Relating natural language aptitude to differences in learning programming
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Re: Relating natural language aptitude to differences in learning programming
#2I had always hypothesized that numeracy is, besides being generally useful of itself, mostly useful to programmers as application domain knowledge rather than as an indicator of learning programming concepts or programming languages. That said, an awful lot of certain problem domains are heavy in mathematics.
Re: Relating natural language aptitude to differences in learning programming
#3I had always hypothesized that numeracy is, besides being generally useful of itself, mostly useful to programmers as application domain knowledge rather than as an indicator of learning programming concepts or programming languages. That said, an awful lot of certain problem domains are heavy in mathematics.
(This is armchair psychologist speculation...)
I wonder if numeracy is actually a correlated factor to the propensity for modeling systems, which is very much relevant to learning to program in any programming language. In my anecdotal experience, being fluid with numbers often comes from having a bias towards thinking about the world in terms of numerical relationships.
Re: Relating natural language aptitude to differences in learning programming
#4The scatter plots seem quite spread out. If you removed one outlier person from the numeracy plot it would be much more correlated. Maybe that person is a special case somehow.
I wonder if there is any formalized way in statistical hypothesis testing to quantify how much the conclusions could be changed by removing at most k (e.g. k=1) number of datapoints.
Re: Relating natural language aptitude to differences in learning programming
#5The scatter plots seem quite spread out. If you removed one outlier person from the numeracy plot it would be much more correlated. Maybe that person is a special case somehow. I wonder if there is any formalized way in statistical hypothesis testing to quantify how much the conclusions could be changed by removing at most k (e.g. k=1) number of datapoints.
Good point but the solution is not stats for how much the results depend on k data points. The solution is to have a much larger sample. N=36 clearly does not support far reaching generalizations.
Re: Relating natural language aptitude to differences in learning programming
#6I don't understand how an n=36 study got into Nature in this day and age.
Re: Relating natural language aptitude to differences in learning programming
#7The scatter plots seem quite spread out. If you removed one outlier person from the numeracy plot it would be much more correlated. Maybe that person is a special case somehow. I wonder if there is any formalized way in statistical hypothesis testing to quantify how much the conclusions could be changed by removing at most k (e.g. k=1) number of datapoints.
That's actually pretty typical for behavioral science scatter plots. Getting p>.95 on a regression line is not something that is obvious from looking at the raw data.
Re: Relating natural language aptitude to differences in learning programming
#8I'm curious as to whether the correlation between natural language aptitude and programming aptitude runs both ways.
Re: Relating natural language aptitude to differences in learning programming
#9The scatter plots seem quite spread out. If you removed one outlier person from the numeracy plot it would be much more correlated. Maybe that person is a special case somehow. I wonder if there is any formalized way in statistical hypothesis testing to quantify how much the conclusions could be changed by removing at most k (e.g. k=1) number of datapoints.
It is suspicious that they had so many with 0 correct answers out of 8. Here are the 4 easier ones of the questions:
> If the chance of getting a disease is 10%, how many people would be expected to get the disease? Out of 1000?
> If the chance of getting a disease is 20 out of 100, this would be the same as having a _____% chance of getting the disease.
> Imagine that we roll a fair, six‐sided die 1000 times. Out of 1000 rolls, how many times do you think the die would come up as an even number?
> In the BIG BUCKS LOTTERY, the chances of winning a $10.00 prize are 1%. What is your best guess about how many people would win a $10.00 prize if 1000 people each buy a single ticket from BIG BUCKS?
https://onlinelibrary.wiley.com/doi/10.1002/bdm.1751
I guess a lot of people never learn what % mean, and since basically all questions are related to % they would fail just from that. So I'm not sure if this is a good test of numeracy, feels like it relies too much on a few key items.
Edit: Also that test was designed so it gave a normal distribution for a typical population, in this study we see a lot of 0's and then no 1's and 2's. So it feels like the population tested wasn't representative, or some of the students asked just didn't bother with the math questions. Since students are forced to participate I wouldn't be surprised if a few of them just skipped trying at all on the math parts.
Re: Relating natural language aptitude to differences in learning programming
#10> one participant was excluded because he was an extreme outlier in learning rate (>3 sd away from the mean).
Can anyone explain why did they do this? Was it just to prove their hypothesis? I bet this guy had really high numeracy scores as well.
Edit: They also botched some of the numbers:
> (Fig. 1A: mean learning rate = 1.25, range = 0.81–2.0, sd = 0.24)
The 2.0 point is not included in Fig. 1A, the highest learning rate is 15 lessons over 10 sessions or 1.5, so they included the outlier in that range but removed it in the plots.
Also I don't find it unreasonable that a person completed 20 lessons compared to the others 15, so I don't see why this was so extreme that he had to be removed. You could argue that the later lessons are harder so it was unreasonable, but then you just confessed that the learning rate metric is not linear so your regression models doesn't work since it assumes that going from .5 to .6 is equal to going from 1.5 to 1.6! Even though as we can see from the plot many of the best learners got stuck at lesson 16 for an entire session without making progress, while the slowest learners took 10 sessions to reach roughly the level these fast learners reached after the first. That is almost a 10x difference, but this plot shows it as just 2.5x.