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Enseigner dans un programme universitaire innovant : de nouveaux rôles à apprivoiser, des actes pédagogiques à diversifier

2012· article· fr· W2064654211 on OpenAlexaffvenueabout
Lise St-Pierre, Denis Bédard, Nathalie Lefebvre

Bibliographic record

VenueThe Canadian Journal for the Scholarship of Teaching and Learning · 2012
Typearticle
Languagefr
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsHumanitiesContext (archaeology)SociologyArtGeography

Abstract

fetched live from OpenAlex

Cette recherche vise à décrire, à partir d’observations empiriques, les rôles exercés et les actes pédagogiques effectués par les enseignantes et les enseignants engagés dans quatre programmes universitaires canadiens jugés innovants. L’observation directe en classe a permis de recueillir leurs propos qui ont été analysés à l’aide de deux grilles construites progressivement au cours de l’étude: la liste des dimensions théoriques et la grille des niveaux de centration sur l’apprentissage. Les résultats obtenus démontrent que les rôles et les actes pédagogiques observés apparaissent moins diversifiés et moins centrés sur l’apprentissage que ce qui est attendu dans un contexte novateur de formation. This research, based on empirical observations, describes the roles and teaching acts carried out by professors engaged in four innovative university programs in Canada. Classrooms observations were used to collect data, which were then analyzed using two grids built progressively during the study: a list of theoretical dimensions and a grid showing degrees of focus on learning. In terms of results, the roles and actions of the four university professors were found to be less diversified and less centered on student learning than was expected from their innovative instructional context.

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)0.006
metaresearch head score (Gemma)0.012
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.005
Scholarly communication0.0080.002
Open science0.0010.003
Research integrity0.0010.002
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 head0.133
GPT teacher head0.368
Teacher spread0.235 · 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

Citations5
Published2012
Admission routes3
Has abstractyes

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Same venueThe Canadian Journal for the Scholarship of Teaching and LearningSame topicEvaluation of Teaching PracticesFrench-language works237,207