Crazy Eight

Is it just me or is every year a “big year”…?

Well 2018 marked a decade of blogging by yours truly, and that alone is something that I’m proud of.

Throughout the highs and lows that life gifted me this year, I was able to share another 8 thought bubbles in addition to my annual list of conferences.

I call them my Crazy Eight and I recall them here for your enjoyment and critique…

1. Battle scars – We can’t fight an “ism” with yet more ism.

2. 25 more real-world examples of Virtual Reality – Yes, VR is being used in the real world.

3. My decade of provocation – 10 years ago I made one of the best decisions of my professional life.

4. The foundations of innovation in L&D – The 70:20:10 model informs the building blocks of long-term efficiency, flexibility and creativity.

5. The best of both worlds – I love Design Thinking because it’s evidence based and it delivers.

6. Gift horses – Let’s empower the experts whom we have hired to practise their expertise.

7. Back to the future – Add these museums to your bucket list.

8. Figure it out – Instead of being the expert who knows the solution, be the one who solves the problem.

I’d be delighted if you were to add a comment to one or two of the above, either in support or offering a constructive alternative point of view.

In the meantime, I wish you joy and safety over the Christmas season, and here’s to a big 2019!

Back to the future

I’m both a science nerd and a history buff, so naturally I’m fascinated by the history of science.

When I visited Bern several years ago, the Museum für Kommunikation was at the top of my “to do” list. This captivating institution is dedicated to the history of technology-mediated communication, from the cuneiform tablets of the Sumerians, through the gamut of the postal service, telephony, telegraphy, radio, television, computers and the Internet. Upon my return from Switzerland I eagerly blogged my highlights from the museum.

More recently, I’ve just come back from a trip to the UK, where of course I continued my exploration of geeky curiosities. I was delighted to have discovered three excellent museums, from which I will now share some of my highlights.

My first discovery was the Museum of the History of Science in Oxford, which houses “an unrivalled collection of early scientific instruments”. Indeed this institution houses a wide diversity of vintage apparatus – from microscopes to telescopes, abacuses to astrolabes.

I think the strength of this collection is the sheer age of some the artefacts, such as the spring-operated prosthetic hand (Figure 1) which is thought to be from the 1500’s!

Figure 1. Artificial Hand, 16th Century?

My second discovery I wish to share with you, which I’m ashamed to admit wasn’t originally on my “to do” list, was the Science Museum in London. Tight for time, I had bigger fish to fry, but my good friend in Kensington urged me to visit this place a mere tube stop away. And boy I’m glad I did.

If the Museum für Kommunikation and the Museum of the History of Science are impressively stocked, the Science Museum is the mother lode. I could have spent days poring over its expansive collection, and I intend to return to do so.

I think the strength of this collection is the sheer fame of some the artefacts. For example…

Figure 2. Faraday’s Magnet and Coil, 1831 – that’s Michael Faraday. You know, the godfather of electromagnetism.

Figure 3. Lumière Cine-Camera and Projector, 1896 – the type of camera which recorded that ground-breaking footage of a train arriving at La Ciotat.

Figure 4. Enigma Machine, 1934 – a suitable corollary to Faraday’s magnet and coil, this ingenious electromagnetic device needs no introduction for anyone who’s watched The Imitation Game or The Bletchley Circle.

Figure 5. Watson and Crick’s 3D Model of DNA, 1953 – the glorious double helix.

Figure 6. Babbage’s Difference Engine No. 2, 1985-2002 – in the 1980’s, the museum began building Chuck’s 138-year-old design for a mechanical calculating machine, finally completing it in 2002.

My third and final discovery I wish to share with you was the Wellcome Collection, yet another destination which was inexplicably omitted from my “to do” list. The only reason I visited it was because my wonderful wife pointed it out as we were walking past.

This institution founded by pharmaceutical magnate Henry Wellcome specialises in human health. Hence I think the strength of this collection is focus, particularly the “Medicine Man” exhibition including the following array of medieval surgical equipment (Figure 7).

Figure 7. Centuries-old surgical tools.

There’s just so much good stuff in these museums, highlighting more would make this blog post a mile long and might breach some sort of copyright regulation. For more of their artefacts, including several that are relevant to financial services, follow me on Twitter where I’ll post them over the course of the next week or so.

Unlike the “vintage future” whereby people of the past predicted a largely fanciful civilisation, each of the objects I have highlighted here offered a glimpse of our real future. In their respective moments in time, they weren’t theoretical constructs or figments of imagination; rather, they were manifestations of advances in technology upon which further advancements were rendered possible.

Which begs the question: What will be our next advances in technology, given the manifestations we see in this moment in time?

While we await our brave new world, I hope you have the opportunity to visit the museums I have mentioned and embrace your journey back to the future.

Painting by numbers

A lifetime ago I graduated as an environmental biologist.

I was one of those kids who did well in school, but had no idea what his vocation was. As a pimply teenager with minimal life experience, how was I to know even half the jobs that existed?

After much dilly dallying, I eventually drew upon my nerdy interest in science and my idealistic zeal for conservation and applied for a BSc. And while I eventually left the science industry, I consider myself extremely fortunate to have studied the discipline because it has been the backbone of my career.

Science taught me to think about the world in a logical, systematic manner. It’s a way of thinking that is founded on statistics, and I maintain it should inform the activities we undertake in other sectors of society such as Learning & Development.

The lectures I attended and the exams I crammed for faded into a distant memory, until the emergence of learning analytics rekindled the fire.

Successive realisations have rapidly dawned on me that I love maths and stats, I’ve floated away from them over time, the world is finally waking up to the importance of scientific method, and it is high time I refocused my attention onto it.

So it is in this context that I have started to review the principles of statistics and its contemporary manifestation, analytics. My exploration has been accompanied by several niggling queries: what’s the difference between statistics and analytics? Is the latter just a fancy name for the former? If not, how not?

Overlaying the post-modern notion of data science, what are the differences among the three? Is a data scientist, as Sean Owen jokingly attests, a statistician who lives in San Francisco?

The DIKW Pyramid

My journey of re-discovery started with the DIKW Pyramid. This beguilingly simple triangle models successive orders of epistemology, which is quite a complex concept. Here’s my take on it…

At the base of the pyramid, Data is a set of values of qualitative or quantitative variables. In other words, it is the collection of facts or numbers at your disposal that somehow represent your subject of study. For example, your data may be the weights of 10,000 people. While this data may be important, if you were to flick through the reams of numbers you wouldn’t glean much from them.

The next step up in the pyramid is Information. This refers to data that has been processed to make it intelligible. For example, if you were to calculate the average of those ten thousand weights, you’d have a comprehensible number that is inherently meaningful. Now you can do something useful with it.

The next step up in the pyramid is Knowledge. To avoid getting lost in a philosophical labyrinth, I’ll just say that knowledge represents understanding. For example, if you were to compare the average weight against a medical standard, you might determine these people are overweight.

The highest step in the pyramid is Wisdom. I’ll offer an example of wisdom later in my deliberation, but suffice it to say here that wisdom represents higher order thinking that synthesises various knowledge to generate insight. For example, the wise man or woman will not only know these people are overweight, but also recognise they are at risk of disease.

Some folks describe wisdom as future focused, and I like that because I see it being used to inform decisions.

Statistics

My shorthand definition of statistics is the analysis of numerical data.

In practice, this is done to describe a population or to compare populations – that is to say, infer significant differences between them.

For example, by calculating the average weight of 10,000 people in Town A, we describe the population of that town. And if we were to compare the weights of those 10,000 people with the weights of 10,000 people in Town B, we might infer the people in Town A weigh significantly more than the people in Town B do.

Similarly, if we were to compare the household incomes of the 10,000 people in Town A with the household incomes of the 10,000 people in Town B, we might infer the people in Town A earn significantly less than the people in Town B do.

Then if we were to correlate all the weights against their respective household incomes, we might demonstrate they are inversely proportional to one another.

Thus, our statistical tests have used mathematics to convert our data into information. We have climbed a step up the DIKW Pyramid.

Analytics

My shorthand definition of analytics is the analysis of data to identify meaningful patterns.

So while analytics is often conflated with statistics, it is indeed a broader expression – not only in terms of the nature of the data that may be analysed, but also in terms of what is done with the results.

For example, if we were to analyse the results of our weight-related statistical tests, we might recognise an obesity problem in poor neighbourhoods.

Thus, our application of analytics has used statistics to convert our data into information, which we have then translated into knowledge. We have climbed another step higher in the DIKW Pyramid.

Data science

My shorthand definition of data science is the combination of statistics, computer programming, and domain expertise to generate insight. Or so I’m led to believe.

Given the powerful statistical software packages currently available, I don’t see why anyone would need to resort to hand coding in R or Python. At this early stage of my re-discovery, I can only assume the software isn’t sophisticated enough to compute the specific processes that people need.

Nonetheless, if we return to our obesity problem, we can combine our new-found knowledge with existing knowledge to inform strategic decisions. For example, given we know a healthy diet and regular exercise promote weight loss, we might seek to improve the health of our fellow citizens in poor neighbourhoods (and thereby lessen the burden on public healthcare) by building sports facilities there, or by subsidising salad lunches and fruit in school canteens.

Thus, not only has our application of data science used statistics and analytics to convert data into information and then into knowledge, it has also converted that knowledge into actionable intelligence.

In other words, data science has converted our data into wisdom. We have reached the top of the DIKW Pyramid.

E-Learning = Innovation = Science

Have you ever been to a conference where the presenter asks the audience, “Who’s implemented a mobile learning strategy?”, and only 2 or 3 people raise their hand?

Forgive me: it’s a rhetorical question. I know you have. Because everyone has.

Of course the question might not revolve around mobile learning, but rather gamification, or enterprise social networking, or flipped classrooms, or whatever the hot topic may be.

While a lot of talk is bandied around about e-learning, it’s evident that relatively few of us are actually doing it.

To help bridge the gap, I was honoured to moderate a panel session at last month’s AITD National Conference. I was even more honoured to share the stage with Helen Blunden, Matthew Guyan, Anne Bartlett-Bragg and Simon Crook.

The session was entitled E-Learning: Transforming Talk into Action, and the panellists were hand-picked from multiple sectors to share their insights and expertise with us. And that they did.

Simon explained how his science students are using their iPads in class to enrich their learning experience: “Engage me or enrage me”; Matt described his use of Articulate Storyline to develop online courses in-house; Helen shared her experience in using Yammer to cultivate a collaborative culture in a conservative corporate environment; while Anne dove head-first into MOOCs and ruffled a few feathers along the way.

Regardless of the specific technology or pedagogy discussed by the panellists, the overarching advice provided by each one was to give it a go and see what happens.

In other words, e-learning is innovation.

Now I realise that many of my peers will balk at this assertion. After all, e-learning is decades old, and today’s L&D pro’s are tech savvy and digitally invested.

So let’s take the “e” out of “e-learning” already – I’ve argued that myself in the past. However I put it to you that a great many among us still haven’t put the “e” into e-learning, let alone take it out again.

For these people, e-learning represents making changes in something established, especially by introducing new methods, ideas, or products. And when you think about it, e-learning is that for the rest of us too – it’s just we’re more comfortable with it; or, in fact, excited by it.

For all of us then, viewing e-learning through the lens of innovation offers us a crucial advantage: it reframes failure.

You see, innovators don’t think of failure as most people do. Rather than see it as something to be ashamed of, avoided at all costs, and certainly not to be aired in public, innovators embrace failure, they actively seek it out – and most importantly of all, they learn from it.

They appreciate the fact that if you never try, you never know. A failure isn’t an error or a mistake, but a beautiful piece of intelligence that informs your next move.

The trick of course is to ensure that when you fail, you do so quickly and cheaply. You don’t want to bring the roof crashing down upon you, so protect yourself by taking baby steps. Pilot your innovation and if it doesn’t quite work, modify it and try again; if it tanks miserably, cut your losses and abandon it; but if it does work, scale it up, keep an eye on it, continue to modify it where necessary, and enjoy your “overnight success”.

And still I wish to take this line of thinking further. Beyond innovation, e-learning is science.

My definition of science is “systematic knowledge”. If you want to obtain deep, scientific insight, get systematic.

Scientists frame failure in much the same way as innovators do. Again, rather than seeing it as something to be ashamed of, they see it simply as a result. It’s not good or bad, right or wrong. It just is.

The advantage of viewing e-learning through the lens of science is embedded in its methodology. Classic experimental design is based on two hypotheses: the null hypothesis, in which the treatment has no effect; and the alternative hypothesis, in which the treatment has an effect. By running an experiment, the scientist will either accept or reject the null hypothesis.

For example, suppose a scientist in a soda company is charged with testing whether honey-flavoured cola will be popular. He might set up two sample groups drawn from the target market: one group tastes the regular cola, the other group tastes the honey-flavoured cola, and both rate their satisfaction. After crunching the numbers, the scientist may find no significant difference between the colas – so he accepts the null hypothesis. Or he may find that the honey-flavoured cola tastes significantly better (or worse!) than the regular cola – so he rejects the null hypothesis. Whether the null hypothesis is accepted or rejected, it’s a useful result. The concept of failure is redundant.

The parallel with e-learning is readily apparent. Consider the teacher who allows her students to bring their mobile devices into class; or the trainer who delivers part of her program online; or the manager who sets up a team site on SharePoint; or the L&D consultant who supports a group of employees through a MOOC. In each case, the null hypothesis is that her new method, idea or product has no effect – on what? that depends on the context – while the alternative is that is has. Either way, the result informs her next move.

So my advice to anyone who has never raised their hand at a conference is that you don’t need to don a white coat and safety goggles to transform talk into action. Rather, change your mindset and take a baby step forward.

Science and Faith: A venn diagram

Merry Christmas everyone.

I wish you a 2013 full of provocative ideas, courageous experimentation, loads of fun, and – of course – plenty of learning!