MétaCan
Menu
Back to cohort
Record W4414695974 · doi:10.26443/mje/rsem.v59i2.10149

Exploration initiale des facteurs pour une pédagogie variée et active à l’enseignement universitaire

2025· article· fr· W4414695974 on OpenAlexaffvenueabout
Myriam Girouard-Gagné, Noémie Deschênes, Olivier Bégin‐Caouette, Glen A. Jones, Grace Karram Stephenson, Amy Scott Metcalfe

Bibliographic record

VenueMcGill Journal of Education / Revue des sciences de l éducation de McGill · 2025
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsInstitute for Christian StudiesUniversité de Montréal
Fundersnot available
KeywordsContext (archaeology)Statistical analysisResearch methodologyMedical screening

Abstract

fetched live from OpenAlex

Une approche centrée sur l’apprenant·e impliquant l’utilisation de méthodes pédagogiques actives semble encouragée à l’enseignement universitaire afin de répondre au contexte changeant. Toutefois, aucune étude quantitative n’a été effectuée pour comprendre les facteurs déterminants des pratiques pédagogiques variées et actives chez les professeur·e·s universitaires, ce que cet article propose de faire. Des analyses statistiques descriptives ainsi qu’une régression logistique ont été effectuées à partir des données récoltées auprès de 2 968 professeur·e·s d’universités canadiennes. Il semble que celles et ceux dont la pédagogie est la plus variée ont suivi une formation sur la pédagogie, préfèrent l’enseignement, travaillent plus d’heures par semaine, autoévaluent leur enseignement de façon formelle ou exercent dans les domaines des sciences naturelles et de la santé.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.068
metaresearch head score (Gemma)0.220
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.362

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0680.220
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0080.010
Science and technology studies0.0030.004
Scholarly communication0.0110.009
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0170.002

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 head0.693
GPT teacher head0.532
Teacher spread0.161 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2025
Admission routes3
Has abstractyes

Explore more

Same venueMcGill Journal of Education / Revue des sciences de l éducation de McGillSame topicEducation, sociology, and vocational trainingFrench-language works237,207