Uh Welcome to the exploring data driven advocacy session here at the Women Tech Global Conference.We're super excited to have you and today we'll be talking a lot about how we can uh explore data driven a and use data to empower uh people of diverse backgrounds as well as departments and changing the culture around underrepresented students. So, to guide us through this, uh here is our agenda for the day which can be split split broadly across who we are our data and our findings and how we can really translate data into action through data driven advocacy starting with who we are. I think it's really important that when we're talking about data, we disclose who we are, what our mission is and why we're doing this. It's important for you as a consumer of data to know where your data is coming from. So, without further ado who are we um to introduce myself? My name is Stephanie and I'm the CEO of the percentage project. I'm also a software engineer at Amazon Robotics

and I'm Luna, I'm the chief outreach officer at the percentage project and I'm also a software engineer at so

uh we are part of the percentage project, an organization, a 501 C three nonprofit dedicated to helping every person of every background, embrace the diversity and feel a sense of belonging in the field that they pursue. I've highlighted two key parts of this message. The first is to embrace your diversity, which is our differences and what makes us special, what makes us unique. And the second, a sense of belonging uh which is to include everyone, to bring people together and to feel that you truly belong. And some might argue that these feel like opposing forces,

apologies.

Some may argue that these feel like opposing forces. But I want to make it clear that these are not mutually exclusive rather they are two sides of the same coin. We want to bring people together while also making feel people included, but not through a raio of their identity or pretending that we don't see color or we don't see gender. It's through understanding their backgrounds, their life and who they truly are as a person. And that is what true acceptance and belonging means to us. And that's our guiding mission for today. So we'll talk a little bit about our approach. Um Today, we'll be talking about, you know, our approach to data driven ay at the percentage project and we can define this approach in three prongs. The first is students, we believe that students should have an active say in shaping their educational experience. And so they should be in formed. And how do we inform them every year, we work with student leaders to collect climate data on their campuses. So we go beyond, you know, what students are of demographic. We try to delve deeper and learn about how they actually feel around their peers in their class, their professors and within their community. The second prong is departments. It is absolutely critical that the data that we collect helps to drive concrete progress.

And so we keep believe in keeping departments up to date, detailed analysis of their climates, their campuses and giving individualized data reports and dashboards to help them identify key areas and challenges uh within their departments and help fuel data driven decisions that can actually alleviate and uh the current circumstancess and eventually close the gap.

Finally, it's community. Uh We believe that we are stronger together and our focus is on connecting campuses from all over to advocate for equity in computer science. It's in this vein that we also aggregate all the data from all the different participating campuses that we have every year to release a benchmark. Uh And this is where we have an aggregate benchmark that each school can use to then compare their own personal and individual school data um A towards the benchmark and then drive accountability in technology education. So uh to get started, we're gonna show you firsthand uh a little bit about what we do um and the questions that we ask and the data that we collect and analyze. So if you can um and those who are participating in the chat, if you could join slider dot com on your phones. Um and just enter this number 8056092. I'm gonna get a couple um a couple moments for everyone to just join and check it out and then you should be able to interact with a

poll. Bye bye.

Yeah. It sounds like we could probably uh move to the next piece. So if anyone really uh still needs it, the slide uh slide number is still on the side of the of the screen. So now you should see on your phone that the poll is open. Um note that this is anonymous. So don't worry, you can, you can put whatever you want here. Um We won't know who, who said what just like in our survey. Um So the question here is sometimes I feel or believe that my success in life or in my job has been the result of some kind of error. So it would be great if um you guys could uh give it a shot and answer ra rarely or never, sometimes or often or always

like we have some response couple coming in for rarely or never and then sometimes

Awesome. I'm just gonna give another moment for anyone who hasn't been able to participate yet just to participate. OK, great. So we can see now, uh Right now, I think we have a couple of participants and it looks like about two thirds field, rarely or never and a third field sometimes. OK. So this is kind of our mini poll or mini survey within this group. Let's check out what the stats were for um across 1500 students from this past uh survey year. Awesome. So you can see here that about 53% said rarely or never, about 22% said sometimes and about 24% said often or always. Um So this is our summer statistic. You can see that this is data that's just across all of our survey participants for the past year. And um and the data isn't broken down any further. So let's actually now go and break this down a little bit further. So you can see here uh same graph on the on the left side, uh the summary statistic that we had now on the right. You can see us actually breaking it down across a specific demographic in this case, gender. So you can see here uh Now we can start to analyze the differences that uh we're noticing across students on various campuses that have worked with us.

So you can see here that for those who identified as men, um 18% said often are always versus 27% for those who identified as women. And 44% for those who identified as non binary, a similar kind of trend or trajectory with uh the sometimes, which is for starts at 19.76% for those identified as men, 25.64% for women and 22% for those identified as non binary. And so you can see how this this trend occurs and you can see kind of the gaps that start to form across different demographic groups. So let's try this again and, and do another another poll. So if you go back onto your phone and check out um the question is I often compare my ability to those around me and think that they may be more intelligent than I am. So give, give the pole a little shot. OK, great. So we can see here that right now we're looking at something like 50% sometimes 50% often or always. Um So let's just go and check out the data that we got from the survey. So you can see here that often, it always is about 60.9% sometimes 22.7% and rarely or never was 16.4%. And again, let's break this down into a demographic. In this case, let's break it down again into gender.

And so you can see here that often or always for uh those who identify as men has been 50.59% versus women, which is 69.27% versus non binary, which is 76%. So you can see a gap from the lowest to the highest percentage is around 26% points. Um And then the gap between men and women uh here is around 19%. So very large gaps here in um in feeling whether or not that you're comparing yourself and your own abilities to others and feeling that other people are more intelligent or being worried that other people are more intelligent than you. Uh And this is a classic question that's part of the actual uh test that people do for uh um for first checking if they uh if they're worried about other people's uh beliefs or, or feelings for them. So this is actually a question that's on in a lot of research as well. So next slide,

yeah. So now we kind of move on to dive a little bit more into our data. I know you've seen some of it already. Um But I'll talk more in depth about our methodology and then kind of see um more questions that we survey across different demographics. Um So first discussing kind of our methodology um when it comes to data collection methodology is really important. While statistical biases always exist, we do make it a priority to collect good data. Um So quick summary of kind of our key points around methodology. First, we make sure that um it's specific to all students majoring or minor in computer related a computer related discipline. Um And like we mentioned already, all survey responses are fully confidential and participation is voluntary. And then you might see this in some of our graphs we show later. Um but to prevent any risk of identifying participants, any demographic groups that make up less than 1% of the survey population are excluded from the aggregate analysis. Our survey data is filtered with spam detection and then only aggregate data. So we don't um display school specific data is released as a benchmark and then just kind of our overall metrics. Um We've run six campaign cycles so far uh and have surveyed at a total of 24 college campuses.

Um So across those six campaign cycles and uh 20 plus college campuses, we've had a total of 500 or 5000, sorry, 5545 survey participants and um over 700 of portrait photo participants. And then in terms of our data, um we survey across a lot of different demographic groups.

Um So we kind of all analyze it across um groups like uh race, ethnicity, gender, sexuality, first generation students, low income and disability. So first, we'll kind of look a bit closer at some questions specific to student culture. So one really interesting question we ask um is someone has once claimed to me that blank has unfairly contributed to my acceptance to my university under this question, we have a couple options for um which participants can select multiple such as my race, my gender, my ethnicity to kind of fill in that link.

Um In this kind of slide, we've included the analysis across race. But if you go to our website, you can check out the analysis across each demographic if you want to. This is an important question in particular because it's so integral to not only feeling that you belong, but also feeling that you deserve to be studying and learning at your school here, you can tell there's about a 50% difference where at the highest 61.6% of black or African American students have been told their race has unfairly contributed to their acceptance at their university versus kind of on the lowest end, only 10.6% of white students have been told this so we can move on to another um question for the audience.

So this is the same question. Um or sorry, a similar question. This doesn't have to do with acceptance to university. Um But actually in gaining job opportunities. Um So we'll wait a few minutes to see and it seems like we have some answers come in. So I will let's break it down and kind of how our survey did. Um So this time, similar question to four but specific to job opportunities instead of acceptance to university. Um And then here we're breaking it down by gender instead of race, which is what we looked at last time. Um So yeah, again, kind of a really important question, especially as college students prepare to enter the workforce. These are the people you might recruit the people, you might work with your future coworkers. Um And here we actually see about a 40% difference where 55% of students who identify as women have been told that their gender has unfairly given them an advantage in gaining job opportunities as well as 38% of those who identify as non binary or their gender. Um So this is actually a story from that I heard from Steph, but she said that the funny thing is she once told the recruiter about this stat and that um the recruiter was actually shocked and double checked with her that she had misspoken and that the stats weren't the other way around assuming that under represented students would be told less that they had an unfair advantage compared to groups with a lot of representation.

So it really is interesting to reflect on kind of success is how much of it that is earned who truly has the advantage here and what classifies as unfair or fair. Um Another question on student culture relates to micro aggressions. These are comments that the people around you, your peers, your friends, what they say to you and have a direct effect on your sense of belonging in your groups and communities. The graph I've included analyzes across transgender versus cis gender identifying students and we see a, a 25% gap with 54% of transgender students saying that someone has commented on their identity in a manner that made them feel uncomfortable, angry or insecure. Um So it is important to think about how these comments affect our sense of belonging in a group. How how can they affect how we look at ourselves and how we compare to our peers. How can this change, how we interact with others in and outside of the classroom? So we do survey on a couple other questions similar to this about how kind of um students feel among their peers. Um And uh yeah, just climate and student belonging. Cool. So now moving on to a few questions, more specific to the learning environment. Um kind of speaking of the classroom, it's also critical that we survey about learning environment at the end of the day. Um students go to college for education to learn, to expand their horizons.

So it's really important to understand how effective the current education system is and how it's designed affects people of different backgrounds. So one interesting question we ask is whether students generally feel comfortable asking questions during lecture. Um And people check if they agree or disagree with this statement.

Uh And this uh analysis here specifically for gender identity. Um But we also have it across all the other demographics if you check out our website, so you can check that out on your own time. Um, so you can see here, there's about a 20% difference with men at, um, 51.6% and women at 28% non bent students at 34%. Um, but it is surprising even that, uh, male students, it's only about 50% which is not very high considering lecture is the main mode of education in college these days. Um So that's another thing to look at. Um But yeah, you can see there's a pretty big difference. Um And we kind of, it does bring up questions about whether this education system is actually designed to enable effective learning for everyone. How can we maybe think about um making this more equitable and everyone feeling more comfortable participating. And then another question we ask kind of about learning um learning environment. And this is a pretty important one is how intimidated people feel, studying computer science and related fields, computer science. And I've definitely felt this personally can be an intimidating field.

And the intimidation factor may not only affect academic performance but also who enters the major and retention, who stays in through the four years. This grasp simply is uh broken down by low income students versus non low income students. And we see about a almost a 10% difference where 51% so slightly over half of low income students feel intimidated, studying computer science in related fields. Um Computer science can be a really intimidating field. Um The median annual wage for information technology was 86,320 in 2018, which is 36 th about 36,000 higher than the medium, all other occupations and especially as the field continues to evolve. How can we, how can these discrepancies in particular widen the already growing socioeconomic gap in our country? So you can see from so much of this data, there are noticeable effects and indicators of the student culture and learning environment from across all kinds of dimensions.

Again, I wasn't able to cover every dimension, but I hope this information was not provoking and inspired, it inspires you to check out the rest of the data. Um And this is kind of all our share, all our all I will share on um kind of specific demographic data, but we do have a few more I wanna share on kind of summary data. So things we ask um across uh kind of and don't break it down by demographic. Um So the next one, we'll look at some trends over time and this is actually interesting to look at because now that we've done six surveys, we can see there are some trends over time across our survey population. Um So this data, this question in particular is asking about uh whether people believe diversity organizations um whose purpose is to support underrepresented or marginalized groups such as but not limited to the National Society of Black Engineers Society of Hispanic Professional Engineers and Women in computer Science are still needed today.

So as you can see in 2020 on the left, um 80.7% of our respondents agreed with this statement and three years later when we just surveyed this past year or sorry, this year, um That number has actually increased by 6.5% to 87.2% of respondents agreeing with this statement. Um The percentage of those who disagree has fallen by 2.5% while the percentage of respondents who neither agree or disagree has fallen by 4%. So we're continuing to work with departments to provide analysis of data trends over time to also help evaluate the effectiveness of new diverse in initiatives and programming and their impact the culture and how they they impact the culture on their campuses. So kind of now that we've gone through all this data, we definitely want people to reflect and think about, I think this is something we think about a lot. Um is where this data like where this data actually fits into your life. Um Some things you could think about or do these survey results um about college climate, surprise, you are similar results reflected in your workplace. Um Does your current workplace have any uh internal climate surveys and what impact if any do they have, um can you use these results and bring this back to your community? Do you consider volunteering with high school or college students?

And what impact can you have in changing the narrative here? OK. Um Right. And then of course, like I said, already, multiple times, uh we could only uh present a small portrait of our data and we have all of this on our website. Um So we'd love for you to go check out more of our data on our website. Um We have more specific interactive dashboards as well as a case study on women in stem. Uh So you can go to percentage project dot org slash data to see that.

Awesome. Thanks Luna. So, uh we've gone through the data and once we've seen that data and thought about it and reflect on how it fits into our life and our communities, how can we actually put this data into action? Uh So I'll go over what it, what is data driven advocacy? What are we actually talking about here? Uh discuss how our organization, the present project achieves this as a case study, some future steps and also how you can get involved. So without further ado what is data driven advocacy, so to us, data driven advocacy can be broken down into three main steps. Uh The first step is to inform and this is a crucial first step once we have data, how do we actually effectively present it to others and create awareness. This involves, you know, translating digesting the data in a way that is actually easy for others to learn uh to reason. And to think about the next step is to help others actually reflect. So how do we provide platforms for people to think critically about the data that we're, we're producing? It's important to really allow for conversation and reflection so that people can interpret it and decide for themselves what to think about the data that we're giving them.

And then the final step is advocating and this means using data to make real change. So once we have informed, encourage reflection, how do we actually create action? So now that we've talked about what data driven advocacy is, let's get back to our mission and our goal that we've established as an organization. So as you may remember from our earlier conversation, we talked about our three pronged approach as a nonprofit. We defined three groups that we really hope to empower and to collaborate with and at the end of the day to impact. So as a case study, let me show you about how we've applied data driven advocacy. So everyone here likes matrices, right? Uh We're all women in tech. So to think about data driven advocacy, we kind of use this matrix framework where the columns are steps that we're taking and the rows are the audiences that we hope to advocate for And with so the blue cells are now prompts for us to actually brainstorm and explore actions that we can take.

So in general, it's easiest to start from the top left corner and later expand outwards to further steps and to other audiences. So for example, let's look at cell one a it's to inform students. Um and as a case study, one of our main challenges when we started the project was figuring out, OK, now that we've collected all this data, how do we actually get the word out to other students? Uh We decided to organize a community photo gallery where everyone could sign up for photo shoots and display statistic that they connected most with. So if we go to the next slide, uh so as you can see by putting these faces to the statistics and showing these stories behind the numbers, we found that students felt more connected to the message and we're also excited to learn more. So from there, creating awareness became more organic grassroots and expanding largely through word of mouth. The next step in data driven ay is to reflect on the data and get a deeper understanding of what it says about the organization and its climate. So as an example, uh students and departments can reflect on data in an event that we call a diversity Summit at a uh a diversity summit such as the one that was held in University of Pennsylvania in recent years. Is an event where members of the academic department.

So faculty staff and students can come together and reflect on the data that their climate survey has produced. So they can hear about diversity related issues firsthand and then actually work together to identify ways to address those issues and for form a more inclusive and a supportive community.

And then there's a bit of a waterfall effect here, right. So resulting from the events such as Diversity Summit departments can actually then further inform the academic community and school administrators through data dashboards and uh and data reports and they can advocate and plan for further change through department uh through the department of through a diver, departmental diversity road map.

We've also noticed that action in one area can grow and take on new goals and audiences. So in our case, students from other schools start noticing their friends sharing photos from their gallery um and asked if they could actually bring the project to their university. And that's when we started to actually think about the realize that this could grow. Um That's when we launched a social media campaign that serves to inform the community and power and that is powered by students. So the social media campaign is facilitated via our campus directors at partner schools. So on the same day, every year, everyone that participates in the campaign across all the different schools that we work with, uh share their photos and advocate for underrepresented students in computer science.

It's been a really exciting opportunity for students to share their own stories and their own experiences and how that relates to the data. And it also takes advantage of the power of social media to get the data out there in a consumable and objective form. So over the past four years, by informing students and faculty and impairing, empowering them to take action, we provided a platform where they can advocate and inform others. And we see this as fuel fast paced growth in the number of participating schools, photos and survey respondents each year.

Uh We can see here how the number of schools has grown, the photos has grown as well as survey respondents over time as well, even though our certain methodology changed in the middle of it. So lastly, to encourage the community to reflect on data, we actually share, you can see uh the three C um column, we actually share our data, our aggregate data and hold it. Uh hold it um uh an opportunity on our website for people to look at our interactive dashboard which basically has intriguing data analysis and visualizations. We publish a visualization of our average data across um all of our schools on our website and that serves as a public bench benchmark that can advocate and raise the bar as more schools join the initiative. So, uh without further ado, we're gonna talk a little bit more about what we're working

on now. Yeah. So now um we are focusing on growing school participation and expanding internationally. Um We're expanding on data metrics and visualizations as you can see from our website and leveraging uh community through data. We're expanding the program beyond the campaign with more actionable follow ups to the data. Um like with things with events like the Diversity Summit for instance, repeat and then we're increasing data visibility among students, faculty and school administrators and also expanding partnerships with other nonprofit organizations and hopefully raising awareness um about the work we're doing at uh events like this.

And then in addition to what, what we're working on now, um we always have opportunities to get involved. Um You can join our newsletter, follow us on social media. Um And we are always opening up new positions and looking for more volunteers. Um We actually have a bunch of slots open right now on a number of different teams. Uh So you can check out our link tree there if you're interested in volunteering. Um If you'd like to support us through donations, you can also feel free to go to our website at percentage project dot org slash donate or you can also buy our merch with death is wearing right now. Um 11 version of our merch um and all proceeds for that, go to the nonprofit and funding our programs and research. So, so that's it. Thank you very much and definitely check us out on our website and other social media platforms, we open it up to questions now if there are any which you can use the slider for or the chat, like we have one question. Um Are there ways to analyze the data at intersections of various demographic groups? Yeah, you want to take that? Sure.

Uh I

think uh as we move forward, we have a wealth of data. And uh as you can see, we're really collecting a lot of different questions across all different uh departments across the country. Uh And a lot of different students who are responding to our, our statistics. And so our survey and so it becomes really important to actually leverage all this data and analyze it in as many different possible ways as as we can. Uh And one of those ways is trying to look a little bit more at intersectionality and understanding. So as you can see here with our demographics, we're looking at a specific demographic and the different categories within that. Um But then understanding uh how those, how di different intersections of those demographics uh and how the data looks there uh is definitely an interesting area that we are looking to do in the next uh coming year or years. So hopefully, as you stay tuned with us, um we would we be posting most of most of that information on our website as well as Instagram? Yeah, I think that concludes our Q and a section. So, um thank you so much for stepping in and uh taking a look at our session today. I know we presented a lot of data, we presented a lot of different actionable items that we can take from that data.

And we hope that you feel inspired or intrigued to take a look at our website or to get more involved uh through either volunteering or even applying this data and the learnings that you have to your own life and communities. Thank you again for joining us and hope to see you soon.