Archives for January 2018

As I have hinted, I’ve been working on a project that is related to what learning science has to do with learning design.  And I can finally announce the project!  I’ve been writing a book on debunking learning myths & superstitions, and unpacking some misconceptions. I’m happy to say that it’s finally available for pre-order (ATD members here, Amazon here). It’s myth-smashing time!

The focus here is on workplace learning, as the title suggests. There already has been a book oriented toward the education market, but this one is particularly focused on myths that impact learning & development. The title is Millennials, Goldfish & Other Training Misconceptions:  Debunking Learning Myths and Superstitions.   There are 3 major categories of things addressed:

• Myths: beliefs that are the source of effort and investment that have been proven to be false.  It’s surprising how many there are, but they persist. I have addressed 16 of them.  I talk about the appeal, the possibilities and problems, how research could answer the question, and what the research says.
• Superstitions: these are practices that aren’t really advocated, but continue to be observed in practice. And they’re not necessarily the subject of specific research, but instead we can make principled arguments against them. I have documented five of these, with the approaches, the plausible case, and why it’s not accurate.
• Misconceptions: these are topics that are hotly debated, with typically smart people on both sides, but yet contention remains.  After identifying what both sides are arguing, what I try to point out is what is worth taking away. Or when it’s useful.

In each case,  I identify what you  should be doing.  The point is not to just point out the flaws, but have us using good approaches.  And have a wee bit of fun ;).

This book is very much intended as a tool. It’s to pull out when you have a question, and very specifically when someone wants to push you to do something that’s contrary.  It’s a reference tool that you should have on your shelf for when these questions arise.

While the book won’t be available ’til late April, I can now let you know that it’s already available for pre-order.  In conjunction with ATD, the publisher, we’re finalizing all the aspects.  If you’re not an ATD member, you can also get it here.

I’ll be talking on the topic of myths, covering a limited subset, for Training Mag’s Network in a webinar on April 11 at 9 PT, noon ET.  See you there?

And I’ll be addressing the larger issue of being professional about learning science, including myths, for ATD in a webinar on May 24 at 11AM PT, 2PM ET.

Here’s to busting myths!

 

As a response to my post where I offered to ‘listen’, I’ve had several comments giving me topics, and so I thought I should respond.  One asked about meta-learning (learning to learn), in the particular situation of courses with a variety of expertise levels, and getting into issues of learner responsibility.  The author pointed to a presentation on learning to learn, that had a nice framework, and I thought I should elaborate.

The framework mentioned talked about three stages of expertise: apprentice, journeyman (using the traditional term, is there a move to ‘journey person’ or…?), and mastery.  Within these, you watch as an apprentice, practice as a journeyman, and share as a master.  Which isn’t a bad approximation of the whole ‘cognitive apprenticeship‘ approach.

The article misses some nuances, of course (and the author acknowledged this). For instance, in practice, the role of deliberate practice is important, it’s not  just repetition, but the ‘right’ repetition. And my commenter brought up the role of epistemological stance, that the learners need to  own their own learning.

The starting point from the comment, however, was the fact that the audiences being seen varied in background knowledge; some were relative novices, others were experienced.  To me, that calls for a ‘leveling’ approach.  Here, you have preparatory material that you can test out of, otherwise you go through it. This helps ensure that the audience starts the learning experience with a baseline of at least language. You  don’t want to be presenting content in that valuable face-to-face time!

The details involved in making learning experiences work are many. It’s about what to teach, how, how to address audience diversity, and more. It’s about meta-learning for ourselves and our learners.    That’s why I advocate learning about how we learn, the cognitive science that (should) drive how we do what we do. So, who wants to learn?

I mentioned in yesterday’s post that one thing I do in getting objectives is  focus on decisions. And, simple ones will get automated; we can train AI to handle these. What will make the difference between ordinary and extraordinary organizations is the ability to make decisions in this new VUCA environment (volatile, uncertain, complex, and ambiguous). And it made me wonder how you develop the ability to make better choices.

AI can be trained in a couple of ways to answer questions and make these decisions. We can use machine learning to train a system on a historical database (watching out for bias).  We can use semantic analysis to read documents and make a system that can answer questions about them. But such systems are very limited; they can’t handle questions at the periphery of the knowledge well, and fall apart at related areas. Which people are better at,  if their expertise has been developed.

Now, developing this expertise isn’t straightforward.  If there were simple decision trees, we could automate them as above. Instead, what works best is expert models that have been abstracted across dialog and practice. This needs to be augmented with an awareness of adjacent fields. So, for instance, for instructional design, we should have an awareness of interface design, graphic design, media production, etc. So how do we develop this?

We certainly need to develop the expert models we know play a role. But this gets circular with the above unless we find a way to break out of the predictable. I suggested one approach to this with my ‘shades of grey’ post, having groups work together to make categorization choices: is this or is this not legal.  This was, however, more focused on compliance and there’s a much wider situation.

We first need to identify the situations, the relevant models, and the scope of likely variation. We can’t provide specific data (or we’d train the system on it), so we need to anticipate a spread.  And we could just train that, but I want to go further.

I’d want to use such a process to choose situations, and then design group work, for the reasons I identified here. (Resourced with models and examples, of course.)  We want to get learners working together to address complex problems. We want them to use their various understandings to illuminate the underlying models.  If you can get productive discussion (and this needs to be designed in and facilitated), the learners’ thinking will be enriched. (And they may have folks to call on when the situations  do  arise ;).

Collaboration in learning is second best to collaboration in problem-solving. We should do the latter when we can, but we should do the former anyway. For better learning, and for those times when there isn’t the luxury of working with others.

I reckon this would lead to better decision-making ability. What do you think?