Using Discrimination Response Ideation to Uncover Student Attitudes about Diversity and Inclusion in Computer Science
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".