Expérimentation d’un modèle d’évaluation certificative dans un contexte d’enseignement scientifique
Bibliographic record
Abstract
Le programme québécois de science et technologie est basé sur une approche par compétences. Ce choix implique des défis importants et principalement quand l’évaluation est de nature certificative. Une des compétences à évaluer concerne l’investigation scienti‐ fique. En s’appuyant sur les travaux de Rey et al. (2003), nous avons conçu un modèle d’évaluation qui permet de juger du développement de cette compétence. Différentes situa‐ tions d’évaluation ont été créés et administrées auprès de 560 élèves du secondaire pour vérifier si le modèle : (1) est adéquat pour mesurer le niveau de compétence des élèves et (2) se comporte de la même façon selon le contexte disciplinaire. Les résultats montrent que le modèle permet de classer les élèves selon trois niveaux de maîtrise : compétence assurée, compétence partielle et maîtrise des habiletés. Mots clés : évaluation, compétences, investigation scientifique The Science and Technology curriculum in the Province of Quebec, based on competencies, represents a challenge for science teachers, particularly for high‐stakes assessment. Teach‐ ers who do know how to conduct hands‐on assessment must deal with practical con‐ straints. In this context, we adapted Rey’s et al. (2003) work to construct an assessment model in relation with the Scientific Inquiry Competence. We designed different assess‐ ment situations which we administered to 560 junior high school students to verify whether a) the model is helpful in assessing learners’ level of competency in scientific in‐ quiry b) the results are comparable among disciplines on which the assessment situations are based. The results show that the model works as predicted for different learners’ levels: Full competency, Partial Competency, Skill. Key words: assessment, competencies, scientific inquiry
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".