Mindset Post: The Efficient Learning Curve

I really like graphs, both the data structure and the run-of-the-mill, let’s put some stuff on some axes kind of graphs, so when I hear about learning curves, I think about something like this:

stuff graph

So let’s graph our topic of discussion. Here’s the “Steep Learning Curve”:

steep learning

I don’t hear about the other kind of learning curve very often, but I suppose it’s a “shallow learning curve”, and it looks like this:

shallow learning

Boy, look at the difference! In the first graph, after 18 times, whatever those are, you have almost 800 learnings! In the second graph, you only have about 21 learnings. Sadface.

Bogus units aside, this leads me to an interesting observation: Why do we talk about steep learning curves as if they were bad? Doesn’t the first one look more fun?

Here’s the thing: Learning is good. Time is limited. In every other situation where we graph our ability to attain something good against our expenditure of something limited, steep curves are good. We never talk about a revenue curve that’s “too steep”. (Darn! The graph of this investment’s value over time curves too steeply upward. Better sell!) We never talk about an acceleration curve that’s too steep. (My car just accelerates too quickly! I’m going to trade it in for a ’98 Ford Taurus with a shallower acceleration curve.) So why does the learning curve get picked on? Why is it the only “good stuff over time or effort” type graph that’s not allowed to be steep?

I think it’s because a lot of people label the x-axis wrong. They label it “effort” or “how much this will suck” or “parties I will have to miss while studying.” But if I can convey only one message with my blog, this is it: Learning is fun, and learning about computers and how they work and how to change how they work is especially fun. So let’s throw out our pessimistic x-axis labels and get to work. We can label the x-axis “hours of sustained concentration” if you want. Now steep curves are back to being efficient, not bad.

There’s something I like to say to myself, when I am working on something that’s hard, like taking an algorithms class, or trying to get a really tricky piece of code to behave. My saying is: “You are now entering an area of highly efficient learning.” Doesn’t that sound nicer than the steep learning curve?

My point is not to beat your head against the most difficult thing you can find; rather, my point is to start looking for opportunities to master something that’s a little beyond you. Sometimes, at the beginning of an online course or a programming book, I can tell that I’ll need to learn a whole new way of thinking to get through the course. That’s when I get excited! I say to myself, “You sir, have just entered an area of highly efficient learning! Clear out your schedule, and focus hard! This is an all-you-can-eat buffet of interesting thoughts and creativity!”

Occasionally, after this burst of enthusiasm, I discover that I really do need to calm down and study something easier for a bit. (For example, I once signed up for a hard physics course without taking the math prerequisites. That didn’t work.) But most of the time, this burst of enthusiasm is followed by really learning a lot, and then feeling very happy with what I’ve accomplished.

What was the last book, blog post, online course, or whatever, that you took, that gave you the feeling that you were going to learn exponentially fast to get through it? Post your answers in the comments!

Till next week, happy learning,

-Will

4 thoughts on “Mindset Post: The Efficient Learning Curve”

  1. […] This spring, I’m following along with Eric Lippert as he implements the virtual machine layer for playing Zork. He will be using OCaml. I will be using C#, partly because I don’t want to be able to copy and paste from Mr. Lippert’s masterful code, and partly because if I use a a language I don’t know at all to do something completely new, the learning curve might be a little too efficient. […]

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