Predicting curriculum and test performance at age 11 years from pupil background, baseline skills and phonological awareness at age 5 years
Bibliographic record
Abstract
BACKGROUND: Phonological awareness tests are amongst the best predictors of literacy and predict outcomes of Key Stage 1 assessment of the National Curriculum in England at age 7. However, it is unknown whether their ability to predict National Curricular outcomes extends to Key Stage 2 assessments given at age 11, or also whether the predictive power of such tests is independent of letter-knowledge. We explored the unique predictive validity of phonological awareness and early literacy measures, and other pupil background measures taken at age 5 in the prediction of English, Maths, and Science performance at age 11. METHOD: Three hundred and eighty-two children from 21 primary schools in one Local Educational Authority were assessed at age 5 and followed to age 11 (Key Stage 2 assessment). Teaching assistants (TAs) administered phonological awareness tasks and early literacy measures. Baseline and Key Stage 2 performance measures were collected by teachers. RESULTS: Phonological awareness was a significant unique predictor of all nine outcome measures after baseline assessment and pupil background measures were first controlled in regression analyses, and continued to be a significant predictor of reading, maths, and science performance, and teacher assessments after early literacy skill and letter-knowledge was controlled. Gender predicted performance in writing, the English test, and English teacher assessment, with girls outperforming boys. CONCLUSIONS: Phonological awareness is a unique predictor of general curricular attainment independent of pupil background, early reading ability and letter-knowledge. Practically, screening of phonological awareness and basic reading skills by school staff in year 1 significantly enhances the capacity of schools to predict curricular outcomes in year 6.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".