This study describes our current effort to expand our previous research to a national scale, exploring the implications of students' employability and salary between Bachelor of Arts (BA) and Bachelor of Sciences (BS) in Computer Science (CS) degrees. The literature on broadening participation has identified numerous barriers, but these challenges frequently burden students instead of addressing the underlying systemic issues within the curriculum. Previous research has identified bottleneck courses like calculus and physics as barriers to persistence in CS. Reimagining the curriculum and introducing alternative pathways, such as BA in CS, can eliminate bottlenecks and enhance access and retention without compromising essential skills in computing. To understand the implications of the BA pathway on employability and salary, we conducted a study at a large minority-serving institution (MSI) with a CS department serving around 2,000 students. Our research revealed that the BA degree offers comparable employment opportunities to the BS. Students choose BA due to broader career aspirations, a faster route to graduation, and view math courses as a significant barrier. Despite earning potential being important, we discovered salary differences between the two degrees, emphasizing the need for clear communication to help diverse students make informed decisions about their degree paths in CS. Given the findings from a single MSI, we recognize the importance of expanding this project nationally. This lightning talk will discuss background context, lessons learned, and potential collaborations for broader implementation.

Artificial Intelligence (AI) has become a fundamental tool for cybersecurity researchers and practitioners. It is frequently used to address major security problems such as supply chain attacks, ransomware threats, and social engineering. In this context, integrating AI into cybersecurity workflows requires incorporating AI-driven approaches into the educational training of the cybersecurity workforce. This paradigm shift in academic settings will introduce the necessary skills for cybersecurity professionals to operate modern AI-based systems. Yet, the current cybersecurity curriculum still suffers from the absence of AI resources, particularly the detailed understanding of the appropriate AI mechanisms. Such absence leaves skill gaps for future professionals and practitioners in the industry. To address this, we designed an academic lecture module on AI covering both theory and practice. Then, we taught the module across six cybersecurity courses in our institution. To assess the effectiveness of integrating AI materials into cybersecurity education, we collected data by presenting two surveys before and after the lecture (concluding 81 participants per survey). Specifically, we utilized widely accepted models for unbiased analysis of our data. Our experimental results show positive AI knowledge improvement by 30% of the participants, demonstrating the beneficial impact of the lecture. Then, we observed a high similarity score between the survey responses and the lecture content, reaching 84%. Moreover, our sentiment analysis results reflect positive feedback from the participants with a positive score of 0.50. Overall, our study serves as a reference for instructional designers for developing educational curricula aiming to integrate AI into cybersecurity education.

Postdoctoral training increasingly represents an essential step in advanced research careers in engineering and computer science; however, little is known about postdoctoral mentorship practices, competencies, and needs. The scales evaluated here present an opportunity to investigate global and specific aspects of mentorship competency in postdoctoral engineering and computer science training and education. The global construct of mentorship measured may help assess overall mentor capabilities for postdoctoral mentorship and indicate general needs for mentorship training. Similarly, a global construct assessment from postdoctoral trainees provides an alternative perspective on issues postdocs experience with their mentors. At the same time, individual items may be better used to assess more specific aspects of mentorship capabilities and needs, such as communication or professional development. The mentorship competency assessments for supervisors (MCA-ECS.S) and postdoctoral trainees (MCA-ECS.P) provide mentors and educational institutions opportunities to assess mentor competencies as self-evaluation and mentee-evaluation to understand better the education and training needs of postdoctoral mentors and trainees beyond the structure of graduate education systems.

The demand for computing professionals has highlighted considerate gender and racial disparities in computing degree attainment, specifically for women, Black/African American women, Hispanic/Latin women, and American Indigenous women. Therefore, computing education researchers have been investigating what factors promote persistence for undergraduate female students pursuing computing degrees. Computing identity has been identified as a factor influencing students' persistence in computing degrees, however, few studies have looked at the interplay between identity and self-belief that ability and intelligence are fixed, known as fixed mindset. Prior studies have suggested that this self-belief may be impacted by classroom environments or cultures in other disciplines, serving as a barrier to participation, though it is understudied in computing education research. Prior research suggests that fixed mindset can be influenced through what some scholars call mindset messages. It is currently unknown in what ways mindset messages are communicated in computing classrooms, along with how they impact students' mindsets and computing identities, thus leading to either persistence or attrition. Based on prior studies, mindset messages can be sent through specific pedagogical and instructional practices used in the computing classroom. Therefore, this work-in-progress pilot study investigates the following research questions: 1) What are the relationships between computing identity and each of these factors: computing-specific mindset, persistence, perceived instructional practices, and perceived faculty mindset for computing students? and 2) What are the relationships between computing identity and each of these factors: computing-specific mindset, persistence, perceived instructional practices, and perceived faculty mindset for non-male computing students? This study presents the findings of n=61 students in a mid-level computer science course. Data collection consisted of a survey instrument that measured: demographic information, students' perceptions of instructional practices, a computing-specific adaptation of Dweck's Implicit Theories of Intelligence scale measuring mindset and perceptions of instructor mindset, and a computing identity scale. For all students, growth mindset, the perception of instructor-centered instructional practices, and the perception of student-centered instructional practices were positively correlated with computing identity. Meanwhile, perceived fixed faculty mindset was negatively associated with a computing identity. For non-male students, growth mindset was positively associated with computing identity whereas perceived fixed faculty mindset was negatively correlated with computing identity.

This paper reviews the application of Schlossberg's transition theory in understanding the learning experiences of women in computing. Computing education communities have been consistently exploring approaches and strategies to increase the participation of underrepresented groups. Even though efforts are being made, it remains challenging to address the shortage of highly skilled computing professionals, and gender disparities still persist. A primary goal in computing education is to attract and engage with this historically minoritized population to retain them in the field for long-term contributions. In this pursuit, we propose reviewing Schlossberg's transition theory in the context of women in computing education to understand how to improve their learning experiences and improve their long-term engagement. Transition theory examines how individuals identify with and adapt to a changing situation in their personal and professional lives. In this study, we focus on non-computing women, those who have earned bachelor's degrees in non-computing fields but wish to change their career directions to computing after completing their undergraduate education. We argue that non-computing women have deferred interests in computing, so they leverage these learning experiences to facilitate their computing career transition. It is important to investigate non-computing women's learning experiences to gain a comprehensive understanding of how they navigate the transition process. Schlossberg's transition theory, originally developed for adult education and career counseling, provides a framework to guide our inquiry into women's interpretations of computing career transitions and for determining which resources are best suited to support them during the process. This paper seeks to synthesize relevant literature on Schlossberg's transition theory in engineering and computing education, focusing on the underrepresentation of women in computing. It explores broadening the application of Schlossberg's transition theory by applying it to the study of non-computing women to inquire into how it can help disrupt gender disparities in computing education. Our discussion of Schlossberg's transition theory also demonstrates how this theory is appropriate to study the non-computing women's career transition in computing and outlines future studies that can further the discoveries in engineering and computing education research.

Technology innovation requires a set of diverse employees with computing skills. Yet, it remains a challenge for the global digital labor market to obtain an equitable representation of women. This is particularly true for women who enter the computing workforce after obtaining an initial undergraduate degree in a non-computing field. To resolve discrepancies, it is important to learn more about the factors influencing career trajectories and the possible alternative pathways that may encourage participation in the field. We define alternative pathways as any post-baccalaureate program or training that meets the needs of bachelor's degree-holding women with computing aspirations. This research paper, guided by Schlossberg's Transition Theory, conducted a content analysis on publicly available job profiles to characterize the types and features of alternative pathways commonly chosen by women in the United States (U.S.) to aid in understanding their transitions into a computing career. Findings from this study provide guidance and suggestions to women who are interested in transitioning to computing later in their career paths. It further outlines potential avenues with actionable recommendations for the computing education community to attract and retain women in the computing workforce in an effort to build an inclusive ecosystem.

A call to the computer science education community to make our values match our actions related to broadening participation through epistemological inclusion.

Despite the projection of an increase in the number of jobs in the computer science (CS) field by 13% from 2020 to 2030 in the United States (as reported by the Bureau of Labor Statistics), the representation of women, especially women of color, in the field remains low. Lack of representation for women in computer science negatively impacts the growth of this demographic as it becomes harder for prospective individuals to envision themselves in the field when they do not see others like them already succeeding in CS. Studies have found that the retention of women in the field is stronger when the representation of women is evident in their environment, however, it is hard to come by considering the low population of women computer scientists. While new prospects may find fewer women in their CS departments in their college experience, or at their workplaces, there is a plethora of social media personalities and communities for them to engage in and find like-minded individuals.This full research paper investigates the experiences of women, or lack thereof, in CS communities centered around social media and how it contributes to their sense of belonging in the CS field at large. It is evident that there is limited scope in the existing literature that studies the impact social media participation has on CS women. This literature review distinguishes the narrow scope of literature focused on women’s experiences with open-source software communities in CS from women’s experiences with more generic widespread platforms such as Twitter, or Instagram. It argues for the expansion of knowledge for the effects of CS women’s participation on such platforms and provides insight into approaches, such as photovoice, that may be utilized to study this space. The outcomes of this review reveal the potential of utilizing online platforms in retaining women in the CS workforce effectively. Considering the current status of many organizations that have switched from in-person to remote engagement due to COVID, this review contributes to the analysis of the effective use of technology and its impact at a critical time.

Background and Context: Women remain minoritized in the global digital labor market [4], which is problematic since technology innovation requires a diverse set of employees with computing skills [6]. Existing studies have primarily focused on examining students at the K-12 and/or post-secondary levels [1, 2, 3], but less is known about women who are in the workforce and choose to enter a computing program or who transition into a computing role after obtaining an initial undergraduate degree(s) in a non-computing field. It is critical to learn more about the unique trajectories taken by those with non-traditional backgrounds to encourage the participation and retention of additional women in computing. Objectives: We conducted this pilot study to characterize the types and features of alternative pathways commonly chosen by women in the United States. The framework of Super’s Life Span and Life Space [5] guided our inquiry as we considered the correlation between life spaces for women and their career transitions into computing from other fields. This theory addresses different career development paths as a consequence of career development at distinct stages through progressive efforts in pursuing career growth [5]. It considers factors which may influence career development, such as social learning experiences, personality development, and one’s values, needs, and abilities. We use this theory to explore women’s life roles and the potential impact on career aspirations. Method: Data collection involved leveraging publicly available background information from profiles shared on a professional networking website. Specifically, we examined the profiles of women who entered computing later in their educational and/or career paths and who are currently working in computing-related positions. To characterize their trajectories, we conducted a content analysis, with a focus on their education, computing-related job information, and organizational affiliations. Findings: The exact alternative pathway programs selected by women from the targeted population varied on a case-by-case basis. We observed that women may obtain additional degrees in computing from higher education institutions, although they may also hone their skills through coding bootcamps and via self-learning through online resources. In particular, women switchers often have a background in mathematics, statistics, and electronic engineering. Moreover, we discovered that the majority of women, despite the field of their undergraduate majors, experienced exposure to computing through serving as research assistants. Implications: Exploring the backgrounds of these women, who may enter the field through alternative pathways, furthers our understanding of potential avenues to attract and retain an untapped talent pool. By examining different pathways, we seek to provide insight into ways to better support these women’s transitions and to find additional ways to encourage more women to join the profession. We recommend offering increased flexibility in coursework for learners and increased opportunities to gain exposure through undergraduate research. The results could not only be of interest to program administrators but could also offer suggestions for computing educators looking to make their lessons more inclusive.

The session will present an overview of findings of a recently funded NSF workshop that set out to examine the pressing issues for computing education research for the next 15 years. Based on dialogs for scholars working in this area, the workshop participants developed a series of themes and topics that they felt should become the focus of computing education research efforts for the next 15 years. Main themes that emerged and will be discussed in this session include: diversity, equity, inclusion, ethics, broadening participation, teaching, learning, K-12, research to practice, computing's connection to other fields, and computing education research disciplinary issues. The session will focus on interesting research questions from each of these areas that are ripe to be explored as well as enablers and blockers to the progress of this work.

Underrepresentation of Black and Hispanic women in computer science is a long-standing problem that looks bleak at every level - undergraduate and graduate. This is prompting scholars to explore reasons for these low participation rates. One framework used to understand participation and persistence in STEM fields is identity. Prior work in computer science education suggest that identity is a strong indicator of persistence in these fields. However, it is hard to understand students' perception of identity without also understanding ontological beliefs with regards to a computer scientist. In this study, we explore the nature of a computer scientist. Guided by social identity theory, we designed a study that asked students to describe their definition or ontological belief of what constitutes a computer scientist in contrast to their ability to ascribe a computer science identity to self. Leveraging qualitative methods, we interviewedn = 24 women in computer science (Black and Hispanic, undergraduate and graduate students), in order to explore the role their ontological beliefs had on their computer science identity salience. The research questions guiding this work are: (1) How do Black and Hispanic women describe or define computer scientists? (2) What impact does this definition have on Black and Hispanic women's ability to claim a computing identity? Results suggest that the wide variation in definitions has a negative impact on computer science identity salience. The findings from this work suggest that computing should consider the impacts of the current messaging of what constitutes a computer scientist.

This study was designed to compare salary implications and employability of students who graduated with a Bachelor of Arts in Computer Science (BACS) - primarily distinguished by the removal of calculus and physics requirements from the traditional computer science curriculum versus those that graduated with a Bachelor of Science in Computer Science (BSCS). Given the numerous studies that identify gateway courses like calculus and physics as impediments to students' persistence in engineering and computer science AND their impact on women and people of color, the removal of this barrier has incredible potential for broadening participation in computing. One university's first cohort of BACS graduates (spring 2020) furnished a unique opportunity to compare student's self-reported employment and salary information to their BSCS peers. The study consisted of institutional data and a survey targeting spring 2020, summer 2020, fall 2020 graduates from computer science, with data fromn =134 recent graduates (BAn = 45, BSn =89). Preliminary results indicate there are no statistical significance in enrollment on the basis of gender nor job attainment; however, there is a statistical significance in enrollment on the basis of race/ethnicity and pay. The results of this work could either serve as a cautionary tale for institutions considering similar programs OR it could serve as the basis for a deeper, more critical review of the requirements currently in place in BSCS programs, nationally. Are calculus and physics courses required for prosperity in computing or are they simply a barrier to equity?

Despite increasing demands for skilled workers within the technological domain, there is still a deficit in the number of graduates in computing fields (computer science, information technology, and computer engineering). Understanding the factors that contribute to students’ motivation and persistence is critical to helping educators, administrators, and industry professionals better focus efforts to improve academic outcomes and job placement. This article examines how experiences contribute to a student’s computing identity, which we define by their interest, recognition, sense of belonging, and competence/performance beliefs. In particular, we consider groups underrepresented in these disciplines, women and minoritized racial/ethnic groups (Black/African American and Hispanic/Latinx). To delve into these relationships, a survey of more than 1,600 students in computing fields was conducted at three metropolitan public universities in Florida. Regression was used to elucidate which experiences predict computing identity and how social identification (i.e., as female, Black/African American, and/or Hispanic/Latinx) may interact with these experiences. Our results suggest that several types of experiences positively predict a student’s computing identity, such as mentoring others, having a job, or having friends in computing. Moreover, certain experiences have a different effect on computing identity for female and Hispanic/Latinx students. More specifically, receiving academic advice from teaching assistants was more positive for female students, receiving advice from industry professionals was more negative for Hispanic/Latinx students, and receiving help on classwork from students in their class was more positive for Hispanic/Latinx students. Other experiences, while having the same effect on computing identity across students, were experienced at significantly different rates by females, Black/African American students, and Hispanic/Latinx students. The findings highlight experiential ways in which computing programs can foster computing identity development, particularly for underrepresented and marginalized groups in computing.

The goal of this research methodology paper is to discuss the utility of employing a phenomenographic approach towards empirical examination of experiences in computing fields, and to argue for the application of theory within the process. Phenomenography is a qualitative technique applied to describe the variations in how populations perceive and conceptualize an observable fact, circumstance, or event. In our work, we discuss the benefits and challenges of this methodology, and examine the different approaches that can be employed. Although deviations exist in the styles of treating the data, its analysis, and the interpretation, ultimately, the goal of phenomenography is to develop an outcome space which consists of critical features for the object under investigation. Unlike many other qualitative techniques, the analysis itself is not guided using theoretical frameworks or a priori coding, since categorizations are meant to emerge from the data. While theoretical frameworks are not used during the evolution of the outcome space, we do suggest ways they can be applied in other stages of the process. Theoretical frameworks are valuable tools for limiting the scope of the relevant data by focusing on specific variables and defining a particular viewpoint. When employing phenomenography, we describe how theoretical grounding can be useful during planning and data collection - when establishing research questions, developing interview scripts, or during participant selection - or as part of interpretation and explanation of the results. This work is intended to encourage future researchers investigating experiences in computer science education to use phenomenography, and to assist in demonstrating how theoretical frameworks can enhance the protocol without compromising the integrity of the data-focused analysis.

This research paper serves as a benchmarking study to investigate the types and availability of resources available to postdoctoral scholars on university websites. Postdoctoral education in engineering and computer science disciplines is a forgotten stage of the academic pipeline, with very few scholars investigating the learning and development that occurs through the transient postdoctoral years. The few studies that have been done report postdocs feeling “forgotten” and on a “postdoctoral treadmill,” often without formal mentorship or guidance in developing the skills required to land academic careers. While most postdoctoral scholars do have supervisors to whom they report, most literature indicates that postdocs in engineering and computer science are still lacking mentorship in the peripheral skillsets essential for career success, and these effects are amplified for women and postdocs of color. Given a lack of interpersonal mentorship, it is plausible that postdocs turn to institutional resources for guidance and directions for professional development. To date, literature has not benchmarked the type or extent of resources available that are aimed at postdoctoral scholars. To this end, the purpose of this paper is to characterize university webpages using content analysis methods in order to understand the presence or absence of various types of support for postdocs at universities.

Increasingly companies assess a computing candidate's capabilities using technical interviews (TIs). Yet students struggle to code on demand, and there is already an insufficient amount of computing graduates to meet industry needs. Therefore, it is important to understand students' perceptions of TIs, and other professional experiences (e.g., computing jobs). We surveyed 740 undergraduate computing students at three universities to examine their experiences with the hiring process, as well as the impact of professional and cultural experiences (e.g., familial support) on computing identity. We considered the interactions between these experiences and social identity for groups underrepresented in computing - women, Black/African American, and Hispanic/Latinx students. Among other findings, we observed that students that did not have positive experiences with TIs had a reduced computing identity, but that facing discrimination during technical interviews had the opposite effect. Social support may play a role. Having friends in computing bolsters computing identity for Hispanic/Latinx students, as does a supportive home environment for women. Also, freelance computing jobs increase computing identity for Black/African American students. Our findings are intended to raise awareness of the best way for educators to help diverse groups of students to succeed, and to inform them of the experiences that may influence students' engagement, resilience, and computing identity development.

Currently, the computer science community is experiencing a rise in interest in computer science education research (CSER). However, current structures and belief systems within the discipline have largely relegated computer science education researchers to the margins. Computer science education researchers are mainly 'lone-wolf' scholars in their departments that participate in CSER in addition to their more widely 'accepted' computer science research. As such, there has been a resistance to offer doctoral programs in CSER. Florida International University has not only invested inCSER by hiring a CSER tenure-track faculty member, they have also established a School of Computer Science Education and Engineering Education. Despite this investment, one obstacle remains the low visibility and understanding of computer science education research among undergraduate students. This makes establishing are search group of Ph.D. students challenging. In order to combat this obstacle, a four-year program was developed as a dedicated pipeline to the computer science education Ph.D. through a series of research experiences for undergraduates (REU). Summer 2020 consisted of the first cohort of this four year commitment. Given that 2020 was wrought with a series of unprecedented events, this REU was designed and executed virtually. This paper presents the plan, setting, execution, and subsequent evaluation of this virtual REU experience. Student-feedback was overwhelmingly positive; however, as with any endeavor there were many lessons learned.

Although computing occupations have some of the greatest projected growth rates, there remains a deficit of graduates in these fields. The struggle to engage enough students to meet demands is particularly pronounced for groups already underrepresented in computing, specifically, individuals that self-identify as a woman, or as Black, Hispanic/Latinx, or Native American. Prior studies have begun to examine issues surrounding engagement and retention, but more understanding is needed to close the gap, and to broaden participation. In this research, we provide quantitative evidence from the Multiple-Institution Database for Investigating Engineering Longitudinal Development—a longitudinal, multi-institutional database to describe participation trends of marginalized groups in computer science. Using descriptive statistics, we present the enrollment and graduation rates for those situated at the intersection of race/ethnicity and gender between 1987 and 2018. In this work, we observed periods of significant flux for Black men and women, and White women in particular, and consistently low participation of Hispanic/Latinx and Native American men and women, and Asian women. To provide framing for the evident peaks and valleys in participation, we applied historical context analysis to describe the political, economic, and social factors and events that may have impacted each group. These results put a spotlight on populations largely overlooked in statistical work and have the potential to inform educators, administrators, and researchers about how enrollments and graduation rates have changed over time in computing fields. In addition, they offer insight into potential causes for the vicissitudes, to encourage more equal access for all students going forward.

The lack of diversity in computing fields in the United States is a known issue. Students enter the computing fields with the intention of graduating; however, a large number leave and do not persist after enrolling, due to discrimination and biases. This particularly concerns groups already underrepresented in computing fields, such as women, Black/African American students, and Hispanic/Latinx students. However, there are various experiences that can make students feel more included or excluded in the field. Some of these experiences include internships, undergraduate research, capstone courses, and projects, etc. Drawing on Astin's I-E-O model and applying a random forest algorithm, we measure the feature importance of 14 distinct experiences on 1650 students' feelings of inclusivity in the computing field. We observe that there are gender and racial differences in terms of the opinions of computing fields' inclusivity. For example, tutoring experience, job offers, and job experience are considered some of the most important factors for female's perceived inclusiveness of women. However, men perceived women's inclusivity differently, based on the experiences they engaged in. We also looked at the perceived inclusiveness of computing fields for ethnically and racially underrepresented groups, such as Hispanic/Latinx students. Understanding the effect of different experiences on students of both genders with different races and ethnicities on the perceived inclusion could assist the computing community to provide more cohesive experiences that benefits all students and helps them to feel more welcome.

This research full paper describes how web scraping and natural language processing can be utilized to answer complex questions in computer science education. In this work, we apply connectivism as the theoretical framework, and demonstrate how web scraping can be useful for extrapolating large amounts of data from publicly available web pages to pool data from a wider array of sources and to further knowledge in the field. In addition, we discuss how natural language processing can be used to reliably obtain salient information from textual data, and how it can complement qualitative analysis. To illustrate these techniques in practice, we provide a specific application in which we examine the current trends in the job market for computer science students. The information gathered in this example provides additional areas for educational consideration, such as offering students Python programming language and machine learning. Also, the job postings delineate a clear need for applicants to exhibit programming and testing skills. Although programming may be taught already, testing is widely considered a knowledge deficiency, which suggests that educators should consider placing an increased emphasis on this area to ensure their students are adequately prepared for their career endeavors, and able to transfer the knowledge taught to critically assess and debug their own programs.

This work-in-progress (WIP) paper in the research track explored the muddiest point and self-efficacy for students in introductory computer science courses, starting with one course at a southern public institution. With the increasing demand in computing-related careers, the increased enrollments in undergraduate computer science courses are facing challenges to ensure the average passing rate. In this work, we applied the muddiest point to capture student's perceptions of concepts of difficulty and used self-efficacy to better contextualize students' perfections of self-efficacy in relation to concepts of difficulty. In this paper we present the results of one section of one introductory computing course, that revealed the perceived difficult topics as well as changes in self-efficacy throughout the semester. Furthermore, a positive correlation was found between self-efficacy and performance.

Like other STEM fields, computer science (CS) lacks representation of minorities, such as Black and Hispanic individuals, both in the number of bachelor’s degrees obtained and the number of individuals in the CS workforce. Out-of-school CS programs are often designed with the intent to inspire young people to pursue careers in CS. Much of this programming focuses on developing student interest in CS and CS careers. Nevertheless, it is not well understood how the stereotypes that children hold about computer scientists contribute to CS interest and career choice. In this study, we set out to examine the complex relationships between CS interest, held stereotypes, and CS career choice. We surveyed participants in an after-school CS program offered to middle school boys who identified with racial and ethnic minority groups (N= 110). We tested three linear regression models and confirmed that CS interest and socially divergent stereotypes—those that diverged from societal norms—of computer scientists play unique and contrary roles in young boys’ career decision-making process even when controlling for home and school factors. These models suggest educational CS programs should include curriculum to dispel participants’ socially divergent stereotypes about computer scientists rather than targeting CS interest alone, particularly if a goal is to inspire diverse young people to pursue careers in CS.

Computer science (CS) has been identified as one of the fastest-growing professions, with demand for CS professionals far outpacing the supply of CS graduates. The necessity for a trained CS workforce has compelled industry and academia to evaluate strategies for broadening participation in CS. The current literature in CS education emphasizes the importance of social relationships and supports for individuals from underrepresented groups. Unfortunately, this literature has largely been limited to either the exploration of issues of women or that of underrepresented racial/ethnic groups. These limited views generalize characteristics of specific underrepresented groups without considering intersections between these groups. This quantitative study (n= 3,206) addressed that shortcoming by leveraging inferential statistical methods to examine (i) the similarities and differences between the social CS-related experiences of Black women, Black men, and non-Black women in the United States; (ii) the relationship between these experiences and CS career choices; and (iii) the activities during which significant social experiences might occur. The results indicate that Black women's social experiences are often different from the experiences of both Black men and non-Black women. In particular, both Black men and non-Black women had more CS friends than Black women, whereas having these friends was more significant for the CS career choice for Black women. Introductions to CS in school, before college, were negatively related to career choice for all groups, whereas home support was positive for both Black women and men. This work suggests that considering intersectionality is important to understanding the needs of different individuals, as well as the importance of social supports for persistence in CS.

This Research Full Paper presents the effects of computing identity sub-constructs on the persistence of computer science students. Computer science (CS) is one of the fastest growing disciplines in the world and an emerging critical field for all students to obtain vital skills to be successful in the 21st century. Despite the growing importance of computer science, many university and college programs suffer from low student persistence rates. Disciplinary identity is a theoretical framework that refers to how students see themselves with respect to a discipline and is related to long-term membership in a disciplinary community. The theory has been effectively applied in Science, Technology, Engineering, and Mathematics (STEM) to understand students' success and persistence. This study examines the effects of performance/competence, recognition, interest and sense of belonging on the academic persistence of computer science students. A survey of approximately 1,640 computing students as part of a National Science Foundation (NSF) funded project was developed and administered at three metropolitan public institutions. Confirmatory Factor Analysis (CFA) was performed to validate the sub-constructs of identity for use in a computing identity model. Then, a structural equation model (SEM) was constructed as a snapshot of the structural relationships for describing and quantifying the impact of the identity sub-constructs on persistence. The results indicated that our model for CS aligns with prior research on disciplinary identity but also adds the importance of sense of belonging. In addition, the findings indicate that students' academic persistence is directly influenced by their interest. A better understanding of these factors may leverage insight into students' academic persistence in computer science/engineering programs as well as a meaningful lens of analysis for further curriculum and extracurricular activities.

The U.S. government has called for more STEM professionals in order to remain competitive as a nation. Increased participation of women and minorities in these fields will capture the rich diversity of their lived experiences and contributions. Many studies examine these issues for women or minorities, but less frequently consider the intersection of gender and race. There have been quantitative studies conducted in recent years that attempt to capture why women leave engineering. However, they lack the rich description at the intersection of race and gender necessary to understand the impetus to leave engineering industry. Identity theory is the theoretical framework utilized for understanding how engineering industry culture fits with how these women see themselves and feel like they belong or do not belong in engineering. Identity theory can illustrate the meanings that Black women attribute to themselves as engineers and how they negotiate their perceived membership in engineering based on interpretations of status differences, legitimacy and stability of those status differences. This paper presents insight in to methods of data collection, analysis process, and preliminary research results.