The language brain matters more for programming than the math brain? (2020)

Isn't math somewhat a language in of itself? If not, a language has surely derived from math.

Hold your horses. The summary of that article does not offer any proof of what the title says.

* It's a small sample, and they did not analyze the people who didn't complete the course. That's dubious. Those 6 could have had a massive influence on the outcome.

* The summary does not present the actual numbers. These are: "fluid reasoning and working-memory capacity explained 34% of the variance, followed by language aptitude (17%), resting-state EEG power in beta and low-gamma bands (10%), and numeracy (2%)". Note: numeracy, not math.

* The test result was only partially programming related. 50% consisted of the results of a multiple choice test with questions such as What does the “str()” method do?. Linguistic knowledge indeed.

* It's about completing a 7.5 hour Python course. That's learning indeed, but only the very beginning, where abstraction is not in play. The early phase is about welding bits of syntax into working order.

* The numeracy skills required are very low for such tasks, as the tasks are simple, and mainly require thinking in steps and loops, whereas numeracy aptitude is generally measured on rather problems involving fractions.

Edit: the paper uses the Rasch-Based Numeracy Scale for this, which seems to involve estimation and probabilities.

* 17% explained variance is a rather minimal result, and you cannot easily compare factors in such a small design, even if the other one is only 2%. That's a rather hairy statistical undertaking.

* Linguistic expedience might be explain the speed with which the course was followed, since the instruction is, obviously, linguistic. Hence, this factor is not necessarily related to the actual learning or programming.

* The argument from beta waves is clutching at straws.

* The argument that "perhaps women should have more of a reputation for being “good” at programming" because they score better on tests, is --however well meant-- utterly ridiculous. It reverses correlation to causation and then turns that into a fact.

* That linguistic skills are useful for programmers is widely understood. However, this is not because of the actual coding, but because the coder needs to understand the environment, the specs, the users, etc., all of which is transferred via language.

* And of course, the statistical result relies on Null Hypothesis Test Significance, which is rotten in its very foundations.

* Note that the CodeAcademy course "Learn Python 3" is 23 hours in 14 lessons.

Truly great art, the kind that expands the field of artistry and makes people think, requires creativity; if you make something that's just a rehashing of existing art, that's not truly creative, it's boring and derivative.

This has nothing to do with whether a human or AI created the art, and I don't think it's controversial to say that AI-generated art is derivative; the models are literally trained to mimic existing artwork.

> I mean is it any surprise kids get bored in math?

Indeed. Kids did not evolve to learn things because they would be useful years down the road. But they did evolve to inhale knowledge at very high rates wherever it extends their capabilities in the moment.

My take on this is that math should be tied to kids crafting, experimenting, and learned directly in the context of its fun and creative uses.

Geometry screams out to be developed within a context of design, crafting, art and physical puzzles. Algebra, trigonometry, calculus ... they all have direct (and fun) uses, especially at the introduction stage.

The availability for graphics software, sim worlds, 3D printing, etc. should make math, from the simplest to most advanced, more fun and immediately applicable than ever.

Boredom with math is a failure to design education for human beings.

(Then there is the worst crime of all - moving kids through math in cohorts, pushing individuals both faster and slower than they are able to absorb it. Profound failure by design.)

When I did my CS degree in New Zealand there were just two mandatory maths papers - statistics and discrete mathematics. Would be wrong to say I didn't get anything from them - but I'm not fumbling around truth tables or poisson distributions all that often either. Everything else was pretty standard: intro to programming, DSA, low level programming, compilers and networks. What I do find kind of mind blowing is comparing my lectures with the ones from MIT and CM (on YouTube) where they can't go more than a few seconds without jumping into math. Ultimately I'm left unconvinced I was deprived of anything important as a typical software engineer.