I want to support David Kelly’s Learning Styles Awareness Day, but have written pretty much all I want to say on the matter. In short, yes, learners differ. And, as a conversation with someone reminded me, it helps for learners to look at how they learn, so as to find ways to optimize their chances for success.  Yet:

There’s no psychometrically-valid learning styles assessment out there.

There’s no evidence that adapting learning to learning styles is of use.

So what to do?

Use the best learning you can (at the end of the video).

Then help learners accommodate.

Here’re my previous thoughts, developing towards a proposal for how to  consider learning styles,  in chronological order:

Learning Styles

Learning Styles, Brain-Based Learning, and Daniel Willingham

Rethinking Learning Styles

Situated Learning Styles

My problem with learning styles really is the people flogging them without a) acknowledging the problems, and b) appropriately limiting the inferences.  Sometimes it seems like playing ‘whack-a-mole’…

As I think about slow learning and Sage at the Side, I want to think about a continuum of tech-enablement. I want to include performance support, formal learning, and meta-learning. One way to think about it is layering on support across the learning event.

As I talked about in Making Slow Learning Concrete, the idea is to have little bits of information layered on top of what you’re doing. Thus, the first level might be to have performance support, to optimize the outcome of the event.

However, a second layer, potentially wrapped before and after the event, would be to connect the essence of the performance to a learning framework. Perhaps not all events would have it, but it would connect the event: context and goals, to a learning framework.  It could be a conceptual model, and certainly could be feedback.

A third layer would be a meta-learning layer.  Looking at any resources used (and perhaps a different one this time than the last), some information could be provided that helped the learner understand their own learning.  It could be reflection support, a map of the learner’s actions, even connecting to a learning mentor, whatever would help them look at how they learned with the purposes of improving their own learning.

With this approach, we start de-coupling learning from a particular event, and start wrapping learning around our lives. I’ve used the label ‘slow learning’, but I really believe that this will feel slower, but actually will accelerate learners to competence faster than the ineffective methods we currently are using.  Lots of tuning to make an experience that feels natural and supportive, as opposed to intrusive, and some real system architecture issues, but I think this is doable, and certainly worth exploring.

My colleague Harold Jarche pointed me to a post by Dave Snowden about deliberative practice, which I found interesting for a facet not part of the key article (which makes worthwhile points).  Among a list of important requirements for meaningful activity that is part of effective learning (i.e. it’s not just 10K hours of practice that makes an expert, but what sort of practice has an effect), Dave cites that “at least half of … experiments should fail”.  Think about that for a minute.

What that’s saying is that at least half of the money you invest in new things could be conceived of as being wasted.  You might be considered a very ineffective manager if 50% of your investments don’t yield returns!  Now, first of all, I’m sure you recognize that failed experiments aren’t a complete waste, as long as you learning something (“when you lose, don’t lose the lesson” as the saying goes).  Still, 50%  might still seem like a high failure rate.  But is low risk really good?

I remember hearing a talk by a Canadian AI researcher (who’s name escapes me after all these years) who had studied the optimal ratio of success to failures in helping a system learn. Now this was particular to the learning algorithm he’d chosen, but his result was roughly that you learned fastest if you failed two-thirds of the time, or around 67% failure.  Now that’d be pretty disheartening, but if you could take emotion out of the equation, e.g. made it safe to fail, would learning faster be a big enough argument to support bigger failure?

It depends on a lot: on how well you discern the lessons from failure, how well you tolerate failure, how much social scrutiny and how tolerant that public viewpoint is, but it’s interesting to contemplate what might be an optimal context for failure, and given that, what would be the fastest way to learn, and capitalize on that learning.  You want your experiments to be designed in the first place to yield maximum information, but if they do, what would a valuable success rate be?

I do believe that they who adapt fastest will be the survivors.  That adaptation may be subconscious, but I think conscious reflection is a valuable component.  Certainly for sharing the learning, so no one else has to make the same mistakes.  So are you learning just as fast as you can?

On this, the shortest day of the year here in the northern hemisphere, it seems like a good time to reflect on the past year and look forward to the coming one.  It’s certainly been an interesting year, and a good one personally.  I feel there are lots of reasons to hope for good things to be coming too.  The opportunities are big.

First of all, mobile really has taken off.  I know that many people are still wondering about how to take advantage of mobile, but awareness of the opportunities are growing. I’ve conducted several mobile brainstorming sessions with clients who have started with some apps and are looking to go further. I think more should be thinking about mobile from a strategy perspective as well.  While an initial initiative or two is ok, you have to recognize that mobile is a platform upon which solutions are created, and consequently the approach should go beyond just individual development efforts.  Still, the benefits of supporting workers wherever and whenever they are can make some big impacts on organizational outcomes.

Another area of excitement is social and informal learning. Even firms that have big concerns about security and government scrutiny are finding that they need to address it, and are beginning steps. Hey, if the CIA can deal with it, your organization can.  With the ITA in particular we have been successful in getting some traction in this area, and hope to help more organizations come to grips in the coming year.  The chance to really accelerate innovation and more is hard to ignore.

And I personally am excited about more activity around content: about more quality design, more structure around the content, and more awareness of the need for architectural work around it. I was thrilled to hear more discussion about this in a number of forums, and I think the opportunities to capitalize on finely granulated and articulated content will provide flexibility and personalization going forward.

Finally, it does appear more organizations are getting the message about moving to a strategic position in terms of learning technology.  And, of course, that means getting strategic about learning (big ‘L’ learning), and then aligning with the overall organization strategy. Of course, such an effort will not only result in better outcomes for the organization but also more credibility for the L&D group.

I’m really grateful to all those who’ve had a role in the past year (and in my past, period) in co-developing new understandings, and I look forward to more opportunities in the coming year.