Tag: definition

Great and small

English is a funny language.

Coloured by countless other languages over centuries of war, politics, colonialism, migration and globalisation, many words have been lost, appropriated or invented, while others have changed their meaning.

In Australian English for example, fair dinkum means “true” or “genuine”. Linguaphiles speculate the phrase originated in 19th Century Lincolnshire, where “dinkum” referred to a fair amount of work, probably in relation to a stint down the mines. Add a tautology and 10,000 miles, and you have yourself a new lingo.

Thousands of other English words have their origins in ancient Greek. One pertinent example for L&D practitioners is pedagogy (formerly paedagogie) which derives from the Hellenic words paidos for “child” and agogos for “leader”. This etymology underscores our use of the word when we mean the teaching of children.

And yet our language is nuanced. We may alternately use pedagogy to mean the general approach to teaching and learning. Not necessarily teaching, not necessarily children. In this broader sense it’s an umbrella term that may also cover andragogy – the teaching of adults – and heutagogy – self-determined learning.

For example, when Tim Fawns, the Deputy Programme Director of the MSc in Clinical Education at the University of Edinburgh, blogged his thoughts about pedagogy and technology from a postdigital perspective, he defined pedagogy in the university setting as “the thoughtful combination of methods, technologies, social and physical designs and on-the-fly interactions to produce learning environments, student experiences, activities, outcomes or whatever your preferred way is of thinking about what we do in education”.

When Trevor Norris and Tara Silver examined positive aging as consumer pedagogy, they were interested in how informal learning in a commercial space influences the mindset of its adult patrons.

And when I use the word pedagogy in my capacity as an L&D professional in the corporate sector, I’m referring to the full gamut of training, coaching, peer-to-peer knowledge sharing, on-the-job experiences and performance support for my colleagues across 70:20:10.

A standing businessman facilitating a training session with a group of colleagues seated in a semi circle.

So while I assume (rightly or wrongly) that the broader form of the term “pedagogy” is implicitly understood by my peers when it’s used in that context, I spot an opportunity for the narrower form to be clarified.

Evidently, modern usage of the word refers not only to the teaching of children but also to the teaching of adults. Whether they’re students, customers or colleagues, the attribute they have in common with kids is that they’re new to the subject matter. Hence I support the Oxford English Dictionary’s definition of pedagogy as the practice of teaching, regardless of the age of the target audience.

If pedagogy includes adults, then logic dictates we also review the exclusivity of the term andragogy. Sometimes children are experienced with the subject matter; in such cases, an andragogical approach that draws upon their existing knowledge, ideas and motivations would be applicable. Hence I dare to depart from the OED’s definition of andragogy as the practice of teaching adults, in favour of the facilitation of learning. Again, regardless of the age of the target audience.

With regard to heutagogy, I accept Hase & Kenyon’s coinage of the term as the study of self-directed learning; however in the context of our roles as practitioners, I suggest we think of it as the facilitation of self-directed learning. That makes heutagogy a subset of andragogy, but whereas the latter will have us lead the learners by pitching problems to them, hosting Socratic discussions with them and perhaps curating content for them, the former is more about providing them with the tools and capabilities that enable them to lead their own learning journeys.

A tree structure flowing from Pedagogy down to Pedagogy, Andragogy and Heutagogy; with Instructivism, Constructivism, Connectivism and Novices, Intermediates, Experts aligned respectively.

This reshaping of our pedagogical terminology complements another tri-categorisation of teaching and learning: instructivism, constructivism and connectivism.

As the most direct of the three, instructivism is arguably more appropriate for engaging novices. Thus it aligns to the teaching nature of pedagogy.

When the learner moves beyond noviceship, constructivism is arguably more appropriate for helping them “fill in the gaps” so to speak. Thus it aligns to the learning nature of andragogy.

And when the learner attains a certain level of expertise, a connectivist approach is arguably more appropriate for empowering them to source new knowledge for themselves. Thus it aligns to the self-directed nature of heutagogy.

Hence the principle remains the same: the approach to teaching and learning reflects prior knowledge. Just like instructivism, constructivism and connectivism – depending on the circumstances – pedagogy, andragogy and heutagogy apply to all learners, great and small.

Skills of the present

The meaning of the phrase skills of the future is variable. Like so many other terms in our profession, its definition depends on who you ask.

According to my own heuristic, a “skill of the future” is a capability for which demand will grow disproportionately over the next 5 years. (While the future extends beyond this timeframe, I typically see any crystal balling for it too fantastical to be useful.)

And to be scalable from an organisational development perspective, the skill needs to be transferable across roles and leadership levels, so that it’s applicable to the context in which each individual works.

Arrows in a quiver

The why for investing in skills of the future should be self-evident post Covid. Organisations that neglected basic capabilities such as web conferencing, let alone more complex ones such as remote leadership, found themselves scrambling in the wake of the pandemic.

In contrast, organisations that had already invested in such skills and were using them day to day, experienced a relatively straight-forward transition into lockdown. Moreover they found themselves with a competitive edge, after years of reaping the benefits of the skills on their own merits.

Herein lies the main point of this post: skills of the future aren’t so much about preparing for tomorrow as they are about maximising today. Waiting for the moment when an imagined skill will meet an imagined need misses that point.

Let me return to the pre-Covid environment to elaborate. An organisation that trained its people face-to-face in the classroom may very well have recognised the future need for virtual training. But since the future hadn’t arrived yet, they had no reason to challenge the status quo. If it ain’t broke, don’t fix it.

In contrast, another organisation that also trained its people in the classroom started to diversify its approach by offering some of its training virtually. This new delivery option supported a remote working strategy, which improved employee engagement and scaled up their talent pool from local to national. The company’s smooth transition into lockdown was simply the latest win.

This mindset also applies to those uber sexy skills that seem so out of reach. For example, if data science is an aspiration, start by collecting whatever numbers you can get your hands on and analyse them however basically to inform your decision making now; and if artificial intelligence is intimidating, start by creating something simple like a branched online form to help your colleagues self-service their needs now. Your sophistication in these areas will improve over time while you ground yourself in the fundamental concepts and crystallise new opportunities to pursue.

Which leads me to the supplementary point of this post: skills of the future are a source of power. If you’re the one backing up your proposals with quantitative evidence, they’re more likely to be approved; and if you’re the one meeting the real needs of your end users, you’re more likely to receive positive appraisals from them.

And if Jim (my colleague in Double defence) didn’t like the idea of click-next online courses, he could have used his development skills to build them differently. Furthermore, he could have proved a blended solution by which the e-learning was complemented by a flipped class that drew upon his facilitation prowess.

But he did neither, and so for him the future arrived too soon.

You however have the opportunity to future proof your own career, by making the skills of the future your skills of the present.

Roses are red

It seems like overnight the L&D profession has started to struggle with the definition of terms such as “capability”, “competency” and “skill”.

Some of our peers consider them synonyms – and hence interchangeable – but I do not.

Indeed I recognise subtle but powerful distinctions among them, so here’s my 2-cents’ worth to try to cut through the confusion.

Old style botanical drawing of a rose and violets

Competency

From the get-go, the difference between the terms may be most clearly distinguished when we consider a competency a task. It is something that is performed.

Our friends in vocational education have already this figured out. For example, if we refer to the Tap furnaces unit of competency documented by the Australian Department of Education, Skills and Employment, we see elements such as Plan and prepare for furnace tapping and Tap molten metal from furnace.

Importantly, we also see performance criteria, evidence and assessment conditions. Meeting a competency therefore is binary: either you can perform the task successfully (you are “competent”) or you can not (in the positive parlance of educationalists, you are “not yet competent”).

Capability

Given a competency is a task, a capability is a personal attribute you draw upon to perform it.

An attribute may be knowledge (something you know, eg tax law), a skill (something you can do, eg speak Japanese), or a mindset (a state of being, eg agile).

I consider capability an umbrella term for all these attributes; they combine with one another to empower the behaviour that meets the competency.

Capability is an umbrella term for the attributes that empower the behaviour that meets a competency.

Frameworks

According to the definitions I’ve outlined above, we frequently see in the workplace that “capability frameworks” are mislabelled “competency frameworks” and vice versa.

Terms such as Decision Making and Data Analysis are capabilities – not competencies – and moreover they are skills. Hence, not only would I prefer they be referred to as such, but also that they adopt an active voice (Make Decisions, Analyse Data).

I also suggest they be complemented by knowledge and mindsets, otherwise the collection isn’t so much a capability framework as a “skills framework”; which is fine, but self-limiting.

Deployment

I have previously argued in favour of the L&D team deploying a capability framework as a strategic imperative, but now the question that begs to be asked is: should we deploy a capability framework or a competency framework?

My typical answer to a false dichotomy like this is both.

Since capabilities represent a higher level of abstraction, they are scalable across the whole organisation and are transferable from role to role and gig to gig. They also tend to be generic, which means they can be procured in bulk from a third party, and their low volatility makes them sustainable. The value they offer is a no-brainer.

In contrast, competencies are granular. They’re bespoke creations specific to particular roles, which makes them laborious to build and demanding to maintain. Having said that, their level of personalised value is sky high, so I advise they be deployed where they are warranted – targeting popular roles and pivotal roles, for example.

Semantics

A rose by any other name would smell as sweet.

Yet a rose is not a violet.

In a similar manner I maintain that capabilities and competencies are, by definition, different.

In any case, if we neglect them, the next term we’ll struggle to define is “service offering”.

The leader’s new clothes

From $7 billion to nearly $14 billion.

That’s how much the spend on leadership training by American corporations grew over the preceding 15 years, according to Kaiser and Curphy in their 2013 paper Leadership development: The failure of an industry and the opportunity for consulting psychologists.

Over that same period we witnessed the bursting of the dot-com bubble, the implosion of Enron, and of course the Global Financial Crisis. While the causes of these unfortunate events are complicated, our leaders were evidently ill-equipped to prevent them.

Despite the billions of dollars’ worth of training invested in them.

Undressed mannequins in a shop window

For a long time I felt like the child who could see the emperor wasn’t wearing any clothes. Then Jeffrey Pfeffer visited Sydney.

Pfeffer is the Professor of Organizational Behavior at Stanford University’s Graduate School of Business. He was promoting a book he had published, Leadership BS: Fixing Workplaces and Careers One Truth at a Time, in which he states what I (and no doubt many others) had been thinking: leadership training is largely ineffective.

At a breakfast seminar I attended, the professor demonstrated how decades of development had no positive impact on metrics such as employee engagement, job satisfaction, leader tenure, or leader performance. He posited numerous reasons for this, all of them compelling.

Today I’d humbly like to add one more to the mix: I believe managers get “leadership” training when what they really need is “management” training.

They’re entreated to be best practice before they even know what to do. It’s the classic putting of the cart before the horse.

For example, the managers in an organisation might attend a workshop on providing effective feedback, leveraging myriad models and partaking in roleplays; when what they really need to know is they should be having an hour-long 1:1 conversation with each of their team members every fortnight.

Other examples include training in unconscious bias, emotional intelligence and strategic thinking; yet they don’t know how to hire new staff, process parental leave, or write a quarterly business plan. Worse still, many won’t realise they’re expected to do any of that until the horse has bolted.

I’m not suggesting leadership training is unimportant. On the contrary it’s critical. What I am saying is that it’s illogical to buy our managers diamond cufflinks when they don’t yet own a shirt.

At this juncture I think semantics are important. I propose the following:

  • Management training is what to do and how to do it.
  • Leadership training is how to do it better.

In other words, management training is the nuts & bolts. The foundation. It’s what our expectations are of you in this role, and how to execute those expectations – timelines, processes, systems, etc. It focuses on minimum performance to ensure it gets done.

In contrast, leadership training drives high performance. Now you’ve got the fundamentals under your belt, here’s how to broaden diversity when hiring new staff. Here’s how to motivate and engage your team. Here’s how to identify opportunities for innovation and growth.

$14 billion is a lot of money. Let’s invest it in a new wardrobe, starting with the underwear.

The sum of us

What is the definition of the term “data scientist”…?

In my previous post, Painting by numbers, I offered a shorthand definition of data science based on what I could synthesise from the interwebs. Namely, it is the combination of statistics, computer programming, and domain expertise to generate insight. It follows, then, that the definition of data scientist is someone who has those skill sets.

Fat chance!

In this post I intended to articulate my observation that in the real world, incredibly few people could be considered masters of all three disciplines. I was then going to suggest that rather than seeking out these unicorns, employers should build data science teams comprising experts with complementary talents. I say “was” because I subsequently read this CIO article by Thor Olavsrud in which he quotes Bob Rogers saying, well… that.

Given Thor and Bob have stolen my thunder (18 months ago!) I think the only value I can add now is to draw a parallel with pop culture. So I will do so with the geeky HBO sitcom Silicon Valley.

The cast of Silicon Valley: Dinesh, Gilfoyle, Richard, Jared and Erlich.

If you aren’t familiar with this series, the plot revolves around the trials and tribulations of a start-up called Pied Piper. Richard is the awkward brainiac behind a revolutionary data compression algorithm, and he employs a sardonic network engineer, Gilfoyle, and another nerdy coder, Dinesh, to help bring it to market. The other team members are the ostentatious Erlich – in whose incubator (house) the group can work rent-free in exchange for a 10% stake – and Jared, a mild-mannered economics graduate who could have been plucked from the set of Leave It to Beaver.

The three code monkeys are gifted computer scientists, but they have zero business acumen. They are entirely dependent on Jared to write up their budgets and forecasts and all the other tickets required to play in the big end of town. Gilfoyle and Dinesh’s one attempt at a SWOT analysis is self-serving and, to be generous, NSFW.

Conversely, Jared would struggle to spell HTML.

Arguably the court jester, Erlich, is the smartest guy in the room. Despite his OTT bravado and general buffoonery, he proves his programming ability when he rolls up his sleeves and smashes out code to rescue the start-up from imploding, and he repeatedly uses his savvy to shepherd the fledgling business through the corporate jungle.

Despite the problems and challenges the start-up encounters throughout the series, it succeeds not because it is a team of unicorns, but because it comprises specialists and a generalist who work together as a team.

Purple Unicorn, courtesy of Wild0ne, Pixabay.

And so the art of Silicon Valley shows us how unlikely we would be in real-life to recruit an expert statistician / computer programmer / business strategist. Each is a career in its own right that demands years of education and practice to develop. A jack-of-all-trades will inevitably be a master of none.

That is not to say a statistician can’t code, or a programmer will be clueless about the business. My point is, a statistician will excel at statistics, a computer programmer will excel at coding, while a business strategist will excel at business strategy. And I’m not suggesting the jack-of-all-trades is useless; on the contrary, he or she will be the glue that holds the specialists together.

So that begs the question… which one is the data scientist?

Since each is using data to inform business decisions, I say they all are.