Utilizing decision matrices to validate kindergarten screening measures
Bibliographic record
Abstract
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.
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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 | 0.014 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".