Learnlets

Expertise is an elusive thing. It comes from years of experience in a field.  However, it turns out that it doesn’t just accumulate. You need very specific practice and/or useful feedback to develop it.  And the more expertise one has, the better you are able to apply it to situations. Which has implications for what you do and when and how you do it.

Expertise is valuable. The properties of expertise include that it’s compiled away to be essentially automatic. Which implies it’s not accessible for conscious introspection. (Which is why experts quite literally cannot tell you what they do!)  On the other hand, their responses to situations in their area of expertise are likely to be as good as you can get.  They apply mental models they’ve developed to solve problems.

If you want to develop expertise as an individual, you need to understand how to practice.  Deliberate practice, as Ericsson details, is the key.  You need to practice at the limits of your ability, and consciously learn from the outcomes.  It’s not just doing the job, it’s pushing the boundaries, and actively reflecting.

If you want to develop expertise as an organization  internally, the situation is very much the same.  You need resources to develop people, and stretch assignments with feedback and coaching to optimally develop the expertise.

Of course, you can bring in expertise from outside, as well.  The question then becomes one of when and who.  You can contract out work, which makes sense when the activity isn’t part of your core ability.  Outsourcing to technology or external expertise is fine for things that are in areas that are well developed.

Otherwise, you can bring in consultants. The latter is particularly useful when you are moving in a new direction or want to deepen your understandings. A good consultant will work with you to not only help address the situation, but internally develop your own understanding. The key is working collaboratively and transparently. Yes, I’m a vested interest, but I believe these things are true on principle and should be in practice.

Expertise is core to situations you know you need expertise in, but also in those that are new. When you need innovation, you need expertise in the complementary areas that you are applying to address the situation.  You don’t want to develop learning  except  in the problem.  At least, that’s  my expert opinion. Which, of course, is on tap if needed ;).

Sparked by a colleague, I’m reading The Digital Transformation Playbook, by David Rogers. In the chapter on innovation, he talks about two types of experimentation: convergent and divergent. And I was reminded that I think of two types of innovations as well.  So what are they?

Experimentation

He talks about how experimentation is the key to innovation (in fact, the chapter title is Innovate by Rapid Experimentation). His point is that you need to be continually experimenting, rapidly.  And throughout the organization, not just in separate labs. Also, it’s ok to fail, as long as the lesson’s learned.  And then he distinguishes between two types of experimentation.

The first is convergent. Not surprisingly, this is when you’re trying to eliminate options and make a decision.  This is your classic A/B testing, for example. Here you might try out two or three different solutions, to see which one works best. You create the options, and have measures you’ll use to determine the answer.  You might ask: should we use a realistic video or a cartoon animation? A situation where there isn’t a principled answer, and you need to make a decision.

Divergent experimentation is, instead, exploratory. Here you give folks some ideas, or a prototype, and see what happens. You don’t know what you’ll get, but you’re eager to learn.  What would a scenario look like here?

Innovation

These roughly correspond to the two types of innovation I think of. One is the ‘we need to solve this’ type. I think of this as short-term innovation. Here we are problem-solving or trouble-shooting.  You bring together a team of relevant capabilities and otherwise as diverse as possible. You facilitate the process. And you’re likely to try convergent experimentation.

At the other end is the serendipitous, long-term innovation that happens because you create an environment where ideas can gestate.  You’ve got access to the adjacent possible, and the opportunities to explore and share. It’s safe to experiment and fail.  People are  supposed to take time to reflect! This is more closely aligned to divergent experimentation.

Note that this is  all learning, as you don’t know the answers when you start!  The success of organizational learning, however, is a product of both. You need to solve the problems you know you have, and allow for ideas to generate solutions to problems you didn’t know you had.  Or, more optimistically, to search through idea spaces for opportunities you didn’t know to look for.

Rogers is right that continual experimentation is key.  It has to become baked into how you do what you do.  Individually, and organizationally.  And you can’t really get it unless you start practicing it yourself.  You need to continually challenge yourself, and try things both to fix the problems, and to explore things that are somewhat tangential. Your own innovations will be key to your ability to foster them elsewhere.

Too many orgs are only focused on the short-term.  And while that may solve shareholder return expectations, it’s not a receipt for longer-term organizational survival.  You need both types of innovations. So, the question is whether you can assist your org in making a shift to the serendipitous environment.  Are you optimizing your innovation?

I sat in on a twitter chat yesterday, #DLNChat, that is a higher ed tech focused group (run by EdSurge). The topic was the link between higher ed and job skills, and I was a wee bit cynical. While I think there are great possibilities, the current state of the art leaves a lot to be desired.

So, I currently don’t think higher ed does a good job of preparation for success in business. Higher ed focuses too much on knowledge, and uses assignments that don’t resemble the job activities.  Frankly, there aren’t too many essays in most jobs!

Worse, I don’t think higher ed does a good job of developing meta-cognitive and meta-learning skills. There is little attempt to bridge assignments  across courses, so your presentations in psychology 101 and sociology 202 and business 303 aren’t steadily tracked and developed. Similarly with research projects, or strategy, or… And there’re precious little (read: none) typically found where you actually make decisions like you would need to.

And, sadly, the use of technology isn’t well stipulated either. You might use a presentation tool, a writing tool, or a spreadsheet, maybe even collaboratively, but it’s not typically tied to external resources and data.

Yes, I know there are exceptions, and it may be changing somewhat, but it still appears to be the case. Research, write a paper, take a test.

Yet the role of developing higher skills is possible and valuable.  We could be providing more meaningful assignments, integrating meta-learning layers, and developing both meaningful skills and meta-skills.

This doesn’t have to be done at the expense of the types of things professors believe are important, but just with a useful twist in the way the knowledge is applied. It might lead to a revision of the curriculum, at least somewhat, but I reckon it’d likely be for the better ;).

Our education system, both K12 and higher-ed, isn’t doing near what it could, and should. As Roger Schank says, only two things wrong: what we teach, and how we teach it.  We can do better. Will we?

Ok, so I can be a bit of a pedant.  Blame it on my academic background, but I believe conceptual clarity is important! If we play fast and loose with terminology, we can be be convinced of something without truly understanding it.  Ultimately, we can waste money chasing unwarranted directions, and worse, perhaps even do wrong by our learners.

Where do the problems arise?  Sometimes, it’s easy to ride a bizbuzz bandwagon.  Hey, the topic is hot, and it sounds good.  Other times, it’s just too hard to spend the effort. Yet getting it wrong ends up meaning you’re wasting resources.

Let’s be clear, I’m not talking myths. Those abound, but here I’m talking about ideas that are being used relatively indiscriminately, but in at least one interpretation there’s real value.  The important thing is to separate the wheat from the chaff.

Some concepts that are running around recently and could use some clarity are the following:

Microlearning.  I tried to be clear about this here. In short, microlearning is about small chunks where the learning aggregates over time.  Aka spaced learning.  But other times, people really mean performance support (just-in-time help to succeed in the moment). What you don’t want is someone pretending it’s so unique that they can trademark it.

70:20:10.  This is another that some people deride, and others find value in. I’ve also talked about this.   The question is why they differ, and my answer is that the folks who use it as a way to think more clearly about a whole learning experience find value. Those who fret about the label are missing the point.  And I acknowledge that the label is a barrier, but that horse has bolted.

Neuro- (aka brain- ). Yes, our brains are neurologically based. And yes, there are real implications. Some.  Like ‘the neurons that fire together, wire together’.  And yet there’re a whole lot of discussions about neuro that are really at the next higher level: cognitive.  This is just misleading folks to make it sound more scientific.

Unlearning. There’s a lot of talk about unlearning, but in the neurological sense it doesn’t make sense. You don’t unlearn something.  As far as we can tell, it’s still there, just increasingly hard to activate. The only real way to ‘unlearn’ is to learn some other response to the same situation.  You learn ‘over’ the old learning. Or overlearn.  But not unlearn. It’s an unconcept.

Gamification. This is actually the one that triggered this post. In theory, gamification is the application of game mechanics to learning.  Interestingly, Raph Koster wrote that what makes games fun are that they are intrinsically about learning!  However, there are important nuances.  It’s not just about adding PBL (points, badges, and leaderboards). These aren’t bad things, but they’re secondary.  Designing the intrinsic action around the decisions learners need to acquire is a deeper and more meaningful implication.  Yet people tend to ignore the latter because it’s ‘harder’.  Yet it’s really just about good learning design.

There are more, of course, but hopefully these illustrate the problem. (What are yours?)  Please, please, be professional and take the time to get clear about our cognitive architecture enough to ensure that you can make these distinctions on your own. We need the conceptual clarity!  Hopefully then we can reserve excitement for ideas that truly add value.