Students’ Identities and Collaboration in Mathematics Group Work
Bibliographic record
Abstract
In this study, we examined how students’ different identities, especially gender and students’ relationship with mathematics, influenced leadership and group work in math classrooms. This study took place in two linguistically and racially diverse middle schools in Canada. We collected student surveys, video-recorded group work interactions, and individual video-mediated interviews. We analyzed four mixed-gender groups and 12 interviews. Our analysis revealed that girls took on leadership roles equally as boys. Instead, we found that those who demonstrated a positive math identity tended to take on leadership roles in the group and those who demonstrated a negative math identity did not. We also identified how culturally-constituted gender norms can influence collaboration. Two girls expressed their preference to work with girls. Their preference influenced the way in which they interacted in the group with a boy. Our results complicated the role of gender identities in math by examining its intersection with other identities. We highlight the importance for educators and parents to collectively develop positive math identities among all students to in turn foster leadership and agency in math learning. We also question the common school practice where students are socialized into the unsustainable norm that boys and girls work separately.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".