Saturday, July 04, 2015

On the different stages of learning and teaching (algorithms)

Descending a rabbit hole of links prompted by a MeFi discussion (thanks, +David Eppstein) of Steven Pinker's essay on the curse of knowledge (thanks, +Jeff Erickson), I came across an article by Alistair Cockburn on a learning framework inspired by aikido called 'Shu-Ha-Ri'.

In brief,

  • In the Shu stage, you're a beginning learner trying to find one way to solve a problem. It doesn't matter that there might be multiple ways. The goal is to learn one path, and learn it well. 
  • In the Ha stage, you understand one way well enough to realize its limits, and are ready to encounter many different strategies for reaching your goal. You might even begin to understand the pros and cons of these different approaches. In effect, you have detached from commitment to a single approach. 
  • In the Ri stage, you have "transcended" the individual strategies. You might use one, or another, or mix and match as needed. You'll create new paths as you need them, and move fluidly through the space of possibilities. 
Reading through this article while I ponder (yet again) my graduate algorithms class for the fall, I realize that this three-stage development process maps quite well to what we expect from undergraduates, masters students and Ph.D students learning about an area. 

The undergraduate is learning a tool for the first time (recurrence analysis say) and if they can understand the master theorem and apply it, that's pretty good. 

At the next level, they realize the limitations of the master theorem, and might learn about the Akra-Bazzi method, or annihilators, or even some probabilistic recurrence methods. 

Of course, once you're dealing with some thorny recurrence for the analysis in your next SODA submission, then the standard templates are helpful, but you'll often have to do something creative and nontrivial to wrestle the analysis into a form where it makes sense. 

Pick your own topic if you don't like recurrences. 

Which also explains why it's hard to explain how to prove things. Beginning students expect a standard formula (which is why induction and proof by contradiction get taught over and over). But once you go beyond this, there aren't really good templates. In effect, there's no good second level with a set of proof techniques that you can throw at most problems, which explains why students taking a grad algorithms class tend to struggle with exactly this step. 

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