We are thinking of skill learning as depending on sequence learning in which a set of actions are chained together into a fluid, well-performed execution sequence.  In SISL, like Guitar Hero, the interface steps you through a specific sequence (repeatedly) and you become better at the sequence.  The William James idea of water carving an increasingly deeper trench captures the idea, although you have to assume deeper = better here.

That’s all well and good for skills you learn by stepping through the goal sequence and can improve by simple repetition.  But a friend recently suggested to me that in some skills, there are a lot of possible paths and some of these are inefficient meaning that practicing them won’t help much.  You’ll get better at a  bad strategy, but you’ll never become expert.  He proposed that golf is an example.  There are too many ways to swing a club to hit a ball and just hitting it repeatedly willy-nilly won’t make you a scratch golfer.

I think this is the idea behind why Ward & Ericsson talk about “deliberate practice.”  Either through external coaching or explicit learning and top-down control, you need to guide yourself through the right sequence and then you can practice it.  In addition to the right/wrong strategy, this might also reduce variance helpfully by guiding you to repeat the same sequence.

The “problem space” concept is a way or representing possible strategic paths from your start to your goal state.  In SISL & GH, the path is highly linear.  In a game like chess, you have up to dozens of possible moves at each state, so if you try to map out the possible paths, you get a very bushy problem space.  The degree to which you need “deliberate practice” or coaching or top-down deduction to guide your sequence learning is likely determined by how bushy the problem space is — how easy is it to find the ideal path to solution.

This may not sound very random, but wait: Starcraft 2!

Starcarft 2 is the latest version of the real-time strategy multi-player competition video game from Blizzard.  I’m currently playing it with my boys and therefore have the opportunity to subjectively observe the skill learning process.  Because it’s competitive and you play against other players, Blizzard works very hard to rate your play and try to match you up with appropriate opposition.  Since we are hoping to learn, we expect to see this reflected in increasingly difficult opponents (and increasing rating).

It is also obvious that pure willy-nilly practice does not guarantee robust learning in SC2.  There are tons of people in the lowest division (with me) with records like 500 wins, 500 losses.  One thousand games and they haven’t been promoted yet.  Why not?  Probably because it’s a fantastically complicated problem space with dozens of major strategic decisions, timing decisions, adjustment decisions.  Without some top-down control or coaching, it’s going to take roughly forever to hone the correct sequences.

Coaching?  In a video game?  Yes, and here’s where it gets really random.  SC2 is nearly a major sport in Korea, but gamers all over the world are following the Global Starcraft League Season 3 competition in which some of the best players in the world compete for about ~$175,000 in prizes.

One way they cover the prize money is that they charge to watch the replays with expert commentary at GOMtv (http://www.gomtv.net/2010gslopens3/).  Why pay to see the experts play explained?  So we can get that top-down control.  And also root for Jinro, the one non-Korean in the final field of 32 (which the American commentators seem to do).

Commented SC2 games are available in a number of places including youtube, e.g., http://www.youtube.com/user/ArtosisTV.

An embedded example: