Who does better in person or online, males or females? Gender differences in academic performance of undergraduate sciences students at the University of Ottawa.
Bibliographic record
Abstract
From secondary school, females tend do to do less well in maths than males (Statistics Canada). Do these differences in academic performance between the genders carry on in higher education and influence their performance in other science disciplines? To study whether gender played a role in academic performance at the Faculty of Science at the University of Ottawa, two datasets were collected for in-person (2014-2019) and online courses (2020-2022). Data included gender (student self-identified as male or female at registration) admission average, cGPA, and the final grade in each course of a student’s degree. Non-binary genders were not included because of the lack of representation for statistical analysis. Analysis of covariance and multi-covariance were used to determine whether performance differed between males and females in individual courses, disciplines, year of study, class size and language of study. Surprising results were obtained, some who may warrant some serious further investigation, and may encourage strategic changes to teaching some subjects. Want to know more? Bring your mobile device to the presentation and test your assumptions on gender gaps in science!
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".