Bolstering the Persistence of Black Students in Undergraduate Computer Science Programs: A Systematic Mapping Study
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Résumé
Background: People who are racialized, gendered, or otherwise minoritized are underrepresented in computing professions in North America. This is reflected in undergraduate computer science (CS) programs, in which students from marginalized backgrounds continue to experience inequities that do not typically affect White cis-men. This is especially true for Black students in general, and Black women in particular, whose experience of systemic, anti-Black racism compromises their ability to persist and thrive in CS education contexts. Objectives: This systematic mapping study endeavours to (1) determine the quantity of existing non-deficit-based studies concerned with the persistence of Black students in undergraduate CS; (2) summarize the findings and recommendations in those studies; and (3) identify areas in which additional studies may be required. We aim to accomplish these objectives by way of two research questions: (RQ1) What factors are associated with Black students’ persistence in undergraduate CS programs?; and (RQ2) What recommendations have been made to further bolster Black students’ persistence in undergraduate CS education programs? Methods: This systematic mapping study was conducted in accordance with PRISMA 2020 and SEGRESS guidelines. Studies were identified by conducting keyword searches in seven databases. Inclusion and exclusion criteria were designed to capture studies illuminating persistence factors for Black students in undergraduate CS programs. To ensure the completeness of our search results, we engaged in snowballing and an expert-based search to identify additional studies of interest. Finally, data were collected from each study to address the research questions outlined above. Results: Using the methods outlined above, we identified 16 empirical studies, including qualitative, quantitative, and mixed-methods studies informed by a range of theoretical frameworks. Based on data collected from the primary studies in our sample, we identified 13 persistence factors across four categories: (I) social capital, networking, & support; (II) career & professional development; (III) pedagogical & programmatic interventions; and (IV) exposure & access. This data-collection process also yielded 26 recommendations across six stakeholder groups: (i) researchers; (ii) colleges and universities; (iii) the computing industry; (iv) K-12 systems and schools; (v) governments; and (vi) parents. Conclusion: This systematic mapping study resulted in the identification of numerous persistence factors for Black students in CS. Crucially, however, these persistence factors allow Black students to persist, but not thrive, in CS. Accordingly, we contend that more needs to be done to address the systemic inequities faced by Black people in general, and Black women in particular, in computing programs and professions. As evidenced by the relatively small number of primary studies captured by this systematic mapping study, there exists an urgent need for additional, asset-based empirical studies involving Black students in CS. In addition to foregrounding the intersectional experiences of Black women in CS, future studies should attend to the currently understudied experiences of Black men.
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Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
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Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle