Gender gaps and gendered action in a first-year physics laboratory
Bibliographic record
Abstract
This paper is part of the Focused Collection on Gender in Physics.] It is established that male students outperform female students on almost all commonly used physics concept inventories. However, there is significant variation in the factors that contribute to the gap, as well as the direction in which they influence it. It is presently unknown if such a gender gap exists on the relatively new Concise Data Processing Assessment (CDPA) and, therefore, whether gendered actions in the teaching lab might influence-or be influenced by-the gender gap. To begin to get an estimates of the gap, its predictors, and its correlates, we have measured performance on the CDPA at the pretest and post-test level. We have also made observations of how students in mixed-gender partnerships divide their time in the lab. We find a gender gap on the CDPA that persists from pre-to post-test and that is as big as, if not bigger than, similar reported gaps. We also observe compelling differences in how students divide their time in the lab. In mixed-gender pairs, male students tend to monopolize the computer, female and male students tend to share the equipment equally, and female students tend to spend more time on other activities that are not the equipment or computer, such as writing or speaking to peers. We also find no correlation between computer use, when students are presumably working with their data, and performance on the CDPA post-test. In parallel to our analysis, we scrutinize some of the more commonly used approaches to similar data. We argue in favor of more explicitly checking the assumptions associated with the statistical methods that are used and improved reporting and contextualization of effect sizes. Ultimately, we claim no evidence that female students are less capable of learning than their male peers, and we suggest caution when using gain measures to draw conclusions about differences in science classroom performance across gender.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".