Cognitive Dimensions of Early Numeracy: Exploring Profiles of Early Math Achievement in Canadian Students
Bibliographic record
Abstract
Early numeracy skills are critical for students’ later mathematics achievement. I applied a person-centered approach to investigate distinct mathematics skill development patterns in early childhood, using data from 664 Grade 1 students (Mage = 6.3 years, Females = 54%) in Alberta who completed the Provincial Numeracy Screening Assessment (PNSA) in Fall 2021 and in Fall 2022. A Latent profile analysis (LPA) identified four distinct profiles at the beginning of Grade 1: (1) low performers (22%), (2) students with average verbal counting skills but low performance in other measures (31%), (3) average achievers with strengths in arithmetic (17%), and (4) high achievers (30%). Moreover, profile membership in Grade 1 was strongly predictive of performance in Grade 2. Together, the findings underscore the importance of recognizing heterogeneity in early numeracy skills. Understanding the variability in the development of mathematical skills of students may help create targeted support for students’ mathematical learning.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".