sfswift
commented
7 years ago Thanks for the feedback Tim! I looked up clickstream mapping - that's super cool, thanks for bring that up. I really like your suggestion at the end - showing starting data v completing data. It would definitely help to have, however I haven't been able to find any public data about intended majors.
I also struggled with the point you made about if the analysis is focused on gender bias or more general. I agree with you, a gender study being a single element of a larger project is would be great. I'm not sure I can reframe and incorporate both into this proposal, so I'll try to shift the focus more on the gender bias portion.
Cool project, Sullivan! In summary, you're looking at the course decisions students make (put together, their course paths) and trying to determine if specific course paths lead to a higher rate of success completing a STEM major. Additionally, you want to study the course demographics to find correlations with course success.
I liked your analysis idea of mapping the courses to find precisely when gender biases begin, based on how the demographics change from the very first semester through to graduation. I think everyone who has done a difficult major knows there are certain "weed out" classes, so better understanding those important milestone classes is important. This analysis technique reminded me of clickstream mapping - how website designers look at the path of users moving through their website in order to find frustration points were users fail to complete a transaction, for example.
I think you may want to decide early on if this analysis should be focused on gender bias specifically, or simply the general deflection away from STEM seen from all students - which, by itself is still a relevant topic. You may end up finding really great data that explains when students move away from a STEM track, but doesn't explain the trend of women in STEM specifically. A gender study could still be a single element of a larger, broader STEM course path study.
You may want to add some data that shows the starting % of women in STEM (pre-enrollment major intention) vs % of women completing majors. It would help your argument to show that the low % of women in STEM is not just due to the initial conditions, but due to attrition losses over the course of 4 years of school.