One of the biggest final efforts in “Georgia Computes!” has been trying to get a measure of the whole state’s CS1/CS2 population. Who are they? Where did they come from? What influenced their decision to take a CS course? Did “Georgia Computes!” have any influence on them? Our third ICER2012 paper (available here) documents our effort to answer those questions.
Of the 35 colleges and universities in Georgia, 29 offer computer science coursework, and 19 participated in our statewide survey. (Why only 19 or 29? Great question, and worthy of another study in itself.) In total, 1,434 introductory computer science students (in either a first or second semester course, but all in the same semester without duplication of students) completed the survey. Our analysis had three parts:
General description of who’s taking CS and why An attempt to answer the question, “Did Georgia Computes have an effect?”Regression analysis on what variables impact decisions to pursue computing.
The general description required a GT vs. non-GT lens. 673 of the students in the survey came from Georgia Tech, and most of those were not CS majors, since GT requires everyone to take CS1. When GT is included, the pool is 31% female, but without GT, it’s only 25% female. Most of the pool had no interest in CS in middle or high school, but the percent expressing interest rises dramatically when you take GT out (since there are so many non-majors being forced to take CS at GT). Having some middle school out-of-school computing experience is pretty much the same with GT (57%) or without GT (56%) which is somewhat surprising. Only 56% of students who ended up as CS majors (not at GT) did anything with CS in middle school? Even larger percentage 57% of students (at GT, thus part of the “required” and “not likely to be CS majors” cohort) had some middle school CS, but did not choose a CS major? One explanation might be that GT is a prestigious school and the kids who go there (CS majors or not) had more out-of-school experiences in general.
We did ask students that if they were NOT a computing major, what were the reasons? Here were the top three answers I don’t want to do the kind of work that a computing major/minor leads to, 30%. I don’t enjoy computing courses, 20% I don’t think I belong in computing (don’t fit the stereotype), 13%. In general, GaComputes out-of-school activities were not mentioned by many students. Girl Scout events and summer camps are still too small in Georgia to touch a significant percentage of students who end up in CS. A big part of our analysis was figuring out if the students may have been influenced by a teacher who had professional development through Barbara’s Institute for Computing Education (ICE). We asked every student what high school they went to, then deciphered their scrawl, and figured out if we had an ICE teacher there. (We didn’t try to figure out if the student actually interacted with that teacher.) Yes, in general, schools that have ICE teachers do produce more women in our CS1/CS2 data set and more under-represented minorities (in some categories), but neither is a significant difference. Right direction, not not enough to make a strong claim.
Finally, we looked at what influenced student interest in pursuing computing career, disaggregated by gender and race/ethnicity. There were several statistically significant differences that we noted, like men are more interested in computer games and programming than women, and women are more interested in using computing to help people or society. These aren’t new, but at the size and scope of the survey, it’s an important replication. Most interesting is the mediation analysis that Tom McKlin and Shelly Engelman did. They found that women and under-represented minorities are statistically more influenced by encouragement and a sense of belonging than by a sense of ability, compared to men and white/Asian groups, with outcome variables of (a) satisfaction in choosing to study computing, (b) likelihood in completing a computing major/minor, and (c) likelihood of pursuing a career in computing. Again, these are expected results, but it’s useful to get a large, broad replication.
As I said before, we’re getting to the end of “Georgia Computes!” This was one of our last big analysis efforts. It’s really hard to do these kinds of studies (e.g., each of those school that did not participate still got our time and effort in trying to convince them, then there’s the data cleaning and analysis and…). I’m glad that we got this snapshot, but wish that we got it at an even larger scale and more regularly. That would be useful for us to use as a yardstick over time. (NSF BPC funded “Georgia Computes!”. All the claims and opinions here are mine and my colleagues’, not necessarily those of any of the funders.)