redesigning workflows...

[kirby urner]

Hi Ted --

I'm renaming the subject given we may have exhausted talking

about tensors for now. 

I think we've agreed they have an honored place as a capstone
somewhere above the K-12 level.  We're open to universal

algebra and happy to continue with that program.

My perspective is I'm coaching code schools to rally around

some standards that will allow us to collaborate, given the

demand showing signs of seriously exceed supply, and
given we need to role model collaboration as how serious
engineers get work done. 

We hate unnecessary redundancy as much as the next guy
("guy" in a gender-neutral tone) and the idea of each guy in
a silo, reinventing the wheel, drives us crazy as what a waste
that is, what a dumb way to work.  Any management system

that insults our intelligence can't be immune to reform.

I have some caveats, about the place for secrets, as in "secret
sauce" and all that.  This topic comes up in the second panel
discussion I mentioned, where Google Brain gets a lot of
focus.[1]

Open source is not entirely incompatible with holding back in
some cases.  Richard Stallman is free to use a more restrictive
agreement regarding confidentiality with his hand-picked set
of cronies and friends, even if most his work is freely GNU. 

I'm not saying he'd wanna do that (count me as a fan by the
way, one of my heroes), just that he's free to do so, and our
notion of freedom includes that (the freedom to first help our
friends, our teammates, those upon whom we may mostly
depend).

On Fri, Apr 29, 2016 at 2:07 PM, Ted Kosan <ted....@gmail.com> wrote:

Kirby wrote:

> Apropos of what you're saying, I recommend this panel discussion of
> statisticians and data scientists, asking themselves 'what's the
> difference?'
> (between these two disciplines) and where are we going with education.
>
> https://youtu.be/C1zMUjHOLr4
> Data Science and Statistics: different worlds?
>
> From the panelists, we learn that many with programming skills are
> indeed attracted to data science but may feel intimidated or scared off
> by the statistics aspect, with statistics categorized as a type of math.

This was an excellent discussion. One thing I gathered from it is if
traditional statistic teachers don't modernize their approach ASAP,
they are in danger of having their teaching duties taken over by
people who understand statistics and computer programming. I think
this danger exists for all math teachers.

I'm glad you enjoyed it.  I've been encouraging a similar workflow

on Phys-L, where we brainstorm about physics pedagogy.  I was

invited onto that listserv by one Dr. Robert Fuller, based in the

midwest and highly acclaimed as one of the more effective physics

teachers out there.  I've blogged about him quite a bit.

Anyway, some several months, over a year ago maybe, on
Phys-L, I learned discovered a certain Canadian series of

physics cartoons which has proved really polarizing, with these

teachers taking issue with how Work & Entropy come across

especially.  I was sorry their opinion was so negative as the

use of cartoons (anime) is a big draw in my case, a preferred

way to learn.

My attitude in response is similar to Maria's:  OK fine, it may
be useful to criticize X, Y or Z but lets also find examples of
what we like and approve of.  If those cartoons about Work
& Entropy do not meet your high standards, then lets search
high and low for ones that do! 

I'm not proposing this just to "be nice" but to find some standards
we might agree on.  That's useful work!  In the same spirit I'm

encouraging teachers here to circle / bookmark / share what

jumps out as stellar for them.  Example:  I liked that vimeo of

a lambda track teacher sharing Pyret with Girl Scouts. [2]

The panel discussion above is one of those I think we might
agree is really helpful, if one is hoping to piece the puzzle
together, regarding the state of various subjects.  Where
"Data Science" meets "Statistics" is a fruitful vein, a seam
to mine.  Many jewels of wisdom tumble out when we dive

into this conversation.

 

> I think math teachers might also want to seize the initiative and
> insist on professional development courses that are (a) more
> future-oriented and (b) empowering of these same teachers
> vis-a-vis developing their own curriculum.

In another post you had this brilliant insight:

"As soon as high school math teachers are computer literate, they go
for the higher paying job (in aggregate, I'm not saying there aren't
exceptions)."

I think most math teachers who were interested in learning how to
program computers have already done so, and (as you said) have moved
on to better paying jobs. 

... here's the thing though:  every day, new math teachers wander

in.  It's not like people suddenly stopped becoming math teachers.

Many are idealistic, fresh out of school, open minded, full of high

hopes.  They'd like to stay in teaching.  If they'd wanted jobs in IT,

they'd have trained for that.  Their math skills may be good to excellent.

So here I am, across a canyon, not a college professor, no longer

a math teacher per se, and I'm calling out:  "cross the swinging
bridge, come over to my castle for a tour of duty, learn our ways,
and then go back to your cities and villages empowered with new
insights, new toolz, new toyz!"  ("toyz" spelled with a z because
that's cool, like e-toyz). 

And really it's not that scary, what I'm suggesting. 

The "swinging bridge" is named Tillikum Crossing and its strong
enough to take trains (no private cars though).

The "castle" is made of brick, true, and is perched on a hill, but

there's no dragon and no troll.  Just Python @ <guild />. :-D 
So why are these bright and eager math teachers not encouraged
by their own administrations to save their own careers?  The
drawbridge is down.  We have cookies and soda pop.

 

For the remaining math teachers who have
not learned computer programming yet, it is probably too late to do
so. For years I have tried to figure out if the math/programming
teachers of the future would be math teachers who learned how to
program or programmers who learned how to teach math. Your insightful
post, and the discussion you linked to above, have made it clear to me
that it is programmers who will learn how to teach math who will be
the math/programming teachers of the future.

Ted

Lets remember that in Alan Turing's day, a computer was a person

who came to work at Bletchley Park, likely female, to crunch numbers

all day. 

The idea that a computer might be a machine, running thousands of
detailed steps with no errors, in the blink of an eye, was but a
dream in the minds of cognoscenti and digerati, such as Ada Byron
and those who believed in The Turk. [1]

Turing and his ilk were soon to change all that, in the name of

cracking codes, and to this day our computer sciences owe a lot

to cryptography, another art and science.  Yes, there's lots of math

involved, but as any cyber-security expert will tell you, there's so

much else besides.

What is a person to do today, when the drudgery of perfect accuracy,

the need to get every step right or get fired, when that stress and

pressure has been removed? 

All we need do is get it right one time, using test data with known
answers, lots of proof that the algorithm we've scripted is just what
we imagine it to be.  We code that jewel, add it to the treasury, and

count ourselves that much richer, that much better positioned to

solve the problems that we face.  We have piled up a wealth of

powerful algorithms just itching to be used for the betterment of

humankind.

To what problems shall we apply our algorithms?  That's what the

humans need to figure out.  The machines are fairly neutral about

whether we use our algorithms to play games all day, or try to

feed people or what.  Games as simulations have a lot to tell us

about what it might take to tackle the problem for real.  Billions get

spent on war games for this reason.  World Game was about doing

the same thing, but with more optimism (world domination is more

fun than just blowing it to bits, which takes no genius whatsoever).

Anyway, I'd say there's no shortage of problems to work on.  They've
never been in short supply.  The challenge is matching them up with

the appropriate simulations and algorithms.  Lets use our new lambda

track to build these abilities!

Kirby

[kirby urner]

... and my footnotes.


[1]

https://youtu.be/czLI3oLDe8M
Deep Learning: Intelligence from Big Data

(learn about Google Brain)

[2]
https://vimeo.com/163949506  (about 30 mins --
gives good insights into some of the newer
e-toys 'n tools).

Another one by the same Eric, watching it now...
https://vimeo.com/145672921