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Research Questions: While community college transfer (i.e., upward transfer) represents an important mechanism for advancing equity across STEM fields, existing studies of gender and women’s participation within computer science have largely excluded the perspectives of upward transfer students. We address this gap in the literature by exploring transfer receptivity and gender discrimination within computer science, guided by the following questions: (1) How do upward transfer computer science students report their receptivity experiences, and how might this differ by gender? (2) How do upward transfer computer science students make meaning of receptivity experiences, and how might that meaning making be shaped by gender? Methods: We use a sequential mixed methods design, relying on longitudinal survey and interview data from upward transfer computer science majors, collected throughout students’ first year at the receiving university. Results: Findings reveal that, relative to men, upward transfer women report greater experiences of transfer stigma and challenges accessing resources at the receiving university. Qualitative findings document additional nuances in how upward transfer students—especially women—describe resilience as they navigate the university campus, encounter navigation challenges at the university, and make meaning of various manifestations of transfer stigma on campus. Contributions: In addition to implications for research and theory, we discuss what universities can do to foster a more receptive environment for upward transfer women. Specific recommendations focus on ensuring that spaces for women in computing are inclusive of transfer students and, likewise, creating supportive transfer cohort communities that are inclusive of women.

 

Objective: Upward transfer pathways are an important mechanism for advancing gender equity in STEM. Yet, little is known about how students access lucrative STEM careers and leadership positions via community colleges. This study explores the factors that may promote computing leadership identity among upward transfer students, focusing specifically on gender differences in upward transfer computing student experiences. Methods: This study relies on longitudinal survey data from N = 1,955 computing majors across 15 universities in the United States. I used linear regression with tests of two- and three-way interaction terms to examine how factors associated with computing leadership identity might differ by upward transfer student status and gender. Results: Findings highlight how faculty mentorship, peer support, and involvement in student groups might shape leadership identity among all computing students. Other findings show that time spent working for pay is positively associated with leadership identity for non-transfers but negatively associated with leadership identity for upward transfer students. Significant three-way interactions show that parent career and sense of belonging in computing are uniquely associated with leadership identity for upward transfer women. Conclusions: This study identifies contextual factors that are uniquely associated with computing leadership identity for upward transfer students, while identifying ways in which institutions can increase upward transfer students’ access to college experiences that are positively associated with leadership identity for all students. Future directions for research are discussed, including opportunities for researchers to test the impact of specific interventions to promote leadership identity, aspirations, and behaviors for upward transfer students.

 

While previous research documents that women in STEM doctoral programs tend to fare better when their advisor shares their gender identity, this study provides new insights into the role of student–advisor gender identity congruence, relying on a longitudinal sample of doctoral students in biology and using structural equation and latent growth curve modeling. Findings show that advisor gender played an inconsistent and typically indirect role in predicting student outcomes. Further, all students, regardless of gender, tended to report higher quality advising when their advisor was a woman, pointing to potential gender inequities in advising expectations of faculty. Implications for research, theory, and practice are discussed. 

Developing research self-efficacy is an important part of doctoral student preparation. Despite the documented importance of research self-efficacy, little is known about the progression of doctoral students’ research self-efficacy over time in general and for students from minoritized groups. This study examined both within- and between-person stability of research self-efficacy from semester to semester over 4 years, focusing on doctoral students in biological sciences ( N = 336). Using random intercept autoregressive analyses, we evaluated differences in stability across gender, racially minoritized student status, and first-generation student status. Results showed similar mean levels of self-efficacy across demographic groups and across time. However, there were notable differences in between-person and within-person stability over time, specifically showing higher between-person and lower within-person stability for racially minoritized and first-generation students. These findings indicate that racially minoritized and first-generation students’ research self-efficacy reports were less consistent from semester to semester. Such results may indicate that non-minoritized and continuing-generation students’ experiences from semester to semester typically reinforce their beliefs about their own abilities related to conducting research, while such is not the case for racially minoritized nor first-generation students. Future research should examine what types of experiences impact self-efficacy development across doctoral study to offer more precise insights about factors that influence these differences in within-person stability. 

This article discusses transfer pathways to Science, Technology, Engineering, and Mathematics (STEM) degrees and highlights ways in which existing literature on equity in STEM fails to represent transfer student experiences, limiting opportunities to support transfer students and diversify STEM programs. More specifically, we synthesize findings across research studies—focusing closely on recent studies of disparities in students’ sense of belonging in computing—to make a case that existing research on equity in computing is not generalizable to students following upward transfer pathways to their computing degree. Implications for centering upward transfer students’ experiences in future research and practice are discussed.

 

Purpose This study aims to examine how science, technology, engineering, and mathematics doctoral students interact with postdocs within the research laboratory, identifying the nature and potential impacts of student–postdoc mentoring relationships. Design/methodology/approach Using a sample of 53 doctoral students in the biological sciences, this study uses a sequential mixed-methods design. More specifically, a phenomenological approach enabled the authors to identify how doctoral students make meaning of their interactions with postdocs and other research staff. Descriptive statistics are used to examine how emergent themes might differ as a product of gender and race/ethnicity and the extent to which emergent themes may relate to key doctoral student socialization outcomes. Findings This study reveals six emergent themes, which primarily focus on how doctoral students receive instrumental and psychosocial support from postdocs in their labs. The most frequent emergent theme captures the unique ways in which postdocs provide ongoing, hands-on support and troubleshooting at the lab bench. When examining how this theme plays a role in socialization outcomes, the results suggest that doctoral students who described this type of support from postdocs had more positive mental health outcomes than those who did not describe this type of hands-on support. Originality/value Literature on graduate student mentorship has focused primarily on the impact of advisors, despite recent empirical evidence of a “cascading mentorship” model, in which senior students and staff also play a key mentoring role. This study provides new insights into the unique mentoring role of postdocs, focusing on the nature and potential impacts of student–postdoc interactions. 

Faculty and peer interactions play a key role in shaping graduate student socialization. Yet, within the literature on graduate student socialization, researchers have primarily focused on understanding the nature and impact of faculty alone, and much less is known about how peer interactions also contribute to graduate student outcomes. Using a national sample of first-year biology doctoral students, this study reveals distinct categories that classify patterns of faculty and peer interaction. Further, we document inequities such that certain groups (e.g., underrepresented minority students) report constrained types of interactions with faculty and peers. Finally, we connect faculty and peer interaction patterns to student outcomes. Our findings reveal that, while the classification of faculty and peer interactions predicted affective and experiential outcomes (e.g., sense of belonging, satisfaction with academic development), it was not a consistent predictor of more central outcomes of the doctoral socialization process (e.g., research skills, commitment to degree). These and other findings are discussed, focusing on implications for future research, theory, and practice related to graduate training. 

The doctoral advisor—typically the principal investigator (PI)—is often characterized as a singular or primary mentor who guides students using a cognitive apprenticeship model. Alternatively, the “cascading mentorship” model describes the members of laboratories or research groups receiving mentorship from more senior laboratory members and providing it to more junior members (i.e., PIs mentor postdocs, postdocs mentor senior graduate students, senior students mentor junior students, etc.). Here we show that PIs’ laboratory and mentoring activities do not significantly predict students’ skill development trajectories, but the engagement of postdocs and senior graduate students in laboratory interactions do. We found that the cascading mentorship model accounts best for doctoral student skill development in a longitudinal study of 336 PhD students in the United States. Specifically, when postdocs and senior doctoral students actively participate in laboratory discussions, junior PhD students are over 4 times as likely to have positive skill development trajectories. Thus, postdocs disproportionately enhance the doctoral training enterprise, despite typically having no formal mentorship role. These findings also illustrate both the importance and the feasibility of identifying evidence-based practices in graduate education.