Ten little steps, and how One grew

This entry is part 3 of 21 in the series Ten Steps to Complex Learning (the book).

You’ve probably said to yourself, “Is obair-latha tòiseachadh.” No?  Maybe you’ll agree that getting started is a day’s work.  Part of what I’ve worked on (or fretted about) the past few days was how to move from the overview chapters in Ten Steps to Complex Learning to those deal.ing with the actual steps.

So here’s a chart I made, showing the four components for complex learning (boxes on the left), and the steps aligning with each component.  Remember, the application of the steps is not necessarily linear, but the diagram is, and the discussion will probably be.

Blueprint components and the ten steps

Whew.  On to Step 1…

Admit it: sometimes you think it’s the content.

Van Merriënboer and Kirschner claim, maybe a bit simplistically, that “traditional” instructional design starts with the subject matter and adds practice items.  The Ten Steps, in contrast, start with “whole-task practice tasks” which become the backbone for everything else.

“Whole-task practice tasks” isn’t a felicitous phrase, but by now the idea is clear: meaningful tasks that look like a complete element within the overall complex job.  “Interview a job candidate” is a meaningful task; “ask open and closed questions,” in my opinion, is not–it’s one of those constituent skills that make sense to the learner in the context of the whole task (interviewing).

We’re talking about complex learning here.  In real life, it’s hard to figure out a complex problem; it’s hard to be sure the solution will work.  In fact, recognized experts will disagree about the best solution.  (How do you prevent traffic jams?  How do you unravel an existing jam?)

Using real-life tasks as the basis for learning tasks…[confronts] learners with…the constituent skills that make up complex task performance….it [engages] the learners in activities that directly involve them with the constituent skills…as opposed to activities in which they have to study general information about or related to the skills.

So the French were right:  en forgeant, on devient forgeron. By working at smithing, you become a blacksmith.  You sometimes see this translated as “practice makes perfect,” but that’s not the case.  Practice can reinforce what you’re doing, but on its own doesn’t guarantee you’re doing the right things, let alone doing them right.

vM&K advocate that the learning tasks you design put learning before performance.  By that they mean: have learners focus their attention on the cognitive processes for learning, rather than simply on performing the tasks.

I think that may be one of the biggest challenges in this model.  We’re not accustomed to thinking about how we learn.  We’re not used to stepping back and watching ourselves as we perform.  This isn’t going to please the training-as-sheepdip crowd.

For people who actually want to encourage complex learning, though, the fact that it’s not simple isn’t a deterrent.  They want to know ways to make whole-task learning effective– like by changing the environment in which they perform the tasks, and by providing support and guidance.

Environments: getting real about simulation

What?  No screen shots?  No programming?You can learn many complex skills in the real work environment.  But often, the on-the-job setting hinders learning.  You can’t provide the necessary support (no expert available; no place to put her; she has no time).  You can’t present the right task at the right time (because useful Problem X doesn’t occur predictably).  It’s expensive, inefficient, or dangerous for someone to learn on the job. Finally, the amount of detail in the real-life setting can overwhelm the novice.

Thus, simulation.  And I’ll bet you’re thinking of high-tech machinery or immersive online environments.  Not bad, but not the only way to go.  vM&K argue that simulations can differ from real life in two important ways–physically (looks like the real job) and psychologically (feels like the real job).  And it’s the psychological fidelity that’s more important.  A setting with too much physical realism often provides “seductive detail” that distracts the learner.

That explains why you hate those fatheaded computer simulations where you have to walk through doorways and press elevator buttons and open doors.  You already know how to do that stuff–what you want to know how to do is do a real-life complex task like manage a project or negotiate with a vendor.

Support with the problem, guidance with the process

To design effective learning tasks, you need not only the real-life problem with its situation and details, but also an acceptable solution and, ideally, the problem-solving process that generated the solution.  This means you’ve got a worked example (which provides the task support) and the process-related information (the guidance).

vM&K give an example task: controlling air traffic.  Worked examples  might include radar and voice information about a particular problem situation (the “given”), similar information showing a safe resolution of the problem (the goal), and actions necessary to reach or maintain safety (the acceptable solutions).

Guidance helps the learner approach the problem through useful approaches and heuristics–for example, strategies to help reach or maintain safe air traffic situations.

Setting aside the process stuff for a bit, what you have is a kind of high-level recipe for learning tasks.  Each task has a given state, a goal, and one or more acceptable solutions.  Varying those elements gives you different types of tasks:

  • A reverse task presents a goal and an acceptable solution.  The learner has to figure out the given.  (The images on your web page, which looked fine yesterday [example A], are messed up and look like this [example B].  How come?)
  • An imitation task give you a case study (given, goal, solution) and a “conventional task” (given and goal); you work from the case study to solve the new problem.  (If you need to sell this to clients, it’s “case-based reasoning.”)
  • Tasks with non-specific goals push learners to explore the problem area–to move past solving the immediate problem and think about how problems get solved.  In vM&K’s ongoing research example, the learner receives a research question and a highly specific goal.  The task: come up with as many research queries as possible that could be relevant, and make those queries.
  • Completion tasks provide the givens, criteria for a goal state, and partial solutions.  These require learners to study the partial solutions.  Completion tasks, the authors claim, are especially useful in design-oriented task areas.

All these approaches encourage the learner to think about the problem, the solution, and useful steps.  That means they’re also abstracting like crazy, mining the solutions and using induction to build cognitive frameworks.

For those complex, non-recurrent skills, this means more than lots of the same type of practice.  “The bottom line is that having students solve many problems on their own is often not the best thing for teaching them problem solving.”

Or: extra work doesn’t make you better at math; it just makes for extra work.

Speaking of work, there’s enough of it in this post.  I’m not quite done with Step One, and so the next post in this series will touch on tools for problem solving, guidance, and induction.

CC-licensed mockup of computer app by striatic.

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