Not Just Black and Not Just a Woman: Black Women Belonging in Computing

Solomon, Amber; Moon, Dekita; Roberts, Armisha L.; Gilbert, Juan E.

doi:10.1109/respect.2018.8491700

Cited by 21 publications

(5 citation statements)

References 26 publications

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“…In another study, women who were randomly assigned to male majority teams were less willing to become team leaders than women assigned to women majority teams [8]. It is further important to consider intersectional identities in this [46][47][48]. This literature leads us to strongly believe that we should ensure that no students experience solo or singleton status in study groups.…”

Section: Group Compositionmentioning

confidence: 99%

Inclusive Study Group Formation At Scale

Kohli¹,

Ramachandran²,

Tudor³

et al. 2022

Preprint

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The student peer-group is one of the most important influences on student development. Group work is essential for creating positive learning experiences, especially in remote-learning where student interactions are more challenging. While the benefits of study groups are established, students from underrepresented communities often face challenges in finding social support for their education when compared with those from majority groups. We present a system for flexible and inclusive study group formation that can scale to thousands of students.Our focus is on long-term study groups that persist throughout the semester and beyond. Students are periodically provided opportunities to obtain a new study group if they feel their current group is not a good fit. In contrast to prior work that generates single-use groups, our process enables continuous refinement of groups for each student, which in turn informs our algorithm for future iterations.We trialed our approach in a 1000+ student introductory Electrical Engineering and Computer Science course that was conducted entirely online during the COVID-19 pandemic. We found that of all students matched to study groups through our algorithm, a large majority felt comfortable asking questions (78%) and sharing ideas (74%) with their group. Students from underrepresented backgrounds were more likely to request software-matching for study groups when compared with students from majority groups. However, underrepresented students that we did match into study groups had group experiences that were just as successful as students from' majority groups. Students in engaged, regularly participating study groups had more positive results across all other indicators of the study group experience, and certain positive group experiences were associated with higher exam scores overall. Furthermore, students performing at a B-level on the first class midterm, who participated in high-quality software-matched study groups, demonstrated higher final exam scores than students in lowerquality groups.

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Section: Group Compositionmentioning

confidence: 99%

Inclusive Study Group Formation At Scale

Kohli¹,

Ramachandran²,

Tudor³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This sentiment was echoed by first generation [3] and low income students. Solomon et al [66] reported that Black women feel that they don't belong in CS, and in 2020, Nguyen and Lewis [52] concluded that competitive enrollment policies negatively affected first year students' sense of belonging.…”

Section: Community Belonging and Wellbeingmentioning

confidence: 99%

Teaching through a Global Pandemic

Siegel

Zarb

Alshaigy

et al. 2021

Proceedings of the 2021 Working Group Reports on Innovation and Technology in Computer Science Education

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The coronavirus (COVID-19) pandemic has forced an unprecedented global shift within higher education in how instructors communicate with and educate students. This necessary paradigm shift has compelled educators to take a critical look at their teaching styles and use of technology. Computing education traditionally focuses on experiential, in-person activities. The pandemic has mandated that educators reconsider their use of student time and has catalysed overnight innovations in the educational setting.Even in the unlikely event that we return entirely to pre-pandemic norms, many new practices have emerged that offer valuable lessons to be carried forward into our post-COVID-19 teaching. This working group will explore what the post-COVID-19 academic landscape might look like, and how we can use lessons learned during this educational shift to improve our subsequent practice. Following a multinational study of computing faculty, this exploratory stage will identify practices within computing that appear to have been improved through exposure to online tools and technologies, and that should therefore continue to be used in the online space. In the broadest sense, our motivation is to explore what the post-COVID-19 educational landscape will look like for computing education.

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“…Mentoring is a key contributor to the recruitment, retention, and productivity of underrepresented groups in Science, Technology, Engineering, and Mathematics(STEM) [8] [9] [10]. Each of these disciplines contributes to the domain areas of data science, the focus of our BPDM workshop.…”

Section: Literature Reviewmentioning

confidence: 99%

The Data Science Fire Next Time: Innovative strategies for mentoring in data science

Jackson¹,

Maestre²

2020

Preprint

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As data mining research and applications continue to expand in to a variety of fields such as medicine, finance, security, etc., the need for talented and diverse individuals is clearly felt. This is particularly the case as Big Data initiatives have taken off in the federal, private and academic sectors, providing a wealth of opportunities, nationally and internationally. The Broadening Participation in Data Mining (BPDM) workshop was created more than 7 years ago with the goal of fostering mentorship, guidance, and connections for minority and underrepresented groups in the data science and machine learning community, while also enriching technical aptitude and exposure for a group of talented students. To date it has impacted the lives of more than 330 underrepresented trainees in data science. We provide a venue to connect talented students with innovative researchers in industry, academia, professional societies, and government. Our mission is to facilitate meaningful, lasting relationships between BPDM participants to ultimately increase diversity in data mining. This most recent workshop took place at Howard University in Washington, DC in February 2019.Here we report on the mentoring strategies that we undertook at the 2019 BPDM and how those were received.

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Not Just Black and Not Just a Woman: Black Women Belonging in Computing

Cited by 21 publications

References 26 publications

Inclusive Study Group Formation At Scale

Inclusive Study Group Formation At Scale

Teaching through a Global Pandemic

The Data Science Fire Next Time: Innovative strategies for mentoring in data science

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