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Record W4296622887 · doi:10.4000/ripes.4094

Le développement professionnel en évaluation des apprentissages d’enseignants du supérieur

2022· article· fr· W4296622887 on OpenAlexaff
Isabelle Nizet, Josée-Anne Côté, Christelle Lison

Bibliographic record

VenueRevue internationale de pédagogie de l’enseignement supérieur · 2022
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

L’objectif général de notre recherche est d’outiller le repérage de traces de développement professionnel chez des enseignants-chercheurs de différentes disciplines, se formant en évaluation par le biais du cours Évaluer en situations authentiques offert en ligne. Ce cours fait partie d’un microprogramme conçu selon la démarche du Scholarship of Teaching and Learning (Bélisle et al., 2016). Les artefacts provenant de trois tâches complexes, réalisées dans le cadre de ce cours, sont analysés en référant aux différents paramètres du modèle dynamique de développement professionnel de Clarke et Hollingsworth (2002) et aux dimensions identitaires et culturelles propres aux compétences évaluatives des enseignants universitaires (Nizet, 2015). Nous proposons une exploration de ce modèle à la lumière de l’intelligibilité des pratiques évaluatives, puisque la réalisation de tâches complexes dans le cours amène des prises de conscience et des déplacements identitaires et culturels, signes d’un développement professionnel authentique dans le cadre de contraintes institutionnelles et de cultures évaluatives dominantes.

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)0.013
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0040.005
Scholarly communication0.0080.006
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.002

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 head0.264
GPT teacher head0.401
Teacher spread0.137 · 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 designNot applicable
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

Citations2
Published2022
Admission routes1
Has abstractyes

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Same venueRevue internationale de pédagogie de l’enseignement supérieurSame topicEducation, sociology, and vocational trainingFrench-language works237,207