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Record W3161275293 · doi:10.11575/prism/31969

Students’ Identities and Collaboration in Mathematics Group Work

2018· article· en· W3161275293 on OpenAlexaboutno aff
Silvana Valera, Miwa Aoki Takeuchi

Bibliographic record

VenueOpen MIND · 2018
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsGroup (periodic table)Mathematics educationGroup workWork (physics)MathematicsPedagogyAlgebra over a fieldPsychologyPure mathematicsEngineering

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.370
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.081
GPT teacher head0.472
Teacher spread0.391 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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