Early cognitive predictors of language, literacy, and mathematics outcomes in the primary grades
Bibliographic record
Abstract
• Recent cross-domain research is highlighting the shared and unique early cognitive predictors of later achievements in language, reading, and mathematics. • Early cognitive predictors clustered together as expected as well as overlapped in non-obvious ways (e.g., math tasks cross-loaded with early language and literacy). • Kindergarten verbal and symbolic skills independently predicted grade 1 outcomes. • By grade two, early verbal skills continued to predict language grades as did grade 1 marks, whereas symbolic skills had indirect effects through grade 1. • Results are discussed in terms of screening practices across academic domains. Recently, cross-domain research has shown that some early cognitive precursors of language, reading, and mathematics overlap and predict one another. This study investigated how early cognitive predictors across domains could predict future academic skills across domains using data from 563 students in kindergarten to second grade (ages 5 to 8; 288 males; largely monolingual English). The roles of verbal, symbolic, and magnitude comparison skills as predictors of later academic grades for various language and math subjects were examined. Results found that Grade 1 marks were predicted by kindergarten verbal and symbolic skills, while Grade 2 marks were predicted by verbal skills and Grade 1 as well as indirectly by symbolic skills via Grade 1. Results are discussed in light of the overlapping relationships between language, reading, and mathematics.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".