Amazon’s amazing algorithms – what can we learn from them?

When you use Amazon, you may not realise it, but that screen is actually a set of ‘tiles’ and what you see at any time (recently revamped) is finely tuned to your needs. What you don’t see, is what lies beneath the surface, a powerful AI engine that decides what you are seeing. This invisible hand not only determines what you see, it plays a role in what you do. It nudges you, in realtime, in one direction or another.

Amazon excel in making it easy to buy their product, they are also good at recommending what you buy and cross-selling other products. Their algorithms have been honed over many years taking inputs, such as what you’ve bought before, viewing history, repeat clicks, dwell time. your past search patterns, recently reviewed items, what’s in your cart, what site you were referred from, demographic data (where you live and what type of person you’re likely to be), user segmentation (if you bought books on photography, sell you cameras and accessories) and so on (Your Recently Viewed Items and Recommendations).

This is supplemented by aggregated data from other customers to produce differents sets of recommendations:

‘What Other Items Do Customers Buy After Viewing This Item?’

‘Customers who bought this item also bought this’.

‘Customers who shopped for Equality and Partiality also shopped for…’

‘Recommended for You Based on…’

‘Customers also Bought these Highly-rated Items’

Aggregated data from other similar customers and so on to nudge you towards the ‘Frequently Bought Together’ technique or a range of related items in terms of brands, function, colour and size.  Best sellers are also pushed. They also recommend newer products, such as Kindle versions. Packages of products, such as item plus carrying case, are also pushed. Reductions on postage and discounts also boost sales. The bottom line is that this is a bottom line result, as you buy more. Their conversion rate from website recommendations is very high.

How do they do this?

They do all of this through their own collaborative filtering algorithm, that has the goal of increasing the average order price. Note that this may not be the ideal goal, as some customers may order more through lots of smaller orders, rather than the occasional large one. They do this through A/B testing (trying different things out and measuring the effects), data mining, affinity analysis and collaborative filtering. This is not easy. Algorithms are sensitive things and one technique may require too much data to be practical, take too long, or have odd (weird pricing) sparse or low quality outputs. Performance and scaling problems are big issues. You can reduce the data size by sampling or partitioning, by product or category, but this reduces the quality of the recommendation. It’s all about trade offs. Clustering into groups that are similar is another technique but difficult to scale. What Amazon actually does, is rely on item data i.e. the similarity or relationships between products, not just buyers. It looks for correlated or most popular items. This is called ‘item-to-item collaborative filtering’. This emphasis on item data means their recommendation engine is super fast.

Remember also, they are the biggest online retailer in the word, so they have the biggest data sets. This gives them a real market advantage as their algorithms have more to work with, can be tested on larger groups and use more aggregated data.

Human reviewers a problem?

One of the things that screws algorithms up is bad data. If the reviews are positive but they’ve been fixed by humans, that skews the recommendations. Publishers have been paying people to review by offering discount product, so Amazon has just banned incentivised reviews. In other words, slowly but surely, the human factor is being squeezed out and this is to our benefit.

Algorithmic bias?

One could argue that this is not in itself a good thing, as the algorithms are written by humans and may contain bias. I think this is a red herring. Compare this approach with the average bookstore employee. It outguns them. The algorithm is far less likely to contain bias on gender, race and social background, as it doesn’t make judgements on these. Neither will it have the vast array of cognitive biases that we humans all have. It is ruthlessly objective.

One thing that does screw up this ‘perfect’ market through algorithms is promotion. Amazon will promote products that they get a better margin on. You never know what the behind the scenes deal is with certain suppliers in terms of promotional intent and pricing. You may very well be getting recommendations based on their margins, than your preferences. On the whole, however, their prices tend to be better than physical retail outlets.

Serendipity lost?

Another argument could be that it eradicates serendipity – those chance encounters in bookshops where you find a gem. I have some sympathy with this view, but suspect that when you actually compare browsing in a bookshop with browsing online, that factor is exaggerated by the bookstore supporters (biased towards past behaviour, nostalgia and neophobia). The bottom line is that Amazon has a larger collection of books than any bookshop. Indeed, many small bookshops now survive on Amazon sales. Its recommendation engine also promotes the sort of ‘impulse’ buying you see in bookstores through last minute cart recommendations. Nevertheless, the stronger a recommendation engine becomes, one could argue, the narrower the browsing and buying behaviour, This is a trade off, a common problem when designing algorithms.

It learns

One final point, is that, with machine learning, these algorithms simply get better and better. They are learning from their own generated data and can adapt, sometimes on their own to improve. This is way smarter than any human approach to recommended sales. Remember also that Amazon is increasingly automating its distribution through AI, as people are replaced by robot pickers and loaders. Beyond this AI driven drones may well deliver to your doorstep.

What can we learn from this?

What do we have to learn about learning from all of this. First, that recommendation algorithms will certainly play an increasing role in the learning game. They already have. Google is arguably the number one technology used by learners. We use it to search for things we want to know. That is a fundamental piece of pedagogic technology that has revolutionised the learning process, whether you’re searching for knowledge, instruction on a process or skill on YouTube (owned by Google), or on Google Scholar for research. Beyond this we have tools that use AI to create learning content and experiences, in realtime, such as WildFire. Then we have adaptive learning systems that use AI to navigate learners through courses. On top of this there’s advances on online assessment through digital ID, face recognition and automated answer and essay marking. This is the one area, AI, where significant advances are being made, quickly. These advances promise to do what they’ve done for Amazon and others, offer a massively scalable solution to the problem of teaching and learning through realtime learning design, personalised recommendations, less linear teaching and better assessment. All of this, of course, driven by machine learning, which makes these systems learn as they go. The scalable online teacher is a fast learner that learns as it goes. It just gets better and better.

Above all

Above all, what we can learn from Amazon is that you, the learner, are a valued customer and that they tailor their service to your actual needs, in realtime. Without getting too angsty about the use of the word ‘customer’, which seems to trigger all sorts of people into surface criticism, what education badly needs is this approach to learning, not the blind, batch process we currently have. Let's not over-romantisie batched lectures to hundreds in raked lecture-hall or classrooms of 30+ kids struggling with the intricacies of maths or French. There's plenty of headroom for improvement here, and some of the techniques have already been put to the test over decades with proven efficacy in target groups of the tens and hundreds of millions.

Education – Blair’s poisonous legacy

Can you name the Shadow Secretary for Education? No? Not a single person I’ve asked can and some of those are teachers, some even staunch Labour supporters. How has it come to this?

The answer is to be found in Tom Bower’s excellent book on Tony Blair - Broken Vows. It’s brilliant. Forget Chilcot – this book reveals so much more about Blair and why Labour Party commentators feel as though they’re watching a slow-motion implosion. Prior to this, Polly Toynbee wrote a critique of the Blair years, The Verdict, its successes and failures. It was good book, a balanced summary. What it also revealed was that most of the failures were either Blair’s own idiosyncratic beliefs or his undoubted ability to persuade, pick and promote people who reflected those personal beliefs. Much of the rest was actually quite good.

This was certainly true of his now obviously flawed forays into Iraq but it is even more obvious in his legacy in education – basically a neo-liberal policy that opened the door for diversification of schools, faith schools, obsession with testing, their separation from state control, doomed pilots and projects (truancy, literacy, numeracy), ILAs, the introduction of fees in Higher education and the continued destruction of vocational learning. For these wondrously wrong-headed moves, we have Blair, and Blair only, to thank.

Remember the painfully shy Estelle Morris, who at least had the good grace to resign when she could no longer take Blair’s diktats? Remember Charles Clark with his white heat of technology initiative – eh… whiteboards? Remember Ruth Kelly and ILAs (Individual Learning Accounts)  – which collapsed under the weight of massive fraud? Brown hated the idea and it became a Blair project that existed only to stymie his rival. Remember David Bell who warned Blair about the divisive and odd idea that faith schools reflected diversity, when all they did was divide? Truancy projects that failed miserably. Literacy and numeracy initiatives where huge sums were spent with little or no impact. The mantra was ‘choice’. If only we gave everyone more choice, which simply opened up a market for education where the sharp elbows of the middle classes went to work, as they always do, with a ferocious elbowing aside of the people who needed help the most. This has, in the end, led to from free schools to academies and now t the resurrection of the Grammar School nonsense. None of this would have happened without Blair.

More recently we had Tristram Hunt, a hapless a shadow Education Secretary, who simply reflected what Blair had initiated and went along with what Gove had completed. Ignoring the working class needs on vocational education, despite the fact that the majority of kids do not go to University, he was clueless. Two elections lost and a working class vote that has been betrayed by ignoring their plight.

How did we come to a position where a Conservative government has had more progressive policies on vocational learning than Labour, namely, the apprenticeship levy? Labour stood by while progressive policies, in Conservative manifestos trumped them.

The Blair legacy was an army of technocrats who confused student politics and bookish views with the real world. They were hired, groomed and selected, with a tremendous sense of entitlement. This went on and on with the idea that having NGO experience on your CV meant you had been in the middle of the class war. No – it largely meant a comfortable sojourn and almost no contact with typical, Labour constituents. I saw this in my own constituency, where a good, local MP, with a healthy majority retired, only to be replaced with two candidates who were ‘selected’ not so much for the abilities, as their CV. They lost – badly. This Blair legacy has lost Labour the last two elections. The empirical evidence is now clear. First, in Scotland, Labour has already imploded, as other, fresher politicians land-grabbed. Even the leader of the Scottish Conservative Party demands more respect. Second, Labour lost the last two elections, which clearly shows a long-term rot. Third, Brexit proved that Labour had now officially disengaged from their traditional voters and working class concerns. This all adds up to an existential threat, as Labour fractures down the middle, slogging it out over footnote issues such as anti-semitism and rule-book changes, while the world around them ignores their petty domestic disputes.

I’m no Corbyn fan and O’Connel is a liability but Corbyn’s education policies are quite good, with a core idea being a NES (National Education Service) like the NHS. What I’d like to see is a move towards all schools being secular, drop charitable status for fee-paying schools, force HE to cut costs rather than raising fees and a massive push on apprenticeships.

PS

Her name, by the way, is Angela Raynor, a more invisible and lightweight Shadow Education Secretary is hard to imagine.

Why I got married on Blockchain and paid by Bitcoin

I got remarried on Blockchain this year. Seriously, in Edinburgh, I opened a Bitcoin account and we both entered our details via two iPhones onto a blockchain system and renewed our vows. Why not? When I got married in that same city, 34 years ago, we signed a document and that document lies somewhere in Edinburgh – to be honest I couldn’t tell you where. We were doing the same thing but getting it confirmed by a piece of technology that is arguably more transparent, safe and tamperproof. This piece of tech was provided by my alma mater, the University of Edinburgh. I was determined to learn abut this stuff by doing something. So what role has academia had in Bitcoin and Blockchain?

Who is Satoshi Nakamoto?

Let’s go back to 2008 and the first appearance of Bitcoin and Blochchain. Who is Satoshi Nakamoto? This has become a modern myth, like the Loch Ness Monster, the Yeti or Bigfoot. There have been lots of candidates, one disastrous false claim, lots of denials and a recent false claim. We don’t know. We don’t even know if it is one person. What we do know is that Satoshi Nakamoto published one of the seminal papers in computer science Bitcoin: A Peer-to-peer Personalised Electronic Cash System at the very time that the world’s financial system was on the brink of meltdown.

He has good reason to remain anonymous, as he, or she or they, had created a dangerous piece of technology that threatens to reshape, not only the world’s banking system, but the way government and many other areas of human endeavour operate. Satoshi disappeared just as the US government, the CIA and the FBI, even the department of Homeland Security, were becoming active around Bitcoin. It was clear that they saw it as a potential threat to the dollar and existing banking system, as well as a system that could be used for money laundering and drug sales (they weren’t wrong and closed down Silk Road some time later). When Wikipedia became the target of US agencies, who used Visa, Mastercard and PayPal (disgracefully) to starve it of funds, Bitcoin was associated with Wikipedia, but Satoshi Nakamoto did not want to become another Julian Assange and went to ground.

Academia’s reaction

Academia was slow to react. This was clearly an astonishing technical achievement and serious figures in the computer world agreed that it could be a game changer, some claimed it could be as great a shift as the invention of the internet itself, certainly a major advance. Yet they were nervous of its disruptive and transgressive nature. The main players were not academics but hardcore coders and hackers, often with strong libertarian views. They weren’t fond of institutional values and inertia.

Nakamoto’s astonishing nine-page paper was not published in an academic journal but part of a community of coders and hackers, where the real action was on Reddit and Sourceforge. These are people who comment quickly, contribute and do things. They quickly went on to create exchanges, wallets and dozens of applications in the Bitcoin ecosystem. The players lie largely outside of institutions and prefer the world of doing and action, rather than research and papers.

As things progressed, however, academia started to take an interest. So where is the activity on Bitcoin and Blockchain? There’s a curious dynamic here. Bitcoin and Blockchain were created to decentralise and disintermediate institutions, so why keep it locked up within institutions? And if you offer courses can learners pay in Bitcoin? Should they be decentralised and free MOOCs or MOOC-like? Should the qualifications be on Blockchain?

There seems to be four approaches to Bitcoin and Blockchain, in terms of academic activity. First, the technical stuff around cryptocurrencies and the design and coding of Blockchain. Second, courses and research on the governance, social and policy issues. Third, the practical applications, in different domains. Fourth, the actual use of Bitcoin and Blockchain to deliver education. So who is doing the interesting stuff?

Academic offers

Joichi Iti, college drop-out and Director of the MIT Media Lab (where else could that happen), was first to do something substantial. He saw a role for academia to provide research and stability. In a bold move MIT had already offered 4500 students $100 in Bitcoin. 3110 took up the offer but 40% traded it for cash (so much for their appetite for innovation). Two years later about 14% are using it in anger, the rest holding it as an investment. This, perhaps, says more about the modern student than Bitcoin or Blockchain. Iti wanted something more substantial and set up The Digital Currency Initiative (DCI) with some serious developers and an ex-White House advisor. Interestingly, he sees this as a way of opening up Higher Education to successful entrepreneurs like himself, making it more agile. In his own words “It’s an opportunity to pilot the future of academia.”

Simon Fraser University in Canada has been doing some edgy stuff, as have many other research departments around the world. Courses in business schools have become common, with appropriate price tags for corporate customers, not perhaps entirely in the spirit of the exercise.

There’s even a free textbook on Bitcoin from Princeton, which is both readable and informative. Although, for the less technically minded I’d recommend Dominic Frisby’s Bitcoin: The Future of Money, which is an excellent introduction to the topic. Blockchain Revolution, by the Tapscotts, is a thorough introduction to Blockchain.

MOOCs

One University that offered something substantial was in 2013, when the University of Nicosia, who offer a MSc in Digital Currency, started to deliver courses on Bitcoin but also accepted Bitcoin for tuition fees. Andreas Antonopoulos and Antonis Polemitis also launched the first free MOOC in the area - An Introduction to Digital Currencies. In the true spirit of the thing, you got your certificate on Blockchain.

The University of Cumbria offer a MOOC on Money and Society and you can pay with Bitcoin. Princeton has a Coursera MOOC on Cryptocurrencyand Blockchain. There’s a French MOOC on Blockchain itself and Bitcoin and Blockchain increasingly appear on more general Fintech courses and tech-oriented MOOCs.

EdTech

In our own EdTech world, Audrey Watters has taken a keen interest with her Blockchain for Education: A Research Project. She’s rightly taking neither a hyped nor skeptical approach, simply asking some key questions.

Future

In this age of concern about finance and banking, advances in cryptocurrencies, along with a general trend towards things being decentralised, open, transparent, yet secure, has led to intense interest in Bitcoin and Blockchain. There is a sense in which institutional research and teaching can emasculate a technical movement and Bitcoin/Blockchain started outside of academia and certainly does not rely on academia for its progress. However, it has rightly become a topic of interest in research, formal courses, MOOCs and books. That is a good thing as the Wild West world of Bitcoin could do with some sheriffs. Whether it has any impact on the actual delivery of education remains to be seen.

But there are others, such as academic, Melanie Swan, who set up the Institute for Blockchain Studies, who think that “academia is not the right place to do academic thinking about very new things like the blockchain”. This is a fast moving world where the speed of academic publishing is too slow.  Her vision is one where students are paired with courses, accredited or even more relevantly, she thinks – MOOCs. The decentralised MOOCs could benefit from blockchain functionality around identity, actual attainment, accreditation, cost and payment. Going further she sees the possibility of directing donor funds to poorer countries, straight to families and learners, at little or no cost, based on progress and attainment.

It's an interesting clash between one world and another. Education does;t cope with change very readily, certainly not the rapid rate of technological change that we've seen over the last 20 years. On the other hand technology doesn't often see the nuances in education, and is often too quick to prosthelytise solutions. In a series of blogs, I plan to explore Bitcoin and Blockchain further. The first is here 10 ways blockchain can be used in education. If you're interested, I'm giving a talk on the subject in Berlin at Online Educa in November.

Could AI composed music win a Grammy?

It hasn’t but I’d argue that one day it could. Classical music, many would say, is a crowning human achievement. It’s regarded as high art and its composition creative and complex. Jazz is wonderfully improvisational. Whatever the genre, music has the ability to be transformative and plays a significant role in most of our lives. But can AI compose transformative music?

EMI

At a concert in Santa Cruz the audience clapped loudly and politely praised the pieces played. It was a success. No one knew that it had all been composed by AI. It’s creator, or at least the author of the composer software, was David Cope, Professor of the University of California, an expert in AI composed music. He developed software called EMI (Experiments in Musical Intelligence) and has been creating AI composed music for decades.

Prof Steve Larson, of the University of Oregon, heard about this concert and was sceptical. He challenged Cope to a showdown, a concert where three pianists would play three pieces, composed by:

1. Bach

2. EMI (AI)

3. Larson himself

Bach was a natural choice as his output is enormous and style distinctive. Larson was certain of the outcome, and in front of a large audience of lecturers, students and music fans, in the University of Oregon concert Hall, they played the three pieces. The result was fascinating. The audience believed that:

1. Bach’s was composed by Larson

2. EMI’s piece was by Bach

3. Larson’s piece composed by EMI.

Interesting result. (You can buy Cope’s album Classical Music Composed by Computer.) Conclusion – this is getting somewhere.
Iamus. named after the Greek god who could understand birdsong, created at the University of Malaga, composed a piece called Transits - Into the Abyss, which was performed by the  London Symphony Orchestra in 2012 and also released as an album. Unline Cope's software, Iamus creates original, modern pieces that are not based on any previous style or composer. You can listen to its output here. Their Melamics web site has an enormous catalogue of music and has an API to allow you to integrate it into your software. They even offer adaptive music which reacts to your driving habits or lulls you into sleep in bed, by reacting to your body movements.

Further examples of the Turing Test for music have been applied to work by Kulitta at Yale. But is a Turing test really necessary? One could argue that all we’re doing is fooling people into thinking this has been composed by a machine that cheats. I’m not so sure. Cope has been creating music from computers from 1975, when he used punch cards on a mainframe. He really does believe that computers are creative. Others are not so sure and argue that his AI simply mimics the great work of the past and doesn’t produce new work. Then again, most human composers also borrow and steal from the past. The debate continues, as it should. What we need to do is look beneath the surface to see how AI works when it ‘composes’.

AI techniques in musical composition

The mathematical nature of harmony and music has been known since the Pre-Socratics and music also has strong connections with mathematics in terms of tempo, form, scales, pitch, transformations, inversions and so on. Its structural qualities makes it a good candidate for AI production.

Remember that AI is not one thing. It is very many things. Most have been used, in some form, to create music. Beyond mimicry, algorithms can be used to make compositional decisions. One of the more interesting phenomena is the idea of improvisation through algorithms that can, in a sense, randomise and play with algorithmic structures such as Markov chains and Monte Carlo tree decisions, to create, not deterministic outcomes, but compositions that are uniquely generated. Evolutionary algorithms have been used to generate variations that are then honed towards a musical goal. Algorithms can also be combined to produce music. This use of multiple algorithms is not unusual in AI and often plays to the multiple modality of musical structure, playing to different strengths to produce aesthetically beautiful music. In a more recent development, machine learning, presents data to the algorithmic set, which then learns from that data and goes on to refine and produce composed music.  This is the new kid on the block and brings an extra layer of compositional sophistication.

We, and all composers, are organisms created from a bundle of organic algorithms over millions of years. These algorithms are not linked to the materials from which you create the composer. Whether the composer is man or machine, music is music. There is no fatal objection to the idea that organic algorithms used by organic composers can do things that non-algorithmic algorithms will never be able to replicate, even surpass. The bottom line is that this is going places, fast.

AI and aesthetics

The AI v human composition of music also opens up several interesting debates within aesthetics. What is art? Does ‘art’ reside in the work of art itself or in the act of appreciation or interrogation by the spectator? Does art need intention by a human artist or can other forms of ‘intelligence’ create art? Does AI challenge the institutional theory of art, as new forms of intelligent creation and judgement are in play? Does beauty itself contain algorithmic acts within our brains that are determined by our evolutionary past? AI opens up new vistas in the philosophy of art that challenge (possibly refute, possibly support) existing theories of aesthetics. This may indeed be a turning point in art. If art can be anything, can it be the product of AI? I think Duchamp would have approved.

Conclusion

This area is rich in innovation and pushes and challenges us to think about what music is and could be. Is the defence of the ‘artist’ or ‘composer’ just a human conceit, built on the libertarian idea of human freedom and sanctity of the individual, that makes us repel from the idea of AI generated music and art? The advent of computers, used by musicians to compose and in live performance, has produced amazing music, some created live, even through ‘live coding’. The possibilities of 3D audio in VR (already available) open up other compositional opportunities with interactive music. As in other areas, where AI is delivering real solutions, music is being created that is music and is liked. Early days but it may be that musical composition, with it’s strong grounding in mathematical structures, is one of those things that AI will eventually do as well, if not better, than we mere mortals.

10 deep dive questions to ask about LEARNERS before you start

The whole ‘Blended learning’ thing turned out to be an excuse for ‘Blended teaching’. It's largely used as an excuse for using teaching techniques you've used before with a couple of new things thrown in, whereas the promise had been delivery based on an analysis of learners and learning (the clue was in the second word – learning). To partly rectify this, here’s ten questions you may want to ask to inform your blend, questions about your learners. It’s easy to ignore learners when teaching but teaching, remember, is a means to an end, not an end in itself, and that end is learners and learning. 

Most methods of audience analysis focus on attributes such as age, gender, ethnicity and so on. Sure, you need to know whether you’re learners are children or adults and at what level in the education system but you need to take a deeper dive to get to the issues that inform design. So these are not the usual questions about age, gender, educational background and diversity. That’s dead easy – takes about five minutes. These are the questions that allow you to create, adjust and target your delivery to make it a success. These are the harder, more oblique questions that dig deeper.

1. Personas – mmm…

Personally, I’m not a fan of personas, named profiles of typical audience. You know the line, ‘This is Matt, he’s 23 and loves sports…..” They tend to focus too much on the average, which is dangerous with a wide and diverse audience. I’d much rather get some concrete ideas that will really shape the experience. Nevertheless, they can be useful as a means to communicate intention to a team. Each to their own and if you think this helps – OK by me.

2. Why?

Why? That’s a great question to start with. Why should they do this? Why should they learn this? Understand their motivations (or lack of) and you can respond accordingly. Is it compulsory? Do they care? Why should they take it seriously or even do it at all? Teachers and trainers tend to assume that people are gagging for learning – when much of it makes them gag.

3. What will they NOT like?

People love online stuff, that’s why 1.7 billion are on Facebook and online time is soaring. Yet they often recoil when they get very structured online learning. Why is this? Have a look at these 20 reasons why online learning sucks. You may be surprised to learn that they don’t want all of that jazzy graphics, animation, cartoons, beeps and gamification. Maybe they do. How can you find out what they don’t like? Ask them.

4. What will they like?

What would your target audience like to experience? Do they want the light touch or more solid, challenging learning?

You may be surprised when a bunch of engineers come back and say they want it straight, no frills. How can you find out what they want? Ask them.

5. How distributed are they?

If you have a highly distributed, global audience, you may wish to seriously consider the social side of learning. Or are they all on one site – makes blended learning a lot easier. Different time zones, different cultures, different languages are all issues you may have to wrestle with. Do you really want virtual classrooms across seven time zones? Or should the design be more asynchronous, so that all can access the experience in their own time. What has one location got than another does not? Can you leverage their differences?
6. Language(s)

This matters, especially when learners are taking a course in a second language (often English). This doesn’t mean dumbing it down but being smarter in the level of language you use and media mix. Take it easy on long sentences and jargon when it’s in the target audience’s second language. It may also influence media mix. Ultimately cultural and literal translation may be necessary. Think about this, as you can stay more culturally neutral. They may even want the learning in their second language, if that is the language of business or medicine.

7. Personality types?

Forget Learning Styles – they don’t exist. Let me repeat that – they don’t exist. Forget Myers Briggs – it’s a Ponzi scheme. What does matter is personality types. Are you dealing with extrovert sales people, largely introverted IT types, sceptical academics? The OCEAN (openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism) is a scheme that is well researched, stable and a good guide. Get a feel for this, as it will affect the language you use and media mix. This often uncovers features of the audience that relate directly to design in terms of language used, formality and images.

8. What went before?

How is training currently delivered? Are they used to full-day classroom courses? Do they have experience of online learning? People come with expectations and if you shatter those expectations, explain why. If this is a dramatic change you may have to consider marketing and change management issues.

9. Where do they learn?

Will they learn in a formal learning centre, at their desk, during class, in the library, at home, on the move, while commuting? It may be a combination of these. Know where they are likely to learn and tailor your design to that environment.

10. What time do they have for learning?

When will they learn? Do they have time off their normal work to do this? How much time will they have? This will determine the ‘chunking’ of the content as well as its target devices. If mobile, remember to keep the chunks short.

Conclusion

You can use what you have harvested here in your blended and online learning design. Tell your audience what you learned from them and why you’ve responded with the current solution. That would impress me – it should impress them. Beyond that, be sensitive not just to who, what, when and where they are but their deeper needs – what they want and how they can help you design your learning experiences.
For a further 500 learning design tips click here.