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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.

Underrepresentation of women in computer science (CS) increasingly demands the necessity to find and enhance current learning engagement approaches to bring more women into computing fields. Some researchers have been exploring the influence of gamification on female students as one of these possible learning engagement strategies. Gamification refers to the introduction of video game elements into non-game activities to enhance engagement and motivation. Previous studies have reported mixed results of the impact of gamification on women. In this study, we introduce SEP-CyLE (Software Engineering and Programming Cyberlearning Environment), an online gamified tool that was designed to provide supplemental computing content to students. This paper presents a convergent mixed-methods study guided by social identity theory and self-efficacy to understand women's experiences with this gamified tool. More specifically, this study explores virtual points' and leaderboards' effects on CS identity development, self-efficacy, and performance. The results show that virtual points and the leaderboard contributed to improved performance for students of all genders, suggesting that gamification is a gender-neutral learning engagement strategy that improves female students' performance as much as male students. Regardless of improved performance, most women did not actively enjoy or were motivated by the virtual points or leaderboard in SEP-CyLE. Additionally, gamification had no significant impact on CS identity development or self-efficacy constructs and had little to no impact on women's interest and engagement in the field of computing.

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.

Background: Social identity theory has been used to understand student and professional engagement in engineering. Engineering identity development, however, can be disrupted by the barriers and challenges associated with the racialized and gendered perceptions of engineering. Purpose/Hypothesis: This study examined the engineering identity development and resilience of Black women engineers in industry through the exploration of their experiences in the engineering workplace. Many studies document challenges that underrepresented groups face in engineering. This work, instead, focuses on particular supports and empowerment that enabled continued participation in the engineering workforce over time. Design/Method: This interpretative phenomenological analysis consisted of nine self-identified Black women engineers currently employed in engineering industry with at least 10 years of work experience and explored the background, pathway into and through engineering, and what it means to be an engineer through 90-min interviews with each participant. Results: The results of this study suggest that engineering identity development alone does not contribute to the retention of Black women in the engineering workplace but instead the confluence of race, gender, and role identity that aids in developing a resilient engineering identity. Conclusions: The formal, informal, and structural educational experiences of Black women engineers are critical to the development of a resilient engineering identity. This identity is dependent on the complexities associated with being Black, a woman, and an engineer.

Background Purpose/Hypothesis Design/Method Results Conclusions Postdoctoral training holds an increasingly important place in preparation for leading academic and research positions. While little empirical research has described postdoctoral training beyond the sciences, across all fields, “misaligned expectations” are often touted as a key source of postdoctoral strife.This article describes mentorship competency beliefs within engineering and computer science fields, which increasingly engage in postdoctoral training.An embedded mixed‐methods design was used to quantitatively identify mentorship profiles from survey data using latent profile analysis (LPA) from a sample of n = 118 postdoctoral scholars and n = 165 postdoctoral supervisors. Qualitative thematic analysis of interviews with n = 29 postdoctoral scholars and n = 20 postdoctoral supervisors was used to identify meaning in the differences between quantitative profiles. The combination of LPA with thematic analysis enabled the triangulation of distinct postdoctoral mentorship profile definitions.LPA identified six postdoctoral fellow profiles and four supervisor profiles, which became clearly definable through thematic analysis. Postdoc profiles included Technical Manager, Autonomy Focused Advisor, Stretched Mentor, Well‐Rounded Mentor, Exemplar Mentor, and Leader‐Mentor, while supervisor profiles included Autonomous Mentor, Reflective Mentor, Research Lab Mentor, and Confident Leader‐Mentor. Some of these are aligned, but several are not, giving insight into the phenomenon of “misaligned expectations” in postdoctoral literature.The mentorship profiles illustrate the misalignment in expectations, which leads to negative mentorship experiences for many postdoctoral scholars. [ABSTRACT FROM AUTHOR]

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.

Contribution: The current study finds that female-identified students report stronger associations between "helping others" and interest in bioengineering/biomedical engineering than non-females, while they report less interest in electrical and computer engineering overall, with similar associations to factors such as "inventing/designing things" than non-females. Background: While women have made gains in STEM, electrical and computer engineering programs award 13% of their Bachelor's degrees to women while bioengineering/biomedical engineering programs award over 40%. Prior work suggests that women's persistent under-representation in electrical and computer engingeering may be due to them being drawn into other disciplines. Women persist in engineering at similar rates as men, so a better understanding of early college attitudes is needed. Research Questions: (1) How are career outcome expectations associated to electrical engineering, computer engineering, and bioengineering/biomedical engineering? (2) What are females' interests in electrical engineering, computer engineering, and bioengineering/biomedical engineering? (3) Are outcome expectations and major interests distinct for female-identified students? Methodology: Regression analyses were conducted on multiply-imputed data of introductory engineering students at four public universities in the U.S. Findings: Students associate inventing/designing things and "developing new knowledge and skills" to electrical engineering, and associate inventing/designing things and "working with people" (negative) to computer engineering. Students associate helping others and "supervising others" (negative) to bioengineering/biomedical engineering. Female-identified students are less interested in electrical and computer engineering, more interested in bioengineering/biomedical engineering, and associate helping others to bioengineering/biomedical engineering more strongly.

Educational researchers have explored the importance of performance, recognition, and interest in establishing and maintaining a STEM identity. Research has also demonstrated that the ways students describe themselves and how they participate in STEM communities can provide insight into their role identity salience; however, there has been little work to explore the ontological beliefs of students about STEM people and how this influences their ability to see themselves as possessing a STEM identity. This research explores the ontological beliefs of high school students, with specific attention to the ways in which they describe what constitutes a math person, science person, physics person, or engineer and how these descriptions influence their ability to take on these role identities.

Postdoctoral fellows report experiencing misalignment between their expectations and their experiences in postdoctoral training. Little research explores their experiences with less still attempting to identify advisors' expectations of postdoctoral fellows. This research aims to describe the knowledge, skills, and attributes (KSA) advisors/principal investigators expect when postdoctoral fellows begin and the expectations for developing postdoc KSA during the fellowship. Qualitative semi-structured interviews with postdoctoral advisors provide data about the hiring, starting, and development expectations advisors have for postdocs. Axial coding with KSA and abductive analysis identify advisors' KSA expectations. Postdoctoral advisors describe hiring requirements and development expectations that do not clearly align. This misalignment starts postdocs and advisors in a new relationship with already misaligned expectations. Clarified language in hiring requirements and development expectations can help advisors and postdocs begin the fellowship with better-aligned expectations. The research reported here provides language to advisors and postdocs to assist and guide KSA expectations. [ABSTRACT FROM AUTHOR]