Leveling the Playing Field: How Cold-Calling Affects Class Discussion Gender Equity
Bibliographic record
Abstract
Classroom discussion is widely used and highly valued for actively engaging students in their own learning. A recent study has shown that cold-calling increases the number of students who participate voluntarily in class discussions and does not make them uncomfortable when doing so (Dallimore, Hertenstein, & Platt, 2013). However, there are concerns about whether these findings generally apply to both men and women students since prior research has documented lower participation rates and higher discomfort for women. This study examines the relationship between cold-calling and a) voluntary participation of both men and women students and b) student comfort participating in class discussions. The results show that cold-calling increases the percentage of both men and women who participate voluntarily. Further, the results indicate in high cold-calling classes women answer the same number of volunteer questions as men. Additionally, increased cold-calling did not make either group uncomfortable. However, differences were observed between men and women in low cold-calling environments where women answered fewer questions than men. Thus, cold-calling may help improve the performance of both men and women in class discussions and may make the classroom environment more equitable for women.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".