Tag: knowledge

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”.

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…

The DIKW Pyramid, with Data at the base, Information a step higher, Knowledge another step higher, and Wisdom at the peak.

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.

The DIKW Pyramid, showing statistics converting data into information.

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.

The DIKW Pyramid, showing analytics converting data into knowledge.

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.

The DIKW Pyramid, showing data science converting data into wisdom.

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.

The past tense of open badges

Some commentators are heralding open badges as the nemesis of the college degree. I don’t quite see it that way.

It is true they are uneasy bedfellows. As Mark Smithers observes…

“It’s interesting that the reaction to open badges from senior academic managers is often to dismiss them as being child like and akin to collecting a badge for sewing at scouts.”

…and…

“I also suspect that traditional higher education providers will resist providing them because they don’t fit in with traditional academic perceptions of achievement and credentialing.”

I wonder if these academics have consulted their own faculties of education?

Of course, open badges and college degrees are not mutually exclusive. If a particular university can overcome its initial prejudice, it will see badges for what they really are: representations of achievement – just like those pieces of paper they dole out at graduation ceremonies.

There is no reason why a university couldn’t award a badge upon the completion of a degree. In fact, it could also award badges upon the completion of individual subjects within the degree. That would give the student a sense of accomplishment while in the midst of a multi-year program, and I imagine showcasing one’s backpack on the university’s VLE would become rather competitive.

Open badges

Speaking of competition, I don’t see open badges as a serious disruptor of the higher education system in the way that MOOCs are. And that’s because MOOCs are disrupting the delivery of education, rather than its credentialing.

A degree will always command a certain level of gravitas. It represents a structured, comprehensive education from – according to broader society – an elite bastion of knowledge and research. In short, it equips you with the intellectual foundation to do something in that domain.

In contrast, open badges are task oriented. Beyond the nebulous notion of “study”, they recognise the execution of specific actions. For example, Mozilla issues its Div Master Badge upon successfully using the div tag at least 2 times in its Webmaker Project.

If the task were passing an exam, the badge could indeed represent the acquisition of knowledge; but the spirit of open badges dictates that the task be performed in the real world, and hence represents the mastery of a skill. And this is meaningful to the corporate sector.

For example, if I were an employer who needed a graphic designer, I would seek someone who knows how to take awesome digital photos and edit them in Photoshop. So an applicant who has earned badges for digital photography techniques and advanced Photoshop operations would be an obvious candidate.

Yet if I were seeking a IT executive, I don’t think open badges would cut the mustard. Sure, badges earned by an applicant for various Java programming tasks might be attractive, but a wide-ranging role requires the kind of comprehensive education that a degree is purposefully designed to give.

Magnifying glass

When we look at learning through the lens of the college degree, we see its application in the future tense. The learner has a well-rounded education which he or she intends to draw from. In other words, the degree recognises something you can do.

In contrast, when we look at learning through the lens of the open badge, we see its application in the past tense. The learner has demonstrated their mastery of a skill by using it. In other words, the badge recognises something you have already done.

So the degrees vs badges debate isn’t really about the latter displacing the former. The emergence of badges is merely re-roasting the same old chestnut of whether degrees are necessary for the modern workplace.

And that’s an entirely different matter.

See the wood for the SMEs

In my previous blog post, Everyone is an SME, I argued that all the employees in your organisation have knowledge and skills to share, because everyone is an SME in something.

Sometimes this “something” is obvious because it’s a part of their job. For example, Sam the superannuation administrator is obviously an SME in unit switching, because he processes dozens of unit switches every day.

But sometimes the something isn’t so obvious, because we’re either too blind to see it, or – Heaven forbid – our colleagues have lives outside of the workplace.

Martha the tea lady

Consider Martha, the tea lady. Obviously she’s an SME in the dispensation of hot beverages. That’s her job.

But dig a little deeper and you’ll discover that she’s also an SME in customer service and relationship management. That’s her job, too.

Oh, and she speaks fluent Polish and Russian.

Gavin the IT grad

May I also introduce you to Gavin, the IT grad. Gavin is proficient in several programming languages, as you would expect. In his spare time, he develops iPhone apps for fun.

You’re working on a mobile strategy, right?

Li the BDM

Then there’s Li, the Business Development Manager. Li’s an expert in socratic selling and knows your product specs off by heart, but did you know she’s halfway through a Master of International Business degree?

She also recently emigrated from China – you know, that consumer market you want to break into.

My point is, when we seek subject matter expertise for a project, a forum, a working group, an advisory board, or merely to answer a question, we might not see the wood for the trees are in the way.

Does your organisation have a searchable personnel directory that captures everyone’s expertise? Their experiences? Their education? Their interests? The languages they speak?

If not, you are probably oblivious to the true value of your payroll.

Three illuinated questionmarks among many black question marks.

Clash of the titans

I have really enjoyed following the recent argy bargy between Larry Sanger and Steve Wheeler. From a learning practitioner’s point of view, it raises issues of pedagogy, instructional design, and perhaps even epistemology.

Having said that, I think it all boils down to the novice-expert principle. As a novice, you don’t know what you don’t know. Thankfully, an expert (the teacher) can transmit the necessary knowledge to you quickly and efficiently. In eduspeak, you benefit from “scaffolding”.

Then, after you have acquired (yes – “acquired”) a foundational cognitive framework, I suggest a constructivist approach would be appropriate to expand and deepen your knowledge. In other words, now you know what you don’t know, you can do something about it.

My sector of practice is corporate rather than K-12, but I would assume that because the learners are children, their level of experience and prior knowledge is limited. Hence, having the basic concepts explained up front is a perfectly reasonable teaching strategy.

Teacher in front of K-12 class

I wonder, though, whether conversation (online or otherwise) would indeed be a useful technique after the basics have been bedded down? Perhaps the last third or so of the class could be devoted to discourse facilitated by the teacher? Or assigned to participation in a district-wide online discussion forum? (Moderated, of course, by teachers and class nerds.)

Or – more likely – I’m exposing my ignorance of the logistics of managing a classroom.

My point is that constructivism can complement, rather than substitute, instructivism. This is something that I have argued for previously.

My secondary point is that I am quite getting over the Twitterati’s tendency to devalue the role of the expert in education. Not only is the expert aware of the important facts, but they can also impart their meaning and context.

Googling ability does not a scholar make.