WEBVTT 00:00:08.009 --> 00:00:10.009 Hi, everyone. Welcome, sorry about the confusion. Um Kim Scott is um 00:00:12.960 --> 00:00:16.785 hopefully she'll be able to join us in just a second. Um But I will 00:00:16.818 --> 00:00:21.666 instead kick us off. Um We have a wonderful group of panelists today who 00:00:21.699 --> 00:00:26.015 will be presenting their findings. Uh Welcome. This is our first um 00:00:26.048 --> 00:00:29.686 presentation of findings from the women of color and Computing 00:00:29.719 --> 00:00:35.354 collaborative. Um These set of uh researchers will be talking about their 00:00:35.387 --> 00:00:40.674 research findings related to um barriers and solutions for women of color 00:00:40.707 --> 00:00:44.954 in computing, specifically in K 12 education and higher education. So we 00:00:44.987 --> 00:00:51.076 are very excited to get started and I will um share my screen and get us 00:00:51.109 --> 00:00:59.109 started with the first uh presentation. 00:01:04.097 --> 00:01:12.097 OK. Here we go, 00:01:22.239 --> 00:01:25.376 Allison. Are we up first? 00:01:25.409 --> 00:01:30.477 You sir are OK. So Mia, would you like to kick us off and do a quick 00:01:30.510 --> 00:01:35.447 introduction of your project? Yes. Thank you. Good afternoon. My name is 00:01:35.480 --> 00:01:40.305 Mia, given that this is a virtual meeting and we are all located 00:01:40.338 --> 00:01:43.727 throughout the country. We want to start this presentation with the 00:01:43.760 --> 00:01:48.206 acknowledgment that we are all on indigenous land as part of this 00:01:48.239 --> 00:01:51.885 acknowledgment. We want to recognize and give thanks to the indigenous 00:01:51.918 --> 00:01:57.257 people that historically and currently care for these lands. Today, our 00:01:57.290 --> 00:02:01.745 presentation is entitled I'm Learning to knit culture with science, 00:02:01.778 --> 00:02:07.956 lifting up native identity and computer science for academic persistence. 00:02:07.989 --> 00:02:12.066 We will be talking about our current study, native women and two spirit 00:02:12.099 --> 00:02:17.245 individuals in computer higher education. A photo elicitation study of 00:02:17.278 --> 00:02:23.475 persistence, which we refer to as Knock two. The knock two team is 00:02:23.508 --> 00:02:28.116 composed of Kathy Dewater, who is the Chief program officer at the 00:02:28.149 --> 00:02:33.707 American Indian Science and Engineering Society, Maria Jamat Pascual, who 00:02:33.740 --> 00:02:39.737 was a research scientist at Turk, a stem education nonprofit research 00:02:39.770 --> 00:02:44.707 nonprofit based in Cambridge, Massachusetts, Mia Ong, who is a senior 00:02:44.740 --> 00:02:49.525 research scientist and evaluator at church and Christina BV Silva who is a 00:02:49.558 --> 00:02:52.987 research assistant also at church. 00:02:53.020 --> 00:02:57.737 The knock two study addresses what is known and what is not known in the 00:02:57.770 --> 00:03:01.755 empirical literature about native women and two spirit individuals in 00:03:01.788 --> 00:03:05.927 computer science, undergraduate education through a literature review and 00:03:05.960 --> 00:03:09.936 the factors that have influenced their persistence including barriers and 00:03:09.969 --> 00:03:14.566 enablers through photo elicitation. Interviews. 00:03:14.599 --> 00:03:19.487 In photo elicitation, participants take pictures that respond to a prompt 00:03:19.520 --> 00:03:24.077 that the researchers provide. Then we talk about these pictures in an 00:03:24.110 --> 00:03:29.467 interview. The prompts in our study asked participants about three main 00:03:29.500 --> 00:03:35.656 topics, the barriers they have encountered the supports they have received 00:03:35.689 --> 00:03:39.886 and how they conceptualize their identities as native students in computer 00:03:39.919 --> 00:03:45.916 science. The combination of images and conversation accesses more areas of 00:03:45.949 --> 00:03:51.055 the brain evoking different kinds of information such as more detailed 00:03:51.088 --> 00:03:55.457 memories and emotions than words alone, which often results in powerful 00:03:55.490 --> 00:04:01.666 combinations of words and images. 00:04:01.699 --> 00:04:09.699 Can you make me smart? 00:04:11.338 --> 00:04:16.296 In our interviews? We found four main themes which we will introduce with 00:04:16.329 --> 00:04:22.377 a short description and a quote and picture from participants first giving 00:04:22.410 --> 00:04:29.296 back. If you click for the first picture, 00:04:29.329 --> 00:04:33.486 participants wanted to give back meaning to help their communities, 00:04:33.519 --> 00:04:37.805 strengthen families or mentor and serve as role models for younger native 00:04:37.838 --> 00:04:43.366 individuals. This theme was present in all eight participants and was 00:04:43.399 --> 00:04:48.007 potentially the most salient one in terms of how often they talked about 00:04:48.040 --> 00:04:53.507 it and the passion it elicited. We saw that participants often viewed 00:04:53.540 --> 00:04:58.286 their education in computer science as vehicles for giving back. For 00:04:58.319 --> 00:05:03.627 example, one participant whom we call Kelly talked about using her 00:05:03.660 --> 00:05:08.515 emergent computing skills to preserve the oral traditions of her tribe. 00:05:08.548 --> 00:05:14.356 She said I'm creating an Ojibwe language app which can help language 00:05:14.389 --> 00:05:18.796 learners speak the language better. So I'm creating it using Alexa so that 00:05:18.829 --> 00:05:22.887 the language is preserved orally in the way it was created. And yeah, it's 00:05:22.920 --> 00:05:27.305 mainly there for beginning language learners 00:05:27.338 --> 00:05:34.286 in the next theme. Next picture, 00:05:34.319 --> 00:05:38.596 participants experienced their n their native culture as a source of 00:05:38.629 --> 00:05:43.836 resilience. They used their culture as a source of pride and strength to 00:05:43.869 --> 00:05:48.895 persist in computer science, higher education. They were motivated to 00:05:48.928 --> 00:05:54.116 persist in their studies by a desire to explicitly challenge stereotypes 00:05:54.149 --> 00:05:59.096 of native individuals as not being capable of being successful in computer 00:05:59.129 --> 00:06:04.075 science and tech. This is how a participant we named Lee, a native 00:06:04.108 --> 00:06:08.955 Hawaiian talked about it. I identify as a person who's learning to knit 00:06:08.988 --> 00:06:13.786 culture with science, right? Native Hawaiians have always had science. 00:06:13.819 --> 00:06:17.736 Once I started to learn that and once I started to respect that I had 00:06:17.769 --> 00:06:21.916 pride and that pride has also allowed me to really continue in science. 00:06:21.949 --> 00:06:26.717 You know, I don't have to worry we have another way and it's the exact 00:06:26.750 --> 00:06:31.166 same level. So that's what gave me pride. And that was a barrier that was 00:06:31.199 --> 00:06:36.515 blocking me from really believing in myself into continuing in science. 00:06:36.548 --> 00:06:41.116 The next theme 00:06:41.149 --> 00:06:47.055 uh was uh being too spirit, two partic one participant we call David 00:06:47.088 --> 00:06:53.145 attributed his creativity to being two spirit. Two spirit originated as a 00:06:53.178 --> 00:06:59.075 term for contemporary native LGBT Q individuals that has come to refer to 00:06:59.108 --> 00:07:04.145 a number of past and present native roles and identities around gender and 00:07:04.178 --> 00:07:09.805 sexual sexual cate categories and behaviors that emphasizes the spiritual 00:07:09.838 --> 00:07:14.817 aspect of one's life. However, there's no consensus around the meaning of 00:07:14.850 --> 00:07:20.055 the term to spirit as it varies by individual and culture. David told us 00:07:20.088 --> 00:07:25.495 about how his experiences were different from those of other men and how 00:07:25.528 --> 00:07:30.495 he considered that being two spirit provided him with creativity and two 00:07:30.528 --> 00:07:33.995 different perspectives. When looking at problems, this is how he talked 00:07:34.028 --> 00:07:39.317 about his experience. I feel like I approach computer science in a very, 00:07:39.350 --> 00:07:44.495 very different way in a creative way. Being a two spirit in computer 00:07:44.528 --> 00:07:49.447 science and in college and undergrad in everything made everything I do in 00:07:49.480 --> 00:07:53.567 the class kind of different than all the other kids. And then they want to 00:07:53.600 --> 00:07:58.197 learn off of me too when they were in class, like this typical male, they 00:07:58.230 --> 00:08:03.296 only see the outside or the inside of a computer or they see one way. And 00:08:03.329 --> 00:08:07.906 then me, I see different ways of how to take this computer apart. Just 00:08:07.939 --> 00:08:11.555 being creative and try and find different ways to do the same thing as 00:08:11.588 --> 00:08:16.166 your peers. But do it in a different way or more efficient or a better or 00:08:16.199 --> 00:08:24.127 a faster way. Our fourth and final theme 00:08:24.160 --> 00:08:29.627 is resisting colonization. We saw how participants choices in pursuing 00:08:29.660 --> 00:08:35.196 computer science, resist their tribe's histories of colonization. 00:08:35.229 --> 00:08:40.047 Participants talked about their elders experiences of cultural erasure 00:08:40.080 --> 00:08:45.186 through native boarding schools and forced removal and how in turn their 00:08:45.219 --> 00:08:50.635 own choices as they get their degrees. Fight back the colonizing pressures 00:08:50.668 --> 00:08:55.616 of higher education. This is how Christa talked about her experiences 00:08:55.649 --> 00:08:58.275 resisting colonization. 00:08:58.308 --> 00:09:02.816 I was so confused about if I go to college, will I forget my culture, my 00:09:02.849 --> 00:09:07.505 language, my heritage, I wouldn't be here if my ancestors survived that 00:09:07.538 --> 00:09:11.667 long walk. And so in that sense, when they came back, a lot of them were 00:09:11.700 --> 00:09:15.525 sent to boarding schools to be colonized and to lose their culture, their 00:09:15.558 --> 00:09:21.395 religion by force. And to me, I would, if I had to change to go to an Ivy 00:09:21.428 --> 00:09:25.875 League school or at whatever, I probably would turn it down just because 00:09:25.908 --> 00:09:33.908 it's easy to lose your culture. 00:09:35.469 --> 00:09:39.807 Based on these findings, our recommendations for funders point towards 00:09:39.840 --> 00:09:43.686 supporting changes in the system so that native individuals and 00:09:43.719 --> 00:09:49.145 perspectives are integrated. Some ways this can be done is by supporting 00:09:49.178 --> 00:09:53.547 programs that promote native participation in stem and computer science 00:09:53.580 --> 00:09:57.005 and that validate and integrate native culture and science in the 00:09:57.038 --> 00:10:01.366 curriculum and by supporting research on the effectiveness of support 00:10:01.399 --> 00:10:05.625 programs for native students and faculty and on native culture and science 00:10:05.658 --> 00:10:10.407 curricula as well as ensure that the research is conducted with the 00:10:10.440 --> 00:10:14.336 involvement and support of the native community such as by using 00:10:14.369 --> 00:10:20.275 participatory methods and led by or with native researchers. 00:10:20.308 --> 00:10:24.547 This concludes our presentation. We would like to acknowledge our board of 00:10:24.580 --> 00:10:29.976 advisors, Shana de Begat Rochelle Larson and Stephanie Masta and our 00:10:30.009 --> 00:10:33.686 funders, the Women of Color and Computing collaborative formed by the 00:10:33.719 --> 00:10:38.946 caper center and C at Arizona State University. We would also like to 00:10:38.979 --> 00:10:42.606 thank you for your attention. We appreciate that you took the time to 00:10:42.639 --> 00:10:46.226 learn more about our efforts to bring our the stories of these native 00:10:46.259 --> 00:10:50.057 students to the general public. We will be, we will be glad to answer any 00:10:50.090 --> 00:10:53.616 questions and talk more about any of these issues in the Q and A. Or at 00:10:53.649 --> 00:10:58.336 any time through email. Thank you very much Mia and Nodia for your 00:10:58.369 --> 00:11:01.375 excellent presentation. And I would encourage folks that have any follow 00:11:01.408 --> 00:11:04.686 up questions to just drop them in the chat and we can also share out these 00:11:04.719 --> 00:11:09.875 slides at the conclusion of the presentation. Thank you. Next up, we have 00:11:09.908 --> 00:11:13.797 Kristen Shelton, Wanda Eugene, Shani Daley and Jaquita Thomas to talk to 00:11:13.830 --> 00:11:17.297 us about trends and bachelor's degree completion among women of color in 00:11:17.330 --> 00:11:20.586 computing. 00:11:20.619 --> 00:11:26.157 Um uh Good afternoon. My name is Wanda Eugene. Um myself along with my 00:11:26.190 --> 00:11:30.826 colleagues, Kristen Shelton and Shani Daly of Deep Design, along with 00:11:30.859 --> 00:11:36.005 Akita Thomas of Auburn University um are going to uh briefly discuss our 00:11:36.038 --> 00:11:40.717 findings and trends and bachelor's degree completion among women of color 00:11:40.750 --> 00:11:45.086 in computing. Um Next time 00:11:45.119 --> 00:11:49.967 um when we began this work, it was based upon uh Jaquita Thomas work on um 00:11:50.000 --> 00:11:57.057 Intersectional computing um as well as Sarah Rodriguez and um uh Kathleen 00:11:57.090 --> 00:12:00.986 Lehman work on intersectional computing and identity. Uh what they pointed 00:12:01.019 --> 00:12:05.015 out and what we're building on is that there is little research on women 00:12:05.048 --> 00:12:10.135 of color and computing supporting and understanding um of this unique 00:12:10.168 --> 00:12:13.875 intersection of race and gender is um as experienced by women of color is 00:12:13.908 --> 00:12:20.307 the basis of our work. Um So, what we propose to examine is uh both the 00:12:20.340 --> 00:12:24.885 statistics related to degree um completion uh phase one as well as the 00:12:24.918 --> 00:12:28.686 narratives of women of color um in different computing contexts such as 00:12:28.719 --> 00:12:34.885 work, workforce, graduate school, government and academia. Um uh Can you 00:12:34.918 --> 00:12:39.816 click the side once again, please? Um But what we will focus on in this um 00:12:39.849 --> 00:12:45.005 body of research is uh phase one. And so, with this, we explore um the 00:12:45.038 --> 00:12:50.265 research question of what universities are currently having an impact on 00:12:50.298 --> 00:12:54.496 our population of interest as defined as the number of women of color 00:12:54.529 --> 00:13:02.529 receiving bachelor's degrees in um computing next slide. 00:13:02.950 --> 00:13:07.986 Um So, what we did is um um in terms of our work, we, we use the ipads 00:13:08.019 --> 00:13:14.167 database. Um um And um uh for those who are not familiar with ipads, um 00:13:14.200 --> 00:13:19.696 it's freely available and um uh it's basically for all institutions that 00:13:19.729 --> 00:13:23.775 receive federal funding, they report to them. And so we combine data sets 00:13:23.808 --> 00:13:31.000 um for computing degrees um earned by women from 2011, 2, 2018. Um And so 00:13:31.033 --> 00:13:35.802 , um what we then our next steps to follow after this is we'll create a 00:13:35.835 --> 00:13:41.561 follow up sur survey for institutions who are top producers and design and 00:13:41.594 --> 00:13:45.451 interactive visualization in Tableau. Um This work that we've done thus 00:13:45.484 --> 00:13:48.662 far, basically tells us what's happening. And our next steps is to 00:13:48.695 --> 00:13:56.287 evaluate the why next step, next slide. So our observations, that's why. 00:13:56.320 --> 00:14:00.356 So um if you look at how the numbers have changed with women in computing 00:14:00.389 --> 00:14:06.635 , overall, the growth has been primarily with white and Asian women, with 00:14:06.668 --> 00:14:11.956 Asian women having more representation in computing than they have in the 00:14:11.989 --> 00:14:18.096 entire US population. As such, we decided to focus on two categories. Um 00:14:18.129 --> 00:14:23.265 Women of color, black, Latinx, indigenous, um indigenous people, Pacific 00:14:23.298 --> 00:14:27.236 Islanders, and Asian and a woman of color subset. So we can have a better 00:14:27.269 --> 00:14:32.287 understanding what of what's happening in the um broader landscape next 00:14:32.320 --> 00:14:40.320 slide. 00:14:41.479 --> 00:14:46.657 So um with this, uh once we collected our data and done some analysis um 00:14:46.690 --> 00:14:51.856 to look at, to look in further the of our findings, we ran a logistical a 00:14:51.889 --> 00:14:56.186 lo a lot, excuse me, a logistic regression analysis and found one of the 00:14:56.219 --> 00:15:02.037 most of um uh telltale factor of predictors of graduating women of color 00:15:02.070 --> 00:15:07.116 was um having an M SI designation was the strongest indication um of the 00:15:07.149 --> 00:15:10.946 institution would graduate a woman of color. Um And so what we found here 00:15:10.979 --> 00:15:15.326 was the odds of an institution producing a woman of color in computing, it 00:15:15.359 --> 00:15:21.135 was 2.85 times higher. If the institution had an Alaskan native, serving 00:15:21.168 --> 00:15:25.746 native Hawaiian serving designation and the odds of an institution 00:15:25.779 --> 00:15:33.167 producing a woman of color in computing subset was 2.52 times higher if 00:15:33.200 --> 00:15:36.505 the institution had a historically black college and university 00:15:36.538 --> 00:15:41.336 designation um related to if it did not. And so it was really interesting 00:15:41.369 --> 00:15:47.946 seeing how big of a role those designations um played in um into uh uh the 00:15:47.979 --> 00:15:54.677 predicting uh the roles of graduating women of color. Next slide. 00:15:54.710 --> 00:15:58.226 Um So what we did next is we looked at the zip code, the zip codes are how 00:15:58.259 --> 00:16:02.946 the universities distinguish um all the degrees that fall within the 00:16:02.979 --> 00:16:07.635 respective discipline. So within computing, computer science, um computer 00:16:07.668 --> 00:16:11.787 engineering management information systems, those various majors are, are 00:16:11.820 --> 00:16:15.537 divided by their zip code. So we look that have the deeds play out across 00:16:15.570 --> 00:16:19.606 zip codes. And what we found was um for example, black women earning were 00:16:19.639 --> 00:16:23.427 earning more degrees in computer information systems and management 00:16:23.460 --> 00:16:28.236 information systems. Um And that is shown in this table um by zip code 00:16:28.269 --> 00:16:34.005 1101 and 5212 in the blue, 00:16:34.038 --> 00:16:37.456 next slide. 00:16:37.489 --> 00:16:43.246 Um then um we, we followed up by looking at um the top 25 Universes and 00:16:43.279 --> 00:16:47.446 see who were the top producers of women of color there. And so here we 00:16:47.479 --> 00:16:52.236 show the top producers that have AM SI designation. Um And so what we can 00:16:52.269 --> 00:16:56.186 see here is um University of Washington, Seattle Campus, University of 00:16:56.219 --> 00:17:00.797 Houston and University of California Irvine are the top producers of women 00:17:00.830 --> 00:17:05.045 of color. But when we look at the subset, we see a slight change where 00:17:05.078 --> 00:17:09.295 Florida International University, Georgia State University and University 00:17:09.328 --> 00:17:14.728 of Houston are top producers. Um What we also took in the um consideration 00:17:14.761 --> 00:17:18.649 here, um You can click it one more time um is that we looked at how these 00:17:18.682 --> 00:17:23.008 , we took into consideration how these U universities ranked in terms of 00:17:23.041 --> 00:17:28.159 um the top the top uh 20 rankings, 25 rankings. So University of 00:17:28.192 --> 00:17:32.711 Washington, Seattle um is ranked number 22 and University of California 00:17:32.744 --> 00:17:37.232 Irvine is um ranked number nine. Both of those are public institutions and 00:17:37.265 --> 00:17:41.882 we also looked to see if any of those programs, how do they place in terms 00:17:41.915 --> 00:17:45.732 of their rankings of top computer science programs? Um And so University 00:17:45.765 --> 00:17:52.776 of Washington um also ranks as um top 20 computer science program next 00:17:52.809 --> 00:17:55.815 slide. 00:17:55.848 --> 00:17:59.117 So then we looked at women's colleges, right? And so how are women 00:17:59.150 --> 00:18:03.647 colleges playing out and producing women of color? Um And so when we 00:18:03.680 --> 00:18:08.647 looked at um across, you know, women, all women of color, we noticed um 00:18:08.680 --> 00:18:13.805 Wellesley University um had like the, the largest footprint in that space. 00:18:13.838 --> 00:18:17.926 But again, we look at the subset and we see a slight shift where Spelman 00:18:17.959 --> 00:18:22.246 College surpasses Wellesley followed by um Spelman College, Wellesley and 00:18:22.279 --> 00:18:27.617 then Texas University are top producers 00:18:27.650 --> 00:18:30.746 next slide. 00:18:30.779 --> 00:18:38.779 So basically, um a summary um of our observation is that um uh we found um 00:18:38.848 --> 00:18:43.647 um this, excuse me, uh growth in computing degrees is dominated by women. 00:18:43.680 --> 00:18:47.035 Um white and Asian women with the latter having a greater percentage 00:18:47.068 --> 00:18:51.426 representation in the US, almost no growth in an indigenous indigenous and 00:18:51.459 --> 00:18:56.467 Pacific Islander population. Um Between the 2011 and 2018 time frame that 00:18:56.500 --> 00:19:00.946 we looked at uh black women earning more degrees in uh were earning more 00:19:00.979 --> 00:19:04.717 degrees in computer information systems. And managed information systems. 00:19:04.750 --> 00:19:09.585 Having an M SI was the biggest um designation um sta or statistically 00:19:09.618 --> 00:19:15.147 significant predictor of graduating women of color in computing, H BC US, 00:19:15.180 --> 00:19:20.065 outperform other M SI S with respect to uh percentage of total degrees 00:19:20.098 --> 00:19:24.055 earned by women of color. Um And Wellesley College and Spelman College 00:19:24.088 --> 00:19:28.706 graduated the most women of color and women of color subset in computing 00:19:28.739 --> 00:19:35.467 um of all women's colleges. Um And so these are the sets um are important 00:19:35.500 --> 00:19:38.726 for the groundwork for what we want to do for the next phase. Um For the 00:19:38.759 --> 00:19:43.446 surveys we would like to create and administer and that concludes our 00:19:43.479 --> 00:19:46.166 presentation. 00:19:46.199 --> 00:19:49.877 Thank you so much, Wanda. Excellent findings. And again, I encourage folks 00:19:49.910 --> 00:19:54.676 to just drop questions into the chat or the Q and A. Um and Wanda and team 00:19:54.709 --> 00:19:57.976 can answer those. 00:19:58.009 --> 00:20:03.686 OK. Next up, we have Ursula Nn and Katherine Regal Crum who are going to 00:20:03.719 --> 00:20:07.607 present on who is a computer scientist, counter stereotypical views and 00:20:07.640 --> 00:20:12.176 the stem major intentions of Black and Latinx students. 00:20:12.209 --> 00:20:16.186 Thank you, Alison. Good afternoon, everyone. Uh It's a pleasure to be here 00:20:16.219 --> 00:20:19.835 and part of this fantastic panel of presenters and researchers. We 00:20:19.868 --> 00:20:23.026 appreciate this opportunity. I'm Catherine Nagle Rem at the University of 00:20:23.059 --> 00:20:27.367 Texas at Austin and I'm gonna hand things over to Ursula Wen, who's my uh 00:20:27.400 --> 00:20:33.367 collaborator and phd student, Ursula. Hi, good afternoon, everyone. Um As 00:20:33.400 --> 00:20:37.217 Doctor R, Crm said, I'm Ursula Wen and I'm presenting on this work that we 00:20:37.250 --> 00:20:42.295 have uh collaborated together on as you all pro you know, Black and Latinx 00:20:42.328 --> 00:20:46.535 women are under presented in stem occupations. And this actually can be 00:20:46.568 --> 00:20:50.535 traced back to their under representation in stem majors and stem major 00:20:50.568 --> 00:20:55.666 declarations wu which usually occurs um during the transition from high 00:20:55.699 --> 00:21:01.137 school to college. Moreover, prior, uh social psycho psychological 00:21:01.170 --> 00:21:05.535 research shows that adolescence is a critical time when uh young men and 00:21:05.568 --> 00:21:09.976 women are actually articulating who they are and who they want to become. 00:21:10.009 --> 00:21:14.847 So it's a, it's a critical time to also investigate um what predicts the 00:21:14.880 --> 00:21:19.026 formation of their future plans um including their intentions, what major 00:21:19.059 --> 00:21:23.726 they intend to pursue in college, taking this into consideration. Um 00:21:23.759 --> 00:21:26.967 There's a critical need to understand what factors promote girls, future 00:21:27.000 --> 00:21:31.055 stem plants um and decisions to enter stem fields. Certainly there is 00:21:31.088 --> 00:21:34.831 prior research that focuses on the negative roles of stereotypes, which 00:21:34.864 --> 00:21:38.101 includes student perceptions of stereotypes about scientists. For example 00:21:38.134 --> 00:21:42.281 , there are plenty of studies that um actually use the draw scientist test 00:21:42.314 --> 00:21:47.541 to examine what stereotypical indicators students draw. Um And this 00:21:47.574 --> 00:21:50.972 usually includes like drawing a white old scientist or someone that works 00:21:51.005 --> 00:21:55.847 alone um or someone that works the lab coat. It is very superficial. And 00:21:55.880 --> 00:21:59.736 so our study builds on that need to understand what factors promote Black 00:21:59.769 --> 00:22:04.906 and Latinx uh students f stem intentions and plans while also departing 00:22:04.939 --> 00:22:09.776 from prior work that focuses on stereotypes um which focuses on negative 00:22:09.809 --> 00:22:14.565 portrayals of scientists to instead focus on positive use of scientists. 00:22:14.598 --> 00:22:19.420 Those that run counter to stereotypes that may help young women and men 00:22:19.453 --> 00:22:23.092 of color identify with stem fields. So in this study, we asked several 00:22:23.125 --> 00:22:27.180 research questions which include, are there gender differences in students 00:22:27.213 --> 00:22:31.041 counter stereotypical views of scientists and then do these counter 00:22:31.074 --> 00:22:34.762 stereotypical views of scientists matter for stem college major intentions 00:22:34.795 --> 00:22:42.315 ? Next slide please? 00:22:42.348 --> 00:22:48.906 Yeah. Um Yes, thank you. So, in order to understand these research 00:22:48.939 --> 00:22:52.986 questions, we actually draw a sample from a large that school district 00:22:53.019 --> 00:22:57.666 that is predominantly Latinx with about 62% of stu students being Latinx, 00:22:57.699 --> 00:23:05.315 25% being black, nine and 4% being white and Asian respectively. Um And 00:23:05.348 --> 00:23:10.585 our data basically is focused on surveys and transcripts uh that students 00:23:10.618 --> 00:23:14.996 completed uh during middle school and high school and the file, the 00:23:15.029 --> 00:23:18.967 transcripts from administrative files provided by the schools, but a study 00:23:19.000 --> 00:23:23.696 focuses on a sub sample of students of about 1000 and Latinx, 1000 Latinx 00:23:23.729 --> 00:23:26.486 and Black students that were followed from middle school to the beginning 00:23:26.519 --> 00:23:31.916 of high school. And in this study, our key independent variable is a scale 00:23:31.949 --> 00:23:36.347 that measures students counter stereotypical views of scientists. The 00:23:36.380 --> 00:23:39.926 skill is composed of several survey items that were measured when students 00:23:39.959 --> 00:23:45.226 were in eighth grade and asked students uh the extent to which they 00:23:45.259 --> 00:23:48.815 strongly agree, strongly disagree with several items which include uh 00:23:48.848 --> 00:23:53.315 statements like scientists are nerds, scientists work alone in labs and 00:23:53.348 --> 00:23:58.137 scientists don't have other interests. And our key are dependent variables 00:23:58.170 --> 00:24:01.815 were measured later at the beginning of high school, either in ninth grade 00:24:01.848 --> 00:24:06.607 or 10th grade and includes expectations of students um intentions to major 00:24:06.640 --> 00:24:10.416 in several stem fields which include computer science, engineering, math, 00:24:10.449 --> 00:24:14.835 physical science, and biology. And so in all logistic regression models, 00:24:14.868 --> 00:24:18.496 we also include control variables such as student science, self efficacy, 00:24:18.529 --> 00:24:23.377 math test scores, parent education level and school characteristics. And 00:24:23.410 --> 00:24:26.325 so next, I will present the results and the implications in the next slide 00:24:26.358 --> 00:24:28.867 , please. 00:24:28.900 --> 00:24:32.996 So as you can see um for our first research questions where we asked 00:24:33.029 --> 00:24:36.535 whether there are gender differences in views of scientists, we did find 00:24:36.568 --> 00:24:42.117 that girls had stronger counter stereotypical views of scientists and boys. 00:24:42.150 --> 00:24:45.416 And this was particularly true for black girls. They actually had 00:24:45.449 --> 00:24:50.861 stronger counter stereotypical views of scientists more than uh Latin boys 00:24:50.894 --> 00:24:55.722 and girls. Uh Next, we also ask whether these kind of stereotypical views 00:24:55.755 --> 00:24:59.773 of scientists mattered for college major intentions and we find that they 00:24:59.806 --> 00:25:04.222 do but for certain stem fields. Um So if you look at the right um at the 00:25:04.255 --> 00:25:11.553 plot, you see that for both um black and Latinx girls and boys, conor took 00:25:11.586 --> 00:25:16.829 coding stronger counter stereotypical views of scientists predicted their 00:25:16.862 --> 00:25:21.388 intentions to major in computer science, which is uh indicated by the 00:25:21.421 --> 00:25:26.168 solid lines in green and yellow respectively. So for the green. One is the 00:25:26.201 --> 00:25:30.399 girl's intentions to major in computer science and the yellow solid yellow 00:25:30.432 --> 00:25:34.785 lines for boys intentions to major in computer science. And the similar 00:25:34.818 --> 00:25:39.075 trend pattern is observed for their intentions to major in engineering, 00:25:39.108 --> 00:25:44.887 which is indicated by the green and yellow dash lines for girls and boys 00:25:44.920 --> 00:25:50.926 respectively. So as they hold stronger counter stereotypical views of 00:25:50.959 --> 00:25:54.851 scientists, they average which predict the probabilities of intending to 00:25:54.884 --> 00:26:00.012 major um in computer science and engineering increases. Um And lastly, we 00:26:00.045 --> 00:26:05.022 also observe for, for only for Black and Latinx boys um as they hold 00:26:05.055 --> 00:26:09.262 stronger counter stereotypical views of scientists that also predicts um 00:26:09.295 --> 00:26:15.335 their intentions to major in biology. Um So, in all our, our research 00:26:15.368 --> 00:26:20.285 really highlights the importance of looking at counter stereotypical views 00:26:20.318 --> 00:26:24.085 of scientists. Um These are important findings specifically because a lot 00:26:24.118 --> 00:26:29.097 of research focuses on the negative portrayals of scientists. And very few 00:26:29.130 --> 00:26:33.976 actually look at the views that students of color, particularly Black and 00:26:34.009 --> 00:26:37.467 Latinx students hold. Um So, as we found in our studies, these are 00:26:37.500 --> 00:26:41.815 important to, to examine and investigate because they are predictive of 00:26:41.848 --> 00:26:48.035 their stem major intentions. Um And lastly, we um our implications show 00:26:48.068 --> 00:26:52.436 that this is important specifically for the educational practice where we 00:26:52.469 --> 00:26:58.147 um think that not only classrooms but also curricula and broader me, 00:26:58.180 --> 00:27:03.276 social media messaging, social messaging should include a broader or more 00:27:03.309 --> 00:27:08.266 multidimensional portrayals of scientists. Um And also departing from the 00:27:08.299 --> 00:27:11.906 negative portrayal of scientists. Um this means including various types of 00:27:11.939 --> 00:27:15.565 scientists and the work that they do while also showing diverse 00:27:15.598 --> 00:27:20.686 representations of stem uh workers and scientists. Um This concludes our 00:27:20.719 --> 00:27:24.956 presentation, thank you for your time. Thank you very much Ursula and 00:27:24.989 --> 00:27:28.226 Catherine and, and really, really important findings agreed and as we 00:27:28.259 --> 00:27:31.656 think about implications for classrooms and and broader media messaging. 00:27:31.689 --> 00:27:37.887 So thank you next up. Um We have Luis Leva. Dr Leva is going to be talking 00:27:37.920 --> 00:27:41.835 to us about exploring perceptions of entry level mathematics instruction 00:27:41.868 --> 00:27:45.325 as racialized gender, gendered experience among women of color in 00:27:45.358 --> 00:27:49.766 computing and engineering. Luis. You up. Thanks so much, Alison. Hi 00:27:49.799 --> 00:27:53.607 everybody. It's a pleasure to be here. Um We can go to the next slide. You 00:27:53.640 --> 00:27:57.835 did a wonderful job with the title. I love it. All right. So um this is 00:27:57.868 --> 00:28:01.696 the theoretical perspective that anchors my inquiry around women of colors 00:28:01.729 --> 00:28:06.756 , perceptions of entry level math instruction, particularly precalculus 00:28:06.789 --> 00:28:11.686 and calculus. In my prior work, I've been able to theorize how math thema 00:28:11.719 --> 00:28:15.766 context function as white patriarchal spaces. And you'll see on the right 00:28:15.799 --> 00:28:20.196 hand side here on the slide a frame that framework um itself that consists 00:28:20.229 --> 00:28:23.867 of three different dimensions, the ideological, the institutional and the 00:28:23.900 --> 00:28:28.217 relational and how these different dimensions really interplay to shape 00:28:28.250 --> 00:28:32.166 racialized and gendered experiences of mathematics and for the purposes of 00:28:32.199 --> 00:28:36.847 this presentation, calculus instruction. Um And then also in my prior work 00:28:36.880 --> 00:28:41.967 , I've been able to uh 00:28:42.000 --> 00:28:46.967 some findings around how precalculus and calculus instruction um are 00:28:47.000 --> 00:28:51.107 shaped by broader ideological forces. So for example, stereotypes of 00:28:51.140 --> 00:28:55.656 mathematical ability as well as broader institutional influences such as 00:28:55.689 --> 00:28:59.347 the representation rates in calculus classrooms, and also rates of 00:28:59.380 --> 00:29:03.607 representation in stem fields like computing and how those broader forces 00:29:03.640 --> 00:29:07.426 really shape how students experience, particularly historically 00:29:07.459 --> 00:29:11.226 marginalized students experience calculus instruction in the classroom. 00:29:11.259 --> 00:29:15.676 And so I extend sociologists, Amanda Lewis and John Diamond's work where 00:29:15.709 --> 00:29:19.467 they theorize this idea of what's called mechanisms of inequality where 00:29:19.500 --> 00:29:22.795 there are these aspects of schools. Um And their work there in K through 00:29:22.828 --> 00:29:27.736 12 schools where policies or the ways in which teachers might engage with 00:29:27.769 --> 00:29:32.522 students might be seemingly neutral and well intended, but have um a 00:29:32.555 --> 00:29:36.101 impressive impacts for students from historically marginalized backgrounds. 00:29:36.134 --> 00:29:39.722 And so the nature of this work is to really begin to explore how does 00:29:39.755 --> 00:29:43.391 calculus instruction have these embedded racialized and gendered 00:29:43.424 --> 00:29:48.141 mechanisms of inequality and how that impacts um historically marginalized 00:29:48.174 --> 00:29:51.430 students. And for the purposes of this talk, uh women of color, 00:29:51.463 --> 00:29:58.476 particularly black and Latinx women. Next slide, please. 00:29:58.509 --> 00:30:02.347 So through um uh the support of the National Science Foundation Grant 00:30:02.380 --> 00:30:06.065 called courage, challenging, operationalized and understanding, racialized 00:30:06.098 --> 00:30:10.186 and gendered events in undergraduate mathematics coupled with the support 00:30:10.219 --> 00:30:14.285 of the collaborative, um I was able to carve out a line of inquiry that 00:30:14.318 --> 00:30:19.361 looks specifically at two black women and three Latinx women's perceptions 00:30:19.394 --> 00:30:22.920 of calculus destruction. And they were all pursuing either computing or 00:30:22.953 --> 00:30:26.641 engineering majors. And for this talk, I'm focusing specifically on 00:30:26.674 --> 00:30:32.561 computing. Uh the nature of the study design was all participants um 00:30:32.594 --> 00:30:36.847 participated in either an individual or group interview that was designed 00:30:36.880 --> 00:30:42.006 around 4 to 5 stimulus events. And those stimulus events really derived 00:30:42.039 --> 00:30:46.637 from participants journaling of positive and negative moments in their 00:30:46.670 --> 00:30:50.926 classroom spaces, particularly of instructional moments that they found to 00:30:50.959 --> 00:30:56.295 either be socially affirming or dis of their social identities. Um And so 00:30:56.328 --> 00:31:00.647 , taking that purpose of journaling entries, we created 4 to 5 stimulus 00:31:00.680 --> 00:31:05.295 events where we remove students reactions to the original event as well as 00:31:05.328 --> 00:31:10.226 removed um perspectives about race and gender to keep it as a more open 00:31:10.259 --> 00:31:15.276 ended experience of those moments in the interview space and then created 00:31:15.309 --> 00:31:18.686 questions to be able to ask students about the different features of those 00:31:18.719 --> 00:31:23.075 instructional events that they found to be potentially racialized and or 00:31:23.108 --> 00:31:28.456 gendered to explore students responses through the interviews. I used the 00:31:28.489 --> 00:31:31.835 framework that I presented in the previous slide of white patriarchal 00:31:31.868 --> 00:31:35.746 space to be able to examine the racialized and gendered functions of 00:31:35.779 --> 00:31:40.055 instruction. And so the the crux of this presentation really answers this 00:31:40.088 --> 00:31:43.976 research question. What are racialized? What racialized and gendered 00:31:44.009 --> 00:31:49.045 mechanisms of inequality? Do Black and Latinx women and CN AC NE Computing 00:31:49.078 --> 00:31:52.295 and Engineering majors report and their perceptions of calculus 00:31:52.328 --> 00:31:59.526 instruction. Next slide. 00:31:59.559 --> 00:32:06.766 Thanks. So I have two themes here. Um So one mechanism that came up in the 00:32:06.799 --> 00:32:10.986 findings was this mechanism of activating exclusionary ideas of who 00:32:11.019 --> 00:32:15.406 belongs in stem across these two themes. I focused particularly on 00:32:15.439 --> 00:32:20.486 students responses to one instructional event called instructor mistake. 00:32:20.519 --> 00:32:24.531 And in the event it when an instructor makes a mistake on the board, um 00:32:24.564 --> 00:32:29.081 and a student volunteers a correction to the mistake and the instructor 00:32:29.114 --> 00:32:32.460 interrupts the student and says something along the lines of like, yeah, I 00:32:32.493 --> 00:32:36.242 know that was pretty intentional. Whereas when other students tried to 00:32:36.275 --> 00:32:40.467 correct the instructor, other students were thanked for the correction. Um 00:32:40.500 --> 00:32:44.075 So this is Jasmine, self identifies as a black woman pursuing a computer 00:32:44.108 --> 00:32:48.766 science major responding to the nature of this event. And as you can see 00:32:48.799 --> 00:32:52.565 in her responses, on the left hand side, she invokes this idea of a 00:32:52.598 --> 00:32:57.897 racialized and gendered distribution of who is deemed to hold mathematical 00:32:57.930 --> 00:33:01.776 authority. And so when you volunteer a correction because of this 00:33:01.809 --> 00:33:06.371 racialized and gendered way of, of us understanding who holds mathematical 00:33:06.404 --> 00:33:10.930 ability, the ways in which that correction might be taken up may differ 00:33:10.963 --> 00:33:15.131 along those lines. And so we can see how she begins to invoke that there 00:33:15.164 --> 00:33:19.492 is this double standard that exists within the classroom space of who 00:33:19.525 --> 00:33:24.401 actually holds access to space of being able to correct the instructor who 00:33:24.434 --> 00:33:28.631 by and large, traditionally, our position is holding all authority in 00:33:28.664 --> 00:33:33.097 calculus classrooms. And the second response that she has here, she starts 00:33:33.130 --> 00:33:37.726 to talk about how this relates to some of her work in computer science 00:33:37.759 --> 00:33:41.676 where as a field of computer science, it's oftentimes this idea of 00:33:41.709 --> 00:33:46.276 collaboration is valued. And so when you're correcting someone that might 00:33:46.309 --> 00:33:50.147 be a bid for being able to participate in a collaborative way, but she 00:33:50.180 --> 00:33:54.397 talks about how that bit of, of collaboration might be rendered in a 00:33:54.430 --> 00:33:58.585 racialized and gendered way which you can see then then withholds women of 00:33:58.618 --> 00:34:02.107 color. And for this particular case, a black women's ability to be able to 00:34:02.140 --> 00:34:07.387 tap in to that collaborative ethos in a computing in a computing 00:34:07.420 --> 00:34:11.936 environment. And so the takeaway message here is, and we can see how 00:34:11.969 --> 00:34:16.236 calculus instruction can be a source of racialized and gendered messaging 00:34:16.269 --> 00:34:20.267 which then implicates black and Latinx women's sense of belongingness in 00:34:20.300 --> 00:34:27.456 stem fields. And in this case, computing next slide, 00:34:27.489 --> 00:34:31.727 the second theme or mechanism that we identified in our findings was 00:34:31.760 --> 00:34:35.747 limiting opportunities for classroom participation and instructor support. 00:34:35.780 --> 00:34:38.885 And again, this is based off of that same event about correcting an 00:34:38.918 --> 00:34:42.557 instructor. And so you can see here on the left hand side, I have two 00:34:42.590 --> 00:34:46.675 excerpts from two Latinas. Uh one being a computer science major and one 00:34:46.708 --> 00:34:50.845 from uh from animal science who participated in a group interview together 00:34:50.878 --> 00:34:55.095 responding to this design and what they invoked in their responses was 00:34:55.128 --> 00:34:58.575 this again, this distribution of mathematical authority, but it wasn't 00:34:58.608 --> 00:35:01.995 specific to race or gender. At first, it was just about how instructors 00:35:02.028 --> 00:35:06.135 tend to hold all the power. Um And by and large, when students were asked 00:35:06.168 --> 00:35:09.414 about this event, they talked about how this type of event doesn't 00:35:09.447 --> 00:35:13.265 frequently happen in the classroom. But it's very representative of the 00:35:13.298 --> 00:35:17.155 power structures that exist in entry level math classrooms in higher 00:35:17.188 --> 00:35:21.666 education. And so Melanie really talks about this idea that participating 00:35:21.699 --> 00:35:25.885 in entry level math classroom spaces is generally a vulnerable experience. 00:35:25.918 --> 00:35:29.796 So she talks about this idea about speaking up in a big lecture hall. Um 00:35:29.829 --> 00:35:34.885 for example, but then Laura kind of extends that perspective and talks 00:35:34.918 --> 00:35:39.026 about how there's that generally vulnerable experience. But that there's 00:35:39.059 --> 00:35:43.577 this unique challenge, uniquely racialized and gender challenge that comes 00:35:43.610 --> 00:35:49.057 into play when you couple that participation with the ethos of stereotypes 00:35:49.090 --> 00:35:53.126 around who is positioned as being mathematically able. And so while 00:35:53.159 --> 00:35:57.046 participation already is challenging and vulnerable in general, when you 00:35:57.079 --> 00:36:01.267 couple that with those broader ideological forces of stereotyping, you can 00:36:01.300 --> 00:36:05.126 see that that form of participation then becomes more limited for women of 00:36:05.159 --> 00:36:10.066 color. And so I argue that without challenging the, without explicitly 00:36:10.099 --> 00:36:14.247 challenging that hierarchy of mathematical ability through instruction, 00:36:14.280 --> 00:36:18.086 women of color are are faced with inequitable opportunities to participate 00:36:18.119 --> 00:36:24.506 in classroom spaces. Final swipe 00:36:24.539 --> 00:36:28.066 and for concluding remarks. So two are more research oriented and one's 00:36:28.099 --> 00:36:32.376 more practice slash policy oriented. We can see that marginalizing forms 00:36:32.409 --> 00:36:35.896 of instruction may not necessarily be explicitly racialized or gendered. 00:36:35.929 --> 00:36:39.467 So that event that I talked about in this presentation, it didn't invoke 00:36:39.500 --> 00:36:43.115 race, it didn't invoke gender, but we can still see how that destruction 00:36:43.148 --> 00:36:47.173 is very much still embedded in what white supremacists and patriarchal 00:36:47.206 --> 00:36:50.762 ethos in the broader structure. And so even though you might not be 00:36:50.795 --> 00:36:55.001 explicitly naming those things, the ways in which students are receiving 00:36:55.034 --> 00:36:59.081 those messages through instruction are are having racialized and 00:36:59.114 --> 00:37:02.883 engendered impacts. The second is drawing on higher ed research around the 00:37:02.916 --> 00:37:07.349 very significance of adopting racially and gender forms of consciousness 00:37:07.382 --> 00:37:10.747 of instructional practices. So one event that I didn't share in this 00:37:10.780 --> 00:37:14.608 particular presentation was called Courser, where an instructor says, if 00:37:14.641 --> 00:37:17.948 you can't do this fast enough, you should reconsider staying in calculus 00:37:17.981 --> 00:37:22.468 even though again, not invoking race, not invoking gender. When we think 00:37:22.501 --> 00:37:25.856 about the broader conception of who's represented in these spaces, 00:37:25.889 --> 00:37:30.126 thinking about consciously, if I if I make this message to an entire class 00:37:30.159 --> 00:37:33.796 , how might those from historically marginalized backgrounds and 00:37:33.829 --> 00:37:38.006 stereotype backgrounds, how are they receiving a message like you should 00:37:38.039 --> 00:37:41.956 consider dropping down from calculus if you can't do it fast enough? And 00:37:41.989 --> 00:37:45.316 last but not least, unfortunately, calculus serves as a gatekeeper, a 00:37:45.349 --> 00:37:48.816 racialized and gendered gatekeeper in the ways in which we've designed 00:37:48.849 --> 00:37:52.655 higher education. So to be able to access computing majors, to be able to 00:37:52.688 --> 00:37:56.787 access engineering majors, you have to go through calculus and it's used 00:37:56.820 --> 00:38:00.541 very much. So as a gatekeeper where you hear discourses like being 00:38:00.574 --> 00:38:05.352 calculus ready for example. So to what extent can we begin to reimagine 00:38:05.385 --> 00:38:09.671 policies in higher education? Um and also the ways that we design our 00:38:09.704 --> 00:38:13.720 programs of study where we begin to challenge the ways in which calculus 00:38:13.753 --> 00:38:18.990 gets kind of glorified to be able to access um these different majors. The 00:38:19.023 --> 00:38:22.412 last slide is set of references for your reference and thanks so much for 00:38:22.445 --> 00:38:27.655 your time and all of your support today. Thank you so much Louise for your 00:38:27.688 --> 00:38:30.095 presentation. 00:38:30.128 --> 00:38:33.595 And um again, feel free to pose questions either into the chat or the Q 00:38:33.628 --> 00:38:38.365 and A box. Um Next up, we have Takita Thomas who will be talking about 00:38:38.398 --> 00:38:42.166 speaking truth to power, exploring the intersectional experiences of Black 00:38:42.199 --> 00:38:48.256 women in computing, Takita. You're up Alison. Um I actually dropped my 00:38:48.289 --> 00:38:51.566 contact information in the chat. I was trying to recon inside my kind of 00:38:51.599 --> 00:38:57.816 like information so great and, and you're a little bit uh muffled. So if 00:38:57.849 --> 00:39:00.336 you could try to speak up just a little bit, I think that would be super 00:39:00.369 --> 00:39:04.497 helpful. 00:39:04.530 --> 00:39:09.686 It's slightly better. OK. I, I had broken my toe and so my leg is popped 00:39:09.719 --> 00:39:14.345 up at the same time. So I'm trying to like manage this laptop on my, on my 00:39:14.378 --> 00:39:19.037 lap, on a box. So uh please bear with me. I'm gonna try to speak as 00:39:19.070 --> 00:39:24.686 clearly as I can. No worries. Go for it. So this project, a continuation 00:39:24.719 --> 00:39:29.506 of ongoing work exploring the intersectional experiences of Black women in 00:39:29.539 --> 00:39:34.376 computing. Currently, there is a Darth of research that focuses on that 00:39:34.409 --> 00:39:37.517 double bind and on other intersections that exist within the field. 00:39:37.550 --> 00:39:41.925 Although that body of work is beginning to grow, which is exciting. Um But 00:39:41.958 --> 00:39:48.997 if we want to have a major effects on um the representation of women of 00:39:49.030 --> 00:39:52.675 color, and if we want to truly broaden the same participation and 00:39:52.708 --> 00:39:56.986 engagement in computing, we have to have a more complex understanding of 00:39:57.019 --> 00:40:01.155 the experiences of marginalized groups in computing who lives at the 00:40:01.188 --> 00:40:05.967 various intersections of racism, sexism, xenophobia, hetero, sexism, 00:40:06.000 --> 00:40:10.106 ableism and so on and so forth. Um It's a area of research that we call 00:40:10.139 --> 00:40:14.256 intersectional computing next slide. 00:40:14.289 --> 00:40:18.095 So as such, this project examines the narrative of black women in 00:40:18.128 --> 00:40:21.666 different computing contexts such as undergraduate, graduate school, 00:40:21.699 --> 00:40:26.526 workforce government and academia. This work is really significant because 00:40:26.559 --> 00:40:30.405 the digital narrative of black women de demonstrate counter narratives to 00:40:30.438 --> 00:40:34.905 the perceived shared understandings of computing as a meritocratic claim 00:40:34.938 --> 00:40:38.706 level playing field that only really requires addressing access to 00:40:38.739 --> 00:40:43.135 computing to successfully broaden participation. This project also 00:40:43.168 --> 00:40:46.247 contributes to the field because it leverages intersectional framework and 00:40:46.280 --> 00:40:51.497 system to eliminate black women's experiences, oppression and identity 00:40:51.530 --> 00:40:56.425 politics. And this project also explores the role of power, the matrix of 00:40:56.458 --> 00:41:00.577 intersecting oppression and white supremacy in in in computing, leveraging 00:41:00.610 --> 00:41:05.046 work like a white supremacy culture characteristics which we leverage to 00:41:05.079 --> 00:41:09.086 code the digital narrative of 22 black women in computing So in this 00:41:09.119 --> 00:41:13.236 project, we aim to explore understand and address the three research 00:41:13.269 --> 00:41:16.997 questions that you see on the slide. But this presentation is going to 00:41:17.030 --> 00:41:21.626 focus primarily on the second question. So what do the experiences of 00:41:21.659 --> 00:41:25.577 black women in computing, tell us about white supremacy culture and its 00:41:25.610 --> 00:41:31.077 characteristics in the field of computing? Next slide. 00:41:31.110 --> 00:41:36.675 So given, given the time that I have today, um I'm going to present the um 00:41:36.708 --> 00:41:40.385 White supremacy culture characteristics that showed up most prevalently in 00:41:40.418 --> 00:41:44.885 the data. Um I also have some exempt quotes from the data to share, but I 00:41:44.918 --> 00:41:48.236 won't share with them during the presentation um to ensure that I stay 00:41:48.269 --> 00:41:50.925 within the time, but I'll certainly be willing to read some of those 00:41:50.958 --> 00:41:54.506 during the Q and A. If if people are interested, I'm currently working on 00:41:54.539 --> 00:41:57.717 a manuscript that's going to present a full set of findings for all of the 00:41:57.750 --> 00:42:01.905 research questions presented earlier. Um But in terms of a White supremacy 00:42:01.938 --> 00:42:05.666 culture characteristics, four of these characteristics embody those 00:42:05.699 --> 00:42:10.106 described by Oun. One is newly identified, it's a newly identified White 00:42:10.139 --> 00:42:14.566 supremacy culture characteristic that emerged from the data itself. So 00:42:14.599 --> 00:42:18.936 over one quarter of the data describe instances of perfectionism which 00:42:18.969 --> 00:42:23.307 involves having little appreciation. Um It express among people for the 00:42:23.340 --> 00:42:27.017 work that others are doing to point out either how the person of the work 00:42:27.050 --> 00:42:31.126 is inadequate to talk to others about the inadequacies of a person or 00:42:31.159 --> 00:42:35.336 their work without ever talking directly to them. And it's uh it's um a, a 00:42:35.369 --> 00:42:38.896 culture characteristic where mistakes are seen as personal. In other words 00:42:38.929 --> 00:42:42.856 , they reflect badly on the person, making them as opposed to them being 00:42:42.889 --> 00:42:46.655 seen for what they are, which is just a mistake. So making a mistake is 00:42:46.688 --> 00:42:50.956 often confused with being a mistake, doing wrong is often confused with 00:42:50.989 --> 00:42:54.865 being wrong. So one quote says, I think my experience in dealing with 00:42:54.898 --> 00:42:59.077 expectations, I put a lot of pressure on myself. A lot of times what I 00:42:59.110 --> 00:43:02.577 think people expect from me. And really, I mean, mostly what I expect from 00:43:02.610 --> 00:43:06.526 myself and what I should look like and how like how do I fit into the 00:43:06.559 --> 00:43:10.195 world? They expect me to either be able to do it all because I have a, we 00:43:10.228 --> 00:43:13.276 had a welcome experience or they pretty much expect me to be there and 00:43:13.309 --> 00:43:17.436 just coign whatever they want to do. So at one extreme or the other, not 00:43:17.469 --> 00:43:20.986 much in the middle, I tend to operate a little bit better, more in the 00:43:21.019 --> 00:43:24.776 doing it all space. But I know that most people, for most people, often 00:43:24.809 --> 00:43:29.717 the expectation is fairly low. One part of the data describes instances of 00:43:29.750 --> 00:43:34.206 paternalism. This involves when decision making is clear to those who have 00:43:34.239 --> 00:43:38.296 power, but un yourself without it, those with the power think that they're 00:43:38.329 --> 00:43:43.115 capable of making decisions for and in the interest of those without power. 00:43:43.148 --> 00:43:46.287 And those with power often don't think it's important or necessary to 00:43:46.320 --> 00:43:49.356 understand the viewpoint or experience of those for whom they're making 00:43:49.389 --> 00:43:54.135 decisions. Those without the power understand they do not have it and they 00:43:54.168 --> 00:43:57.997 understand who does have the power and those without power do not really 00:43:58.030 --> 00:44:02.037 know how decisions get made and who makes what decisions. But yet they are 00:44:02.070 --> 00:44:05.945 completely familiar with the impact of those decisions on them. So one 00:44:05.978 --> 00:44:10.206 quote was so his advice to me was, you know, maybe computer science is not 00:44:10.239 --> 00:44:14.115 for you. He was like, if you're struggling on these basic courses, you're 00:44:14.148 --> 00:44:17.486 probably not going to make it in the field. And he actually advised me to 00:44:17.519 --> 00:44:20.477 switch to business, which was like, I really have no interest in that 00:44:20.510 --> 00:44:25.606 whatsoever. It's important to note that paternalism and perfectionism 00:44:25.639 --> 00:44:29.706 often occur together in the data. And in addition, it's also important to 00:44:29.739 --> 00:44:33.307 note that our analysis revealed that power hoarding often occurred in 00:44:33.340 --> 00:44:39.037 concert with either fraternal or perfectionism. Power hoarding involves 00:44:39.070 --> 00:44:43.586 little if any value of being seen around sharing power, power is seen as 00:44:43.619 --> 00:44:47.385 limited. There's only so much to go around and those with power feel 00:44:47.418 --> 00:44:50.577 threatened when anyone suggests changes in how things should be done in 00:44:50.610 --> 00:44:53.767 the organization because they feel those suggestions for change are a 00:44:53.800 --> 00:44:57.486 reflection on their leadership. Those with power don't often see 00:44:57.519 --> 00:45:01.626 themselves as supporting power or as feeling threatened and they assume 00:45:01.659 --> 00:45:04.606 they have the best interests of the organization at heart and assume that 00:45:04.639 --> 00:45:10.287 those wanting change are ill informed, stupid, emotional or inexperienced. 00:45:10.320 --> 00:45:13.666 So the quote was, it's frustrating to encounter someone that doesn't see 00:45:13.699 --> 00:45:17.736 how fast, fantastic and capable I am and try to limit my experience and my 00:45:17.769 --> 00:45:23.396 exposure. 20% of the data describe instances of individualism. This 00:45:23.429 --> 00:45:27.416 involves little experience or comfort working as part of a team. People in 00:45:27.449 --> 00:45:31.537 the organization believe they are responsible for solving problems alone, 00:45:31.570 --> 00:45:35.827 accountability if any goes up and down but not sideways to peers or 00:45:35.860 --> 00:45:39.997 organization or those for whom the organization is set to serve. The 00:45:40.030 --> 00:45:43.945 desire for individual recognition and credit leads to isolation and 00:45:43.978 --> 00:45:49.126 competition is more highly valued than cooper operation. So one quote was 00:45:49.159 --> 00:45:52.727 so for most of my career in the it field, I was the only one who was 00:45:52.760 --> 00:45:57.006 represented from an IT standpoint as a black woman. What's interesting now 00:45:57.039 --> 00:46:00.445 is that now I kind of shifted into the mode of black security. And once 00:46:00.478 --> 00:46:04.166 again, I'm seeing the same thing that's happening. Many of the conferences 00:46:04.199 --> 00:46:07.566 that I attend. Many of my colleagues are white men, especially when you're 00:46:07.599 --> 00:46:11.561 talking about technology and how and digital forensics and things like 00:46:11.594 --> 00:46:15.541 that. We rarely see people of color, black women involved in those types 00:46:15.574 --> 00:46:20.030 of activity. So I think all I can say and unfortunately, I feel that I 00:46:20.063 --> 00:46:24.470 always felt that I was the only one in many of these situations. The bad 00:46:24.503 --> 00:46:27.490 thing about it is that it seems like we're still hearing about that and I 00:46:27.523 --> 00:46:31.827 just wish we could do more to make things happen a little bit better. 00:46:31.860 --> 00:46:36.456 Finally, 11% of the data describe instances of what we call knighting or 00:46:36.489 --> 00:46:39.896 blessing, which is a new white supremacy culture characteristic that 00:46:39.929 --> 00:46:44.497 emerged from the data. It's one of the most prevalent ones um that emerged. 00:46:44.530 --> 00:46:48.666 This involves the words, ideas and work of outside viewpoints not being 00:46:48.699 --> 00:46:52.006 acknowledged, endorsed for champions until someone white, preferably a 00:46:52.039 --> 00:46:56.736 white male champions that acknowledges it or endorses it. Person in power 00:46:56.769 --> 00:47:00.217 has to bless or endorse capability and knowledge. And that endorsement, 00:47:00.250 --> 00:47:04.106 blessing or night may have to be given multiple times before it's accepted. 00:47:04.139 --> 00:47:07.626 Prior work, performance, success may not be remembered, acknowledged or 00:47:07.659 --> 00:47:11.467 held up as evidence of endorse of competition. And then that endorsement 00:47:11.500 --> 00:47:15.816 of blessing may have to be re aow or reissued if the given situation 00:47:15.849 --> 00:47:19.787 changes, which is often outside of the person without power of control. So 00:47:19.820 --> 00:47:23.767 if there's a new manager, new members join the team, new, move to a uh or 00:47:23.800 --> 00:47:27.546 a person moves to a new department. And so this the quote was and so this 00:47:27.579 --> 00:47:30.876 gentle went with my boss and we discuss projects and it got to the point 00:47:30.909 --> 00:47:33.807 where I felt like he didn't think I knew what I was doing, didn't think I 00:47:33.840 --> 00:47:37.365 knew how to do what I was supposed to do. And there was, they hired 00:47:37.398 --> 00:47:40.885 another guy, a white gentleman, if I call him that and within a matter of 00:47:40.918 --> 00:47:44.432 months, he had promoted that guy ahead of me and the guy and I were with 00:47:44.465 --> 00:47:47.811 friends, no issues with him at all. He was a great guy, but I knew there 00:47:47.844 --> 00:47:50.981 was nothing that he could do that I couldn't do. But I was not given those 00:47:51.014 --> 00:47:54.490 opportunities anymore. So I felt like probably that had something to do 00:47:54.523 --> 00:47:57.570 with me being a woman and being black. And that was part of the beginning 00:47:57.603 --> 00:48:01.615 of where I started looking to move on to other places. There were other 00:48:01.648 --> 00:48:04.905 new white supremacy culture characteristics that emerged from the data. Um 00:48:04.938 --> 00:48:07.896 I'd be happy to talk about those during Q and A. But for the sake of time 00:48:07.929 --> 00:48:13.445 , um next time I'd like to move on to some of the takeaways. So OK, 00:48:13.478 --> 00:48:16.546 describes antidotes to each of the white supremacy culture characteristics 00:48:16.579 --> 00:48:21.467 , but instead of outlining specific specific recommendations, I think in 00:48:21.500 --> 00:48:25.595 this moment and especially in this climate, giving a charge and a call to 00:48:25.628 --> 00:48:28.977 action to the communi to the computing community in specific ways is the 00:48:29.010 --> 00:48:33.267 most appropriate thing to do. So first, we have to acknowledge and put to 00:48:33.300 --> 00:48:36.497 rest once and for all that computing and computer science are color blind 00:48:36.530 --> 00:48:40.936 meritocracy. It's just not true rather rather they are matrices of 00:48:40.969 --> 00:48:44.517 intersecting oppression, an interconnected system grounded in white 00:48:44.550 --> 00:48:48.905 supremacy as experienced by black women in computing the concept of a 00:48:48.938 --> 00:48:52.845 system is a foundational concept in computer science. The way systems form 00:48:52.878 --> 00:48:56.706 how they function their outcomes, their economies, their components and 00:48:56.739 --> 00:49:01.557 other aspects of system and system thinking under the field. So we can 00:49:01.590 --> 00:49:05.756 apply that same understanding and rigor to ana analyzing how this do this 00:49:05.789 --> 00:49:09.816 matrix of domination and oppression marginalizes group within computing. I 00:49:09.849 --> 00:49:13.336 think this is necessary for dismantling systems, instructors that have 00:49:13.369 --> 00:49:17.695 yielded what Collins called saturated types of violence in CS education 00:49:17.728 --> 00:49:21.595 and gendered racism in much of computing as a field. When we consider 00:49:21.628 --> 00:49:25.537 transforming computing and to be clear, I am of the mind that computing 00:49:25.570 --> 00:49:30.155 should be transformed and not reformed. We must begin to think about that 00:49:30.188 --> 00:49:34.365 transformation at the interpersonal level as well as at the systemic level 00:49:34.398 --> 00:49:39.256 as both are required for true transformation. This work serves as a first 00:49:39.289 --> 00:49:42.365 step in covering these oppressive power structures and making the role of 00:49:42.398 --> 00:49:46.095 white supremacy and power more difficult in computing. So I charge the 00:49:46.128 --> 00:49:49.526 community to think about structural changes and not just interpersonal 00:49:49.559 --> 00:49:53.537 ones that can create a more equitable and just field of computing. For 00:49:53.570 --> 00:49:57.236 those who wield power and benefit from those systems of oppression, they 00:49:57.269 --> 00:50:01.095 must do the work of dismantling them. If marginalized groups understood 00:50:01.128 --> 00:50:03.967 how these oppressive structures were originally constructed, we would have 00:50:04.000 --> 00:50:07.675 dismantled them all of the uh we did not build these systems and we should 00:50:07.708 --> 00:50:12.037 not be responsible for dismantling them. We argue that further research 00:50:12.070 --> 00:50:16.615 that examines systemic oppression in CS is necessary for three reasons. 00:50:16.648 --> 00:50:20.316 First, we need to hold the field of computing accountable for its role in 00:50:20.349 --> 00:50:23.856 enabling oppression and upholding white supremacy, which has resulted in 00:50:23.889 --> 00:50:27.916 the marginalization of black women as well as others. To hold the field 00:50:27.949 --> 00:50:31.695 accountable, we must expose how interlocking systems of power rooted in 00:50:31.728 --> 00:50:34.896 white supremacy enable oppression in the field so that we know what it 00:50:34.929 --> 00:50:39.517 looks like. And so we'll be able to assess its impact. Second, black women 00:50:39.550 --> 00:50:43.807 in computing need more allies on what I like to call co-conspirators. Our 00:50:43.840 --> 00:50:48.017 lived intersectional experiences are evidence of how power and privilege 00:50:48.050 --> 00:50:51.967 operate in the field of computing to build coalition. We need more people 00:50:52.000 --> 00:50:55.385 to empathize with the collective standpoint of black women in computing 00:50:55.418 --> 00:50:58.967 and be willing to act even in ways that might result in a loss of 00:50:59.000 --> 00:51:03.296 previously held benefits to the dominant culture. Finally, if we're to 00:51:03.329 --> 00:51:07.175 truly transform computing into an inclusive diverse and equitable 00:51:07.208 --> 00:51:10.646 community for all, then we have to take steps to dismantle systemic 00:51:10.679 --> 00:51:14.146 oppression and its many manifestations to the field of computing. And we 00:51:14.179 --> 00:51:17.776 have to be committed to that work. We call for the computing community to 00:51:17.809 --> 00:51:21.967 build coalitions with Black Latina native Asian and specific Islander 00:51:22.000 --> 00:51:25.816 scholars who do this work within their community. This creates a pathway 00:51:25.849 --> 00:51:29.365 to understand the particular matrix of intersecting power dynamics that 00:51:29.398 --> 00:51:33.186 affect each of these groups. Such research will dispel this notion of 00:51:33.219 --> 00:51:36.497 women of color and computing as being homogeneous or having the same 00:51:36.530 --> 00:51:40.796 challenges. This suggests that within computing, we need to develop new 00:51:40.829 --> 00:51:44.997 theories, methods and approaches to engaging understanding and supporting 00:51:45.030 --> 00:51:50.296 marginalized groups in relationship and in coalition with those groups as 00:51:50.329 --> 00:51:54.655 they contend with saturated types of epidemic violence and white supremacy. 00:51:54.688 --> 00:51:58.517 I appreciate your time and I thank the women of color and fortune for the 00:51:58.550 --> 00:52:03.477 support for this work, Takita. Thank you very much for your um amazing 00:52:03.510 --> 00:52:06.655 research findings and also for your charge to the broader computing 00:52:06.688 --> 00:52:13.845 community. Next up. Last, but definitely not least we have 00:52:13.878 --> 00:52:17.675 uh Linda Sachs Kate Lehman, Sarah Rodriguez and Daisy Ramirez who are 00:52:17.708 --> 00:52:21.086 going to talk to us about a mixed method study of the experiences of 00:52:21.119 --> 00:52:26.175 Latinas in computing. 00:52:26.208 --> 00:52:30.066 OK, great. 00:52:30.099 --> 00:52:33.717 I know you're getting this. That's OK. Yeah, no problem. All right, Linda. 00:52:33.750 --> 00:52:38.986 Thank you so much. All right. So I'm Linda Sachs, as Alison said, I'm a 00:52:39.019 --> 00:52:43.967 professor in the School of Education and Information Studies at UCL A. And 00:52:44.000 --> 00:52:48.166 we're really pleased to be here to share the findings from the study that 00:52:48.199 --> 00:52:51.796 we've been conducting over the past year, which is a mixed method study of 00:52:51.829 --> 00:52:56.486 the experiences of undergraduate Latino students in computing. Uh This 00:52:56.519 --> 00:53:01.925 project is a collaboration between the Momentum research team at UCL A 00:53:01.958 --> 00:53:06.506 which includes phd student Daisy Ramirez and Doctor Kate Lehman along with 00:53:06.539 --> 00:53:11.807 our dear colleague, Doctor Sarah Rodriguez at Texas A and M Commerce. This 00:53:11.840 --> 00:53:16.155 study comes out of research conducted on the braid initiative. And I know 00:53:16.188 --> 00:53:20.606 some of you know what braid is and maybe not everybody does. But braid um 00:53:20.639 --> 00:53:25.747 was established in 2014. It's a coalition of 15 universities that are 00:53:25.780 --> 00:53:30.106 engaged in efforts to diversify their undergraduate computing programs. 00:53:30.139 --> 00:53:35.195 And at UCL A, we have been engaged in research on uh what's happening at 00:53:35.228 --> 00:53:38.467 the great institutions, what's effective, what's not and, and tracking 00:53:38.500 --> 00:53:43.615 students trajectories over time. So, uh as part of our study, we've been 00:53:43.648 --> 00:53:49.695 doing a survey of 10,000 students who enrolled in introductory computing 00:53:49.728 --> 00:53:54.345 courses uh four or five years ago and have been followed up annually to, 00:53:54.378 --> 00:53:59.481 to track their trajectory through or sometimes out of computing fields. Uh 00:53:59.514 --> 00:54:02.751 Before we move on, I want to acknowledge funding for the surveys comes 00:54:02.784 --> 00:54:06.892 from a variety of sources including Anita b.org, the Computing Research 00:54:06.925 --> 00:54:10.521 Association, the National Science Foundation and of course funding for 00:54:10.554 --> 00:54:14.501 this particular study uh which collects some additional qualitative data, 00:54:14.534 --> 00:54:17.211 came from the Kapor Center and the Women of Color and Computing 00:54:17.244 --> 00:54:22.206 collaborative. So, next slide. 00:54:22.239 --> 00:54:26.026 So really briefly before I turn it over to my colleagues uh over the past 00:54:26.059 --> 00:54:30.416 few decades um and you can advance uh you probably just click through, 00:54:30.449 --> 00:54:34.126 I'll just tell you. OK. Yeah. Yeah, it's just, that's good. That's perfect. 00:54:34.159 --> 00:54:37.606 Um Over the past few decades, we've seen an increase in the number of 00:54:37.639 --> 00:54:41.037 Latino students entering higher education and earning uh bachelor's 00:54:41.070 --> 00:54:46.595 degrees currently earning 8.2% of bachelor's degrees. But uh you know, it 00:54:46.628 --> 00:54:50.276 sort of reflected in a lot of the presentations we've heard so far. Uh 00:54:50.309 --> 00:54:55.046 Latinas, you earn only 2% of computing bachelor's degrees and are 00:54:55.079 --> 00:55:00.901 represented 5% of those who are tech employees. So this project uses a 00:55:00.934 --> 00:55:04.631 mixed methods approach using both grade survey data and then interview 00:55:04.664 --> 00:55:08.131 data collected more recently to understate the experiences of Latina 00:55:08.164 --> 00:55:11.211 students who pursue computing in a college are the ones who actually did 00:55:11.244 --> 00:55:14.990 pursue computing. And then looks at the factors that promote their 00:55:15.023 --> 00:55:23.023 persistence in the field. So I'm going to turn it over to Daisy. 00:55:23.628 --> 00:55:26.905 Uh Yeah. Can you look please? 00:55:26.938 --> 00:55:31.845 Um And you can click a couple times here. Um So in the study, we utilize 00:55:31.878 --> 00:55:34.747 the mixed methods approach to understand the trajectories of Latina 00:55:34.780 --> 00:55:39.195 students who enrolled in intro computing courses and through the computing 00:55:39.228 --> 00:55:44.166 major or minor. So for the quantitative stream, we utilize data from the 00:55:44.199 --> 00:55:48.727 braid longitudinal survey to descriptively analyze data from Latina 00:55:48.760 --> 00:55:52.736 respondents to understand their backgrounds when they arrived to the intro 00:55:52.769 --> 00:55:57.767 courses. And we followed their pathways into computing majors or minors. 00:55:57.800 --> 00:56:01.126 And for the qualitative stream, we reached out to the pool of Latina 00:56:01.159 --> 00:56:04.655 respondents on the braid survey to ask if they would be willing to 00:56:04.688 --> 00:56:08.675 participate in interviews. We then conducted two interviews with each 00:56:08.708 --> 00:56:13.445 student, one initial and a post. Uh a follow up interview. A couple of 00:56:13.478 --> 00:56:18.477 weeks later with 10 students and nine of those were competing majors and 00:56:18.510 --> 00:56:23.456 we had one competing minor and the sample included one junior, five 00:56:23.489 --> 00:56:28.675 seniors and four participants that had graduated within the past year. And 00:56:28.708 --> 00:56:34.750 all of them were working um in computing or were in computing grad school. 00:56:34.869 --> 00:56:36.869 Uh Next, please. So I'm gonna share some of our quantitative findings and 00:56:39.289 --> 00:56:43.706 some of the questions that we asked, you can click again. So we asked, how 00:56:43.739 --> 00:56:49.106 do Latinas arrive to the introductory computing course? We found that 64% 00:56:49.139 --> 00:56:52.557 of Latina students in the intro course were first generation college 00:56:52.590 --> 00:56:56.717 students, meaning that neither of the parents attended college. So we 00:56:56.750 --> 00:57:00.396 could assume that at least the first year students might arrive to college 00:57:00.429 --> 00:57:05.876 without or with limited knowledge about how to navigate campus. Um And 00:57:05.909 --> 00:57:09.436 they could benefit from being informed about the resources available to 00:57:09.469 --> 00:57:17.095 them such as tutoring or other support services. Click, please. 00:57:17.128 --> 00:57:21.106 Um We also found that 12% of students in the sample had at least one 00:57:21.139 --> 00:57:25.807 parent working in a computing career while an additional 20% had at least 00:57:25.840 --> 00:57:30.986 one parent working in the stem non computing career. In addition, we found 00:57:31.019 --> 00:57:35.706 that 72% of students had prior computing experience in the form of a high 00:57:35.739 --> 00:57:40.717 school course, college course, computing camp or were self taught before 00:57:40.750 --> 00:57:46.767 enrolling in the introductory course. Next. 00:57:46.800 --> 00:57:53.106 Uh Yeah, you can click and stop there. Yes. So next, we wanted to find um 00:57:53.139 --> 00:57:58.836 how did Latinas uh self ratings change from the start to the end of the 00:57:58.869 --> 00:58:02.796 introductory computing course. So we asked students to rate themselves 00:58:02.829 --> 00:58:07.706 compared to their peers on a number of items in the survey. And um these 00:58:07.739 --> 00:58:11.307 graphs represent the proportions of Latinas in the study who rated 00:58:11.340 --> 00:58:16.227 themselves in the top 10 compared to their peers in these categories, 00:58:16.260 --> 00:58:20.217 computer skills, intellectual, self-confidence, math, ability, creativity 00:58:20.250 --> 00:58:28.115 , and drive to achieve. So on the left, uh we have the beginning of course 00:58:28.148 --> 00:58:33.586 ratings and on the right, we have the end of course ratings. Um We notice 00:58:33.619 --> 00:58:37.206 that the students are developing their skills over time. And while we can 00:58:37.239 --> 00:58:41.635 attribute all of these changes to their experience in the intro course, we 00:58:41.668 --> 00:58:45.356 find a large increase in computing skills which is directly tied to their 00:58:45.389 --> 00:58:49.376 intro computing course and some of the other big gains are in their math 00:58:49.409 --> 00:58:54.606 ability, creativity, and drive to achieve 00:58:54.639 --> 00:58:58.026 next. 00:58:58.059 --> 00:59:02.977 So finally, we wanted to know um or understand La Latina students 00:59:03.010 --> 00:59:07.865 persistence in computing majors over time. So although most computing stu 00:59:07.898 --> 00:59:12.086 Latina students enrolled in the intro computing course were computing 00:59:12.119 --> 00:59:16.095 majors just over half of the students in the sample did not persist in the 00:59:16.128 --> 00:59:20.477 major two years after the intro course. 00:59:20.510 --> 00:59:25.577 So at the top there, we see um that of the Latina students who took the 00:59:25.610 --> 00:59:31.327 intro course and responded to the surveys, 73.3% of those who said they 00:59:31.360 --> 00:59:35.365 were computing majors at the end of the intro course, persisted one year 00:59:35.398 --> 00:59:43.155 later. And on the second, the bottom chart there, uh we find that from the 00:59:43.188 --> 00:59:48.916 end of the intro course to two years later, 66.7% of Latina students 00:59:48.949 --> 00:59:53.385 persisted as computing majors. So we do notice a significant drop off for 00:59:53.418 --> 00:59:58.796 Latina Latina students over this period of time. 00:59:58.829 --> 01:00:03.666 Uh Next, we'll have Doctor Sarah Rodriguez present some of the qualitative 01:00:03.699 --> 01:00:09.686 findings. Thanks Stacey. So, in the qualitative stream, um we're gonna go 01:00:09.719 --> 01:00:14.227 over three findings that we had for that. So you can go ahead and click 01:00:14.260 --> 01:00:17.307 through. Uh There are three of them. Um So the first one is about 01:00:17.340 --> 01:00:21.646 leveraging capital, uh familial capital specifically to navigate computing. 01:00:21.679 --> 01:00:26.086 And so really with this, we saw a counter narrative that so often we, we 01:00:26.119 --> 01:00:30.345 see that Latinx families are are portrayed negatively, but really in our 01:00:30.378 --> 01:00:33.557 findings, we saw that there was a lot of familial capital that was going 01:00:33.590 --> 01:00:38.456 into these Latina students and their experiences in computing. Uh families 01:00:38.489 --> 01:00:41.816 were providing knowledge, skills exposure, helping them to make 01:00:41.849 --> 01:00:45.905 connections within in computing. Um Also, we saw that there was an 01:00:45.938 --> 01:00:50.146 importance of curricular extracurricular and internship experiences. So 01:00:50.179 --> 01:00:54.686 really getting in there and connecting with identity based organizations 01:00:54.719 --> 01:00:58.885 that are either racial, ethnic or gender in this case, um or attending 01:00:58.918 --> 01:01:04.231 conferences. So things like Tapia uh or Grace Hopper that really allow 01:01:04.264 --> 01:01:08.122 Latino students in computing to connect with their racial and gender 01:01:08.155 --> 01:01:12.510 diverse peers. Um And really, that helped to build this sense of belonging 01:01:12.543 --> 01:01:15.481 and computing identity. So kind of like we've seen in prior presentations 01:01:15.514 --> 01:01:22.231 , um really um getting a sense of belonging and computing uh very early, 01:01:22.264 --> 01:01:26.066 these were very important to them. The third finding that we had was about 01:01:26.099 --> 01:01:29.865 experiencing the proving process and paving the way for others. So 01:01:29.898 --> 01:01:33.787 particularly with this, we saw that Latino students were very aware that 01:01:33.820 --> 01:01:37.635 they were being uh that they were minoritized that they were marginalized 01:01:37.668 --> 01:01:41.767 in these computing settings. And, and they very, very much articulated 01:01:41.800 --> 01:01:46.776 that um in their interviews. However, they also showed a lot of agency in 01:01:46.809 --> 01:01:51.175 the fact that they said that, you know, I'm proving the, I'm, I'm really 01:01:51.208 --> 01:01:55.086 paving the way for others um specifically for women of color in computing 01:01:55.119 --> 01:02:02.486 , but especially for Latinas in computing, we can go to the next slide. 01:02:02.519 --> 01:02:07.477 So this is just a snapshot. Um you know, for the interest of time of the 01:02:07.510 --> 01:02:11.836 proving process and paving the way for others and I won't read this all to 01:02:11.869 --> 01:02:16.876 you. But essentially the students um in our data set were really specific 01:02:16.909 --> 01:02:19.945 and articulate about saying, you know, they understand that they are 01:02:19.978 --> 01:02:23.405 minorities in computing, they understand that they are trying to bridge 01:02:23.438 --> 01:02:28.356 the gap and really pave the way for students to come after them in 01:02:28.389 --> 01:02:30.606 computing. 01:02:30.639 --> 01:02:36.115 OK, we can go to the final slide. So in terms of recommendations, we have 01:02:36.148 --> 01:02:40.396 three. So you can go ahead and click through those. Um So the first one is 01:02:40.429 --> 01:02:44.195 about enhancing sense of belonging and computing. So as you saw in both 01:02:44.228 --> 01:02:47.827 our quantitative and our qualitative, you know, those those initial 01:02:47.860 --> 01:02:50.791 experiences are really important, but it's also important throughout the 01:02:50.824 --> 01:02:55.682 entire pipeline to, you know, formalize mentorship programs to really, you 01:02:55.715 --> 01:02:59.881 know, think about extending funding for conferences to places like Tapia 01:02:59.914 --> 01:03:03.671 and Grace Hopper, which are great experiences for Latinas in computing to 01:03:03.704 --> 01:03:07.486 connect to other Latinas in computing. Um We also need to think about 01:03:07.519 --> 01:03:12.046 leveraging students as familial capital in the computing context. So not 01:03:12.079 --> 01:03:15.646 forgetting that students are coming with, with such capital to these 01:03:15.679 --> 01:03:20.635 spaces. And it's up to us to figure out how to leverage that for their 01:03:20.668 --> 01:03:24.675 success. The second recommendation that we have is around the intro 01:03:24.708 --> 01:03:29.376 courses. So really thinking about intro courses as a space of support and 01:03:29.409 --> 01:03:33.296 we know based on prior research that these intro courses are important but 01:03:33.329 --> 01:03:39.477 really being specific about connecting students to specific resources um 01:03:39.510 --> 01:03:45.106 that can help Latinas in CS, you know, normalize their use and finally 01:03:45.139 --> 01:03:48.345 providing students with the skills and information that they need 01:03:48.378 --> 01:03:53.175 regarding internships, um undergraduate research experiences and other 01:03:53.208 --> 01:03:57.046 supports that are going to enrich their experience, not just for the intro 01:03:57.079 --> 01:03:59.787 course, which is very important, but also throughout their entire 01:03:59.820 --> 01:04:04.256 experience. So that wraps up our uh presentation if you have any questions 01:04:04.289 --> 01:04:09.385 , definitely feel free to drop them in the chat or to connect with us. Um 01:04:09.418 --> 01:04:13.905 because we would love to share the work with you. 01:04:13.938 --> 01:04:20.666 Amazing. Thank you so much Daisy Kate and uh Sarah. So I'm going to stop 01:04:20.699 --> 01:04:23.936 sharing my screen now that I can see all the questions rolling in and it 01:04:23.969 --> 01:04:28.936 looks like we have about 20 minutes um to have a little bit of discussion. 01:04:28.969 --> 01:04:32.807 Um I know I, I think there's been a bunch of questions coming in. Some of 01:04:32.840 --> 01:04:37.146 the questions have been answered already. Um So maybe I will just pick one 01:04:37.179 --> 01:04:45.179 of these and see if we can get a discussion going. 01:04:48.139 --> 01:04:51.856 I know there was one question that came into the chat about um de 01:04:51.889 --> 01:04:55.655 disaggregation of data and how we think about when we're talking about 01:04:55.688 --> 01:04:59.977 women of color. Um And I'd love for any of the presenters to talk about 01:05:00.010 --> 01:05:04.175 how you um classified the populations that you were looking at and why you 01:05:04.208 --> 01:05:12.208 chose to focus on specific populations. Maybe that can get us started. 01:05:19.949 --> 01:05:26.845 Would anyone like to dive in on that? 01:05:26.878 --> 01:05:32.077 I can just talk about our decision, my team's decision to study native 01:05:32.110 --> 01:05:37.115 women in computing um and two spirit individuals in computing education. 01:05:37.148 --> 01:05:42.017 Um It really had to do with the fact. Um As as many of, you know, my team 01:05:42.050 --> 01:05:48.217 is involved in for the past two decades, studying women of color and stem 01:05:48.250 --> 01:05:56.250 um And what we had noticed in the many, you know, um literature reviews 01:05:58.239 --> 01:06:04.385 and literal synthesis we've done is very, very little exist on, on native 01:06:04.418 --> 01:06:11.736 women in stem education, let alone computing, but, and nothing exists on 01:06:11.769 --> 01:06:19.769 two spirited individuals in um in computing or stem education. So, um when 01:06:20.398 --> 01:06:28.267 this opportunity from the WOCCC came up, um Nuria um who initiated the 01:06:28.300 --> 01:06:32.717 queries and um we're, we're, we feel very fortunate to be able to 01:06:32.750 --> 01:06:36.247 contribute just a little bit. And I'm also happy to say that we have 01:06:36.280 --> 01:06:40.997 leveraged what we've learned here in this, this project into a, a newly 01:06:41.030 --> 01:06:47.816 funded NSF project that will be a uh four year investigation on uh native 01:06:47.849 --> 01:06:53.057 students and professionals in the stem fields. 01:06:53.090 --> 01:06:57.115 Thank you very much. It seems like there's another question. Let me find 01:06:57.148 --> 01:07:01.077 it here in the chat um from Erica Cruz who was asking about um Asian women 01:07:01.110 --> 01:07:06.077 as a broad umbrella term. Um What about Filipino Mong Vietnamese women? 01:07:06.110 --> 01:07:09.706 Are they also experiencing an increase in re uh representation? Or do, do 01:07:09.739 --> 01:07:13.467 we not yet know because it hasn't been disaggregated? And I think that was 01:07:13.500 --> 01:07:18.566 a question for Wanda's uh presentation on the um the data and computing 01:07:18.599 --> 01:07:22.236 degrees. 01:07:22.269 --> 01:07:26.227 Yeah, that was a question for us. So the ipads data that we use does not 01:07:26.260 --> 01:07:30.436 disaggregate um the population in that way where we can tell like we know 01:07:30.469 --> 01:07:34.106 anecdotally that the populations that were mentioned are probably 01:07:34.139 --> 01:07:37.706 underrepresented with respect to the rest of uh to other Asian populations 01:07:37.739 --> 01:07:44.436 , but we just don't have that data to talk through it. 01:07:44.469 --> 01:07:50.577 Thank you. I may add, may I add a little bit more Alison? Um So before I, 01:07:50.610 --> 01:07:55.077 before I decided to study women of color and stem, um I was an Asian 01:07:55.110 --> 01:08:01.945 Americanist studying Filipino American identity. Um And this question is 01:08:01.978 --> 01:08:07.057 actually near and dear to my heart. And I, I think that as stem education 01:08:07.090 --> 01:08:10.885 scholars, we really need to be careful not to dismiss Asian or Asian 01:08:10.918 --> 01:08:16.027 American women and to really pay attention to all of the subgroups that 01:08:16.060 --> 01:08:20.836 fall within that category. Um And I think that, you know, the chat um 01:08:20.869 --> 01:08:27.586 raises some excellent questions. Um And to also look at immigrant versus 01:08:27.619 --> 01:08:32.746 us born because there is a disparity there in between those groups. And, 01:08:32.779 --> 01:08:37.246 you know, and so you just keep, keep in mind. And I think I'm, I'm very 01:08:37.279 --> 01:08:42.946 uncomfortable with um the data that, that shows this ipad data or similar 01:08:42.979 --> 01:08:47.397 data. And, and basically concludes that Asians are fine and we need to 01:08:47.430 --> 01:08:50.946 just to focus on, you know, other groups of women of color because in fact 01:08:50.979 --> 01:08:58.826 , um we, my team and I have just conclude um we're con concluding a um a 01:08:58.859 --> 01:09:04.264 study on literature synthesis on women of color in computing from the last 01:09:04.297 --> 01:09:11.245 15 years, 1520 years. And um when we are looking at what do we know about 01:09:11.278 --> 01:09:17.796 Asian or Asian American women in the US um in computing computing science. 01:09:17.829 --> 01:09:22.796 The answer is very, very little. It's actually the least, um probably 01:09:22.829 --> 01:09:28.055 alongside native American women. It's, it's what is it, Nuria one or two 01:09:28.088 --> 01:09:33.476 less than a handful of pieces. Um So what do we know about Asian American 01:09:33.509 --> 01:09:38.857 women in computing education, qualitatively about their experiences about 01:09:38.890 --> 01:09:42.937 whether or not they suffer from microaggressions or discrimination of any 01:09:42.970 --> 01:09:47.217 kind. The answer is we don't know that, that, that research has not been 01:09:47.250 --> 01:09:52.305 done, it's not been funded um very extensively at all. So I think we 01:09:52.338 --> 01:09:55.226 should just, you know, it's not an either or it's a both and, and we 01:09:55.259 --> 01:10:01.286 should just stay curious and stay open to um further investigation. Um 01:10:01.319 --> 01:10:05.277 Just to follow up on that um our, our intentions were not to be dismissive 01:10:05.310 --> 01:10:09.956 of Asian women in that. Uh We were strictly looking at the numbers of what 01:10:09.989 --> 01:10:13.385 was happening and stating that our analysis because of what we were seeing 01:10:13.418 --> 01:10:18.326 , we chose to look at the groups in different ways. So we chose to look at 01:10:18.359 --> 01:10:21.675 women of color as a whole, which was including Asian women because we 01:10:21.708 --> 01:10:25.286 think all women of color will all women. Really, we're all 01:10:25.319 --> 01:10:29.437 underrepresented, right are important to look at. But we also saw that 01:10:29.470 --> 01:10:33.666 there was some differentiation there between some Asian women and then 01:10:33.699 --> 01:10:37.357 other women of color. And so we wanted to look at both groups. So it was 01:10:37.390 --> 01:10:42.857 making sure that the bit of disaggregation that we could do, we we would. 01:10:42.890 --> 01:10:46.116 Um And then there's just some nuances that we can't look at because we 01:10:46.149 --> 01:10:49.385 don't have more granular data. So it wasn't meant at all to be dismissive 01:10:49.418 --> 01:10:52.406 and say we, you know, Asian women are fine and they're doing well. 01:10:52.439 --> 01:10:55.746 Therefore we don't need to do anything. It was being really particular 01:10:55.779 --> 01:10:58.906 about, hey, there's some differences here and it's important to pay 01:10:58.939 --> 01:11:03.666 attention to. I appreciate that Shannon. That wasn't, yeah, that wasn't my 01:11:03.699 --> 01:11:07.397 intention to criticize you. But um oh I didn't take it as such. I just 01:11:07.430 --> 01:11:11.826 wanted to make sure that it was not meant to be dismissive was super clear. 01:11:11.859 --> 01:11:16.027 And thank you both Mia and Shani for um for those insightful answers. And 01:11:16.060 --> 01:11:20.675 I just wanted to reiterate um the purpose behind the women of color and 01:11:20.708 --> 01:11:24.607 computing collaborative originally was because we saw such a dearth in 01:11:24.640 --> 01:11:30.357 research on what are the barriers for all women of color. Um And we also 01:11:30.390 --> 01:11:33.421 thought it was super important to be able to break down and look at the 01:11:33.454 --> 01:11:36.362 categories of women of color. And I think this is a really important point 01:11:36.395 --> 01:11:41.121 to think about what, what's the next step in continuing to build this um 01:11:41.154 --> 01:11:45.241 this field and this area of literature as it relates to all of these 01:11:45.274 --> 01:11:50.041 populations that have very distinct and unique challenges. And um how are 01:11:50.074 --> 01:11:54.687 we ensuring that we're addressing each of those? So, thank you. Um I have 01:11:54.720 --> 01:11:59.536 one other question that came in from Jeff Forbes. Hello, Jeff. Um And I 01:11:59.569 --> 01:12:02.675 think it was answered by Luis, but I thought it was worth uh mentioning 01:12:02.708 --> 01:12:06.906 for the broader uh for a broader conversation. So Louis, um can you talk 01:12:06.939 --> 01:12:10.226 about how the impact of math culture on computing students possibly 01:12:10.259 --> 01:12:14.067 differs if first, if the first college course was discrete math versus 01:12:14.100 --> 01:12:17.866 calculus? And this real, this idea about um calculus being kind of the 01:12:17.899 --> 01:12:22.385 gatekeeper course. Yeah, I had so much fun with that question. So thank 01:12:22.418 --> 01:12:26.746 you, Jeff. Um because it got me thinking about how often times in the math 01:12:26.779 --> 01:12:32.196 world we tend to really like put on a pedestal, the more theoretically 01:12:32.229 --> 01:12:37.885 inclined subfields of mathematics. And so calculus arguably falls into 01:12:37.918 --> 01:12:42.437 that category. And so I've seen that oftentimes like discrete mathematics 01:12:42.470 --> 01:12:46.786 and probability and even sometimes statistics gets rendered to be more 01:12:46.819 --> 01:12:52.281 applied and so therefore, has less kind of clout or um is kind of less 01:12:52.314 --> 01:12:57.510 glorified if you will in mathematical spaces. So I think by and large, the 01:12:57.543 --> 01:13:01.732 cul the culture of calculus is really reinforcing this idea of who can 01:13:01.765 --> 01:13:06.180 understand these really theoretically dense and abstract ideas. And so the 01:13:06.213 --> 01:13:10.251 fact that we put it at the very beginning of students programs of study is 01:13:10.284 --> 01:13:15.010 really reinforcing the ways in which we really are um perpetuating the a 01:13:15.043 --> 01:13:19.031 lot of the the attrition that we see in these stem in the stem fields. And 01:13:19.064 --> 01:13:23.576 so I love this question because it's a reimagining. So what if it wasn't 01:13:23.609 --> 01:13:28.286 calculus? What if we had something else? Right. And I think even when we, 01:13:28.319 --> 01:13:32.226 I think part of the question to address issues of students who have access 01:13:32.259 --> 01:13:35.836 to calculus in high schools such as the advanced placement courses. And 01:13:35.869 --> 01:13:39.095 this makes me wonder about issues of equity. So who, which school 01:13:39.128 --> 01:13:42.527 districts have access to be able to offer a P classes, which school 01:13:42.560 --> 01:13:47.055 districts have access to math teachers who are able to teach calculus. And 01:13:47.088 --> 01:13:50.805 we see the ripple effect of that in our entry level math classes at the 01:13:50.838 --> 01:13:54.555 university level where students who do have access to that, those high 01:13:54.588 --> 01:13:59.036 school math taking patterns are able to maybe they feel more com confident 01:13:59.069 --> 01:14:02.487 in being able to participate in the classroom space because they were 01:14:02.520 --> 01:14:06.055 already able to kind of engage in some of that in their high school 01:14:06.088 --> 01:14:09.857 experiences. And so how, and I think that this then becomes an 01:14:09.890 --> 01:14:14.726 institutional responsibility of how are institutions addressing some of 01:14:14.759 --> 01:14:18.717 that because it can't just be on the students themselves. This is a 01:14:18.750 --> 01:14:23.706 structural issue and not an individual issue. 01:14:23.739 --> 01:14:27.277 Thank you so much, Louise and, and Luis, I know we've had this 01:14:27.310 --> 01:14:31.576 conversation like about math pathways like and how your work like connects 01:14:31.609 --> 01:14:36.576 to the pathways um in terms of like, I, I don't know where all is doing it 01:14:36.609 --> 01:14:39.385 , but like in the state of Texas specifically, like trying to 01:14:39.418 --> 01:14:43.281 institutionalize those math pathways in a really direct way, but then 01:14:43.314 --> 01:14:47.001 there's always the worry of tracking and like who gets placed into which 01:14:47.034 --> 01:14:51.161 one. And I, I don't know, I always get a little bit nervous um in terms of 01:14:51.194 --> 01:14:55.121 math pathways specifically or like as someone who didn't do well in 01:14:55.154 --> 01:14:59.321 calculus, like how much of an identity do you, are you able to form? Like 01:14:59.354 --> 01:15:03.001 , do you immediately like skip out of CAL, you know, skip out of doing CAL 01:15:03.034 --> 01:15:07.135 two and then not go a stem pathways. So I, yeah, I think that this work is 01:15:07.168 --> 01:15:10.706 super important for thinking about who gets into those pathways. You, 01:15:10.739 --> 01:15:14.706 that's such a good point, Sarah, because even in, in my in earlier work 01:15:14.739 --> 01:15:18.416 where I've done work around math, identity students shared in their 01:15:18.449 --> 01:15:22.576 interviews around how tracking happens when you have the engineering 01:15:22.609 --> 01:15:27.135 calculus versus the calculus for liberal arts, right? And so there's 01:15:27.168 --> 01:15:30.836 already status development that's being constructed by the ways in which 01:15:30.869 --> 01:15:34.226 we offer these different types of calculus for different purposes. So if 01:15:34.259 --> 01:15:38.446 engineering and stem already has some kind of status and you then 01:15:38.479 --> 01:15:40.956 associate it with the nature of the calculus courses that you're offering 01:15:40.989 --> 01:15:45.826 , you're essentially just doing tracking at the at the higher education 01:15:45.859 --> 01:15:49.385 level like as you experienced it in high school. And so even though it's 01:15:49.418 --> 01:15:53.675 not in it's probably not intended, again, seemingly neutral, well intended 01:15:53.708 --> 01:15:58.107 , but it has certain implications on how students begin to see themselves 01:15:58.140 --> 01:16:01.786 as, oh, I'm taking the calculus for, you know, for liberal arts, not the 01:16:01.819 --> 01:16:05.437 engineering calculus. 01:16:05.470 --> 01:16:09.437 Thank you both. Thank you, Sarah and Luis. Um So we have about 10 minutes 01:16:09.470 --> 01:16:13.756 left of this. We could probably keep this conversation going on forever. 01:16:13.789 --> 01:16:17.317 Um But I think since we have all of you here and each of you have really 01:16:17.350 --> 01:16:21.946 compelling findings, really building the literature around of what we know 01:16:21.979 --> 01:16:25.317 about the challenges facing women of color and what we should be doing to 01:16:25.350 --> 01:16:29.086 address those challenges. Um I would love to have you all answer a 01:16:29.119 --> 01:16:34.246 question that came in from uh my colleague and KP I Kim Scott about um it 01:16:34.279 --> 01:16:36.967 was directed towards Wanda, but I think each of you could maybe answer 01:16:37.000 --> 01:16:41.897 this question. Um What are some policy recommendations that you all think 01:16:41.930 --> 01:16:45.196 could come out of this work? So what are some of the next steps that we 01:16:45.229 --> 01:16:49.635 can um that we can recommend to the field? And we have about, I think we 01:16:49.668 --> 01:16:54.467 had about 80 participants. Um It's down to about 66 now. But what else do 01:16:54.500 --> 01:16:57.706 you want um participants in this webinar to really take away from your 01:16:57.739 --> 01:17:05.739 findings um to inform how we might improve the, the field 01:17:06.208 --> 01:17:14.208 and anyone can take that? 01:17:14.829 --> 01:17:18.326 Sure, I'll jump in. Um Thank you very much. So, I think what's been really 01:17:18.359 --> 01:17:22.286 interesting for me in hearing all the talks is to think about uh what 01:17:22.319 --> 01:17:27.357 we're learning about how the underrepresentation and exclusion of women of 01:17:27.390 --> 01:17:30.737 color, we really need to think about this as something that happens 01:17:30.770 --> 01:17:33.402 throughout the life course, right? And so we need to think about what's 01:17:33.435 --> 01:17:36.911 happening in adolescence and what are the barriers um that are happening 01:17:36.944 --> 01:17:39.121 and what are the positive things that are happening? You know, our study 01:17:39.154 --> 01:17:42.101 tried to focus some on the positive as opposed to always the sort of 01:17:42.134 --> 01:17:45.640 negative um views. So what are the things that are happening in 01:17:45.673 --> 01:17:49.746 adolescence and how are those similar or different than what's happening 01:17:49.779 --> 01:17:53.897 in the Higher Ed space? Right. So Luisa's work about the Higher Ed Space 01:17:53.930 --> 01:17:57.586 and Linda and Sarah and Daisy and Kate's work about the higher Ed space, 01:17:57.619 --> 01:18:00.336 right? J's work about the higher Ed space. So really thinking about how 01:18:00.369 --> 01:18:03.717 all of these things come together and what is similar and different at 01:18:03.750 --> 01:18:08.951 these different stages because um if we intervene uh or put all of our 01:18:08.984 --> 01:18:12.890 efforts only at one stage, um that's not gonna work, right? So if we 01:18:12.923 --> 01:18:16.842 really want to, to move the needle and have um women of color represented 01:18:16.875 --> 01:18:20.741 in computing, we need to be thinking about this as a, as a lifelong task 01:18:20.774 --> 01:18:27.036 um to bring women and young girls into this space. I also think to, to add 01:18:27.069 --> 01:18:30.696 on to what Katherine has said. Um I think something that's reflected in a 01:18:30.729 --> 01:18:35.116 lot of the work that we're all doing is also thinking about the computing 01:18:35.149 --> 01:18:38.967 experience as a, as an identity experience like, and I know that that 01:18:39.000 --> 01:18:43.726 seems really like, oh, well, Du Sarah, of course, we should. Um but you 01:18:43.759 --> 01:18:49.206 know, we don't really come to policy with an identity building framework. 01:18:49.239 --> 01:18:52.756 And I would really suggest that policymakers think about like in 01:18:52.789 --> 01:18:57.595 particular because it's my area, the higher ed space as like what are we 01:18:57.628 --> 01:19:01.206 doing from the intro course to the end to make that happen? And there are 01:19:01.239 --> 01:19:03.706 some really great models of people who are doing that. So University of 01:19:03.739 --> 01:19:06.805 Michigan is doing it for their engineering, you know, other people are 01:19:06.838 --> 01:19:10.576 thinking about engineering as spaces for engineering, identity development 01:19:10.609 --> 01:19:13.845 over time. And so there are models out there in terms of policy 01:19:13.878 --> 01:19:17.626 development and implementation that we can really think about borrowing 01:19:17.659 --> 01:19:23.135 from as computing. 01:19:23.168 --> 01:19:27.765 I can, I can add something from AAA different study that we're working on. 01:19:27.798 --> 01:19:33.196 We're looking at the predictive power of taking A P CS principles versus 01:19:33.229 --> 01:19:39.567 A P CS A on students decisions to enter uh computer science majors in 01:19:39.600 --> 01:19:42.726 college. And we're, we're finding, I mean, most it's more correlational 01:19:42.759 --> 01:19:47.366 than predictive. So being clear there. But we're finding that whereas the 01:19:47.399 --> 01:19:53.286 traditional A P CS A which which continues to be uh uh you know, uh 01:19:53.319 --> 01:19:57.496 continues to underrepresent women of color, women in particular, as I mean 01:19:57.529 --> 01:20:02.555 , women and women of color, um the traditional course is the one that 01:20:02.588 --> 01:20:06.317 consistently predicts going into the CS major. Whereas the principals 01:20:06.350 --> 01:20:09.656 course that attracts a more diverse group of students, including women and 01:20:09.689 --> 01:20:15.156 women of color doesn't predict uh going into the CS major. So that is 01:20:15.189 --> 01:20:19.027 related to a, you know, a policy, uh question that we ought to have about 01:20:19.060 --> 01:20:24.286 , you know, if we're designing programs that are geared to uh expand the 01:20:24.319 --> 01:20:29.217 pool are very well intentioned, uh really trying to figure out how to, can 01:20:29.250 --> 01:20:33.706 they take that next step to really uh sustain interest in the field. So it 01:20:33.739 --> 01:20:38.746 goes back to uh the earlier comment about tracking. It's, it's, it's even 01:20:38.779 --> 01:20:44.067 at that very high level of A P that kind of tracking is happening. 01:20:44.100 --> 01:20:47.376 Great point, Linda and I think that connects the dots between the 01:20:47.409 --> 01:20:51.055 presentation on the counter stereotypical role models that might get 01:20:51.088 --> 01:20:54.256 students interested and then the courses that they can take and then what 01:20:54.289 --> 01:20:58.786 happens at the intro um course level and then where folks are dropping off 01:20:58.819 --> 01:21:02.256 um after the intro courses and maybe even even other gatekeeping 01:21:02.289 --> 01:21:06.656 mathematics courses, 01:21:06.689 --> 01:21:10.567 any other thoughts about recommendations you wanna lift up for the uh 01:21:10.600 --> 01:21:12.946 participants. 01:21:12.979 --> 01:21:19.647 Um So from the perspective of our findings and I think um other studies 01:21:19.680 --> 01:21:26.696 like Sarah S, um I think one of the recommendations would be in working on 01:21:26.729 --> 01:21:33.095 the systems that house this women of color so that it's that all the 01:21:33.128 --> 01:21:41.128 efforts don't go to fixing the woman and they go to fixing the place that 01:21:41.399 --> 01:21:46.366 where they receive all this, they're the recipients of all this um 01:21:46.399 --> 01:21:52.937 microaggressions or where they are not valued or where they are dismissed 01:21:52.970 --> 01:21:59.046 and all of these things. So, um I the I think the recommendations should 01:21:59.079 --> 01:22:07.079 very much go toward helping um professors not be so discriminatory, learn 01:22:07.569 --> 01:22:15.569 how to work with diverse populations. Um have funding for um students with 01:22:16.798 --> 01:22:23.406 diverse backgrounds and who may come with fewer resources because of where 01:22:23.439 --> 01:22:27.826 they come from in terms of what kinds of schools they attended or because 01:22:27.859 --> 01:22:33.345 of their not having the economic resources. Um You know, all those things 01:22:33.378 --> 01:22:39.746 like hiring more women of color as professors who will be um some of these 01:22:39.779 --> 01:22:45.996 mentors and role models um for uh women of color who come into these 01:22:46.029 --> 01:22:50.616 spaces. So, um 01:22:50.649 --> 01:22:56.607 I think we need to go beyond the individuals and work on the systems 01:22:56.640 --> 01:23:02.567 themselves to really have an impact on how things work. Because otherwise 01:23:02.600 --> 01:23:09.496 , even if we get a lot of women of color in these spaces, if they continue 01:23:09.529 --> 01:23:16.076 being dismissed and mistreated, um it will be very hard for them to feel 01:23:16.109 --> 01:23:21.737 like they belong and for them to stay. 01:23:21.770 --> 01:23:27.147 Excellent point. Thank you, Maria. I, I also, I also wanted to add that. I 01:23:27.180 --> 01:23:32.286 think it's really important that we start to question the like baseline, 01:23:32.319 --> 01:23:38.135 foundational assumptions of the field of computing, right? So why is it 01:23:38.168 --> 01:23:42.616 that the first thing that a student is gonna hit oftentimes in a computer 01:23:42.649 --> 01:23:48.175 science program is an introductory programming class and now one, why is 01:23:48.208 --> 01:23:52.567 it created that way? Where did that come from? Why did those decisions get 01:23:52.600 --> 01:23:58.706 made and who do those decisions serve and who do they um who do they not 01:23:58.739 --> 01:24:02.925 serve? And in what ways are people being benefiting and not benefiting or 01:24:02.958 --> 01:24:07.937 being penalized for the ways in which the underlying assumptions are set 01:24:07.970 --> 01:24:12.666 up? Um I think that we have to do that in addition to looking at the 01:24:12.699 --> 01:24:16.956 system as a whole and think about transforming that system. But there are 01:24:16.989 --> 01:24:22.956 baseline assumptions in stem that I think are doing a disservice to women 01:24:22.989 --> 01:24:28.116 of color and actually are um harming us in ways as we try to navigate our 01:24:28.149 --> 01:24:31.357 way, you know, through the, through the through stem and through computing 01:24:31.390 --> 01:24:35.666 specific things. Thank you Jaquita. And I think those last two points are 01:24:35.699 --> 01:24:39.305 , are incredibly important about structural change and challenging the 01:24:39.338 --> 01:24:43.555 status quo of computing. So I, I really appreciate you both lifting those 01:24:43.588 --> 01:24:47.765 up and leaving us there with um with those comments. So just to make sure 01:24:47.798 --> 01:24:52.607 we can end right on time, um I will wrap up the Q and A there. Um And just 01:24:52.640 --> 01:24:56.487 want to say a huge thank you to all of our panelists and researchers for 01:24:56.520 --> 01:25:00.506 doing such amazing research over the course of the last year and a half. 01:25:00.539 --> 01:25:04.326 Um As a reminder, the women of color and computing collaborative um when 01:25:04.359 --> 01:25:08.765 we launched, we sought to help to build this um area of literature where 01:25:08.798 --> 01:25:12.317 we thought there was such a dearth of information. Um and really with the 01:25:12.350 --> 01:25:16.675 intention that this information will be utilized to inform strategies and 01:25:16.708 --> 01:25:20.175 practices and policies that will move the needle for women of color. Um 01:25:20.208 --> 01:25:24.126 We're very thankful to each of you because through your research, we will 01:25:24.159 --> 01:25:27.687 be able to disseminate this information and, and we hope that it will have 01:25:27.720 --> 01:25:32.187 um some lasting and and effective impact. Um I'd also like to say thank 01:25:32.220 --> 01:25:35.635 you to my co P I Kim Scott. Unfortunately, she wasn't able to log in 01:25:35.668 --> 01:25:39.845 although I can see her as a participant. Um So thank you, Doctor Kim Scott. 01:25:39.878 --> 01:25:43.647 Uh my co P I and collaborator for the Women of Color and Computing 01:25:43.680 --> 01:25:47.845 Collaborative. Um and uh the director of the Center for Gender Equity and 01:25:47.878 --> 01:25:51.706 Science and Technology at Arizona State University. Um And thank you to 01:25:51.739 --> 01:25:56.765 pivotal ventures which funded this collaborative. Um So next steps for the 01:25:56.798 --> 01:26:00.607 panelists that are, are for the participants that are still on. We will 01:26:00.640 --> 01:26:06.397 have part two of the virtual summit on September 21st at 1 p.m. We will 01:26:06.430 --> 01:26:10.456 send out some invitations for everyone to attend and that uh summit will, 01:26:10.489 --> 01:26:15.786 will focus on um the tech workforce and entrepreneurship of the same exact 01:26:15.819 --> 01:26:20.476 things about barriers and um solutions for women of color in, in tech and 01:26:20.509 --> 01:26:25.857 entrepreneurship. Um And we will also be working on um synthesizing this 01:26:25.890 --> 01:26:30.156 information into different formats so potentially a report and or policy 01:26:30.189 --> 01:26:34.675 briefs. So stay tuned for that. Um And we will also share out the uh 01:26:34.708 --> 01:26:38.675 presentations and a recording of the session. Um So if folks want to dive 01:26:38.708 --> 01:26:42.536 deeper into any of the findings or if they need the resources to share um 01:26:42.569 --> 01:26:45.425 in your networks and communities, we will give that information out as 01:26:45.458 --> 01:26:50.726 well. Um And one last comment, um we are also Kim Scott and I are also 01:26:50.759 --> 01:26:56.046 part of a uh committee, a National Academies Committee on Women of Color 01:26:56.079 --> 01:27:01.286 um in um the underrepresentation of women of color in tech along with uh 01:27:01.319 --> 01:27:05.777 uh Mia Ong as well. And so some of this information will also be included 01:27:05.810 --> 01:27:08.756 in that report, which hopefully will come out by the end of this year or 01:27:08.789 --> 01:27:13.786 early next year. So we are uh busy trying to build a field and appreciate 01:27:13.819 --> 01:27:17.916 each of you for your contributions and uh to the participants for joining 01:27:17.949 --> 01:27:24.416 us today. Thank you all and have a wonderful weekend. 01:27:24.449 --> 01:27:27.357 Thank you, everyone. Bye. 01:27:27.390 --> 01:27:30.539 Thank you. Bye.