Pragmatic Thinking and Learning
Page 24
head and walks back to their own office. To save some wear and
tear on the carpet (and your co-worker), we suggest you place a
surrogate—a yellow rubber duck, for instance—near your screen
and, when you get stuck, just “talk to the duck.”
Another useful approach is to try to explain your
material to a child or someone outside your field
of expertise. The trick is to do so in terms they
can understand. This is a great opportunity to
explain what you really do for a living to your great
Aunt Edna, and it’s a great exercise to start to see
things from your audience’s point of view and to develop metaphors
that will help explain and clarify the material you’re working with.
You may be surprised by what you learn and what insights come
to you during the process.
Finally, you can reach out and try to teach a larger, more respon-
sive audience. Start by offering to give a talk at a local user group
meeting, or pen an article for a newsletter or magazine. There’s
nothing like the prospect of a lot of bright people picking at your
every word to help clarify your thinking. And that’s the real bene-
fit to teaching in general; it clarifies your own understanding and
reveals many of your underlying assumptions.
Remember the medical school mantra:
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See it. Do it. Teach it.
As I mentioned earlier, constant retrieval is very effective for learn-
ing. Having to “go back to the well” while preparing to teach, and
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TAKE IT TO THE STREETS
192
while having to think on your feet to respond to questions, all helps
strengthen your neural connections.
6.11 Take It to the Streets
So far, we’ve seen the Dreyfus model and seen what it means to be
an expert. I’ve shown you some of the myriad wonders of the brain,
including an entire half that’s probably underutilized.
In this chapter, we’ve taken a good look at what learning is—
and what it is not. We’ve looked at using SMART objectives and
a Pragmatic Investment Plan and some specific techniques includ-
ing reading techniques, mind maps, and learning by teaching.
But all this learning is only part of the equation; next we need to
look at putting learning into action and see the best ways to gain
experience. We’ll play with that in the next chapter.
In the meanwhile, it’s time to begin to take it to the streets—to leave
the relatively safe cloisters of the cubicle and begin to interact with
the world to advance your personal learning.
Next Actions
! Take a new topic, and try to teach it to a co-worker or relative.
What did you learn from teaching—and the preparation for
teaching?
! If you haven’t been going to a local user group, start going.
Java, Ruby, and Linux groups are plentiful, but you might
also find groups devoted to Delphi, agile or XP development,
OOP, specific vendor products, and more.
! Listen carefully to the speakers. Make a mind map of the topic
area. What would you add to it? What would you do differ-
ently? Write up a critique for the group based on your mind
map.
! Contact the organizers, and offer to speak on your topic for
an upcoming meeting.
! If that’s not comfortable for you, then write an article on your
topic or blog on it.
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We should be careful to get out of an experience only
the wisdom that is in it and stop there; lest we be like
the cat that sits on a hot stove-lid; he will never sit on a
hot stove-lid again—and that is well; but also he will
never sit on a cold one anymore.
Mark Twain
Chapter 7
Gain Experience
Gaining experience is key to learning and growth—we learn best by
doing.
However, just “doing” alone is no guarantee of success; you have
to learn from the doing for it to count, and it turns out that some
common obstacles make this hard. You can’t force it either; trying
too hard can be just as bad (if not worse) than slogging through the
same old motions.
In this chapter, we’ll look at how to make each experience count.
We’ll see how to do the following:
• Build to learn, not learn to build.
• Fail efficiently with better feedback.
• Groove your neural pathways for success.
That is, we’ll take a look at some key aspects to real-world learning
and then see what you need to create an efficient learning environ-
ment for yourself. After that, we’ll take a look at how to get better
feedback—to avoid the issues of Mark Twain’s overly generalizing
cat (in this chapter’s opening epigraph). Finally, we’ll finish up with
an interesting approach to gain experience virtually.
7.1 Play in Order to Learn
Your brain is designed such that you need to explore and build
mental models on your own. You’re not really designed to passively
sit by and try to store received knowledge. There’s a time and a
place for both of these activities, but in the normal course of events,
we get it wrong: exploring, or “playing with,” the material should
come before studying facts.
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194
We seem to have a cultural tendency to put the cart before the
horse: you struggle to shovel in information first and then hope
to maybe use it later. That’s the basis of most formal education
and corporate training. But the real world doesn’t work that way.
For instance, imagine you were taking a dance class, only to find
you had to pass a test on “dance facts” before actually dancing.
Sounds absurd when I put it that way, doesn’t it? Seymour Papert
thinks so.
Papert is perhaps the leading expert on using technology to cre-
ate new ways of learning.1 He invented the programming language
Logo: a “toy” that children could play with and, in the playing,
learn deep mathematical concepts. His early work with Logo led
to the LEGO Mindstorms robotic toys, named for his hugely influ-
ential book, Mindstorms: Children, Computers, and Powerful Ideas
[Pap93]. Papert worked with world-renowned Swiss psychologist
Jean Piaget and also believed that real learning—the learning that
sticks with you—comes from experience and cognition, not from
explicit teaching or rote practice. Their approach is called construc-
tivism: we build to learn, not learn to build.
He designed the Logo language expressly to provide an environ-
ment where children could learn math concepts via direct experi-
ence by commanding a virtual “turtle” to move around and trace
pattern
s on a virtual canvas. The young, grade-school students
learned geometry, trig, and even recursive algorithms. When kids
got stuck on a problem, they were told to imagine themselves as
the turtle and walk through their own instructions from the tur-
tle’s perspective. By changing their viewpoint to that of the turtle,
the students could leverage their existing real-world knowledge of
walking, turning, and so on, to explore the microworld of the tur-
tle. That’s an important point: structuring learning so that you can
build on top of existing experience.
The Meanings of Play
As I’m using it here, the first meaning of the word play is similar
to what we’ve talked about earlier in the book, in the sense of non-
goal-directed exploration. We’re not really designed to just receive
information but rather to explore and build mental models on our
1.
Papert and Marvin Minsky founded the Artificial Intelligence Lab at MIT; he also was one of the founders of the famed MIT Media Lab.
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PLAY IN ORDER TO LEARN
195
own. We need to be able to poke at a problem, to explore it, or to
“get used to it” (as we talked about back in Section 4.3, Engage
an R-mode to L-mode Flow, on page 92). Playing with a problem
doesn’t make the problem any easier, but it gets us closer to how
we’re wired to learn.
Of course, in this sort of environment,
you’ll make mistakes. As a student, you’re Real life has no
not being led down the garden path of the curriculum.
“one right answer” according to the cur-
riculum. As in real life, there is no curriculum. You’ll make mis-
takes; it will get messy. But those messes give you exactly the kind
of feedback you need.
Mind maps get better the more you play with them (Section 6.8,
Visualize Insight with Mind Maps, on page 181). With a mind map,
looking for opportunities to annotate, decorate, and draw relation-
ships helps you gain insight. This is an extension of that idea—a
more active engagement, playing directly with the ideas or technol-
ogy in question, not sure what you’ll find, but looking to see how
you can extend them, relate them, and so on.
The second sense of the word play introduces a sense of whimsy,
or dare I say, fun.
I was on a business trip last week, and the flight attendant gave a
little twist to the usual boring preflight speech: the entire speech,
including the canned, legally specified parts, was set in a Dr.
Seuss–style rhyme. From proper use of the seat belt to the dire
warnings about disabling the smoke detector in the lavatory2 to
proper handling of the oxygen masks and life rafts, it all rhymed
in a well-orchestrated meter. And for a change, people actually lis-
tened to the announcement. It was a novel presentation and was
very engaging—you listened closely to see where she was headed
with the talk, anticipating the stress and rhyme.
Because it was fun, the presentation was
much more effective. Normally, no one Fun is OK.
pays any attention to the standard talk.
Everyone is busy reading the Airline Catalog of Useless Merchan-
dise or already dozing off. But a fun speech changes the game.
2.
Which begs the question, shouldn’t there be stiff penalties for “disabling or destroying” any part of the aircraft, not just the smoke detector? But I digress....
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196
Smart People and Dumb People
I think most people are a lot more capable than they
give themselves credit for. Papert noted that we tend to
sort people (including ourselves) into two categories: smart
people and dumb people. We’re confident that smart
people have all the answers on a clipboard, dressed in
their crisp white lab coats. Dumb people drive the car in
front of us on the highway.
That’s a grotesque oversimplification, of course. Remem-
ber that the Dreyfus model is a model per skill, not per per-
son. The world isn’t filled with smart people and dumb peo-
ple; it’s filled with smart lab researchers and dumb drivers,
smart cooks and dumb politicians.
But regardless of any specific skill deficiencies, in general
we are amazing learning machines. Consider how much
a young child has to absorb in a short space of time: lan-
guage, motor skills, social interaction, the effectiveness of
a well-timed tantrum, and so on. We don’t bombard two-
or three-year-olds with vocabulary drills or make them dia-
gram sentences to understand grammar. Instead, you just
point to the toy and say “ducky,” and they get it. Ducky
swims. Ducky is yellow. We can grok a lot without explicit
training or exercises.
One of the definitions of fun, according to my dictionary on the
Mac, is “playful behavior.”
That doesn’t mean that it’s easy, non-business-like, or not effec-
tive. In fact, Papert notes that his students called their work fun
because it was hard, not in spite of being hard. It’s hard fun: not
so hard as to be insurmountable (and so not engaging) but chal-
lenging enough to maintain interest and progress at solving the
problem domain.
Working with new material or solving a problem
in a playful manner makes it more enjoyable, but
it also makes it easier to learn. Don’t be afraid of
fun.
Make a game of it—literally. Create flash cards, or invent a card or
board game; use tinker-toys or Lego blocks to act out the scenario.
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For example, you could create a board game that simulates visitors
to a website. Where do they go next when they land on a random
square? What if they never pass Go or go to Home?
I mentioned using Lego blocks for design back in Chapter 4, Get
in Your Right Mind, on page 85 for the same reason: the idea is
to engage as much of your entire being in the learning process:
verbal, visual, musical, numerical, gross-motor body movement,
fine-motor finger movement, and so on. All of that helps you to
really play with the material and learn it more effectively.
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Play more in order to learn more.
Next Actions
! On your next problem, put yourself in the picture. Anthropo-
morphism helps leverage experience.
! Explore and get used to a problem before diving into the facts.
Come back to more exploration after absorbing the formal
facts. Then go back to exploration; it’s a continuous cycle.
! Play, in every sense of the word.
7.2 Leverage Existing Knowledge
Papert was careful to allow students to leverage existing knowledge
in skills in their learning of new skills. We do this all the time,
sometimes consciously, sometimes less so.
When faced with a sticky problem, there
are a couple of classic approaches you’ll Try mind-size bites.
probably take. First, can you break the
problem down into smaller, more manageable parts? This sort of
functional decomposition is bread-and-butter to software develop-
ers: break things down into mind-size bites. The other very popular
approach to take is to look for any similar problems you may have
solved previously. Is this problem like some other? Can you use
a similar solution or adapt the other solution to match this new
problem?
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LEVERAGE EXISTING KNOWLEDGE
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Problem Solving with George Pólya
To solve a problem, ask yourself these questions:
• What are the unknown aspects?
• What do you know? What data do you have?
• What constraints and what rules apply?
Then make a plan, execute it, and review the results. Some
of the techniques Pólya suggests might sound familiar:
• Try to think of a familiar problem having the same or
similar unknowns.
• Draw a picture.
• Solve a related or simpler problem; drop some con-
straints or use a subset of the data.
• Were all the data and constraints used? If not, why
not?
• Try restating the problem.
• Try working backward from the unknown toward the
data.
George Pólya wrote a very influential book on concrete steps to
problem solving that covers these and other classic techniques
(How to Solve It: A New Aspect of Mathematical Method [PC85];
see the sidebar on the current page for a brief synopsis).
One of Pólya’s key bits of advice is to look for similarities to pre-
vious solutions: if you don’t know this, do you know how to solve
something similar? Maybe the similarity is literal (this is just like
a bug I saw last week), or maybe it’s metaphorical (this database
works just like a fistful of water). In a similar manner, Papert’s stu-
dents were able to leverage their existing, tacit knowledge of body