Views on Teaching and Learning Preferences for Women and Men in Undergraduate Computer Science
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it