March 29, 2021

As part of continuing research, the Women in Tech Initiative would like to enlist students to show percentages and changes over time in the participation of women in tech fields, and specifically the participation and persistence of women on the Berkeley campus in various STEM majors. This semester, a new focus of the project aims to identify interventions to make STEM more equitable and inclusive for students from under-included groups.

Research will look at intersectionality and potential opportunities for effective interventions to retain diverse talent in STEM majors. Participants will define research questions, conduct in-depth analysis, and create compelling data visualizations. To conduct their research, students will be given access to work with protected datasets from CalAnswers, UCOP, and OPA.

Previous teams chose to analyze the relationship between socioeconomic status and educational outcomes in computer science. Other possible research questions may involve analyzing equity in the Berkeley STEM departments such as Chemistry and Physics, or evaluating the effectiveness of various university policies. At the end of the semester, the team will also have the opportunity to present their research to faculty.

About the Researcher:

Jill was a political economy major at Berkeley. Her first job outside Berkeley was in Ed-Tech. From there, she got a job at eBay where she was in-charge of the toys business, responsible for running the online marketing and product development for that unit. Tha brought her to the Internet space and ended up working for a foundation with social entrepreneurs who innovate and try to solve big issues around the world. She worked for a number of startups before running an incubator, at Singularity University, for startups using exponential technologies to solve global, grand challenges. She realized we need more women and people of color involved in solving all of these global grand challenges, making sure they have the opportunities to be exposed to and leverage these technologies and to build diverse teams. They can then develop these technologies and solve global problems as well.


What was the motivation to initiate this project? What was the pivotal moment when you decided to formulate this team?

So this was sort of a two part process, the very first reason I embarked on having a data discovery team was to get transparency, to understand the landscape, to understand what is the current state of women in tech at Berkeley, to be able to better understand where we might have opportunities for improvement. Through the course of that first project it raised a couple of really interesting issues and one in particular was around the requirement to have a certain grade point average to declare the computer science major. So the next data discovery team delved into that in much greater detail and we were able to present the findings that we had to a faculty advisory group that was re-evaluating the admissions programs for computer science. And there are a lot of factors that went into them re-evaluating that but we actually do feel like our research was very timely and contributed some good data and insights to help formulate a new proposal that was presented at the recent town hall.

Can you describe the phase of your project and what each phase was building up to? What were your expectations about the outcome or the challenges you were going to face, and what potential impact would this have on the admissions committee?

Yeah it's definitely been a learning process so for the first semester we didn't necessarily have all the data sets or the individual level data sets that we needed to assess persistence in the stem field, and so we learned a lot about the data sets. Fortunately, we had support from campus staff who had those accesses and we found out the processes to make sure that our students were trained and then we were given access to the level of data that allowed them to see some of these different insights. What's also been really great is I actually hired a student from the first data discovery team as a student employee to run the next discovery team, which provided continuity and helped create infrastructure to transfer knowledge from one data discovery team to the next. And so, we have a notion space, we have a github space where we're accumulating the research that has been done so that the next team can sort of take off, from where the last team ended, which has been really, really helpful. The other thing that came from the prior project was that one of the Faculty that we presented to was so impressed by the students’ work that he asked that there be a data discovery project to focus on the Jacobs Institute for design. They saw the value in the research and the analysis that the student did and that prompted them to want to do their own data discovery team. Now we're co-leading a second project that is leveraging similar research and data sets but on a more targeted department basis. I think that that's been one of the benefits of building on prior work. Now we have a better understanding of what's happening in computer science, but now we can look at Jacobs, we can look at chemistry, we can look at physics and applied math. We can start to see if the patterns and the issues that we saw in computer science are replicated or different in other departments, so that we can share the learnings and improve our understanding by looking at other departments. For example, we looked at there's a disproportionate A's for men in some of the stem courses, but there's also a disproportionate number of women in the social sciences getting A's. Giving you a more rounded perspective when you start to look rather than just looking at an isolated department you start looking at it in the context, not only compared to the larger university but also compared to other departments.

Could you elaborate on certain aspects of the project that you find to be the most challenging?

I think there's always a challenge of getting everybody up to speed and learning the data sets. There's a lot of tagging and nuance in data sets, but that is the purpose of the program, right? It’s to teach people what it's like to work with these large data sets, to understand the tagging, to understand how to clean up the data, how to look at your data with a skeptical eye. You can then say, “You know, intuitively this doesn't seem right, let me go back and look at my data. Maybe I left out a group!” Like the summer grading might be different from the fall and spring grading, so if you roll them all up together, you start to get some inconsistencies and how the data presents. There's a lot of learning around what's in your data set and what's not in your data set. There's going to be increasing challenges around gender identification because we're splitting from the binary where it's just male and female. Now people can opt out of giving that data, which makes it more difficult to compare over time if people don't provide that data. And so, I think there's a lot of nuance that comes from working with the data and it forces you to be more thoughtful about your hypotheses and your research questions, so I think that's something that we've been more focused on is getting very clear research questions.

And how has your experience been with discovery and your student researcher so far?

Oh they're fabulous. I'm always really pleased with their shared values, because people choose to do this project, and so we bring in a very diverse group of people to work on this project, but they are brought together on a shared interest and shared focus on wanting to improve equity and inclusion. And so, that's always been really great, and I think the students really appreciate this. Especially in this remote learning environment, having a smaller group of people that they can work with that they can collaborate with that they can discuss offline with. We run it sort of like an agile stand up meeting, so everybody kind of talks about where they are what they're working on, what problems they may have, and then the team can peer-to-peer help each other solve problems and find ways to kind of answer those questions. So, it's sort of like having a little built in mini focus group when you're asking these questions and so that's worked really, really well. We put together a presentation at the end, and I think that this is a tremendous thing, because it really showcases not only the work that they've done, but the insights that they garnered over the course of the Semester, and it allows them to present in an actionable way. So, it's not just charts and data, it's about ‘here's how this data might inform a policy or a decision’, ‘here's how this data could help a department understand where there are opportunities to improve inclusion’, so I think, having that presentation at the end, both gives students a sense of closure and also gives them almost like a portfolio or a project that they can share when they're applying for other research projects or when they're applying for jobs and gives them something very concrete and tangible to share.

That's always great to hear, so I think we already talked a little bit about the most rewarding part of working with students, but what is your greatest challenge?

Well, let me say one more thing on the rewarding side, which is you get ideas you would not have gotten on your own right like. As much as I can think about these things, and I can research, and I can look across working with the students, we surface questions that we wouldn't have otherwise done because of the conversations that we have. So, I think that that's one of the most rewarding pieces. The greatest challenge is time, right? Like people are spread fairly thinly with all of their other courses and midterms and finals and so what's nice about this project, though, is that it allows students to work at their time that works for them. You know, we just have one hour a week where we meet up together, but they can arrange their schedule and they can, you know, shift their priorities as they need to do. But yeah, not really a challenge for me, more a challenge for them. 

And how has discovery helped accelerate your project and what is it like for you to be a partner?

So, I am a unit of one, I have no staff so for me. As a person who runs an initiative to be able to tap into the energy and research skills of students, it has been really, really helpful because it allows me to do more right, it allows me to take on bigger projects. It allows me to, you know, engage in this two-way conversation where the students are learning for me, but I'm also gaining the insights and the knowledge that I need to advance these different initiatives, so for me it's really around adding to the capacity of what I can do. So, like I'm working currently, I also have an undergraduate research apprenticeship program. So, I'm working with students from that group to do things like write grants. What's nice is a lot of this research that the data discovery teams are doing can feed into the other efforts that I'm doing where we're applying for grants to support initiatives around these areas or could inform our decal courses on non traditional pathways into text. There's a nice dovetailing of what the data discovery teams are doing and what the initiative’s doing, so we can bring that learning and knowledge and it adds depth to the proposals that we're putting out there.