Using Discrimination Response Ideation to Uncover Student Attitudes about Diversity and Inclusion in Computer Science
Notice bibliographique
Résumé
Helping students learn to identify and respond to situations involving discrimination is important, especially in fields like Computer Science where there is evidence of an unwelcoming climate that disproportionately drives underrepresented students out of the field. While students should not be considered responsible for fixing issues around discrimination in their institutions, they do have a role to play. In this paper, we present the results of a study in which 318 undergraduate computer science majors were presented with scenarios of discrimination and asked to identify the issues, rate the severity of the issues, and ideate 3–5 responses to address the described situations. They were also asked to identify which of their responses would likely be most effective in addressing discrimination and which of their responses they would be most likely to use if they were in the situation described in real life. Our results show that while students generally are able to identify various forms of discrimination (sexism, racism, religious discrimination, ethnic discrimination, etc.), any ambiguity in a scenario led to students describing the scenario as less severe and/or as an example of oversensitivity. We also show that students come up with many passive responses to scenarios of discrimination (such as ignoring the situation or wishing it had not happened in the first place). Students in our study were more likely to say they would deploy passive responses in real life, shying away from responses that involve direct confrontation. We observed some differences between student demographic subgroups. Women and BIPOC students in CS tend to think these issues are more severe than men and White and Asian students in CS. Women are more likely to ideate direct confrontation responses and report willingness to use direct confrontation responses in real situations. Our work contributes a methodology for examining student awareness and understanding of diversity issues as well as a demonstration that undergraduate computer science students need help in learning how to address common situations that involve either intentional or unintentional discrimination in an academic environment.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».