Increasing Engagement of Underrepresented Groups Using a Novel Mathematics Communication Tool
Bibliographic record
Abstract
Many recent studies, based largely on face-to-face classroom experiences, extol the benefits of new interactive pedagogical models, including peer-based learning.Others have shown that out-of-class student-teacher interaction (e.g., office hour attendance) leads to improvements in many key academic measures, including student performance, retention, and satisfaction.At the same time, however, it has been shown that, relative to their male peers, women are less likely to engage in both in-and out-of-class discussion in post-secondary mathematics and statistics courses.In this paper, we discuss our experience with a mathematics service course in which online communication technology that allowed for anonymity was used.This technology dramatically improved office-hour participation rates, and students reported that it helped alleviate their anxieties surrounding communication.We then explore how these ideas can be extended to develop new communication models for the technologically-enhanced classmodels that may help overcome social barriers to create a more inclusive student-centred environment, leading to further democratization of learning, including increased participation by 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".