Our brains are good at certain things, and not so good at others. We’re good pattern-matchers and meaning-makers, but not so good at doing things by rote. We make mistakes, almost by necessity (evolutionary advantage: if you do something a little different by chance and it’s better, it can get rewarded and more likely).  And we simplify the world, partly to save energy for what we care about, but also because complexity is taxing.

And, in general, this is good.  Our simplifications help us cope, make us more effective.  However, given our nature, at times this can fail us.  We may think we’ve taken a necessary step when we haven’t.  Henry Petroski, in  To Engineer is Human, helps us understand that we continue to push boundaries and take consequent risks.  Atul Gawande, in  The Checklist Manifesto, helps us understand the usefulness of support if we’re not going to make mistakes.

But sometimes this expediency can mask complexities and lead us astray.  For a simple example, the term ‘learning management system’ can actually lead us to believe we’re achieving learning, instead of courses.  And just because you have a course doesn’t mean something was learned.

There are many ways we can mislead ourselves.  We can talk about a concept that we all realize has to be true, that learners differ, and then believe we can identify how someone learns.  We may eventually be able to do so, but existing instruments aren’t valid, and learners change in different contexts. Plus, if we label learners as X or Y, we may limit them.  When I humorously compared the ‘generational differences’ argument to age discrimination, someone deeply involved in that field corrected me that real age discrimination is a serious problem not to be taken lightly!

It may seem like an ‘angels dancing on the head of a pin‘ type of argument, but we have to be careful of the words we use and their import.  We have to carefully consider the ways in which phrases can be used, or misused, and perhaps structure our use of language appropriately.  It’s branding, and perhaps we need to treat it as such.  At least, be careful of what terms you use and what inferences you’re making easy and which you might be inadvertently making hard.

I was asked, somewhat out of the blue, what I wish I’d learned in school, and I thought it an interesting question. I also thought it worth putting out to a slightly broader audience.

So, here’re some off-the-cuff thoughts about what I wish I’d learned in school:

• meta-cognitive and meta-learning skills (e.g. the SCANS competencies beyond the basic skills)

• that it is ok to fail, and that persistence and effort is as much a part of learning as achievement

• that your epistemological beliefs – what you believe learning is – affect your outcomes

• how to work in groups on projects

• about design, and the value of feedback and revision

• how reflection on process is as important as reflection on product

These are just a few thoughts, but it’s a start. And let me ask what you wish you’d learned in school?

There’s much talk about 21st Century skills, and rightly so: these skills are the necessary differentiators for individuals and organizations, going forward.  If they’re important, how do we incorporate them into systems, and track them?  You can’t do them in a vacuum, they only can be brought out in the context of other topics.  We can integrate them by hand, and individually assess them, but how do we address them in a technology-enabled world?  In the context of a project, here’s where my thinking is going:

First, you have some domain activity you are having the learner engage in. It might be something in math, science, social studies, whatever (though ideally focused on applied knowledge). Then you give them an assignment, and it might have a number of characteristics: it might be social, e.g. working with others, or problem solving. You could choose many characteristics, e.g. from the SCANS competencies (using information technology, reasoning), that the task entails.  That task is labeled with tags associated with the required competency, and tracked via SCORM or more appropriately with the Experience API.  There may be more than two, but we’ll stick with that model here.

So, when we then look across topics that the learner is engaging in, and the characteristics of the assignments, we can look for patterns across competencies. Is there a particular competency that is troubling or excelling?  It’s somewhat indirect, but it’s at least one way of systematically embedding meta-learning skills and tracking them.  And that’s a lot better than we’re doing now.

Remember the old educational computer games that said ‘develops problem solving skills’?  That was misleading. Most of those games ‘required’ problem-solving skills, but no real development of said skills was embedded.  A skilled parent or teacher could raise discussion across the problems, but most of the games didn’t.  But they could. Moreover, additional 21C resources could be made available for the assignments that required them, and there could be both programmatic or mentor intervention to develop these.

We need to specifically address meta-learning, and with technology we can get evidence.  And we should.  Now, my two questions are: does the concept make sense?  And does the diagram communicate it?