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Record W3004172140 · doi:10.7202/1066598ar

Élargir conceptuellement le modèle de l’alignement curriculaire pour comprendre la cohérence des pratiques évaluatives sommatives notées des enseignants : enjeux et perspectives

2019· article· fr· W3004172140 on OpenAlexvenueno aff
Raphaël Pasquini

Bibliographic record

VenueMesure et évaluation en éducation · 2019
Typearticle
Languagefr
FieldSocial Sciences
TopicEducational Assessment and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhilosophyPolitical science

Abstract

fetched live from OpenAlex

L’alignement curriculaire désigne les liens de cohérence existant dans tout processus d’enseignement-apprentissage entre les objectifs du curriculum, les tâches d’apprentissage et les démarches d’évaluation. Ce modèle permet notamment de comprendre la cohérence de toute démarche évaluative. En mobilisant des données issues d’une recherche collaborative menée avec huit enseignants de mathématiques et de français du secondaire, nous montrerons toutefois que son acception est limitée lorsqu’il s’agit de comprendre cette cohérence saisie dans des pratiques évaluatives sommatives notées et que, dès lors, le modèle demande à être conceptuellement élargi. Pour cela, nous nous appuierons sur un exemple d’épreuve sommative modélisée dans ce sens. Nos résultats soulignent la pertinence d’analyser les pratiques évaluatives sommatives à l’aide du modèle élargi, tout en considérant le rôle que joue le contexte sur certaines de ses dimensions spécifiques.

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)0.015
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.205
GPT teacher head0.442
Teacher spread0.237 · 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 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

Citations10
Published2019
Admission routes1
Has abstractyes

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