Confidence, Connection, and Comfort
Bibliographic record
Abstract
The computer science education community has long strived to create more equitable opportunities for students, such as initiatives to foster inclusion of women and other people from historically marginalized groups in CS. Despite these efforts, the gender gap has persisted, with less than a quarter of CS Bachelor's degrees awarded to women in the United States in 2019. As a community, we must strive to improve women's experiences in CS. This paper describes work conducted at a large research university which has traditionally offered CS1 through lecture sections ranging in size from 400-650 students. In Fall 2019, we offered an alternative small all-women's class (35 students) in addition to the traditional lecture class (601 students; 149 women). Both classes covered the same CS concepts but were led by different instructors. Students reported on their experience through a survey administered at the end of the semester. Students in the all-women's class reported significantly greater social connections and comfort collaborating with their peers compared to women in the traditional class. They also reported significantly greater feelings of support within their class, more confidence in their CS knowledge, and a more welcoming classroom environment compared to women in the traditional class. Additionally, the drop rate for students in the all-women's class was significantly lower (5.7%) than the drop rate for women in the traditional class (24.8%). In light of these positive results, we provide actionable insights for CS educators and discuss how to better support women in their CS endeavors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".