Cedric Chin Profile picture
Sep 8, 2021 24 tweets 7 min read Read on X
1/ Let's talk about accelerating expertise.

You want to get good. You want to get good fast. How do you do this?

In 2008 and 2009 the US Department of Defence convened two meetings on this very topic.

Here's what they found. (Hint: the answer is NOT deliberate practice). Image
2/ First: let's put ourselves in the position of a training program designer.

You are told that you need to get a bunch of novices to a good level of competency in 3 months.

It used to take them a year.

How do you do this?
3/ If you're like most people, you'd probably say "Ok, let's create a skill tree. Let's map out all the skills needed from most basic to most advanced. Then let's design a syllabus, where complex skills build on simpler skills."

In other words, you'd replicate school.
4/ Aaand you would have failed your assignment.

The researchers who the DoD consulted said, basically: "No. Stop. Get rid of all that."

Your approach, the mainstream pedagogical approach, is too slow.

But why is it too slow?
5/ For two reasons:

1. Teaching novices atomised skills means they will build incomplete mental models of a domain. At some point, these incomplete models will interfere with progress. They become knowledge shields. You now have more work: you will need to break those shields.
6/ But the second reason is more pernicious.

2. Experts are able to see connections between concepts that novices cannot. Teaching novices a hierarchy of skills usually prevents them from learning those linkages early.

In other words, they're likely to get stuck.

So: SLOWWWW.
7/ So what do you do?

Well, you cheat.

It turns out that if you can go to domain experts and EXTRACT their mental models of expertise, you can use those models for training!

This means you'll be able to train for what the experts ACTUALLY HAVE in their heads.
8/ The set of techniques that allow you to extract mental models of expertise is called 'Cognitive Task Analysis'. It's been around for 30 years now.

You know how experts can't really explain how they 'know' things? Yeah. CTA gets around that.
9/ I've written about CTA in the past. For instance, I helped @johncutlefish with some skill extraction a few weeks ago. You may read about that experience here: commoncog.com/blog/john-cutl…

And the most comprehensive book on it is this one: goodreads.com/book/show/4433…
10/ Anyway, back to accelerating expertise. So you now know there is this superpower called CTA. Well, how do these researchers use it?

The short answer is that they use it to create training simulations, so that students CONSTRUCT the mental model that the experts have.
11/ Here's how they do it:

1. They identify the domain experts.
2. They do CTA.
3. During CTA, they collect details of difficult cases to build a case library.
4. They turn that case library into a set of training simulations.
5. They sort the scenarios according to difficulty.
12/ The training simulations serve as the training program.

This is much better, because:

1. Good simulations have good cognitive fidelity to the real work task. Performance transfers.
2. There is no artificial atomisation of concepts! Learners must deal with full complexity!
13/ Ok, here's an example. Trigger warning: Afghanistan, IEDs, military. Skip ahead if necessary.

After 9/11 the US military had problems with IEDs. These were roadside bombs. Think: Hurt Locker. The DoD started spending a lot of money to detect and defeat IEDs. Image
14/ As part of that effort, the DoD commissioned a CTA. Apparently some of the Marines and Soldiers were able to detect IEDs. They would 'have a bad feeling', and take measures to avoid a danger zone.

The military wanted to know how. If they could extract, they could train.
15/ The group of NDM researchers quickly realised this was a bloody difficult skill domain. Think about it: Iraq is large. Within Iraq, different towns and even neighbourhoods had different IED tactics. And Afghanistan was different still.

Plus the enemy was constantly adapting.
16/ And they needed to extract something general. Something that would work regardless of where a young Marine was deployed.

Eventually they realised that the most skilled Marines were putting themselves in the insurgent's shoes.

They could think like an IED emplacer. Image
17/ Think about it: if you wanted to emplace an IED, how would you trigger it? Say you trigger wirelessly. You would need a spotter. You would need to know when the Marine convoy was near enough to the bomb.

So the insurgents would use a marker. Like a pole, or a rock formation.
18/ These were the cues the Marines were picking up on.

The researchers had successfully extracted this mental model of expertise. Now: how to train?

Ask yourself this: would you set up a Powerpoint presentation? A lecture of IED tactics?

That would be dumb.
19/ Here's what the researchers did: they took a video game that the military used for training (called VBS) and built a module for it.

The players had to play AS an insurgent.

They had to emplace IEDs and target blue team convoys. This is what one of the researchers said: ImageImage
20/ Note how rapid the training could be. Note how quickly you could enable the construction of the actual mental model.

Eventually, Marines and Soldiers would play a few scenarios before deployment. It saved lives.
21/ Let's wrap up. I've described an accelerated expertise training program, developed by applied researchers in military and industry contexts.

It is remarkably novel. I've written about some of the underlying theories before:

22/ And it's just scratching the surface. For a full summary, including some other uses of the research, read my blog post here: commoncog.com/blog/accelerat…
23/ Follow for more threads about expertise, business decision making, and so on.

Or subscribe to my newsletter if you don't want to miss out on longform essays here: commoncog.com/blog/subscribe…

Thank you for reading!
PS: If you want to learn CTA, you may sign up for a course here: cta.institute

It's run by the OG researchers who invented some of the techniques. 😊

One of them was involved with the IED project.

I've signed up, and I encourage you to do so too!

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More from @ejames_c

Apr 11
When @sjataylor and I started working on Xmrit together, one of the big questions we had was: why haven’t these methods spread outside of manufacturing?

In a previous life, Sam worked IN manufacturing. He was highly skeptical that it could be applied more generally.
“Except for healthcare” he joked, “in Six Sigma healthcare was always the pet example they rolled out when they wanted to say ‘Look! It’s used outside factories!’”
Well, the existence of (and my essays on Commoncog) is clear evidence that XmR charts ARE more broadly usable.

So what did we learn?

Xmrit.com
Read 16 tweets
Mar 24
I'm actually starting to suspect that the best manufacturers have more to teach software folk than vice versa.

This is not a strong opinion, and I want to retain the ability to revise it.

But manufacturing is harder, older, and lower margin.
This is, incidentally, the premise of an entire book: amazon.sg/Lessons-Titans…
But also I've been testing process control over the past 1.5 years, and the overwhelming reaction I get from software folk (after teaching them these methods) is "my god, we software engs are some of the least data driven folk out there."

70% gross margins hide a lot of sins.
Read 16 tweets
Mar 20
I think some operator folk shy away from writing publicly because they think “ugh, I don’t want to be an internet intellectual.”

But there’s actually a writing-related advantage from being a doer not a writer.
This is a tension that I’ve come up against a lot. @KrisAbdelmessih likes to say that I’m a ‘scientist trapped in an operator’s body’ — I take that as a compliment! — but there have also been folk who go “I think you’re a scholar not an operator” when they first find my writing.
And of course it’s not clear when you’re reading my writing that I’m one or the other — I, too, talk about frameworks and ideas and strategy, just like any other Internet blowhard, uh, I mean, business intellectual on the net.
Read 20 tweets
Feb 24
About four years ago, I started a series on tacit knowledge.

The latest entry was earlier this week: an interview with @stephen_zerfas. Stephen used some of the ideas in the series to accelerate his own software engineering expertise.
If you ask experts how they're able to do what they do, they'll often say something like "it just feels right."

So it's often not useful to ask 'why'.

Stephen used a model of expert intuition called Recognition Primed Decision Making, which gave him better questions. Image
(Ok that diagram of RPD is a bit much, which is why you'll probably want to read this: )commoncog.com/how-to-learn-t…
Read 13 tweets
Feb 3
I hope one of the implications of my Becoming Data Driven essay is clear: that you need to be able to act in order to establish causality.

e.g. you THINK X is a causal factor for Y, but the quickest way to verify is to go do X. Then stop. Then start again.
But I realise this is so easy to say but so hard to do for most org contexts.

Say for instance in order to do X you need sign off from multiple departments: product needs to know, engineering needs to do some work, marketing, etc on down — and this can be hard to get.
I was a little confused as to why so many folk were reaching out to me with notes on Judea Pearl’s ideas on Causal Inference … but then I realised: oh, these folk often don’t have the power to just DO X, to check for causality.

So they hope the solution is MORE analysis.
Read 6 tweets
Jan 31
A huge part of why I feel very strongly about ‘Becoming Data Driven’ is that I feel that data analysts aren’t well served by existing writing on the topic.

(The other part is that I’m a business nerd and want to get better at operating, but I think that’s obvious).
It strikes me that the bulk of online writing about data focuses on second order topics.

- modern data modeling
- the modern data stack
- should your data team be organised as a product or a service org?
- how to build data products

These are important, don’t get me wrong …
But they seem … not as important as “are your business users even getting value out of data?”

Or “is your business data driven?”

Or “what are politically savvy ways to get your org to climb to higher data maturity?”
Read 16 tweets

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