Cross-national assessment of Arithmetic Strategies: Exploring the influence of mathematics teaching curricula
Bibliographic record
Abstract
Arithmetic strategies (AS) can be defined as a procedure used to solve number manipulation problems (e.g., addition and subtraction; Siegler, 1996). Common strategies used in the process of solving arithmetic tasks can be broadly classified into two main categories: fact retrieval (i.e., recovering numerical facts from memory) and procedural calculation (e.g., finger-counting, decomposing problems into mul,ple steps, etc.; Zamarian et al., 2009). However, math strategies are not explicitly taught in many countries. For instance, the Cuban math curriculum establishes the guidelines for teaching a limited set of strategies for solving arithmetic problems, mainly learning math facts and algorithmic procedures (i.e., stacking). Meanwhile, the Ontario curriculum for mathematics, in addition to the above procedures, encourages the development of a broad range of mental math strategies that allow the manipulation of numbers to solve arithmetic problems. In view of this, we plan to conduct cross-national research to analyze the influence of different educational approaches on the development of AS in school-age students and to evaluate its relationship with math abilities (math fluency) and general-domain processes (working memory and metacognition).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".