Friday, November 02, 2018

World’s first hologram lecture (not really) but are they necessary?

Imperial College, London, claimed to have held the World’s first hologram lecture. What they haven’t mastered is the art of being honest or doing a modicum of research.  There have been hologram lectures before, most notably, by Stephen Hawking. But let’s put to one side the usual hyperbole by ‘Women in Tech’ and look at this critically.

For the Imperial event, 3D figures were beamed in live from the US. They are projected on to a glass screen, with a backdrop giving the illusion of depth of field and interact with other panellists and the audience. Nice idea but will it fly?
Before I start, I’m no fan of slabbing out academic lectures as a method of teaching and learning and would much rather institutions either, recorded lectures, or made sure that the people who deliver them receive some training on teaching and presentation. The number of students who simply don’t turn up is evidence enough that they are a failure. Only 60% turn up, even at Harvard. The very words ‘Lecture’ or ‘Lecturers’ says everything about the appalling state of pedagogy in Higher Education.
One of the saddest learning stories I’ve ever heard was from the actress Tilda Swinton. She was the only student who turned up to a lecture at Oxford by Raymond Williams where he read out his lecture, from notes, from behind the lectern, and neither of them even acknowledged each other. How sad is that? Almost every University has even worse tales of lectures where not one student turned up.

Samuel Johnson saw the folly of it all:
‘Lectures were once useful; but now, when all can read, and books are so numerous, lectures are unnecessary. If your attention fails, and you miss a part of a lecture, it is lost; you cannot go back as you do upon a book... People have nowadays got a strange opinion that everything should be taught by lectures. Now, I cannot see that lectures can do as much good as reading the books from which the lectures are taken. I know nothing that can be best taught by lectures, except where experiments are to be shown. You may teach chymistry by lectures. You might teach making shoes by lectures!’

As David Hume, observed, it is the content, not the person who matters:
‘...as you know there is nothing to be learnt from a Professor, which is not to be met with in books, and there is nothing to be required in order to reap all possible advantages from them, but an order and choice in reading them...I see no reason why we should either go to a University, more than to any other place, or ever trouble ourselves about the learning or capacity of the professor.’

On the other hand I have no problem with talks by experts (not adjuncts and/or PhD students) who command respect and give students a feel for what it is to be a physicist or psychologist. Lectures as inspirational and motivational events I have no problem with, where world class speakers and teachers do their thing – but few have that presence or possess those skills. The problem with a focus on just the technology here, is that a hologram of a bad lecturer won’t solve the problem.
This idea is interesting in terms of getting World Class experts and teachers to talk and teach in institutions around the world. If, as the evidence suggests, it increases presence, that’s great, especially if you feel as though they are really there. But I’m not convinced that hologram lectures are much more than a gimmick. They’re technically difficult to organise, expensive and try too hard to mimic what is, essentially, a flawed pedagogic technique. It perpetuates the traditional lecture format, rather than moving things on. It’s taking something that’s not that good in the real world and mirroring it virtually.

Skype or Zoom
On the other hand, we have to probe this a little? Wouldn’t it be easier and simpler to use video, either Skype or Zoom? These have tools that supplement the experience. For example, Skype translator can translate voice, in real-time, in 10 languages. Its text translator works in 60 languages. For global transmission, this makes sense.

Webinar software
Lectures as webinars make even more sense, as the supplementary tools allow for as much interaction as you wish and it is scalable. It is rather odd that educational institutions don’t make more use of this form of delivery. Questions, chat, polls, links and many other features are available in this type of software. It talks some skill to do this but it is a skill worth learning as it results in better teaching and more importantly, better learning.

VR
Full immersion gives the advantage of full immersion and full attention. One of the most compelling features of VR is the fact that you really are in that environment and the brain finds it difficult to jump out or be distracted. We know that attention is a necessary condition for learning, so this could be the optimal solution. One problem is the difficulty of taking notes, although speech dictation would be possible.

Conclusion
All technologies have their affordances. We need to identify what we want, then use the best technology available. Holograms seem like overreach. If students aren’t even turning up, let’s reflect on that first. If we’re not recording lectures, despite the overwhelming evidence that they are good for students being taught in their second language or those who are absent due to illness. In addition, learners can stop and rewind if they miss something, want to find something out or want to take more detailed notes. But the biggest argument is that they can be used for learning through repeated use and revision.

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Wednesday, October 31, 2018

Agile training at Jurys Inn– used AI to produce in minutes not months

So you walk into a hotel, you’re in the queue, and it’s taking ages to check-in. Or at breakfast, there’s no milk on the table…. it’s all those little things that matter in the hospitality trade. In the age of Tripadvisor, it matters. Jurys Inn take this seriously by training their staff to a set of defined, exacting standards.

Agile AI solution
This means agile training – relentless attention to training and the behaviour of customer facing staff. By agile they mean the use of AI to create training quickly, that really does deliver high retention training to front-line staff on any device. WildFire, an AI online content creation service, quickly turned a set of 209 standards, into 6 online learning modules on: 
  • General standards
  • Front office
  • Breakfast
  • Restaurant
  • Bar
  • Room service 
Each module focused on essential knowledge - what staff need to know.

Agile learning
Every module has an AI generated audio introduction (text to speech), that explains what you have to do and to relax about getting things wrong, as it is OK to make mistakes while learning. Staff then read the standards and, rather than click on multiple-choice questions, have to bring to mind and type in the behavioural standards. This ‘effortful’ learning was inspired by recent research in learning that shows recall, with open input, is superior for retention to simply recognising answers from a list. 

Agile production
The six modules were literally created in a day, as the AI created the content, by identifying the relevant learning points and automatically creating the learning experiences. This superfast production process meant that quality assurance could be done on the real modules, without the need for design documents and scripts. There was no need for multiple iterations by SMEs, as the original document contained all the necessary knowledge. The look and feel, logo, images, palette numbers for screen features were quickly agreed. Everything from brief to final delivery was done through Skype. Not a single face-to-face meeting was necessary.

Agile data
Finally, the modules were SCORM wrapped for delivery on the LMS – Totara. As SCORM is rather limited, we also embedded extra data gathering capability which WildFire harvests for further analysis. This allows detailed analysis of who did what when and within the training, the specific times taken by each individual. Beyond this. WildFire have been looking at data and correlating it with other data on sales.

Agile preparation
One important lesson in being agile is the ‘Garbage IN: Garbage OUT’ rule. When you use an agile production process, you need agile preparation. An intensive look at the input material pays dividends. Eliminate all of that extraneous material and text, cut until it bleeds and cut again, catch those pesky spelling and punctuation errors, make sure things are consistent. In our Jurys Inn and TUI work the source material was edited down to the essential ‘need to know’ content.

Agile project management
Another essential ingredient is an agile project manager. In both the Jury's Inn and TUI projects, we spoke frequently, but didn’t have a single face-to-face meeting. It was all quick decisions, problems solvedon the spot and process change to get things done. The project managers never saw problems, only issues to be solved – quickly.
It was made easier by the fact that AI was used to produce content in minutes not months. That means the quality control was on real content, not paper documents. And as we use approved documents, PowerPoints and videos, there was no real need for intensive SME input, which is the main brake on agile  production.

Agile production
This is where the real gains lie. AI is now being used to create content and add curated resourced at the click of a button. WildFire will take any document, PowerPoint or video and turn it into high retention online learning, in minutes not months. Want an audio podcast or audio introduction to the course?  It takes seconds using AI to do text to speech. The time savings are enormous, as well as costs. AI is used not only to identify learning points, it also constructs the questions, assesses open input (words or full short answer) and locates external resources for further learning.

Agile promotion
I’ve lost count of the number of times we’ve bust a gasket getting something finished, only to see it languish on a server before release. Agile marketing should also be part of our mindset. This means blogs like this and good internal (and external if required`) marketing. Managers and learners are intrigued by the use of AI in learning – use that to best advantage.

Conclusion
Agile is as much a state of mind as process. Yet Learning and Development still works to an old model of months not minutes. We procure slowly, prepare slowly, produce slowly and deliver slowly. Despite relentless calls to align with the business and respond to business needs, we are too slow. L and D needs things in days, yet we deliver in months. This is what needs to change.
Online learning has traditionally been a rather slow in design and production. We can now use AI in WildFire to create content quickly, to produce agile learning and data that allows us to adapt to new circumstances. I may have used the word 'Agile' too often here but it captures, in a word, what is now necessary. The days of seeing online learning production as some sort of feature film project with matching budgets and timescales – months, should be re-examined. Sure some high-end content may need this approach but much can be automated and done at 10% of the cost, in minutes not months.

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Monday, October 22, 2018

Agile online learning in 'impossible timescales' saves £453,000 with 36% increase in sales

This agile learning project was a revelation. We used an AI tool to deliver a project to a large multinational (TUI).  The project delivered 138 modules on the locations for its holidays, flights, airport codes and so on. Recognising that they could never produce content on this scale as the estimated costs for external development were just under £500,000, and it had to be delivered in weeks not the estimated 8 months, they opted for WildFire. This uses AI to create content in minutes not months, along with supplementary curated content, also selected by AI.

Agile production
As WildFire does most of the design and build, an agile approach to production and project management was intrinsic. No scripts were necessary, as the software built the content so quickly that quality assurance could be done on the actual modules.
As it was ‘agile’, so let’s cut to the quick...
  • 95% rated the design and approach as good or very good
  • 62% confirmed they could identify a specific sale based on knowledge gained
Their knowledge of the countries, locations, attractions, currencies, airport codes and so on was reported, time and time again, by front-line staff as having helped them sell holidays and flights. Remember - this is a location-driven business. If you want to sell holidays and cruises, you have to know the destinations and attractions.

ROI
Total savings, compared to traditional online learning production, were calculated as: 
  • “£438,000 with extra savings of £15,000 in salary costs”
  • “freed up 15% of manager time” to do other things.
  • “With a bit of lateral thinking and a lot of tenacity – seemingly impossible timescales were met”
Further benefits
When the modules were released to the wider business, sales benefits started to emerge:
  • 36% increase in sales has already been recognised in the first few months the training has been available”
Within the company the “wider business now recognises the benefits of being bold with new learning technologies” and sees the project as an “outstanding example of achieving our strategy to invest in and develop our people”.

Agile mindset
This was a ground-breaking project, delivered without a single face-to-face meeting. It shows what can be achieved when a training department is innovative and brave. We must get past the model that says it takes months to produce content at prohibitive costs. Agile production needs agile tools, agile production methods and agility of thought and mindset. Organisations have traditionally moved faster than training delivery. That means we’re often out of phase with the business. An agile mindset and production attitude allows us to transcend that historical gap. Once we become responsive to the business they will respect us more and we are more aligned with their natural speed.
PS
It has been shortlisted as Best Online Learning Project in this year’s Learning Technologies Awards.

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Has L&D been hijacked by ‘identity’ politics?

I got asked an interesting question last week on a panel at an L&D conference. Where is L&D? Is it advancing or regressing? Interesting question.
In many ways it has advanced by embracing technology. Few organisations of any scale ignore online solutions to learning and development. There has also been some movement towards taking research more seriously. I meet far fewer people who believe in learning styles or NLP. There seems to be much more awareness of learning theory and by that I mean cognitive science.
On the other hand, these advances have been matched by a serious reversal - the swallowing, lock, stock and both barrels of ‘identity’ politics. Gone are the days when HR and L&D were at the forefront of personal development. Much ‘training’ is now targeted at protecting the organisation from their own employees. A tsunami of compliance and identity training has overwhelmed L&D. Much of what is actually delivered via an LMS is actually protective training, or as almost everyone says, ‘tick-box’ training. We know it and learners know it. We have to be honest about the fact that they are often openly contemptuous about this form of ‘training’. Somewhere along the line our agenda got hijacked.
Identity and training
Training that establishes difference also implies exclusion. Fixing favoured identity groups, seeing some as oppressive and others victims, is a destructive force. And when HR aggressively promotes and ‘us’ and ‘them’ culture it does us all a disservice. When we become the managers and administrators of difference, serving up a never-ending diet of identity and diversity training courses, we swirl around in our own echo-chambers. We become purveyors of ‘wrong-think’, policing ordinary people, as if they were stained by original sin. It results, not in rational consensus within an organisation, but the false exaggeration of difference.
Leadership
One specific identity group that has been put on a pedestal is ‘Leaders’. We have thrown resources at Leaders and Leadership training, as elitist an approach to employee engagement as I’ve seen in my 35 years in this business. In a desperate attempt to appear legitimate we have turned ‘elitism’ into a sort of training cult. No one would seriously call themselves a ‘Leader’ without being ridiculed. This form of single  group identity is a serious distortion of real needs in organisations.
Values
Another facet of identity politics has been the unedifying sight of organisations forcing faux values down the throats of their employees. I have values and have no interest in HR telling me what my values should be. Strangely alliterative – all starting with ‘I’ or “C’ they are abstract nouns that bear no relation to the real world or the workplace. Worse still are those values that fit some acronym, where at least some of the values have been made up to fit that word. They want to shape your identity by imposing their values on you. They are, of course, largely ignored or treated with contempt. Few can recall them, fewer still care.
Diversity
The rot set in long ago, when HR thought it should take the role of therapeutic diagnosis. Myers Briggs, a flawed and crude tool, has been used to determine people’s lives. As if this weren’t enough, a slew of interrogative techniques, from learning styles (a fiction) to NLP and mindfulness, were employed to caricature, categorise and, in some cases, condemn ordinary people. There has been no end to this slicing and dicing, based on dubious diagnostic techniques. The evidence is clear. Diversity training does not work.
Unconscious bias
It has now reached surreal levels of interrogative nonsense, with courses on ‘unconscious bias’. Not satisfied with superficial courses making us consciously compliant, HR suddenly started to probe our unconscious. How did that happen? What on earth gives anyone in HR or L&D the right to even think that my unconscious is open to their investigation, something to be probed by some half-baked questionnaire that has no scientific validity? This is completely out of control. The assumption is that certain out-of-favour groups - white, male, working class - are guilty by virtue of being born, so leaden with bias that they need to be re-educated. It’s a pathological view of human nature, where minds must be interrogated and forced to admit their guilt. This was never our goal. HR and L&D is full of good people but it has been hijacked by a form of policing that has abandoned personal development for personal identity.
Conclusion
All of this is fuelled by so called ‘experts’ who design courses on ‘X’, train others to deliver those courses as ‘practitioners’ who sell their courses for ‘$Y’ so the Ponzi schemes begin. Rather than focus on the real needs of organisations, real knowledge, skills and competences, we have been sucked into a world of exaggerations, abstract concepts and fictions, where people have to be identified, shamed and re-educated, to be ‘correct’ and ‘compliant’. Time and time again I hear pleas for HR and L&D to be more business focussed. What we’ve done, in reality, is turn our back on real business issues, to focus on therapeutic concerns, that invade people’s privacy. So let’s get off the identity bandwagon and get back to business.

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Friday, October 19, 2018

"On average, humans have one testicle”... that's why most data analytics projects in education are misleading....

AI will have a huge impact in learning. It changes everything… why we learn, what we learn and how we learn. Of course, AI is not one thing. It is a huge array of mathematical and software techniques. Yet looking at the spend in education and training, people have been drawn to one very narrow area – data analytics. This, I think, is a mistake.
Much of this so-called use of AI is like going over top of head with your right hand to scratch your left ear. Complex algorithmic and machine learning approaches are likely to be more expensive and far less reliable and verifiable than simple measures like using a spreadsheet or making what little data you have available, in a visualized, digestible form to faculty or managers. Beyond this, traditional statistics is likely to prove more fruitful. Data analytics has taken on the allure of AI, yet many of it is actually plain, old statistics.
Data problems
Data is actually a huge problem here. They say that data is the new oil, more likely the new snakeoil. It is stored in weird ways and places, often old, useless, messy, embarrassing, personaland even secret. It may need to be anonymised, training sets identified and subject to GDPR. To quote that old malapropism, ‘data is a minefield of information’. It may even be massively misleading, as in the testicle example.
Paucity of data
In the learning world, the data problem is even worse as there is another problem – the paucity of data. Institutions are not gushing wells of data. Universities, for example, don’t even know how many students turn up for lectures. I can tell you that the actual data, when collected, paints a picture of catastrophic absence. Data on students is paltry. The main problem with the use of data in learning, is that we have so little of the stuff. 
SCORM, which has been around for 20 plus years literally stopped the collection of data with its focus on completion. This makes most data analytics projects next to useless. The data can be best handled in a spreadsheet. It is certainly not as large, clean and relevant, as it needs to be to produce genuine insights.
Other data sources are similarly flawed, as there's little in the way of fine-grained data about actual performance. It's small data sets, often messy, poorly structured and not understood.
Data dumps
Data is often not as clean as you think it is, with much of it in:
·      odd data structures
·      odd formats/encrypted
·      different databases
Just getting a hold of the stuff is difficult.
Defunct data
Then there’s the problem of relevance and utility, as much of it is:
·      old
·      useless
·      messy
In fact, much of it could be deleted. We have so much of the stuff because we simply haven’t known what to do with it, don’t clean it and don’t know how to manage it.
Difficult data
There are also problems around data that can be:
·      embarrassing
·      secret
There may be very good reasons for not opening up historic data, such as emails and internal social communications. It may open up a sizeable legal and other HR risks for organisations. Think Wikileaks email dumps. Data is not like a barrel of oil, more like a can of worms. 
Different types of data
Once cleaned, one can see that there are many different types of data. Unlike oil, it has not so much fractions as different categories of data. In learning we can have ‘Personal’ data, provided by the person or actions performed by that person with their full knowledge. This may be gender, age, educational background, needs, stated goals and so on. Then there’s ‘Observed’ data from the actions of the user, their routes, clicks, pauses, choices and answers. You also have ‘Derived’ data inferred from existing data to create new data and higher level ‘Analytic’ data from statistical and probability techniques related to that individual. Data may also be created on the fly.
Just when you thought it was getting clearer. You also have ‘Anonymised’ data, a bit like oil of an unknown origin. It is clean of any attributes that may relate it to specific individuals. This is rather difficult to achieve as there are often techniques to back engineer attribution to individuals.
In AI there’s also ‘Training’ data used for training AI systems and ‘Production’ data which the system actually uses when it is launched in the real world. This is not trivial. Given the problems stated above, it is not easy to get a suitable data set, which is clean and reliable for training. Then, when you launch the service or product, the new data may be subject to all sorts of unforeseen problems not uncovered in the training process. This is a rock on which many AI projects founder.
Data preparation
Before entering these data analytics projects, ask yourself some serious questions about 'data’. Data size by itself, is overrated, but size still matters, whether n = tens, hundreds, thousands, millions, the Law of Small Numbers still matters. Don’t jump until you are clear about how much relevant and useful data you have, where it is, how clean it is and in what databases.
New types of data may be more fruitful than legacy data. In learning this could be dwell time on questions, open input data, wrong answers to questions and so on. More often than not, what you have as data are really proxies. 
Action not analytics
The problem with spending all of your money on diagnosis, especially when the diagnosis is an obvious limited set of possible causes, that were probably already known, is that the money is usually better spent on treatment. Look at improving student support, teaching and learning, not dodgy diagnosis.
In practice, even when those amazing (or not so amazing) insights come through, what do institutions actually do? Do they record lectures because students with English as a foreign language find some lecturers difficult and the psychology of learning screams at us to let students have repeated access to resources? Do they tackle the issue of poor teaching by specific lecturers? Do they question the use of lectures? Do they radically reduce response times on feedback to students? Do they drop the essay as a lazy and monolithic form of assessment? Or do they waffle on about improving the ‘student experience’ where nothing much changes?
Conclusion
I work in AI in learning, have an AI learning company, invest in AI EdTech companies, am on the board of two other AI learning companies, speak on the subject all over the world, write constantly on the subject . You’d expect me to be a big fan of data analytics and recommendation engines - I’m not. Not yet. I’d never say never but so much of this seems like playing around with the problem, rather than facing up to solving the problem. That's not to say you should ignore its uses - just don't get sucked into data analytics projects in learning that promise lots but deliver little. Far better to focus on the use of data in adaptive learning or small scale teaching and learning projects where relatively small amounts of data can be put to good use.
AI is many things and a far better use of AI in learning, is, in my opinion, to improve teaching through engagement, support, personalised, adaptive learning, better feedback, student support, active learning, content creation (WildFire) and assessment. All of these are available right now. They address the REAL problem – teaching and learning. 

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Tuesday, October 16, 2018

Nudge learning

Things move fast in organisations and when Standard Life merged with Aberdeen Asset Management, an agile learning approach to changing behaviour in the new organisation was implemented, a training intervention that is itself agile and resulted in actual behavioural change. A huge traditional course, whether face-to-face or online, based on a diet of knowledge would have been counterproductive in this fast moving, post-merger commercial environment and be seen as a bit old-school and non-agile. Whereas a series of short, sharp interventions that nudge people into applying agile in their own context and work environment was likely to work better. At least, that's what Peter Yarrow, Head of Learning, thought – and I think he's right. It was his brainchild.
His successful project used the 'nudge' technique. Nudge theory recommends small interventions to push people into changing behaviour. Famous examples include the image of a fly in men’s urinals, to improve aim and reduce cleaning costs! Opting out, rather than into organ donation is another. The psychological theory is laid out in the book 'Nudge: Improving Decisions About Health, Wealth and Happiness’ by Thaler and Sunstein. They could well have added ‘Learning’ to the title.
Nudge solution
In learning, Standard Life Aberdeen sent small, professionally shot videos (on average 1min 30secs long), mainly talking heads from leaders and experts in the organisation, out via email. In addition to the video, there was a ‘challenge’ to apply the lesson in their own working environment. I like this approach, and it is a truly fresh and agile 'nudge'intervention.In their case it was general management techniques but I feel that agile techniques could be applied in response to all sorts of needs. Each starts with a proposition, or problem, followed by a suggested solution and finally, and crucially, a call to action. This is based on techniques also used in web and online design.
Example 1
Video on importance of comms
It’s hard to be a high performing team if colleagues don’t know each other well. Without trust, mutual respect and goodwill, performance will most likely remain middle of the road. Exceptional performance is fuelled by positive working relationships. Take this week’s challenge to get to know your colleagues better.
Example 2
Video on mentoring
Being mentored is a great way to develop and progress. But how do you get started? Begin by identifying someone you trust who has taken a career path you aspire to. Take this week’s challenge to learn more about making mentoring relation ships work.
These videos and challenges were sent out by email and usage tracked. The take-up across the organisation surprised the training department and the feedback was very positive. People felt that it was integrated into their natural workflow (they were not too long and intrusive) and that it was made more relevant by virtue of nudging people towards action by them as individuals in their specific job.
Suggestions for nudge learning
Great start but rather than batch emails, I'd use an algorithm to decide personal needs and, take data from usage and get more precise in timing and targeting. This means harvesting more data, which one can do, even with internal email systems. People get habituated out of responding if they get too many emails.
On the challenges I’d use more of a pure marketing approach, a strong command verb at the start, really concise, with reason and emotional pull. Give your audience a reason why they should take the desired action, maybe a bit of FOMO (Fear Of Missing Out), maybe some compelling numbers. Writing calls to action is both a science and an art and it’s worth being a little creative.
I'd also do slightly more than just state the challenge. I'd get the user to do something there and then to make sure they got the main points in the video (we've done this by grabbing the transcript and getting the user to check they've understood the main points) using AI generated, open-input experiences with WildFire.
None of this is a criticism of Peter’s pioneering project, merely suggestions to make it more potent.
Conclusion
I really liked Peter’s fresh thinking around the ‘nudge’ thing. It has legs and could go in all sorts of directions. It is the combination of proven marketing techniques with learning that make this approach fly. Few in marketing want to slab out hours and hours of content – they think first audience, second channels and third action. Their whole way of thinking is around ‘less is more’. This also happens to be exactly what the psychology of learning tells us about learning experiences. The limits of working memory, cognitive overload, forgetting and the need for transfer mean doing less but doing it better. 

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Monday, October 15, 2018

Why Blockchain is a busted flush

I recently wrote a piece explaining why I think Blockchain may not be such a good idea in education and training, arguing that it is an opaque solution looking for problems, that is hungry on energy consumption, storage and bandwidth. In discussing this piece with others I uncovered what I think may be a deeper problem – that Blockchain itself may be a busted flush.
Cryptocurrencies based on Blockchain (or variants) have plummeted in value, many by 70-80%. In a blizzard of scams and frauds, ‘trust’, the very thing Blockchain was supposed to capture, has evapourated. 
Professor Nouriel Roubini, from NYU’s Stern School of Business, was the Senior Economist for International Affairs in the White House's Council of Economic Advisers during the Clinton Administration, and has worked for the International Monetary Fund, the US Federal Reserve, and the World Bank, describes Blockchain as nothing more than a ‘glorified spreadsheet’. He argues that it’s a dangerous neo-liberal idea that tries to disengage from governments, banks and other trusted sources. He may be right as it has all the hallmarks of the bait and switch tactic, where you promise democratisation, decentralisation and disintermediation with one hand… then concentrate power in miners and a few techo-folk and businesses on the other. The messiahs who suck you in, when hacked, simply fork to another currency. To allow this to flourish beyond the law and normal protections of sovereign states may be a big mistake. Far from creating wealth it may concentrate wealth.
More specifically, why would any organisation want to put their transactions on public decentralized peer-to-peer permissionless ledgers? We have database technology that works, under the supervision and governance of organisations. In the end people employ governance, and not just coders or Blockchain companies. In practice another bait and switch process is taking place. Private blockchains with guarded permissions are not really blockchain at all but relatively small private databases under the control of the bank or organisation. They are not decentralised.
After giving a talk in 2016, where I saw some potential in Blockchain, I’m not ashamed to say I’ve flipped. It’s a bait and switch technology, a busted-flush. Almost all of the projects in education will die.

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Thursday, October 04, 2018

Blockchain looking more and more like a ball and chain?

I was at a conference of CEOs and analysts yesterday and saw presentation after presentation on Blockchain. Then, when I chatted to those in the audience, found that not a single one:
  • Knew what it was
  • Actually used it
  • Could think of uses for it
In over 30 years in the tech industry, I’ve never know a concept so opaque that caused so much fuss. More worrying was the complete lack of awareness about its:
  • energy consumption
  • storage needs
  • bandwidth needs
  • origins in crypto-currency
I gave a talk on Blockchain in learning two years ago in Berlin, where I was pretty upbeat, outlining its structure and possible uses in learning around trusted legers for qualifications and badges. I even got married using Bitcoin on Blockchain! Yet I’ve fallen out of love with this technology. I’ve still to see a single implementation in learning that is worth the candle. In truth education and training does not want to be decentralised and democratised or disintermediated, as almost everyone in the field works in an institutions that will protect themselves to the death. Put aside payment, as one could well imagine that this will happen as the reduction of transaction costs makes commercial sense, Blockchain looks increasingly like it is largely irrelevant in the learning world.
Opaque
Despite all the diagrams and explanations about blocks, hashes and distributed databases, Blockchain remains an opaque term, even, I suspect, to those shovelling out grants and research money. It is not easy to grasp and needs some level of technical knowledge to unravel. I remember Jeff Staes, standing with his back to the audience in Berlin, screaming ‘What the fuck IS Blockchain?’. His point was that if we don’t know what it is why are we so sure about its uses in education and training?
Solution looking for problem
Software fails when there is no market demand, and people start looking for problems that are already solved by simpler and cheaper solutions. Many of the e-portfolio and qualifications problems are quite simply satisfied by existing systems, where centralised storage and security is already in place, especially in these times of data regulation (more of that later). Locking these problems down in a complex and opaque distributed database, is starting to seem like overkill.
Badges
Badges are one of those ideas that I wish had worked but haven’t. Like Blockchain itself, the badges idea has run its course and demand has faltered. Lacking credibility, objectivity, transferability and motivational pull, they simply hit institutional resistance. So to depend on badges and other forms of mico-credentialism is, sadly, no longer the platform on which Blockchain solutions can flourish.
Data regulation
Decentralised databases make sense but there are serious problems in terms of data regulations. Data has, historically, been almost universally stored centrally in databases. On public blockchains, however, this data is massively replicated across the entire distributed database. One solution is to cleave off the trusted hardware for the management and privacy of transactions. Management and privacy are big issues here and I’m not convinced that those recommending Blockchain have solutions that will escape the massive potential fines that are enshrined in EU law. This is a legal minefield that may well block Blockchain projects, especially in the learning world. It will be interesting to see how this plays out. For the moment, I’d stay well clear, as the risks are too high.
Energy consumption
Bitcoin, which uses blockchain, has been estimated to use more electricity than the whole of Ireland and that consumption is rising, rapidly. The huge number of hash calculations across the distributed database gobbles up energy. As long as there is a margin in mining for bitcoin, this energy consumption will continue. One bitcoin mining facility in Russia was shut down because it defaulted on paying its vast electricity bill. In these days of sustainable growth and climate change, Blockchain has a BIG problem and it’s getting bigger.
Huge storage
Some see Blockchain as the new internet, in the sense that it will be the new form of co-operative cloud storage. But that is still a pipe dream. First, we need bigger pipes and second, way more storage. Remember that storage prices have fallen dramatically but Blockchain is a famously bloated system and still costs money to run. 
Disagreements
You may imagine that Blockchain is a simple, unitary piece of technology. It is not. The community is full of road-map wars and disagreements between factions. It will be some time before all of this is stable enough for real implementations.
Trust a problem
For a system that promises a trustworthy leger, Blockchain is built on the premise of ‘trust’. Yet its origins in Bitcoin and crypto-currency may well be its undoing. Few would deny that Bitcoin, in particular, is soaked in money laundering and other forms of criminal activity. Being secure and unhackable is a technical issue, gaining the trust of institutions and consumers is a psychological issue. Blockchain may never cross that Rubicon.
Conclusion
A chain is no stronger that its weakest link and although Blockchain is a distributed, unhackable chain, it has weak links are outwith that technical chain - in terms of opacity, complexity, regulation, energy consumption, bandwidth needs, storage needs, and trust. Software fails when it is just too damn complex to get your head around and the consequences were not thought through in terms of regulation and other demands on bandwidth, processing power and storage. There is a chance that Blockchain may sneak into be the underlying, secure technology for the entire internet, with individual private keys but it has to overcome the problems above and overturn the existing system. This, I think, is unlikely. Blockchain is starting to look less like a great solution and more like a ball and chain.

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Saturday, September 22, 2018

Learning Designers will have to adapt or die. Here’s 10 ways they need to adapt to AI….

Interactive Designers will have to adapt or die. As AI starts to play a major part of the online learning landscape, right across the learning journey, it is now being used for learner engagement, learner support, content creation, assessment and so on. It will eat relentlessly into the traditional skills that have been in play for nearly 35 years. The old, core skillset was writing, media production, interactions and assessment.
In one company, in which I’m a Director, we see a shift towards AI services and products and we’re having to identify individuals with the skills and attitudes to deal with this new demand. This means understanding the new technology (not trivial), learning how to write for chatbots and dealing more with AI-aided design and curation, rather than doing this for themselves. It’s a radical shift.
In another context, using services like WildFire, means not using traditional interactive designers, as the software largely does this job. It identifies the learning points, automatically creates the interactions, finds the curated links and assesses, formatively and summatively. It creates content in minutes not months. This is the way online learning is going. This stuff is here, now.
The gear-shift in skills is interesting and, although still uncertain, here’s some suggestions based on my concrete experience of making and observing this shift in three separate companies.
1. Technical understanding
Designers, or whatever they’re called now or in the future, will need to know far more about what the software does, its functionality, strengths and weaknesses. In some large projects we have found that a knowledge of how the NLP works has been an invaluable skill, along with an ability to troubleshoot by diagnosing what the software can, or cannot do. Those with some technical understanding fare better here.
This is not to say that you need to be able to code or have AI or data scientist skills. It does mean that you will have to know, in detail, how the software works. If it uses semantic techniques, make the effort to understand the approach, along with its weaknesses and strengths. With chatbots, it is all too easy to set too high en expectation on performance. You will need to know where these lines are in terms of what you have to do as a designer. Similarly with data analysis. With traditional online learning, the software largely delivers static pages with no real semantic understanding, adaptability or intelligence. AI created content is very different and has a sort of ‘life of its own’, especially when it uses machine learning. At the very least get to know what the major areas of AI are, how they work and feel comfortable with the vocabulary.
2. Writing
Text remains the core medium in online learning. It remains the core medium in online activity generally. We have seen the pendulum swing towards video, graphics and audio but text will remain a strong medium, as we read faster than we listen, it is editable and searchable. That's why much social media and messaging is still text at heart. When I ran a large traditional online learning company I regarded writing as the key skill for IDs. We put people through literacy tests before they started, no matter what qualifications they had. It proved to be a good predictor, as writing is not just about turn of phrase and style, it is really about communications, purpose, order, logic and structure. I was never a fan of ‘storytelling’ as an identifiable skill.
However, the sort of writing one has to do in the new world of AI has more to do with being sensitive to what NLP (Natural Language Processing) does and dialogue. To write for chatbots one must really know what the technology can and cannot do, and also write natural dialogue (actually a rare skill). That’s why the US tech giants hire screenwriters for these tasks. You may also find yourself writing for ‘voice’. For example, WildFire automatically produces podcast audio using text to speech and that needs to be written in a certain way. Beyond this, coping with synonyms and the vagaries of natural language processing needs an understanding of all sorts of NLP software techniques.
3. Interaction
Hopefully we will see the reduction in the formulaic Multiple Choice Question production. MCQs are difficult to write and often flawed. Then there’s the often vicariously used ‘drag and drop’ and hideously patronising ‘Let’s see what Philip, Alisha and Sue think of this… ‘ you click on a face and get a speech bubble of text. I find that this is the area where most online learning really sucks.
This, I think, will be an area of huge change as the limited forms of MCQ start to be replaced by open input; of words, numbers and short text answers. NLP allows us to interpret this text. We do all three in WildFire with little interactive design (only editing out which ones we want). There is also voice interaction to consider, which we have been implementing, so that the entire learning experience, all navigation and interaction, is voice-driven. This needs some extra skills in terms of managing expectations and dealing with the vagaries of speech recognition software. Personalisation may also have to be considered. I'm an investor and Director in one of the word's most sophisticated adaptive learning companies CogBooks, believe me this software is sophisticated and the sequencing has to be handled by software not designers, that's what makes personalisation on scale possible. With chatbots, where we've been designing everything from invisible LSM bots to tutorbots, the whole form of interaction changes and you need to see how they fit into workflow through existing collaborative tools such as Slack or Microsoft teams. there's a lot of opportunities out there.
4. Media production
As online learning became trapped in ‘media production’ most of the effort and budget went into the production of graphics (often illustrative and not meaningfully instructive), animation (often overworked) and video (not enough in itself). Media rich is not necessarily mind rich and the research from Mayer and others, showing that the excessive use of media can inhibit learning is often ignored. We will see this change as the balance shifts towards effortful and more efficient learning. There will still be the need for good media production but it will lessen as AI can produce text from audio, create text and dialogue. Video is never enough in learning and needs to be supplemented by other forms of active learning. AI can do this, making video an even stronger medium. Curation strategies are also important. We often produce content that is already there but AI helps automatically link to content or provides tools for curating content. Lastly, a word on design thinking. The danger is in seeing every learning experience as a uniquely designed thing, to be subjected to an expensive design thinking process, when design can be embodied in good interface design, use A/B testing and avoid the trap of seeing learning as all about look and feel. Design matters but learning matters more.
5. Assessment
So many online learning courses have a fairly arbitrary 70-80% pass threshold. The assessments are rarely the result of any serious thought about the actual level of competence needed, and if you don’t assess the other 20-30% it may, in healthcare,for example, kill someone. There are many ways in which assessment will be aided by AI in terms of the push towards 100% competence, adaptive assessment, digital identification and so on. This will be a feature of more adaptive AI driven content. 
6. Data skills
SCORM is looking like an increasingly stupid limit on online learning. To be honest it was from its inception – I was there. Completion is useful but rarely enough. It is important to supplement SCORM with far more detailed data on user behaviours. But even when data is plentiful, it needs to be turned into information, visualised to make it useful. That is one set of skills that is useful, knowing how to visualise data. Information then has to be turned into knowledge and insights. This is where skills are often lacking. First you have to know the many different types of data in learning, how data sets are cleaned, then the techniques used to extract useful insights, often machine learning. You need to distinguish between data as the new oil and data as the new snake oil.
We take data, clean it, process it, then look for insights – clusters and other statistically significant techniques to find patterns and correlations. For example, do course completions correlate with an increase in sales in those retail outlets that complete the training? Training can then be seen as part of a business process where AI not only creates the learning but does the analysis and that is all in a virtual and virtuous loop that informs and improves the business. It is not that you require deep data scientist skills, but you need to become aware of the possibilities of data production, the danger of GIGO, garbage-in/garbage out and the techniques used in this area.
7. User testing
In one major project we produced so much content, so quickly, that the clients had trouble keeping up on quality control at their end. You will find that the QA process is very different, with quick access to the actual content, allowing for immediate testing. In fact, AI tends to produce less mistakes in my experience as there is less human input, always a source of spelling, punctuation and other errors. I used to ask graphic artists to always cut and paste text as it was a source of endless QA problems. The advantage of using AI generated content is that all sides can screen share to solve residual problems on the actual content seen by the learner. We completed one large project without a single face-to-face meeting. This quick production also opens up the possibility of A/B testing with real learners. This is an example of A/A testing being used with gamification content – with surprising results.
8. Learning theory
In my experience, few interactive designers can name many researchers or identify key pieces of research on, let's say the optimal number of options in a MCQ (answer at foot of this article), retrieval practice, length of video, effects of redundancy, spaced-practice theory, even the rudiments of how memory works (episodic v semantic). This is elementary stuff but it is rarely taken seriously.
With the implementation of AI, the AI has to embody good pedagogic practice. This is interesting, as we can build good, well-researched, learning practice into the software. This is what we have been doing in WildFire, where effortful learning, open input, retrieval and spaced practice are baked into the software. Hopefully, this will drive online learning away from long-winded projects that take months to complete, towards production that takes minutes not months and learning experiences that focus on learning not appearance.
9. Communications
Communications with AI developers and data scientists is a challenge. They know a lot about the software but often little about learning and the goals. On the other hand designers know a lot about communications, learning and goals. Agile techniques, with a shared whiteboard are useful. There are formal agile techniques around identifying the user story, extracting features then coming to agreed tasks. Comms are tougher in this world so learn to be forgiving.
Then there’s communications with the client and SMEs. This can be particularly difficult, as some of the output is AI generated, and as AI is not remotely human (not conscious or cognitive) it can produce mistakes. You learn to deal with this when you work in this field, overfitting, false positives and so on. But this is often not easy for clients to understand, as they will be used to design document, scripts and traditional QA techniques. I had AI once automatically produce a link for the word ‘blow’, a technique nurses ask of young patients when they’re using sharps or needles. The AI linked to the Wikipedia page for ‘blow’ – which was cocaine – easily remedied but odd.
We have also worked to reduce iterations with SMEs, the cause of much of the high cost of online learning. If the AI is identifying learning points and curated content, using already approved documents, PPTs and videos, the need for SME input is lessened. As tools like WildFire produce content very quickly, the clients and SME can test and approve the actual content, not from scripts but in the form of the learning experience itself. This saves a ton of time.
10. Make the leap
AI is here. Few argue that it will change the very nature of employment and therefore it will change what you learn, how you learn and even why you learn. We are, at last, emerging from a 30 year paradigm of media production and multiple choice questions, in largely flat and unintelligent learning experiences, towards smart, intelligent online learning, that behaves more like a good teacher, where you are taught as an individual with a personalised experience, challenged and, rather than endlessly choosing from lists, engage in effortful learning, using dialogue, even voice. As a Learning designer, Interactive designer, project Manager, Producer, whatever, this is the most exciting thing to have happened in the last 30 years of learning.
Most of the Interactive Designers I have known, worked with and hired over the last 30  plus years have been skilled people, sensitive to the needs of learners but we must always be willing to 'learn', for that is our vocation. To stop learning is to do learning a disservice. So make the leap!
Conclusion
In addition, those in HR and L&D will have to get to grips with AI. It will change the very nature of the workforce, which is our business. This means it will change WHY we learn WHAT we learn and HOW we learn. Almost all online experiences are now mediated by AI - Facebook, Twitter, Instagram, Amazon, Netflix.... except in learning! But that is about to change.

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