not the answer. No need to be flashy, Flash died for a good reason. You need to cut things down, get rid of those extraneous graphics – those stock photos of people in offices, looking at computer screens, managers smiling inanely at each other., patronising cartoons.... You also need to cut the text until it bleeds, then cut it some more. A good editor is of more use than a graphics designer. Forget those dull learning objectives at the start of your course, all of that Michelin-man padding. Sure, adhere to some simple rules on branding, through logo, palette and font – that usually means pre-defined colours but don’t get fixated on superfluous elements that distract. Your goal is learning and retention not aesthetic pleasure.
3. Get smart
Stuck in a flat HTML world where all the effort goes into page design and a flashy CSS, the online learning world hasn’t learnt from Google, Amazon, Facebook, Twitter and Netflix. AI is the new UI. As all the effort goes into the surface skin, there is no smart delivery behind the front-end. Google is pure AI, Amazon’s huge AI platform delivers what you see with subtle recommendations based on your personal behaviour and the behaviour of others. Social media is mediated by AI as is Netflix, which is why it has conquered the globe in the entertainment industry. Yet in online learning we are stuck with flat pages of HTML, with a few branches. Look at AI, that is now the real world.

We are in this pickle because we do not pay enough attention to learning theory. Anyone can say ‘that looks nice’ few can say ‘that’s great learning’ and justify their claim. What to do? Let’s get smart by using smart, behind the scenes software to drive the delivery of online learning. Let’s be honest and say that what we had was OK for that time but it’s time to move on. Let’s drop the idea that it’s all about ‘design’ and focus on functionality and leaning outcomes – what we actually retain and recall. Let’s stop being a nation of online shop, window-dressers and focus on learning, which is why we need newer tools and services, that can deliver effortful learning and work to principles of cognition that lead to learning not just looking.
If you're interested in this direction contact us on WildFire - the world's first AI-driven content creation tool. Or try a different approach.... adios....

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Tuesday, February 20, 2018

We don’t need no stinkin’ badges? Why the badges movement has literally run its course

I’d have loved the idea of learning badges to have worked – motivational dynamo, more fine-grained rewards and accreditation. The inconvenient truth is that the idea has failed. This is not for want of trying but a classic case of supply not matched by demand. To put it another way, we built it and they didn’t come. Sure you’ll find some localised examples of success but overall, as a significant movement, it has literally run its course - few are now interested.
1. Lack credibility
The main problem has been credibility. When explicit accreditation is not anchored in a major accreditation body with quality and standards, there’s no real anchor in the real world. You’re up against recognised accreditation with branding, marketing, frameworks, objective assessment and longevity. Overbadging and weak badging have added to the problem of credibility. Badge projects are here today gone tomorrow, mosquitos not turtles.
2. Lack objectivity
A lack of objectivity, in terms of recognition in the real word has plagued their progress. What happens when you take your badges outside of your institution or course, and no one has ever heard of them and don’t care? Simply badging content is a mistake. This is about real people feeling that they are useful, not lapel badges. If your currency is not recognised in the currency exchange, then you’re left with useless paper.
3. Motivationally suspect
They were always motivationally suspect. Extrinsic rewards should always be treated with suspicion. And there’s something suspect about badges for online, but not offline, stuff. You can’t slice and dice learning by mode of delivery. The ‘Overjustification effect’ shows that Intrinsic motivation will decrease when external rewards are only given for completing a particular task or only doing minimal work. This is not to say that all extrinsic motivation is useless, only that superfluous extrinsic motivation is damaging to learning. The failure to escape this trap is a major problem for most badge schemes.
4. Not really gamification
The idea that they are a great gamification feature is misleading. Pavlovian rewards have a limited effective learning, which is why so much Pavlovian gamification runs out of steam – leaderboards, collecting badges and so on. Real gamers are intrinsically motivated by the game, its reputation, their experiences of games, their peers views of games and so on. They do not buy and play games because of the scoring system or badges. Bad learning games or gamification techniques are often just a pale imitation of massively popular gaming.
5. No form of transfer
When your badges get stuck in a proprietary system, repository or e-portfolio, with little in the way of interoperability, they’re effectively imprisoned. Badges are often rendered useless by their failure to escape the bounds of their small ecosystems, technical and cultural. Mozilla have, since 2011, tried to provide a framework and structure. I applaud their efforts but the early paper “Open Badges for Lifelong Learning” was hopelessly utopian. A more achievable vision was needed. The most successful badge system I’ve seen is in IBM – but it is in IBM – that’s it. Badges don’t travel well.
6. Awful branding
Another problem was branding. Making your badges look like silly, clip-art stickers, makes the whole thing look amateurish. For badges to work they needed some serious marketing and design – Mozilla tried but what we got was almost no marketing and sometimes comically bad design. In addition, it always had that boy scout, girl guide feel – something suitable for earnest young people but not adults. Perhaps it was the word ‘badge’ that was a mistake – something with almost trivial connotations.
7. Measurement
When people started to get badges for simply attending conferences, I got worried. The motivation for conference attendance is not always learning. It is often the extrinsic reward of travel and time off. How do you measure the usefulness of that attendance? We could say, did you tweet out session, blog and distribute your findings to your fellow employees, write a paper suggesting new implementations based on what you learnt? Badges for just turning up don’t wash it for me. A real problem here is that badges often don’t match real learning and are rarely measured in terms of impact.
Foursquare and Gowalla allowed you to check-in, tag your location and record what you did/are doing at those locations, through badges, points, whatever. They were like a spiced-up Twitter, with points for prizes. They died. Reduced to adding GIFs badges to Snapchat, they've had their day. Whether you see badges as motivational devices, credentials, actual assessments, even evaluative, if they don’t catch on, they’re dead in the water. In short, they’re dead in the water. The truth is that this has happened, sad but true.
There is one hope, a technology that avoids some of the problems outlined here – Blockchain. I’ve written about this here…. Time will tell but time is a cruel judge.

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Sunday, February 18, 2018

Tyranny of time – why learning is a waste of time...

The learning world, at all levels, including offline and online learning, suffers from an obsession that leads to massive waste and low productivity – an obsession with time. This is why learning, rather than increasing competence, performance and productivity, often exhibits failure, poor performance and low productivity. The metrics almost universally cost ‘teaching and learning’ like sausages…. by the pound/kilo - face time, contact time, fixed length courses, and hour of learner time in online learning. All are metrics that work against efficient delivery.
Higher Education
The one hour lecture, that pedagogic staple in HE, is an hour long simply because the Sumerians had a base-60 number system -  hence the ‘hour’. It bears no relation to the psychology of attention or efficient pedagogy. It is quite simply the slavish adherence to a fossilised method of delivery that is easy for faculty to timetable. Even then, attendance is often appalling (even at Harvard), and often not recorded, rendering even rudimentary attempts at measurement meaningless. University terms still adhere to an 18th century agricultural calendar, with long holidays, that could have been designed as periods of forgetting. Fixed three and four year length degree courses with only one start date per year, take no account of actual needs. Oh, and lets build and market ‘Masters’ Degrees to that we can add yet another year. Nowhere is the tyranny of time more crude and obvious than in Higher Education.
Similarly in schools, that mimic Universities, as they must be kept in sych, another form of tyranny as schools have been their feeders, despite the fact that the majority of young people do not take that route. The ‘period’ in schools mimics the ‘lecture’ where millions of young people pack up, stand up and shuffle through crowded corridors to another identical room where they have to unpack, sit down and settle again. This waste of time is immense. Imagine running a company where all employees have to rise on the hour and move somewhere else? And again the tyranny of the agricultural calendar, where unhealthy doses of forgetting punctuate the year, determine the rhythm of learning, which should be stead, not full on, nothing, full on, nothing…..
An obsession with ‘courses’ from compliance to whatever fad arises (Emotional Intelligence, NLP. Mindfulness and so on) means days of wasted time doing courses that have little or no effect on performance. Get people to travel from all over, then batch people through in dull rooms with round tables, bowls of mints, coloured pens and some half-baked attempt at collaboration, where you throw out a vague question, discuss at the table, feedback on flipchart paper, which gets pinned on the wall, then the promise that the results will be sent to you – they never are. These courses are always delivered by the half-day, full day, or worse, days on end and when it comes to impact the adherence to a ridiculous mode of evaluation (Kirkpatrick) means very little is meaningfully measured.
Online learning
Just as bad is online learning, bought and sold by the ‘learner hour’, mimicking the University and school model. Rather then focus on value and the idea that this really can save time, it encourages vendors to over-deliver so that they can charge more. The net result is overdesigned content, with oodles of meaningless, illustrative graphics, thinly punctuated by multiple-choice questions, and maybe some Pavlovian gamification (so that a premium price can be paid). Even MOOCs were foolishly deigned to match University semesters, with a drip feed of content over up to 10 weeks – and they wonder why people fell to the wayside?
What to do?
So the tyranny of time comes in many guises, the lecture, period, semester, term, course and degree. Some make it worse by recommending lifelong learning, in the form of going back to college – life as one long courses. No thanks. Life is far too short for that nonsense. By and large all of these take too long as they suffer from the following flaws:
1. Fixed form of delivery
Most ‘teaching and learning’ is shaped by pre-existing, fixed modes of delivery, the lecture, period, term, module, course and so on. This ‘ass before elbow’ mode of delivery should be shaped by the type of learning, needs of learners and resources, not mode of delivery. The solution is to imagine that the learning experience doesn’t exist, take it back to a blank slate, now re-design. Match modes of delivery to the typology of learning, learning needs and resources. Look to make everything shorter and more efficient for the learner. Some call this Blended Learning - that doesn’t mean a bit of online bolted on to a bit of classroom, let’s call that Velcro Learning, and don’t confuse Blended ‘Learning’ with Blended ‘Teaching’, where you simply slice and dice a bit of your old and new delivery methods and call it a ‘Blend’. Escape the tyranny of time and focus on value.
2. Sheep dip
Most teaching is a one-off event. It is ridiculous not to record lectures, even if you think it’s a poor form of pedagogy (which I do). Denying learners a second and third bite of the cherry is ridiculous – they may be ill, miss points, not understand at first pass, have trouble note taking, have the language of teaching as a second language. Above all the psychology of learning shows that repeated access for reinforcement and retrieval through revision is necessary for efficient learning. There is a strong argument for doing the same in schools. I’ve seen this work magnificently in an Italian school, yet few have ever thought about doing it.
3. Forgetting
Let’s not forget that single, fixed timetabled events ignore a well known principle in learning – that the brain forgets almost everything it’s taught. Ebbinghaus showed us this in 1885 and the learning world has studiously ignored the principle that learners need, not repetition but retrieval and deliberate practice. Learning needs to be repeatedly accessible, say through recorded lectures right through to spaced practice techniques such as top and tailing, note taking, repeated testing, up to algorithmically determined, personalised deliberate practice. Deliberate, spaced-practice frees learners from the tyranny of single event, sheep-dip learning.
4. Batching
Courses tend to batch learners who have to go through the linear course at the same pace. In any group you will have a distribution curve, where you only hit those in the middle. There will be tails of learners who find the experience too slow or too fast. Personalised delivery, now possible through adaptive, online learning, allows you to deliver learning to an individual, informed by their progress and aggregated data from all who took the course before. This results in increased attainment and lower dropout.
5. Less is more
In designing learning experiences, the ‘Garbage-In Garbage-Out’ rule is not taken seriously enough. I’ve seen far too many long compliance documents and over-engineered courses throw far too much detail at learners. Lecturers pad out lectures to fit their ‘hour’. Course designers fill out a timetable with unnecessary content and activities. The net result is actually lower learning, retention and recall. Cognitive overload results in less, not more, being retained. Research from large data sets has shown that video in learning tends to fall of a cliff at around 6 minutes. The consequence being that video should be that length or shorter.
The psychology of learning screams ‘less is more’ at us. Cut down documents until they bleed then cut them down again, so that the content is learning ready. There are few courses I’ve seen that can’t have up to 25-30% cut out – all the padding. There is no doubt that lecturers pad out to the hour, the same with classroom teachers and organisational trainers. Rather than plan to fill the time, like an empty vessel that needs to be topped up, look at making the learning experience as short as possible. Think about what learners ‘must’ learn, not generally what they ‘could’ learn. Of all the techniques to free learners from the tyranny of time this one is by far the most productive.
6. Failure to chunk
Chunking is a pretty basic pie of learning theory – that our working memory is limited and that throwing overlong learning experiences at the learner is counter productive. It happens all the time. We teach people to write essays by repeatedly getting them to ‘write essays’ rather than breaking that task down into its constituent parts. Whole word teaching was an almost perfect example of this approach to teaching that resulted in catastrophic failure in reading in UK schools. Learning experiences have to have focus.
7. Digital by default
Rethinking learning around, not existing modes of delivery and fixed timetables, but more flexible methods of delivery that suit the type of learning, learners and resources is badly needed. More often than not this means more 365/24/7 availability by being online. Being digital by default, wherever is practical, turns time-tabled learning experiences into anytime learning. Asynchronous often makes more sense than synchronous, even of its recorded lectures and resources. Switch away from a dependence on courses to an on-demand model.

In practice, as you get older and become a more self-sufficient learner, you realise that freedom from the tyranny of time is the real trick to learning. You literally ‘learn’ how to learn by being measured, having focus, rehearsal, retrieval – by avoiding the waste of time that are courses and degrees. That’s lifelong learning. Life is short, it's made even shorter by wasting so much time learning and not living.

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