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In UK Study, Girls Best Boys At Making Computer Games
New submitter Esteanil writes Researchers in the University of Sussex's Informatics department asked pupils at a secondary school to design and program their own computer game using a new visual programming language. The young people, aged 12-13, spent eight weeks developing their own 3D role-playing games.
The girls in the classroom wrote more complex programs in their games than the boys and also learnt more about coding.
The girls used seven different triggers – almost twice as many as the boys – and were much more successful at creating complex scripts with two or more parts and conditional clauses.
Boys nearly always chose to trigger their scripts on when a character says something, which is the first and easiest trigger to learn.
...while the boys are focused on learning how to be seen and how to claim territory and space. Are we really surprised when the tables are turned later?
I like how it's been less than 10 days and already the editors did not think to link to the Barbie: Computer Engineer story, where she only thinks up a design and then has to go to the boys to get the coding done.
Ironic the fictional land of Barbie, with a supposedly positive message for girls about careers in tech, is more misogynistic than the reality it seeks to change.
I've been 12. From experience, no, girls are not more developed at that age. That's the age where girls and boys take off in different directions. Girls imitate more, which makes them look more developed (and more well behaved), because doing your own stuff isn't quite as impressive when you can hardly do anything yet. They're just different, not further along the development axis.
What, you have to do a complete double-blind study with thousands of people just to see if a hypothesis MIGHT be true? Or just to see what happens?
Here's how the real world works. You have a hypothesis. You design a test to try to test it (this is hard and you may inadvertently introduce an unknown that you're actually measuring). Next comes the expensive bit - data gathering.
But if your hypothesis isn't cut and dry and your experiment isn't necessarily well controlled (common most of the time because you're not testing stuff like "is the sky blue?" but more open-ended ones like "can girls write nicer than boys?"), you're not going to run a test on thousands because it costs too much. And you may find a flaw in your test that invalidates the whole result.
So you run a small scale test, and do your data gathering and analysis. Like say, you do it on a class. It won't be well controlled nor a population sample, but it will reveal several things. First, it will tell you if you're blowing smoke up your ass by having a hypothesis that's invalid. Second, it can help reveal issues with your data gathering. Third, it helps you do data analysis quickly - far easier to ensure your results are accurate when you're only analyzing 30 people versus 3000. The former is small enough that you can double-check your analysis programs with manual hand calculations.
Plenty of research fails because the expected hypothesis was wrong to begin with. Better to have done it and wasted a few thousand dollars than tens of thousands or more. If you're trying to present something to a grant committee, it's far easier to have small scale results that show promise than hand-waving "we thinks".
And yes, it's possible a larger scale study proves no significant difference. Which isn't a failure - it means you forgot to control a variable. Which means it's still a result and you analyze why you got the results you did that you didn't get int he later study.
And, a small scale test may even reveal more questions to ask so when you enlarge the dataset, you can do more analysis and maybe if your original hypothesis is invalid, you can still salvage something. Plenty of science was done by doing a study and "hey, that's kinda interesting..."
30 people may not be population representative, but it certainly produces a result worth studying further, no? You can even do secondary studies like what's the gender population at this level, and compare it a few years later. We always ask why there aren't more females in IT, and inevitably people say "why should we care?" If the population isn't as skewed in the early years, longer term studies can be performed that see why it gets skewed later on. Is it a structural problem, a societal problem, or is it even a problem with IT workers purposely discouraging females from joining? The first we can't do much about (in which case the "why should we bother" crowd is correct), the second we can look at, and the third points the blame solely at IT workers as the main problem (where perhaps it's "IT workers are a bunch of immature boy cliques that treat females as having the cooties").