This is the first part in a series outlining how I determined the leading factors that determine undergraduate introductory Computer Science experience by gender.
I once listened to an episode of Star Talk Radio with Neil deGrasse Tyson titled “The Future of Humanity with Elon Musk.” Ten minutes into the interview, Musk talks about having sophomoric philosophical wanderings as a student in college where he spent his time musing about the five things that would MOST affect the future of humanity. He thought they were the Internet, sustainable energy, artificial intelligence, rewriting human genetics, and space exploration. Bill Nye, who was also on the show suggested that he would add one more, “the education of women and girls.”
Before I delve into the issue of educating women and girls, I will make a quick detour into the importance of retraining. What are my talking about? Am talking about self-driving trucks!!! Recently, Uber debuted its truck which autonomously drove across the country on a beer run. Take a look at the two maps below. The map on the left shows the results of the 2016 Presidential Election, while the one on the right shows the most common jobs in each state. Some have said President-elect Trump won because of the loss of jobs and general neglect of America’s rural areas. I happen to be one of those that believes this hypothesis. If I am correct, then self-driving trucks are going to destabilize the US even more. Except for Utah and Florida, the most common job in the states that voted for Trump is Truck Driver!.
Once the Otto Uber beer run happened, like all the other techies out there, I was thrilled!!!! But then the reality set in. I thought we would have a generation before this piece of AI would go into production. I thought there would be enough time for us to figure out how to retrain adults en masse for the new economy jobs. Turns out that wasn’t true. The future is here.
As automation continues to gain ground, so too are the new industries it helps to create. This new era is creating a new kind of worker, the highly-skilled knowledge worker, in particular, the highly-skilled technology knowledge worker.
This shift in the workforce towards highly skilled, technical knowledge workers poses a challenge on the supply side; mostly because of a lack of presence of computer science in K-12 education; the under-production of post-secondary degrees in computer science; the underrepresentation of women and/or the underrepresentation of ethnic minorities. Which brings me back to Bill Nye the science guy.
I think of this problem as a big-data opportunity where we can kill two birds with one stone. We can scale adult technical education for workforce readiness while leveraging that opportunity to equalize participation.
As Internet adoption increases, so too will be the opportunity to leverage online education to close the gap between the genders, particularly in emerging countries. A solid understanding of the factors that determine women’s participation in computer science can help guide how we design these future learning environments. This project is the start of my journey into understanding those factors.
As part of my doctoral study, I decided to investigate the socio-curricular factors that affect the decision to participate in introductory computer science through a data-driven lens. To do this, I designed a research study examining the role of computer science self-identity centered around the experiences of undergraduates in two introductory computer science classes at UC Berkeley. Once that study was completed, I didn’t stop, I decided to ask new questions of the data. Specifically what were the leading factors that made female students choose intro CS? With that, the project I titled IntroCSExperiencePrediction was born.Share