Archives for September 2014

I’ve been on quite the roll of late, calling out some bad practices and calling for learning science. And it occurs to me that there could be some pushback.  So let me be clear, I strongly suggest that the types of learning that are needed are not info dump and knowledge test,  by and large.  What does that mean? Let’s break it down.

First, let me suggest that what’s going to make a difference to organizations is not better fact-remembering. There are times when fact remembering is needed, such as medical vocabulary (my go-to example). When that needs to happen, tarted up drill-and-kill (e.g .quiz show templates, etc)  are the way to do it.   Getting people to remember rote facts or arbitrary things (like part names) is very difficult. And largely unnecessary if people can look it up, e.g. the information is  in the world  (or can be).  There are some things that need to be known cold, e.g. emergency procedures, hence the tremendous emphasis on drills  in aviation and  the military. Other than that, put it in the world, not the head.  Look up tables, info sheets, etc are the solution.    And I’ll argue that the need for this is less than 5-10% of the time.

So what  is  useful?  I’ll argue that what is useful is making better decisions.  That is, the ability to explain what’s happened and react, or predict what will happen and make the right choice as as consequence.  This comes from model-based reasoning.  What sort of learning helps model-based reasoning? Two types, in a simple framework. You need to process the models to help them be comprehended, and use them in context to make decisions with the consequences providing feedback.  Yes, there likely will be some content presentation, but it’s not  everything, and  instead is the core model with examples of how it plays out in context. That is, annotated diagrams or narrated animations for the models; comic books, cartoons, or videos for the examples.  Media, not bullet points.

The processing that helps make models stick includes having learners generate products: giving them data or outcomes and having them develop explanatory models. They can produce summary charts and tables that serve as decision aids. They can create syntheses and recommendations.  This really leads to internalization and ownership, but it may be more time-consuming than worthwhile. The other approach is to have learners make predictions using the models, explaining things.  Worst case, they can answer questions about what this model implies in particular contexts.  So this is a knowledge question, but not a “is this an X or a Y”, but rather “you have to achieve Z, would you use approach X, or approach Y”.

Most importantly, you need people to use the models to make decisions like they’ll be making in the workplace.  That means scenarios and simulations.  Yes, a mini-scenario of one question is essentially a multiple choice (though better written with a context and a decision), but really things tend to be bundled up, and you at least need branching scenarios. A series of these might be enough if the task isn’t too complex, but if it’s somewhat complex, it might be worth creating a model-based simulation and giving the learners lots of goals with it (read: serious game).

And, don’t forget, if it matters (and why are you bothering if it doesn’t), you need to practice until they can’t get it wrong.  And you need to be facilitating reflection.  The alternatives to the right answer should reflect ways learners often go wrong, and address them individually. “No, that’s not correct, try again” is a really rude way to respond to learner actions.  Connect their actions to the model!

What this also implies is that learning is much more practice than content presentation.  Presenting content and drilling knowledge (particularly in about an 80/20 ratio), is essentially a waste of time.  Meaningful practice should be more than half the time.  And you should consider putting the practice up front and driving them to the content, as opposed to presenting the content first.  Make the task make the content meaningful.

Yes, I’m making these numbers up, but they’re a framework for thinking. You should be having lots of meaningful practice.  There’s essentially  no role for bullet points or prose and simplistic quizzes, very little role for tarted up quizzes, and lots of role for media on the content side and  branching scenarios and model-driven interactions on the interaction side.  This kind of is an inverse of the tools and outputs I see.  Hence my continuing campaign for better learning.  Make sense?

In other fields of endeavors, there is a science behind the approaches.  In civil engineering, it’s the properties of materials.  In aviation, it’s aeronautical engineering.  In medicine, it’s medical science.  If you’re going to be a professional in your field, you have to know the science.  So, two questions: is there a science of learning, and is it used.  The answers appear to be yes and no.  And yet, if you’re going to be a learning designer or engineer, you should know the science and be using it.

There is a science of learning, and it’s increasingly easy to find.  That’s the premise behind the Serious eLearning Manifesto, for instance (read it, sign it, use it!).  You could read Julie Dirksen’s  Design for How People Learn  as a very good interpretation of the science.  The Pittsburgh Science of Learning Center is compiling research to provide guidance about learning if you want a fuller scientific treatment.  Or read Bransford, et al’s summary of the science of  How People Learn,  a very rich overview.  And Hess & Saxberg’s recent  Breakthrough Leadership in the Digital Age: Using Learning Science to Reboot Schooling  is both a call for why and some guidance on how.

Among the things we know are that  rote and abstract information isn’t retained, knowledge test doesn’t mean ability to do, getting it right once doesn’t mean it’s known, the list goes on.  Yet, somehow, we see elearning tools like ‘click to learn more’ (er, less), tarted up quiz show templates to drill knowledge, easy ways to take content and add quizzes to them, and more.  We see elearning that’s arbitrary info dump and simplistic knowledge test.  Which will have a negligible impact on anything meaningful.

We’re focused on speed and cost efficiencies, not on learning outcomes, and that’s not professional.  Look, if you’re going to do design, do it right.    Anything less is really  malpractice!

Last week I had the opportunity to attend the inaugural meeting of the Global Learning Council.  While not really global in either sense (little representation from overseas nor from segments other than higher ed), it was a chance to refresh myself in some rigor around learning sciences. And one thing that struck me was folks talking about learning engineering.

If we take the analogy from regular science and engineering, we are talking about taking the research from the learning sciences, and applying it to the design of solutions.  And this sounds like a good thing, with some caveats.  When talking about the Serious eLearning Manifesto, for example, we’re talking about principles that should be embedded in  your learning design approach.

While the intention was not to provide coverage of learning science, several points emerged at one point or another as research-based outcomes to be desired. For one, the value of models in learning.  Another was, of course, the value of spacing practice. The list goes on.  The focus of the engineering, however, is different.

While it wasn’t an explicit topic of the talk, it emerged in several side conversations, but the focus is on design processes and tools that increase the likelihood of creating  effective learning practices.  This includes doing a suitable job of creating aligned outcomes through processes of working with SMEs, identifying misconceptions to be addressed, ensuring activities are designed  that have learners appropriately processing and applying information, appropriate spread of examples, and more.

Of course, developing an accurate course for any topic is a thorough exercise.  Which is desirable, but not always pragmatic.  While the full rigor of science would go as far as adaptive intelligent tutoring systems, the amount of work to do so can be prohibitive under pragmatic constraints.  It takes a high importance and large potential audience to do this for other than research purposes.

In other cases, we use heuristics.  Sometimes we go too far; so just dumping information and adding a quiz is often seen, though that’s got little likelihood of having any impact.  Even if we do create an appropriate practice, we might only have learners practice until they get it right, not until they can’t get it wrong.

Finding the balance point is an ongoing effort. I reckon that the elements of good design is a starting point, but you need processes that are manageable, repeatable, and scalable.  You need structures to help, including representations  that have support for identifying key elements and make it difficult to ignore the important elements.  You ideally have aligned tools that make it easy to do the right things.

And if this is what Learning Engineering can be, systematically applying learning science to design, I reckon there’s also a study of learning science engineering, aligning not just the learning, but the design process, with how we think, work, and learn.  And maybe then there’s a learning architecture as well – where just as an architect  designs the basic look and feel of the halls &  rooms and the engineers build them – that designs the curriculum approach and the pedagogy, but the learning engineers follow through on those principles for developing courses.

Is learning engineering an alternative to  instructional design?   I’m wondering if the focus on engineering rather than design (applied science, rather than art) and learning rather than instruction (outcomes, not process), is a better characterization.  What do you think?