Relations Between Mathematical Vocabulary and Children’s Mathematical Performance
Bibliographic record
Abstract
Mathematical vocabulary comprises terms that have mathematical meanings and which people use to communicate mathematical concepts (e.g., large, digit, circle, equal sign).Students' general vocabulary and their mathematical vocabulary are both correlated with mathematical skills.I asked whether mathematical vocabulary fully or partially mediates the relation between general vocabulary and mathematical performance.Canadian students in grade 3 (N = 234, mean age = 8.7 years) completed measures of general and mathematical vocabulary and several different mathematical outcomes (i.e., arithmetic fluency, pre-algebra, and wordproblem solving).Students were either learning mathematics in English or in French; the latter group were in immersion schools and thus had English as their first language.The results showed that students' mathematical vocabulary partially mediated the relation between general vocabulary and applied mathematical skills (i.e., pre-algebra and problem solving), and fully mediated the relation between general vocabulary and arithmetic fluency; and second, that students' domain-general cognitive skills (e.g., working memory, nonverbal reasoning) partially mediated the relation between mathematical vocabulary and applied mathematical skills, but not arithmetic fluency.Lastly, Numeration Words partially mediated the relation between general vocabulary and applied mathematical skills (i.e., pre-algebra and problem-solving), and fully mediated the relation between general vocabulary and arithmetic fluency.These analyses provided information about how individual differences in domain-specific and domain-general skills are related to students' mathematical performance.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.009 | 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".