Clusters, chains, and part-task sequencing

This entry is part 6 of 21 in the series Ten Steps to Complex Learning (the book).

(Note: this is a continuation of the previous post in this series.
Together they deal with Step 2, sequencing the task classes.)

Clusters, chains, and parts

It's not just an exhibition; it's a complex.“In exceptional cases,” van Merriënboer and Kirschner say in Step 2 of Ten Steps to Complex Learning, “it might not be possible to find a task class easy enough to start the training with.”  They’re thinking of very complex training–like, say, complete programs for doctors or pilots. “If you are not dealing with such an exceptional case, you may skip this section.”

Thanks, guys.

I’m extending this discussion of Step 2  ( “sequence the task classes” ) because, while vM&K say part-task sequencing is an exception to usual practice, I’ve used some of the techniques in less-than-complex situations.

Parts: on the whole, they’re incomplete

“Part-task sequencing” has a special meaning in the Ten Steps approach.  It refers to how you decide which clusters of constituent skills to deal with, in which order, because you see the overall skill–the whole task–as too complex to confront a learner with.

A cluster is a group of interrelated constituent skills that make up an authentic real-life task, even if it’s not the complete task.  If you think of “diagnosing a patient” as the whole task, one cluster might involve conducting physical exams while another might involve reviewing test results and reports from exams done by others.  Each cluster makes sense on its own (it’s something a physician would do in the real world); each is a part of the whole task of diagnosis.

I’m feeling a little sympathy for vM&K.  There’s lots of terminology like “task” that can apply at different levels.  The authors end up with lots of phrases like “whole task” and “part-task sequencing.”  I suppose  the alternative is to try nailing a term to a particular level.  Good luck.

One advantage to clusters is that they reduce difficulty: the learner has fewer things to attend to.  The tradeoff is that the clusters hinder integration (since you’re leaving out some of the skills) and limit opportunities to coordinate the skills.

In a sense, that coordination is headquarters for “the whole is more than the sum of the parts.”  The complex task is not simply what you get when you add up its constituent skills; you also have to retrieve and deploy combinations of those skills–coordinate them–on the job.

Linking the tasks

Imagine the job of a first-level supervisor.  For the sake of example, we’ll look at the managerial parts of the job (as opposed to industry-specific ones).  As a supervisor/manager/leader, you’ve got whole tasks like:

  • Maintain a skilled team (e.g., make sure people have or acquire necessary skills; make sure they get to develop them)
  • Manage the performance of your team
  • Assess the performance of your team (monitor, provide feedback, conduct performance reviews)

You might choose different clusters, and so might I.  You’ll likely agree that each of the three bullets at least implies a related group of skills, and that each cluster might itself be so complex as to need additional breaking down.  Concentrating on one of these clusters at a time, and training within that cluster, is what part-task sequencing means.

And, yes, you’ll still have to integrate the clusters eventually.  Yes, that’s going to take time and cost money.  Complexity is complex.

In forward chaining, you address the constituent skills in a logical order, typically the way they’re performed.  Take the task of “managing the performance of your team.”  In forward chaining, you might work through these constituent skills:

  • Communicate requirements and standards
  • Monitor individual performance
  • Discuss performance with individual
  • Document results of discussion

Backward chaining works in the opposite direction; you’d train tasks in this order:

  • Document results of discussion
  • Discuss performance with individual
  • Monitor individual performance
  • Communicate requirements and standards

With either method, you can use snowballing.  You combine each subsequent task with what’s been taught before.  The forward version would have task classes like these:

  • “Communicate” tasks
  • “Communicate and monitor” tasks
  • “Communicate, monitor, and discuss” tasks
  • “Communicate, monitor, discuss, and document” tasks

Van Merriënboer and Kirshner believe the most effective combination is backward chaining with snowballing.  Since you start near the completion of the task, you need to provide the learner with outcomes from earlier stages not yet covered:

  • “Document” tasks (given: the outcome of “communicate, monitor, and discuss”)
  • “Discuss and document” given: the outcome of “communicate and monitor”)
  • “Monitor, discuss, and document” tasks (given: the outcome of “communicate”)
  • “Communicate, monitor, discuss, and document” tasks

This last approach, they argue, should be the default mode for part-task sequencing, which in turn is an exception reserved for only highly complex whole tasks.  As for the other combinations:

  • Backward chaining without snowballing is what you do when you lack time for including snowballing.
  • Forward chaining with snowballing is only for part-task practice
  • Forward chaining without snowballing is for part-task practice when you lack time.

“Part-task practice” is another specialized term; it’s Step 10 of the Ten Steps, and it’s meant to develop automaticity for recurrent tasks.  That means the procedural stuff you do the same way each time.  It’s how you learned your times tables; it’s how you practice scales on the cello.

Two more combinations

Earlier we talked about whole-task sequencing: each task class presents the whole task, with the early task classes providing simple examples and the late ones providing complex examples.  This post looked at part-task sequencing, something you do when the whole task is too complex for a learner to tackle.  So, naturally,

Whole-task sequencing and part-task sequencing may be combined in two ways, namely whole-part sequencing and part-whole sequencing.

See why I broke Step 2 into two posts?

It’s not as bad as it sounds–if I understand vM&K.  Here’s what I think they’re talking about.  Remember that constituent task for the supervisor, “Manage performance of your team?’  Its four skills (communicate, monitor, discuss, document) might apply in two contexts: coaching someone to improve current performance, and counseling someone to correct a deficiency.  Or “how to do even better” versus “you need to do better.”)

Here’s how I think that works on a whole-task class for the “manage performance” skill.  This class is made up of relatively simple, straightforward situations.  Individual task problems may involve coaching or may involve counseling.

  • First skill cluster: documenting skill. Learning via backward chaining and snowballing, the supervisor is asked to document various simple counseling and coaching situations.   (The givens include the outcomes of communicating, monitoring, and discussing.)
  • Second cluster:  the supervisor would now works on problems that involve discussing performance with an employee, then documenting the discussion. (Givens: communicating and monitoring.)  Again, some are coaching situations, some are counseling.

And so on.  This is whole-part because the class involves the whole task (“managing performance”); within the class, we sequence by parts of the performance.

vM&K say you could also have part-whole sequencing.  I take that to mean, in terms of this example, that you have a class where you document easy cases, then medium ones, then difficult ones, and then move on to a class where you discuss and document, and so on.

Since their clear preference is for whole-part (if you have to have part-task sequencing), and since they didn’t provide any examples of their own, I’m going to tiptoe away from Step 2.

Next in the series: Step 3, Setting Performance Objectives.

CC-licensed image of Vasarely works by notalike.

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