Analyses diagnostiques cognitives des résultats du test du Programme international de recherche en lecture scolaire (PIRLS) 2011
Bibliographic record
Abstract
Malgré une importante demande de recevoir des informations diagnostiques sur les difficultés en lecture des élèves, il existe très peu d’outils d’évaluation conçus spécifiquement pour cet usage. Plusieurs recherches en approche diagnostique cognitive (ADC) utilisent donc les résultats d’épreuves à grande échelle pour fournir de la rétroaction diagnostique fine et fiable sur les forces et les faiblesses des élèves. Les modélisations de données permettent de s’éloigner des scores ou des rangs percentiles habituellement obtenus, et de fournir des pistes d’intervention appropriées. Cette étude vise à vérifier la faisabilité d’appliquer des modélisations à visée diagnostique aux résultats de 4762 élèves canadiens ayant fait le cahier 13 du test du PIRLS de 2011. Les résultats suggèrent un potentiel de recevoir de la rétroaction diagnostique détaillée de leurs forces et faiblesses sur les habiletés sous-jacentes du test.
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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 | 0.019 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
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".