CS1 Students' Perspectives on the Computer Science Gender Gap: Achieving Equity Requires Awareness
Bibliographic record
Abstract
There are numerous initiatives to improve diversity within the computer science field. However, women still disproportionately drop CS majors and earn less than one quarter of CS bachelor's degrees in the United States. The extent to which CS students-especially male students-are aware of this gender gap is an open question. This paper reports on a study to investigate that question. We analyzed 325 CS1 students' survey responses and found significant differences between women's and men's awareness of the CS gender gap. Men were significantly less aware of the gender gap than women, and men had significantly milder beliefs about whether women experienced adversity in CS and whether there should be targeted efforts to support women. Twenty students (10 women and 10 men) participated in follow-up interviews after the survey, where they discussed their experiences and perceptions of the computer science gender gap. Even among students who were aware of the gender gap, many had a superficial understanding of its cause, believing it to be due to women having less natural interest in CS. We argue that these findings are a call-to-action: university CS curricula need to include diversity, equity, and inclusion (DEI) training so that students have a more complete understanding of this complex issue, and so that these misconceptions do not continue to be perpetuated into the workplace.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".