10+ years later and this is still a crucial finding for achieving diversity in STEM -
"...encouraging women and girls to gain spatial experiences, such as computer usage, might help to bridge the gap in spatial ability between the sexes." (Terlecki & Newcombe, 2008) #my1stthread
Beginning of quote: "Path analyses showed that computer experience substantially mediates the gender difference in spatial ability observed on the MRT. These results collectively suggest that the “Digital Divide” is an important phenomenon and that.."
As a young girl I was always interested in computers and truly had a knack for it - but I never thought I was 'as good' as other people. When I got into college this continued, as I saw mainly white men in the computer sci dept and again, didn't think I was good enough.
While I love where I have ended up in my doctoral studies I wish I would have gotten the minor I wanted in computer science. Shaping my experiences now to become skilled in data science, I would have realized what I was capable of long ago 🤓
I hope one day I can help someone realize they are more than worthy of their STEM interests 💻🧠👩🏻🔬
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✨🧵✨ After that insane response to my job news - here’s a thread about how I ended up at my dream job w/Disney & an illustration of why an untraditional path to tech should never stop you (& is maybe better?):
When I was little I loved computers/math/science/the brain/language. My mom was a school psychologist so she always called me her little speech therapist (which makes sense bc to her that was a job that she knew of that involved language).
From the time I was 7yo I was that kid that stomped around saying ‘I’m going to be a speech pathologist!’ I still liked math/science/computers, but figured I could worry about that later.
I went to the 50th Anniversary of The Walt Disney Archives exhibit @BowersMuseum today! Here is a thread of some of the cool Disney history straight from @TWDCArchives (including the first ticket ever purchased at Disneyland!😱) :
I can't believe I used to avoid using code from tutorials. Please do NOT think this is plagiarizing if you're first starting out in data science. Once I started pulling bits+pieces from different tutorials for my personal projects, it was unbelievable how quickly I started 1/4
understanding concepts. By pulling a few lines here and there from different tutorials doing 'the same thing' I start to see what is actually happening in the code and adjust it accordingly for my use. 2/4
Oftentimes this results in something that doesn't even look like where the tutorial started.
Please also always be sure to give credit where credit is due + comment on tutorial pages. 3/4
Literally getting my phd at the only university designed for deaf and hard of hearing students in the world, all of my classes, lectures, comps, defenses, and proposals are in sign language and, even with almost four years’ experience with this...
I learn something new about this culture and language every. single. day. The depth and complexity is equivalent to that of any spoken language culture and, in my humble opinion, tends to be vastly more interesting.
I think bc it INSTANTLY challenges any person’s preconceived (unconscious) notion of what language is.