At the Guild’s recent mLearnCon, I was having a conversation in which I was reminded that designing performance interventions is a probabilistic exercise, and it occurred to me that we’re often not up front enough about it.  And we need to be.

When we start our design process with a performance vision, we have an idea of what ideal performance would be.  However, we make some assumptions about the performer and their ability to have comprehended our learning interventions and any resources we design or are available in the context.  We figure our learning was successful (if we’re doing it right, we’re  not letting them out of our mitts until they’ve demonstrated the ability to reliably perform what we need), and we figure our performance solutions are optimal and useful (and, again, we should test until we know).  But there are other mitigating factors.

In the tragic plane crash in the Tenerife’s – and pilots train as much as anybody – miscommunication (and status) got in the way.  Other factors like distraction, debilitating substances, sleep deprivation and the like can also affect performance.  Moreover, there’s some randomness in our architecture, basically.  We don’t do everything perfectly all the time.

But more importantly, beyond the actual performance, there’s a probability involved in our learning interventions.   Most of our research based results  raise the likelihood of the intervention affecting the outcome.  Starting with meaningful objectives, using model-based concepts, contextualized examples, meaningful practice, all that increases the probability.

This holds true with performance support, coaching/mentoring, and more.  Look, humans don’t have the predictable properties of concrete or steel.  We are much more complex, and consequently variable. That’s why I went from calling it cognitive engineering to cognitive design. And we need to be up front about it.

The best thing to do is use the very best solutions to hand; just as we over-engineer bridges to ensure stability (and, as Henry Petrovski points out in To Engineer is Human, on subsequent projects we’ll relax constraints until ultimately we get failure), we need to over-design our learning.  We’ve gotten slacker and slacker, but if it’s important (and, frankly, why else are we bothering), we need to do the right job.  And tarting up learning with production values isn’t the same thing.  It’s easier, since we just do it instead of having to test and refine, but it’s unlikely to lead to any worthwhile outcomes.

As I’ve argued before, better design doesn’t take longer, but there is a learning curve. Get over the curve, and start increasing the likelihood that your learning will have the impact you intend.

I’ve been thinking a lot about the higher education situation, specifically for-profit universities. One of the things I see is that somehow no one’s really addressing the quality of the learning experience, and it seems like a huge blindspot.

I realize that in many cases they’re caught between a rock and a hard place. They want to keep costs down, and they’re heavily scrutinized.  Consequently, they worry very much about having the  right  content.  It’s vetted by Subject Matter Experts (SMEs), and has to be produced in a way that, increasingly, it can serve face to face (F2F) or online.  And I think there’s a big opportunity missed.  Even if they’re buying content from publishers, they are focused on content, not  experience.  Both for the learner, and developing learner’s transferable and long-term skills.

First, SMEs can’t really tell you what learners need to be able to do. One of the side-effects of expertise is that it gets compiled away, inaccessible to conscious access.  Either SMEs make up what they  think  they do (which has little correlation with reality) or they resort to what they had to learn. Neither’s a likely source to meaningful learning.

Even if you have an instructional designer in the equation, the likelihood that they’re knowledgeable enough and confident enough to work with SMEs to get the real outcomes/objectives is slim.  Then, they also have to get the engagement right.  Social engagement can go a good way to enriching this, but it has to be around meaningful tasks.

And, what with scrutiny, it takes a strong case to argue to the accrediting agencies that you’ve gone beyond what SMEs tell you to what’s really needed. It sounds good, but it’s a hard argument to an organization that’s been doing it in a particular way for a long time.

Yet, these institutions also struggle with retention of students.  The learners don’t find the experience relevant or engaging, and leave.  If you took the real activity, made it meaningful in the right way, learners would be both more engaged and have better outcomes, but it’s a hard story to comprehend, and perhaps harder yet to implement.

Yet I will maintain that it’s both doable, and necessary.  I think that the institution that grasps this, and focused on a killer learning experience, coupled with going the extra mile to learner success (analytics is showing to be a big help here), and developing them as learners (e.g, meta-learning skills) as well as performers, is going to have a defendable differentiator.

But then, I’m an optimist.