A Methodology for Investigating Women's Module Choices in Computer Science
Bibliographic record
Abstract
At ITiCSE 2021, Working Group 3 examined the evidence for teaching practices that broaden participation for women in computing, based on the National Center for Women & Information Technology (NCWIT) Engagement Practices framework. One of the report's recommendations was "Make connections from computing to your students' lives and interests (Make it Matter) but don't assume you know what those interests are; find out! " The goal of this 2023 working group is to find out what interests women students by bringing together data from our institutions on undergraduate module enrollment, seeing how they differ for women and men, and what drives those choices. We will code published module content based on ACM curriculum guidelines and combine these data to build a hierarchical statistical model of factors affecting student choice. This model should be able to tell us how interesting or valuable different topics are to women, and to what extent topic affects choice of module - as opposed to other factors such as the instructor, the timetable, or the mode of assessment. Equipped with this knowledge we can advise departments how to focus curriculum development on areas that are of value to women, and hence work towards making the discipline more inclusive.
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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 | 0.050 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".