Views on Teaching and Learning Preferences for Women and Men in Undergraduate Computer Science
Bibliographic record
Abstract
This article explores differences between women’s and men’s views on teaching and learning in undergraduate computer science studies at a Canadian university. The research focuses on perceptions and experiences about learning activities and teaching computer science and how students and teachers view these aspects as valuable for these activities. To better understand research problems and complex phenomena, a mixed-methods concurrent approach was developed for this research, with the qualitative part being the major component (QUAL + quant). The data collected was based on interviews with students and academic staff, surveys, and class observations. Quantitative data from surveys were converted into narratives that were analyzed qualitatively (meaning we qualitized the data). The results show that students who identify as women relied more on formal teaching, while students who identify as men found informal teaching and smaller class sizes more important in their learning approaches. The interaction with the teaching assistants (TAs) was found to be more important for the students who identify as women than for the students who identify as men. As for learning preferences, women preferred more direct instruction, while male students were interested in more complex settings flexibly commuting between competitive, cooperative, and individual learning approaches. Neither women nor men preferred single-gender classes. It was noticed that a small class size is not automatically a solution, as in our case, male students benefited from small classes, while some women felt without adequate support.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".