Predicting Early School Achievement With the EDI: A Longitudinal Population-Based Study
Bibliographic record
Abstract
School readiness tests are significant predictors of early school achievement. Measuring school readiness on a large scale would be necessary for the implementation of intervention programs at the community level. However, assessment of school readiness is costly and time consuming. This study assesses the predictive value of a school readiness measure, the Early Development Instrument (EDI), which relies on kindergarten teachers' ratings of children's well-being and social, emotional, and cognitive development. We also compared the predictive value of the EDI with that of a direct school readiness test and a battery of cognitive tests. Data were collected when the children were in kindergarten and a year later, as part of Quebec's Longitudinal Study of Child Development. We found that that the EDI alone explained 36% of the variance in school achievement. The complete battery of measures explained 50% of the variance in early school achievement. Two of the EDI domains (Physical Health and Well-Being and Language and Cognitive Development) contributed uniquely to the prediction of school achievement over and above the cognitive assessments and direct school readiness test. The social and emotional domains of the EDI were at best marginal predictors of school achievement. In spite of this limitation, we conclude that the EDI predicts early school achievement as accurately as measures that take more time and resources to administer.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".