AI builds e-learning at 10% of cost, in minutes not months, with higher retention and recall

Paying 20k an hour for e-learning content, over many months, that is laden with noisy pages of text/graphics, punctuated by low retention multiple-choice questions? AI can help you to build content at 10% of the cost, in minutes not months, with higher retention and recall.

Problem

The problem with the traditional, online learning, bespoke content paradigm is the tools. They push vendors and buyers into producing content that has ten major flaws:

1. Expensive to produce

2. Too long to produce

3. Difficulties with SMEs

4. Media (but not mind) rich

5. Weak Multiple-Choice

6. Low effort learning

7. Pavlovian gamification

8. Impersonal learning

9. Low retention and recall

10. No practice

1. Expensive to produce

I ran a large, bespoke, content company. It was very successful but that was back in the day and used the tools of the day. Yet the content produced today costs and looks much as it was 30 years ago, despite the fact that computers are faster, better, cheaper and online. Why does it still cost 20k an hour to produce e-learning content? Because we’re still in the old paradigm of traditional authoring tools and a mindset besotted with appearance, not learning. Imagine reducing that cost by 90% through a radically different approach, using AI and automation. You can.

2. Takes too long to produce

E-learning projects can take three to six months or longer, with lots of process and angst. It’s a highly iterative process, and takes a huge amount of management, definition, design, development and delivery time to produce anything. Imagine doing all of this in minutes, not months. You can.

3. Difficulties with SMEs

By far the most difficult step in the production of online learning is getting the knowledge and expertise from the mind of the subject matter expert (SME) across and into the course. It’s a tricky process and often full of angst and recrimination. Imagine taking SME content – any document, PowerPoint or video - and turning it automatically into online learning. You can.

4. Media (but not mind) rich

As computers have allowed us to deliver media rich experiences, we have, often blindly, ignored the research by Mayer, Clark and many others, showing that media-rich is not always mind-rich. This has resulted in an often garish concoction of movement, graphics, cartoons and animation, that inhibits, rather than enhances learning. There’s maybe not enough ‘edu’ and too much ‘tainment’ in ‘edu-tainment’. We need to pay attention to the research, reduce cognitive overload and focus on the learning. Less is more.

5. Weak Multiple Choice Questions

Long the staple of online learning, yet when in real life does anyone choose an option from a list, as if the mind was a simplistic ATM, choosing from menus? Besotted with MCQs, we have produced low-effort, low-retention and low-recall learning. It doesn’t have to be this way. Use more effortful, open response learning. The research shows it is superior.

6. Low effort learning

The illusion of mastery is what much online learning produces, the feeling that you’ve learnt things through light-touch exposure and occasional selections from lists. In truth, real learning is learning by doing, real effort, not page turning and exposure to fancy media. Recent reseach turns traditional onine learning on its head. Make the learner make the effort – that’s what results in high retention and recall. Read to remember.

7. Trivial Pavlovian gamification

Does gamification play Pavlov with learners? It so often does, collecting coins, trivial games that just put more cognitive effort into the mix. Or, you can focus on the sort of gamification that Demis Hassabis uses in AI learning – repeated deliberate practice. Don’t make it too easy to sail through, allow learners to fail, make them work, make them do things until they get 100% competence…. That’s true gamification.

8. Impersonal

Good online learning is always a balance between directed and open-learning. There needs to be structure, often quite directed, but there also has to be the opportunity for support, expansion and curiosity. Allow the learner to explore by providing automatically generated links out to extra content. Make your course porous. This is possible with AI.

9. Low retention and recall

Over the last ten years, evidence has emerged (summarized in Make It Stick) that effortful learning matters, that open response is superior to multiple choice and that deliberate practice matters. We have the ability to use AI to embody and deliver practice based on contemporary learning theory.

10. No practice

The once only, sheep-dip experience was the target of online learning, with its anytime, anywhere offer. Yet online learning simply replaced one type of sheep-dip (offline) with another (online). The fact that they rarely delivered opportunities for reinforcement and practice, was the same in both camps. AI, the algorithmic delivery of simple and effective online learning, through effortful and deliberate practice, can change this.

Conclusion

We can now, for certain types of leaning, produce content at 10% of the cost, in minutes not months, with higher retention and recall. We can avoid the trap of whizz-bang graphics, weak MCQs and Pavlovian ‘collect the coins’ gamification. The new approach, using AI, creates e-learning which is effortful and allows the learner to expand on their learning with access to external resources and further opportunities for practice. It’s called WildFire.

WildFire takes any document, PowerPoint or video and turns it into online learning, within minutes, using AI. More than this, it uses effortful open-response learning to increase retention and recall. Beyond this it automatically curates links to content beyond the course and, in real time, can create online learning from this content. It is unique. For more information see the WildFire website.

AI will have a huge effect on education & training. But what is it?

How does AI work?

AI is not one thing. It is many things. There are also many uses of AI in education and training. Machine learning, in particular, will have huge impact on the actual world of learning – education and training. It is like a huge whale shark heading towards us gobbling up all in its wake. It learns about the learner as the learner learns. More than this it can in some domains, learn faster than humans. There are profound implications here, for future employment and even what it is to be human. It’s an existential threat or opportunity. That’s why we need to know more about it.

Five species of AI

Beneath all of this lies several species of AI, schools if you want, that have shaped the AI landscape. A useful guide is the classification used by Pedro Domingo in The Master Algorithm.

1. Symbolists

2. Connectivists

3. Evolutionists

4. Bayesians

5. Analogizers

Symbolists

The symbolists use maths and logic, sets of rules and decision trees, to deal with data. The problem with this approach is ‘overfitting’, the tendency to read into data, things that are not actually there. This form of AI is prone to exaggeration, or prone to being misled by errors in the data. It’s a balancing act between hypotheses produced, and data. Get it wrong and you get it badly wrong. But they have a trick up their sleeve and that is to see induction, predicting the future as the reverse of deduction.  You work back from good hypotheses from good data to determine outcomes. Decision trees are one practical tool in the symbolist armoury, there are many other refinements. Decision trees are used in Microsoft’s Kinect to identify parts of the body from the cameras. Decision trees are often better than humans at predicting diagnoses and legal rulings. Its weakness is that it doesn’t do ‘mess’. It’s bad with many practical and fuzzy problems. It demand exactitude.

Connectionists

Connectionists are inspired, not by logic, but the brain. The brain, or at least neural connections and networks are the connectionists shtick. More specifically ‘backpropogation’ is their kick-ass tool. Imagine having to climb Ben Nevis blind. You tap your foot around until you find the steepest direction and step forward. Repeat until you get to the top. Only there’s a problem; you may only have climbed a secondary hillock. Nevertheless, this idea of a ‘gradient ascent’ (or descent) is fundamental to connectionism. Backpropogation has been used in text to speech, predicting the stock market and in driverless cars. But there’s another serious problem. In layered neural networks, the more layers you have, the more diffuse the findings. Autoencoders, sandwiches where the input is the same as the output but the middle is a form of compression, have allowed layer after layer to be more effective. There’s big money riding on this approach – the EU has put aside a billion euros and there’s £100 million in the US BRAIN project. Some, however, are sceptical about copying the brain. We didn’t learn to fly by copying the flapping of birds’ wings, they say. Neither did we go faster by studying the legs of a cheetah, we invented the wheel.

Evolutionists

Evolution, some argue, is essentially algorithmic. Genetic algorithms produce variability and this is tested in the real world to produce – us! But evolutionary algorithms are more like selective breeding – we inject purpose and goals into the process. It’s less random than biological evolution. We let software breed software, producing variations but also mimicking sexual reproduction. Using all the things we’ve learnt from actual evolution, we let the software rip, in a virtual world. Unlike real evolution, successful solutions don’t need to die. They are immortal, free to breed with their children and grand-children. This approach is great at coming up with new, unimagined solutions. However, it is subject to obesity, producing ever more complexity – the ‘survival of the fattest’ problem. It also, rather cleverly, uses ‘learning’ to accelerate its progress. Allowing genetic algorithms to learn, is the trick to speeding up their success – inspired by the Baldwin effect. This approach has been successful in designing electronic circuits, factory optimisation, even inventions. For a brilliant look at how evolutionary algorithms work in practice, see how one plays Super Mario.

Bayesians

Bayes was an 18^th century clergyman. His theorem (actually created by Laplace), which takes prior probabilities and updates them in the light of new evidence, is the most famous theorem in AI. Sounds easy. You have the theorem and off you go. It’s not. Complexity is its enemy, so the Naïve Bayes Classifier, takes some ‘naïve’ shortcuts to accelerate the process. Beyond Bayes we have Markov chains, originally conceived to apply probability to poetry (Pushkin’s Eugene Onegin). Markov chains do pretty well at dealing with probabilities from structured data, such as language and are used in machine translation systems, such as Google Translate. PageRank (Google’s successful search algorithm) was a Markov Chain which calculated forward-looking probabilities on the basis of incoming links. Hidden Markov Models (HMM) go one step further by predicting the next word from previous words spoken, even from pronounced sounds. They are what make Siri work. In fact, it’s what makes all mobile voice calls work. There’s another trick called Kalman Filters, that eliminate much of the noise, like a barman scraping off the froth from the top of your beer. Beyond this are Monte Carlo techniques, which introduce chance visits across networks to stabilise the results. Bayesian inference is behind a lot of computer vision programmes. Naïve Bayes lies beneath most spam filters and Peter Norvig, of Google, states that it is a staple of search at Google.

Analogizers

Look for something similar, that’s the principle behind the Analogiser approach to AI. There’s the Nearest-neighbour algorithm, Support vector machines and analogical reasoning. Nearest neighbour is super-fast, and is used in face recognition. What makes analogy work is lazy learning. Don’t compare a new face to all faces in your database, use collaborative filters, as Netflix do, to narrow the possibilities. Recommendation engines such as Netflix and Amazon are keen on nearest neighbour algorithms and further tools, such as k nearest neighbour. Analogizers work well in narrow domains, where there’s a limit to what they’re looking for – handwriting recognition, book choices, movie choices and so on. To widen their applicability Support Vector machines (SVMs) optimise solutions for much more complex problems, such as text classification.

Conclusion

This is a complex field but it didn’t come from nowhere. We’ve had 2500 years of maths, with Euclid defining the first ever algorithm, the great Arab scholar Al Khwarizmi, who gave us the word algorithm, and centuries of work in probability theory, and now AI. With the flood of data from the web and massive, yet cheap computing power, we are now able to harness its power for practical uses. This startup, Smacc, has just raised £3.5 to automate accountancy. This AI platform beats expert pilots in ariel combat.  Are there any human skills that AI can't master?

You needn’t worry too much about the mechanics of it all but we do need to understand the ways in which it will impact the field of teaching and learning. It already has. Google’s been around for decades and it is nothing but AI. Almost everything you do online, is powered by AI. Many of the things you do and choose online have been subtly determined by AI. It’s not coming – it’s here.

What does Brexit mean for EdTech?

I know of four successful EdTech entrepreneurs who voted for Brexit. Surprised? Maybe not! I was one of them. Steve Rayson, another, made the point that people in this position like to do detailed analysis, weigh up the risks and make their choice. We were not swayed by Boris and co, and tended to see this as a long-term play, not a proxy general Election. Steve's point was that we are not scared of risk. We rehearsed the arguments openly, online, over several months and made up our minds. For all of us, I suspect, it was marginal. There was also some reflection around what it means for our sector.

The Brexit vote has produced lots of angst around business. Die-hard anti-capitalists have suddenly found a new interest in the stock market and currency trading. They worry about tariffs and trade deals. But for those of us who have been travelling and trading abroad for many years, it’s not such a great problem.

1. Addressable markets

EdTech companies tend to look towards the English speaking market – the US, Australia etc, for the obvious reason that we speak the same language. The EU market is linguistically diverse, compared to the largest, single EdTech market in the world, the US. This is why investment money tends to look for UK companies with a US angle. Content, technology and services are always easier to deliver in one language. Indeed, if one were to look for new, hgh growth markets, it’s not Europe but the US, Middle and Far East that you would turn. So in terms of growth, it’s business as usual.

2. M&A

Most of the M&A business is not investments from Europe or in European entities but in the US, Australia and elsewhere. Similarly, a lot of investment is to and from the US. This has been true for decades and is unlikely to be much affected by Brexit. Every single deal I've been involved in over the last ten years has had nothing to do with Europe.

3. Services

There are no tariffs on services, so it will not make much difference. At present, few in EdTech experience any real differences between selling to EU and other countries. If anything, Ireland, which is in the EU, is the most difficult, as they subtract tax at source, which has to be reclaimed, with some difficulty. So no difference here.

4. Education is devolved

Education is a devolved responsibility in Europe. Pan-European attempts at integration and sharing, Bologna and many others, failed, as countries are, in reality, fiercely independent in education, with funding regimes that encourage institutions not to share students and qualifications. Being in or out, therefore, makes little or no difference.

5. Research

Little has emerged in EdTech from EU research funding. Many business people who have been involved in such projects complain about their bureaucratic and stifling nature and lack of impact. As they have had no significant impact, despite vast sums of money being spent, I don’t see this as something to worry about. Indeed, the country saves large sums if wasted research cash, rather than being spent on diffuse, collaborative, CityBreak research projects, get spent on closer working relationships between researchers and our own UK-based EdTech companies. The EdTech sector in the UK has not grown on the back of University research, it has grown through the efforts of investors, entrepreneurs and small businesses.

6. Outsourcing

Few outsource from the UK to EU countries, as Indian or other countries are cheaper, and often have higher level skills. Even if one does outsource to Eastern Europe, things are unlikely to change.

7. Data regulation

Benedict Evans thinks that the EU is moving towards heavyweight regulation around data (and AI) that will stifle technology company growth, leading to inaction by buyers. The UK has pushed back against this but have been outvoted Post-Brexit, the EU may get worse, while the UK remains relatively light in such over-bearing restrictions. Being out may be an advantage.

8. OER

Wikipedia, Duolingo, Khan Academy, MOOCs, and most successful OER projects, are largely international phenomena, not EU specific. Indeed, despite the huge spend in the EU, the most successful examples of OER seem to come from the US. One wonders whether the steady flow of annual grants has simply stifled innovation. I suspect so.

9. Currency

This is a swings and roundabouts issue. A weak pound makes things difficult if you’re buying from abroad but also makes you cheaper and more competitive to those abroad. As our EdTech industry is a net exporter, this is fine.

10. Education

I covered this in another post but some analysis of the effects of Brexit on the education market as a whole, shows no real, significant change. When it comes to the ededucaytion market, the effects are insignificant.

Conclusion

So not much change then. If anything, a few advantages. One clear advantage for me, is that we’ll rely less on the crutches of EU grants and focus on real businesses and projects that effect real change in terms of better efficiency and outcomes in the EdTech sector. We have the largest and most successful EdTech market in Europe, it is more likely to grow as a result of this vote.

What does Brexit mean for education?

Education is a devolved issue in the EU, so the impact, in my view, will be negligible. Sure there will be adjustments, but that may, in some cases, be healthy.

1. Schools

Brexiters argued that there will be less pressure on schools, as there will be less EU migration in the future. This was one of the reasons so many voted for Brexit. They have a point as it is hard to see how there will be more pressure on schools because of Brexit. With other countries about to join, the number of children coming to the UK was set to increase year on year. EU teachers working here may be affected but that depends on what rules emerge and it is unlikely that qualified professionals will be much affected. Here’s some good news, Nicky Morgan was a fierce remain campaigner, so her tenure may be short - but who knows who we'll get next.

2. Private schools

Patrick Durham, speaking at the Festival of Education, thought that a number of private schools would have to close. Again, it’s hard to see why this would be the case. I think his case is exaggerated. There are about 5000 EU boarders in such schools and a cheaper pound will make their fees lower, attracting even more foreign boarders. There’s far more other nationalities, and let’s face it, this about making money. I’m not exactly crying in my soup over this one.

3. Language schools

This is big business but there’s little sign that the thirst for learning English will change. It may be a little more difficult to come here but a cheaper pound will more than compensate. For me, this will be business as usual.

4. Student fees

Curiously, EU students in Scotland will no longer be classed as home students, and will have to pay fees, allowing the Universities to recruit without a cap. The same is true in Northern Ireland and Wales, which also offer student support. However, there may be fewer EU students in the future as our Borders tighten. So there's a real downside here as well. English Universities will, of course, simply say that EU students will be charged the same as UK to students to avert a dive in 2017 numbers. Easy fix.

5. Research

Complex one this. As part of the £350 million per week we pay (Gross figure before rebate - I know) to the EU, a large portion comes back to us as University research money. We will, in effect, simply be able to pay this direct. It will be under out control, not the agenda of a diffuse set of 28 states. This is generally true of all sources of EU research funding. It’s was never free money in the first place. It means, in effect, that we will have total control.

This is a potential solution. There are already countries that are not members of the EU, that take part in EU research programmes, collaborate within consortia, even have access to European infrastructure. They have ‘Associated Country’ or ‘Third Country’ status, and include Norway, Israel and Switzerland. Time to consider htis as an option.

6. Research quality

One could argue that, free from the constraints of EU research criteria around collaborative participation, the quality of research will rise. I am in that camp. I have witnessed decades of wasted research in my field, in collaborative projects, with lots of meetings in nice European cities but little or no impact outcomes. EU quango have arisen with little of no real value. So we may see a reduction in wasted research, allowing us to focus on the stuff that matters.

7. Vocational learning

The one area that may suffer is vocational education. However, this may, like research money, simply be a case of money that we paid out coming back to us. Indeed, as we move towards a levy-funded apprenticeship model (April 2017), we will be funding this sensibly from employers. Nick Bowles did say that this may have to be delayed if we vote to leave and we did. However, these threats were ten-a-penny during the campaign. The EU was always skewed towards academic HE and not vocational. This has been catastrophic in southern Europe, with huge levels of youth and graduate unemployment. Vocational was always more of a national than international issue and as we rebalance the academic and vocation mixture, it is arguable that we will become more competitive in the long run.

Conclusion

Turns out to be not so bad after all. Win some, we lose some.