Numeracy Screener: Comparing Symbolic and Non-Symbolic Numerical Magnitude Processing in 4–6-year-old Canadian and Singaporean Children
Bibliographic record
Abstract
Background – Students in East Asian countries consistently outperform their Western counterparts on mathematics assessments, with Singaporean students topping all global math competency assessments in 2015. Previous studies suggest that this learning gap between East Asian and Western countries emerges even before students begin formal schooling. Aim - The present study used existing data to test whether Singaporean children display an early advantage in basic numerical knowledge relative to their Canadian peers. Methods - 4 to 6-year-old Singaporean (n = 214) and Canadian (n = 370) children’s symbolic and non-symbolic magnitude processing skills were evaluated using the Numeracy Screener. Participants were presented with pairs of stimuli and asked to identify which of two numbers or symbols (dot arrays) were larger in magnitude. Results - Canadian children outperformed Singaporean children on both the symbolic and non-symbolic magnitude comparison tasks, suggesting that Singaporean children do not begin with an early advantage in basic math skills. After controlling for age, a subset analysis of 5-year-old participants reduced the main effects of format and country on performance scores. Implications & Future Direction - These findings provide novel evidence of a learning gap between younger children that favours a Western country (Canada), suggesting that the learning gap is not unidirectional. This finding conflicts with previous research, highlighting the need for further investigation into the factors underlying the mathematics achievement gap between East Asian and Western countries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".