Utilizing decision matrices to validate kindergarten screening measures
Notice bibliographique
Résumé
The early identification of students at-risk for future learning problems typically forms the basis for the Implementation of early intervention programs designed to prevent, diminish, and/or correct learning difficulties. Kindergarten screening results influence the allocation of special services and are linked with the expenditure of monetary and personnel resources. The extent to which screening contributes to accurate and useful educational decision-making requires evaluation. The purpose of the present study was to investigate the validity and utility of four kindergarten screening measures and their composite screening classification as predictors of third grade achievement. History of school-based intervention and retention status were considered to be additional indices of school performance and their relationship with kindergarten screening results was also investigated. The screening measures included the Draw-A-Person, the Kindergarten Language Screening Test, the Mann-Suiter Visual Motor Screen, and the Deverell Test of Letters and Numbers. The achievement measure employed was the Canadian Tests of Basic Skills. Validity data indicating the degree of accuracy of screening classification decisions (risk/no-risk) was possible through the utilization of decision matrix analysis. Interpretations in this study included percentage calculations of the problem base rate, referral rate, and overall hit rate. Vertical evaluation presents prediction accuracy in relation to criterion (actual) performance versus horizontal evaluation which is calculated in relation to screening (predicted) performance. Prediction-performance matrices presented in this study represent data available for one age coliort of 684 subjects enrolled since kindergarten in one school district located near Vancouver, British Columbia. Seven achieved samples were generated, the number of subjects ranging from 576-663. The results of this study demonstrate that screening referral rates were less than their respective problem base rates, indicating general under-referral of at-risk students. For all analyses, vertical evaluation more appropriately demonstrated greater under-referral rates than did horizontal evaluation. Vertical evaluation also contributed to greater accuracy of interpretation than did horizontal evaluation which proved to be misleading. Specificity rates (vertically calculated true negatives) were much larger than sensitivity rates (vertically calculated true positives), indicating far greater accuracy for the identification of non-risk than at-risk students. Overall hit rates were high but misleading as the proportions of correctly identified at-risk and non-risk students were not indicated.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,091 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».