In recent years, an encouraging trend has begun to accelerate: participation in high school computer science is becoming more diverse. Although the computer science workforce remains overwhelmingly male (79%), white (64%), and Asian (19%),¹ Figure 1 shows that more women and genderqueer folks are taking AP Computer Science exams, as well as more students from historically underrepresented racial/ethnic groups.²

There are many reasons for this trend, including increasing discourse about diversity in computer science, the work of many teachers around the country, and government initiatives focusing on the issue. The Biden administration, for example, just released its National Cyber Workforce and Education Strategy and cited diversity and inclusion as one of the three guiding imperatives for the mission.³

Figure 1: Recent Years Have Seen More Diverse Participation in AP Computer Science

Source: Code.org, “Code.org’s Approach to Diversity and Equity in Computer Science,” (2021). https://code.org/diversity

To be clear, there is still much work to be done at the K-12 level to improve access to and inclusion in computer science. Expansions in high school computer science haven’t reached low-income students in the same proportions as students from higher income households, for example.⁴ And, although more female and gender-expansive students are taking the AP CS exam, that number is just over 30%—short of their overall representation in the United States.

With that context, this post seeks to examine pathways to the CS workforce and, in particular, the role of universities in fostering and maintaining the diversity of increasingly computationally-skilled cohorts. The goal is to make computing a safer and more exploratory space for everyone so that students can choose to join computing if it makes sense for them. Although the primary focus is on higher education, many conclusions apply at the K-12 level as well.

Higher Education is a Chokepoint

Over the last decade, the percentage of computer science bachelor’s degrees awarded to students from underrepresented racial or ethnic groups has stayed roughly the same, as shown in Figure 2. Similarly, the percentage of women receiving CS bachelor’s degrees has creeped from 18% in 2010 to 21% in 2020.⁵

Figure 2: Bachelor’s Graduation Rates in Computer and Information Science by Students from Underrepresented Racial/Ethnic Groups Have Remained Relatively Flat

Source: National Center for Education Statistics, “Bachelor’s degrees conferred by postsecondary institutions, by race/ethnicity and field of study,” https://nces.ed.gov/ipeds/.

For these reasons, researchers at the Pew Research Center argue that “current trends in STEM degree attainment appear unlikely to substantially narrow [diversity] gaps [in the STEM workforce].”⁶

One might expect the trends in CS participation in high school to be reflected in college graduation rates with a four-to-five year delay—the average time it takes for a student to graduate college—but lag time doesn’t fully explain the difference. Many of the same factors that influenced diversification in AP CS participation, like more prominent national conversations about STEM diversity and national policies promoting CS education,⁷ were also felt in higher education at the same time. Since many students decide their major while they’re in college (not before), we might expect an uptick in diversity at the college level happening at the same time as what has been happening at the high school level in response to the same national factors.

To keep up the promising trend of increasing diversity at the high school level and ensure that this trend translates into the workforce, we must pay attention to factors that influence student attrition at the college level. Prior research suggests that additional work is needed to support students from historically underrepresented groups in finding a place in computer science.⁸ Universities should make sure that their computer science curricula are accessible to all students and that it portrays the discipline accurately, allowing for engagement that is creative, multidisciplinary, and culturally sustaining.

Why do Students Leave Computer Science?

To build effective CS programs, universities should address the reasons that students leave computer science and design programs, especially where the attrition is due to stereotypes, misconceptions about the field, or other factors that are rooted in systemic discrimination.

Existing literature has identified several reasons that students might face challenges in—or drop out of—college computer science programs. Studies have looked at students’ prior experience,⁹ interest in the content,¹⁰ sense of belonging,¹¹ and the alignment between students’ identities/interests and the perceived values of the discipline,¹² to name a few. One study conducted a survey of students’ experiences in computer science and identified four key factors that underlie student responses, which they labeled as: lack of sense of belonging, in-class confusion, personal obligations, and lack of confidence.¹³

In this article, we draw together several threads from the work cited above and explore some of the major topics surrounding persistence and attrition in computer science, and their connections to diversity.

Confusion and Lack of Preparation

Being prepared for college-level computer science often begins with access to resources in primary and secondary school. Even though there has been progress (as seen in Figure 1), English-language learners, low-income students, and Black, Native American, and Latine students remain underrepresented in high school CS classrooms.¹⁴ 53% of schools offer a computer science class, but girls still only make up one-third of high school CS students.¹⁵ Even when students from underrepresented groups participate in high school CS experiences, systemic inequities such as poorly funded school districts, less skilled and overworked teachers, and disruptive environments can worsen learning experiences.¹⁶ The result is that students from those backgrounds tend to be less prepared and disproportionately struggle in CS courses in college.¹⁷

Aside from prior preparation, there are a number of other factors that may cause confusion and struggle. Many features of computer science might be especially disorienting for students from underrepresented groups. For example, scholars have recently drawn attention to the ways in which anti-Blackness has long been a part of the field of computer science and computer science education—both at the high school and college level.¹⁸

Technology is often promoted as being “objective” but, in reality, reflects and reproduces existing biases—framing tech as objective might seem particularly dissonant to students who have experienced tech-based harm. Vakil (2020) theorizes about the politics reflected in app features/functions based on descriptions of a lesson sequence where young students build apps. As the students explore their own identities—two students become more comfortable identifying as a “person of color” or as “Black” rather than “mixed”—they modify the features of their apps to reflect the complexities and subjectivities of identity. In one of the apps, the user’s ability to be matched with other people based on their race/ethnicity reflects, according to Vakil, race-conscious politics. Even though the students might not use those terms explicitly, these vignettes highlight how students’ identities may be intertwined with the technology they create and making that visible can lead to more authentic learning experiences.¹⁹ Authenticity, expression, and acknowledging the subjectivity of technology are important, yet often underappreciated, features of CS pedagogy in high school and college alike.

It’s also important to note that confusion and struggle are not inherently bad. “Productive failure” is a term coined to describe how students who fail at solving a problem can, in the process of struggling, learn more about the issue and better transfer their learnings to solve other, similar problems.²⁰ Problems arise, however, when students continue struggling without making progress or even become discouraged from the discipline.

This can be exacerbated when assignments require writing code. Compared to other activities, programming often involves a lot of trial and error before code begins to work—and, unlike in other fields like English, there is relatively little emphasis placed on editing or rewriting working code. The result is that students tend to interact with their code while it’s broken and run it only once when it’s working—there can be a lot of struggle followed by comparatively brief joy.²¹ So, for students who are having trouble with programming, the feeling of frustration can dominate their experience. Women, for example, report significantly higher levels of discouragement, frustration, and stress when they’re working on code alone, as compared to when they have a supportive peer to program with.²² Helping identify productive ways to moderate this frustration through pedagogy, mentorship and other support will require additional and creative action by teachers.

One piece of reducing inequities in CS preparation involves federal investment in broadening access to CS resources in K–12 schools around the country. This can be done in a number of ways: offering teacher professional learning experiences in computer science, funding teacher credentialing programs, and establishing or expanding programs for schools to provide technical support to students. But not every school will be able to offer a class on computer science: some initiatives to broaden access to CS have focused on supporting teachers of other subjects to blend a little bit of computer science into their curricula, helping students learn about two subjects at the same time.²³

Educational institutions can also take steps to help students feel less confused. For one, teachers can design curricula that center joy and focus on allowing students to express themselves in their assignments, like the app-building assignments described earlier—these are core principles behind constructionism and the “maker” movement in education.²⁴ High schools and universities can adopt programs to support differentiated instruction, even in large classes. One approach that’s been shown to be effective is Universal Design for Learning (UDL) which helps teachers design learning experiences that are more accessible and inclusive.²⁵ At the college level, the structure of TA programs are often well-suited to near-peer mentoring models where advanced undergraduate and graduate students serve as mentors—providing assignment help and more general advice—for younger students.²⁶ In addition to academic support, these programs foster social capital and help students connect with others in their community.

Disinterest and Perceived Values

A number of studies have asked students about their perceptions of computer science and reasons they might be interested or disinterested in the subject.²⁷ They generally find that women, especially, have perceptions of computer science that are incongruous with the way CS is presented in classrooms. For example, men tend to focus on gaming and hardware as their primary reasons for pursuing CS while women tend to focus on a desire to use computing in another field and the creative aspects of computing.²⁸ When CS programs lean too heavily toward a particular display of computer science, they risk alienating students—a more inclusive approach could involve presenting a broad range of values and applications of CS.

At the same time, curricula should avoid reinforcing stereotypes. For example, although men tend to cite gaming as a more significant reason to pursue CS in aggregate, that doesn’t mean that men are exclusively interested in this field or that others can’t be interested in gaming. It’s important to welcome and understand each student’s interest in the discipline.

There is evidence that “gamification” features heavily in introductory computer science courses in higher education—popular tools like MIT’s Scratch feature characters, actions, and worlds that are reminiscent of video games.²⁹ Given the possibility for gender disparities stemming from this portrayal, universities should strive to present other characterizations of computer science and take care that the gamified elements of a course contain diverse characters, perspectives, and scenes. Designers of some of the most successful uses of gaming in CS education (as measured by retention) attend to the gender dynamics of gaming and different types of games.³⁰

Student responses to the curricula can be nuanced. Recent work has drawn attention to the way that students reflect on the values of a discipline.³¹ In this framework, computer science is more than a discipline—it’s a “way of being in the world” and students considering CS are considering who they might become if they pursue computer science (not just how interesting the content is). They are trying to determine what the discipline values and whether that aligns with their own values. Often university CS curricula portray a version of the discipline that is misaligned with what many students value.

For example, the terms “master” and “slave” are still commonly used in computer science classes to describe the relationship between two devices.³² Jones and melo, writing about this terminology, argue: “When left unexamined, this malignant language is deemed ordinary. Dismissing Black pain, in other words, becomes a norm disseminated by computing education.”³³ When presented neutrally, students—particularly Black students, but others as well—who encounter that terminology may interpret the presence of these terms as a signal about who is valued in computer science. University CS programs should clarify their values and be critical about their curricula, support systems, and personnel to ensure they are presenting the discipline as intended.

Lack of belonging

Belonging in computer science is about feeling accepted for who you are by the systems and people you encounter in the discipline. The topics discussed previously—confusion, prior knowledge, interest, and values—can contribute to one’s sense of belonging, but even if they’re not confused, they are interested, and they value the material, they may still not feel like they are accepted in computer science. For example, a student who thinks that CS is about gaming might become disinterested in the subject; a female student surrounded by male students may feel like they don’t belong in that environment—both issues disproportionately affect women, but belonging is about a student’s affective experience.

Lack of belonging can happen for a number of reasons, many of which occur in high school and college alike. As such, both high school and college teachers should implement interventions to improve belonging.

Situational environments play an important role in “ambient belonging.”³⁴ The posters on the classroom wall, the way that people are displayed as participating in computer science, and the classroom demographics all have bearing on belonging.³⁵ Belonging, in turn, is connected to whether students stay or leave computer science.³⁶

Early social cues can also affect belonging. One study looked at the effect of the positionality of the presenter of the introduction video to a computer science class. In a random field experiment, they measured persistence among different student populations, depending on whether the introductory video was delivered by a woman, man, or both. They found that it had a more significant effect on women than men, theorizing that the identities of underrepresented groups are more salient in computer science classrooms.³⁷ The presence of role models is powerful: women who have a role model in STEM are much more likely to say they’re interested in a STEM job.³⁸

And, belonging in computer science is not just a matter of the program’s design. Students receive social cues about CS belonging outside of the classroom—in casual conversation at the dining hall, at social events, and in their community. Research shows that, starting in middle school, these peer groups can be quite influential.³⁹ Accordingly, a friend group that values STEM can help women overcome stereotypes and develop a sense of belonging;⁴⁰ the community in which women can either support belonging (if they value computing and know about resources for pursuing it) or harm it.⁴¹

Wise interventions have received much attention in social psychology as precise ways of supporting students in making sense of themselves and their role in the classroom;⁴² this website has a searchable database of research about these interventions.

For example, some students feel imposter syndrome, or a lack of belonging because they feel that their success is unearned—both in high school and college. According to theory, interventions which normalize that feeling can help students feel more supported and a stronger sense of acceptance.⁴³ Indeed, one college intervention where incoming students of color watched videos of older peers talking about their experiences at a predominantly white institution resulted in those students reframing negative experiences and seeking out supportive networks.⁴⁴ For similar reasons, affinity groups and other institutionally-supported, identity-centered communities of practice can support belonging at universities.⁴⁵

Summary

There are a variety of factors that can converge to influence the persistence or attrition of undergraduate students in computer science, many of which have been described above. Even though no single institution can affect all factors, research shows that redundant supports are important for persistence.⁴⁶ Meaningfully advancing computer science diversity in higher education will require federal actors, high schools, universities, and communities—working on different issues—to better support students from all backgrounds and create spaces for safe exploration within the discipline.

The factors surveyed in this article are summarized in the table below:

Social Variable Affecting Persistence	Description	Interventions
Confusion and lack of preparation	Students experience confusion in computer science classes and, rather than being able to resolve it, the feeling of struggle is pervasive enough that they are discouraged from pursuing the discipline.	• Invest in broader access to computer science at the K-12 level, including more CS curricula, computer labs, CS teachers, and opportunities for non-CS teachers to teach computer science. • Implement universal design for learning to create more differentiated instruction in high school and university computer science programs. • Initiate near-peer mentoring models for young students in universities.
Disinterest and perceived values	Because of their experience in class or out-of-class, students don’t feel like computer science is interesting and, therefore, don’t choose to pursue it.	• Edit the ways that computer science is being portrayed in the curriculum with attention to displays that may promote stereotypes in high school or college courses. • Clarify the values that are central to computer science and ensure that college CS programs reflect those values.
Lack of belonging	Students don’t feel like they are accepted in computer science and don’t feel like they can be accepted with more time, so they leave the discipline.	• Ensure that high school and college learning environments avoid displays of stereotypical materials and display diverse participation in CS. • Attend to the early social cues in CS classes, like the identities of lecturers, TAs, and voices represented. • Establish and support diverse social university communities that involve computer science, like affinity groups for students. • Implement wise interventions tailored to the particular needs and situations of the program and students in high school or college.

Parth Sarin is a former Tech, Ethics & Policy Summer Fellow with CSET’s CyberAI Program, and was sponsored by Stanford University’s Institute for Human-Centered Artificial Intelligence.

1. Zippia, “Computer Scientist Demographics and Statistics in the US” (2023), https://www.zippia.com/computer-scientist-jobs/demographics/ (Note: These statistics are interpolated by Zippia based on a survey of 30 million profiles).
2. Code.org, “Code.org’s Approach to Diversity and Equity in Computer Science,” (2021), https://code.org/diversity.
3. Office of the National Cyber Director, Executive Office of the President, “National Cyber Workforce and Education Strategy, Unleashing America’s Cyber Talent,” (2023), https://www.whitehouse.gov/wp-content/uploads/2023/07/NCWES-2023.07.31.pdf.
4. Alyson Klein, “More Than Half of High Schools Now Offer Computer Science, But Inequities Persist,” (Education Week, 2021), https://www.edweek.org/teaching-learning/more-than-half-of-high-schools-now-offer-computer-science-but-inequities-persist/2021/11.
5. National Center for Education Statistics, “Degrees in computer and information sciences conferred by postsecondary institutions, by level of degree and sex of student: 1964-65 through 2019-20,” (Digest of Education Statistics, 2021), https://nces.ed.gov/programs/digest/d21/tables/dt21_325.35.asp.
6.  Richard Fry, Brian Kennedy, and Cary Funk, “STEM Jobs See Uneven Progress in Increasing Gender, Racial and Ethnic Diversity,” (Pew Research Center, 2021). https://www.pewresearch.org/science/2021/04/01/stem-jobs-see-uneven-progress-in-increasing-gender-racial-and-ethnic-diversity/.
7. Michael Hansen, and Nicolas Zerbino, “Exploring the state of computer science education amid rapid policy expansion,” (Brookings, 2022). https://www.brookings.edu/articles/exploring-the-state-of-computer-science-education-amid-rapid-policy-expansion/.
8. Chris Stephenson, Alison Derbenwick Miller, Christine Alvarado, Lecia Barker, Valerie Barr, Tracy Camp, Carol Frieze, Colleen Lewis, Erin Cannon Mindell, Lee Limbird, Debra Richardson, Mehran Sahami, Elsa Villa, Henry Walker, and Stuart Zweben, “Retention in Computer Science Undergraduate Programs in the U.S.: Data Challenges and Promising Interventions,” (ACM, 2018), https://www.acm.org/binaries/content/assets/education/retention-in-cs-undergrad-programs-in-the-us.pdf.
9. Brenda Cantwell Wilson and Sharon Shrock, “Contributing to success in an introductory computer science course: a study of twelve factors,” (SIGCSE Technical Symposium on Computer Science Education, 2001), http://dx.doi.org/10.1145/366413.364581.
10. Lori Carter, “Why students with an apparent aptitude for computer science don’t choose to major in computer science,” (SIGCSE Technical Symposium on Computer Science Education, 2006), https://doi.org/10.1145/1124706.1121352.
11. Sapna Cheryan, Victoria C. Plaut, Paul G. Davies, and Claude M. Steele, “Ambient belonging: how stereotypical cues impact gender participation in computer science,” (Journal of personality and social psychology, 2009). https://psycnet.apa.org/doi/10.1037/a0016239; Allison Master, Sapna Cheryan, and Andrew N. Meltzoff, “Computing whether she belongs: Stereotypes undermine girls’ interest and sense of belonging in computer science,” (Journal of educational psychology, 2016). https://psycnet.apa.org/doi/10.1037/edu0000061; Linda J. Sax, Jennifer M. Blaney, Kathleen J. Lehman, Sarah L. Rodriguez, Kari L. George, and Christina Zavala, “Sense of belonging in computing: The role of introductory courses for women and underrepresented minority students,” (Social Sciences, 2018). https://doi.org/10.3390/socsci7080122.
12. Sepehr Vakil, ““I’ve Always Been Scared That Someday I’m Going to Sell Out”: Exploring the relationship between Political Identity and Learning in Computer Science Education,” (Cognition and Instruction, 2020). https://doi.org/10.1080/07370008.2020.1730374; Colleen Lewis, Paul Bruno, Jonathan Raygoza, and Julia Wang, “Alignment of goals and perceptions of computing predicts students’ sense of belonging in computing.” (ACM Conference on International Computing Education Research, 2019), https://doi.org/10.1145/3291279.3339426.
13. Adrian Salguero, Christine Alvarado, William G. Griswold, and Leo Porter, “Understanding Sources of Student Struggle in Early Computer Science Courses” (ACM Conference on International Computing Education Research, 2021). https://doi.org/10.1145/3446871.3469755.
14. Alyson Klein, “More Than Half of High Schools Now Offer Computer Science, But Inequities Persist,” (Education Week, 2021), https://www.edweek.org/teaching-learning/more-than-half-of-high-schools-now-offer-computer-science-but-inequities-persist/2021/11.
15. Nadia Tamez-Robledo, “Computer Science is Growing in K-12 Schools, But Access Doesn’t Equal Participation,” (EdSurge, 2022). https://www.edsurge.com/news/2022-09-26-computer-science-is-growing-in-k-12-schools-but-access-doesn-t-equal-participation.
16. Ran Liu, “School Disruptions Exacerbated Inequality in High School Completion,” (AERA, 2023). https://doi.org/10.3102/0013189X231167152; Bruce D. Baker, Educational inequality and school finance: Why money matters for America’s students, (Harvard Education Press, 2021).
17. Adrian Salguero, Christine Alvarado, William G. Griswold, and Leo Porter, “Understanding Sources of Student Struggle in Early Computer Science Courses” (ACM Conference on International Computing Education Research, 2021). https://doi.org/10.1145/3446871.3469755.
18. Natalie Araujo Melo, Briana C. Bettin, Francisco Castro, Victoria C. Chávez, Earl W. Huff, Jr, Gayithri Jayathirtha, Yerika A Jimenez, Megumi Kivuva, Minji Kong, Amber Solomon, and Jen Tsan, “An Open Letter To The CS Ed Community” (The Papaya Project, 2022). https://the-papaya-project.github.io/letter; Stephanie T. Jones and Natalie Melo, “‘Anti-blackness is no glitch’: the need for critical conversations within computer science education,” (ACM, 2020), https://doi.org/10.1145/3433134.
19. Sepehr Vakil, “I’ve Always Been Scared That Someday I’m Going to Sell Out”: Exploring the relationship between Political Identity and Learning in Computer Science Education,” (Cognition and Instruction, 2020). https://doi.org/10.1080/07370008.2020.1730374; Colleen Lewis, Paul Bruno, Jonathan Raygoza, and Julia Wang, “Alignment of goals and perceptions of computing predicts students’ sense of belonging in computing.” (ACM Conference on International Computing Education Research, 2019), https://doi.org/10.1145/3291279.3339426.
20. Manu Kapur, “Productive Failure,” (Cognition and Instruction, 2008), https://doi.org/10.1080/07370000802212669.
21. Diane Marie C. Lee, Ma Mercedes T. Rodrigo, Ryan SJ D. Baker, Jessica O. Sugay, and Andrei Coronel, “Exploring the relationship between novice programmer confusion and achievement,” (Affective Computing and Intelligent Interaction, 2011), https://link.springer.com/chapter/10.1007/978-3-642-24600-5_21.
22. Kimberly Michelle Ying, Lydia G. Pezzullo, Mohona Ahmed, Kassandra Crompton, Jeremiah Blanchard, and Kristy Elizabeth Boyer, “In Their Own Words: Gender Differences in Student Perceptions of Pair Programming” (SIGSCE, 2019), https://doi.org/10.1145/3287324.3287380.
23. Mark Guzdial, Emma Dodoo, Bahare Naimpour, Tamara Nelson-Fromm, and Aadarsh Padiyath, “Putting a Teaspoon of Programming into Other Subjects,” (ACM, 2023), http://doi.org/10.1145/3587926.
24. Christine Coolick, “The Impact of the Maker Movement,” (Magazine of the Society of Women Engineers, 2023). https://magazine.swe.org/maker-movement/; Michail N. Giannakos, Monica Divitini, Ole Sejer Iversen, and Pavlos Koulouris, “Making as a Pathway to Foster Joyful Engagement and Creativity in Learning,” (International Conference on Entertainment Computing, 2015), https://doi.org/10.1007/978-3-319-24589-8_58.
25. Alexandria K. Hansen, Eric R. Hansen, Hilary A. Dwyer, Danielle B. Harlow, and Diana Franklin, “Differentiating for Diversity: Using Universal Design for Learning in Elementary Computer Science Education,” (ACM, 2016), https://doi.org/10.1145/2839509.2844570.
26. Leo Porter, Dennis Bouvier, Quintin Cutts, Scott Grissom, Cynthia Lee, Robert McCartney, Daniel Zingaro, and Beth Simon, “A Multi-institutional Study of Peer Instruction in Introductory Computing,” (SIGSCE, 2016). https://doi.org/10.1145/2839509.2844642; Laura S. Tenenbaum, Margery K. Anderson, Marti Jett, and Debra L. Yourick, “An Innovative Near-Peer Mentoring Model for Undergraduate and Secondary Students: STEM Focus,” (Innovative Higher Education, 2014), https://doi.org/10.1007/s10755-014-9286-3.
27. Alexandra Funke, Marc Berges, and Peter Hubwieser, “Different Perceptions of Computer Science,” (IEEE, 2016). https://doi.org/10.1109/LaTiCE.2016.1; Jean J. Ryoo, “Pedagogy that Supports Computer Science for All,” (ACM, 2019). https://doi.org/10.1145/3322210; Michael Hewner, “Undergraduate conceptions of the field of computer science,” (ICER, 2013). https://doi.org/10.1145/2493394.2493414; Lori Carter, “Why students with an apparent aptitude for computer science don’t choose to major in computer science,” (SIGCSE Technical Symposium on Computer Science Education, 2006), https://doi.org/10.1145/1124706.1121352.
28. Alexandra Funke, Marc Berges, and Peter Hubwieser, “Different Perceptions of Computer Science,” (IEEE, 2016). https://doi.org/10.1109/LaTiCE.2016.1; Lori Carter, “Why students with an apparent aptitude for computer science don’t choose to major in computer science,” (SIGCSE Technical Symposium on Computer Science Education, 2006), https://doi.org/10.1145/1124706.1121352.
29. Kara Alexandra Behnke, “Gamification in introductory computer science,” (University of Colorado at Boulder, 2015), https://www.colorado.edu/atlas/sites/default/files/attached-files/gamification-in-introductory-computer-science.pdf.
30. Jessica D. Bayliss, “Using games in introductory courses: tips from the trenches,” (SIGSCE, 2009), https://doi.org/10.1145/1508865.1508989.
31. Sepehr Vakil, “I’ve Always Been Scared That Someday I’m Going to Sell Out”: Exploring the relationship between Political Identity and Learning in Computer Science Education,” (Cognition and Instruction, 2020), https://doi.org/10.1080/07370008.2020.1730374.
32. Ron Eglash, “The Master-Slave Analogy in Technical Literature,” (Technology and Culture, 2007), https://muse.jhu.edu/article/215390.
33. Stephanie T. Jones and natalie araujo melo, “We Tell These Stories to Survive: Towards Abolition in Computer Science Education,” (Canadian Journal of Science, Mathematics and Technology Education, 2021), https://doi.org/10.1007/s42330-021-00158-2.
34. Sapna Cheryan, Victoria C. Plaut, Paul G. Davies, and Claude M. Steele, “Ambient belonging: how stereotypical cues impact gender participation in computer science,” (Journal of personality and social psychology, 2009), https://psycnet.apa.org/doi/10.1037/a0016239.
35. Allison Master, Sapna Cheryan, and Andrew N. Meltzoff, “Computing whether she belongs: Stereotypes undermine girls’ interest and sense of belonging in computer science,” (Journal of educational psychology, 2016), https://psycnet.apa.org/doi/10.1037/edu0000061.
36. Karyn L. Lewis, Jane G. Stout, Noah D. Finkelstein, Steven J. Pollock, Akira Miyake, Geoff L. Cohen, and Tiffany A. Ito, “Fitting in to Move Forward: Belonging, Gender, and Persistence in the Physical Sciences, Technology, Engineering, and Mathematics (pSTEM),” (Psychology of Women Quarterly, 2017), https://doi.org/10.1177/0361684317720186.
37. Rene F. Kizilcec, Andrew Saltarelli, Petra Bonfert-Taylor, Michael Goudzwaard, Ella Hamonic, and Rémi Sharrock. “Welcome to the Course: Early Social Cues Influence Women’s Persistence in Computer Science.” (CHI, 2020), https://doi.org/10.1145/3313831.3376752.
38. Microsoft Research, “Girls with a role model more likely to consider career in STEM, Microsoft research reveals,” (2018), https://news.microsoft.com/en-gb/2018/04/25/62509/.
39. Wendy DuBow, Alexis Kaminsky, and Joanna Weidler-Lewis, “Multiple Factors Converge to Influence Women’s Persistence in Computing: A Qualitative Analysis,” (IEEE, 2017), https://doi.org/10.1109/MCSE.2017.42.
40. Rachael D. Robnett, and Campbell Leaper, “Friendship groups, personal motivation, and gender in relation to high school students’ STEM career interest,” (Journal of Research on Adolescence 2013). https://doi.org/10.1111/jora.12013.
41. Lecia J. Barker, and William Aspray. The state of research on girls and IT, (2006).
42. Gregory M. Walton and Timothy D. Wilson, “Wise interventions: Psychological remedies for social and personal problems,” (Psychological Review, 2018), https://doi.org/10.1037/rev0000115.
43. Terrell L. Strayhorn, College students’ sense of belonging: A key to educational success for all students, (Routledge, 2019).
44. Terrell L. Strayhorn, “Exploring Ethnic Minority First-Year College Students’ Well-Being and Sense of Belonging: A Qualitative Investigation of a Brief Intervention,” (American Journal of Qualitative Research, 2022), https://doi.org/10.29333/ajqr/11422.
45. Robin Cooper, “Constructing belonging in a diverse campus community,” (Journal of College and Character, 2009), https://doi.org/10.2202/1940-1639.1085.
46. Wendy DuBow, Alexis Kaminsky, and Joanna Weidler-Lewis, “Multiple Factors Converge to Influence Women’s Persistence in Computing: A Qualitative Analysis,” (IEEE, 2017), https://doi.org/10.1109/MCSE.2017.42.