Intended mathematics curriculum in grade 1: A comparative study
Bibliographic record
Abstract
Learning mathematics in grade 1 as the formal starting point for learning mathematics in many countries can significantly impact students’ subsequent learnings. One of the most critical factors influencing teacher teaching and student learning is the written intended curriculum materials (official curricula). Despite the importance of this topic, there is little research on how many mathematics topics should be taught in grade 1 and to what depth students should learn these topics until the end of the first grade. In this study, we investigated and compared the grade 1 intended mathematics curriculum of Australia, Iran, Singapore, the Province of Ontario in Canada, and New York State in the USA. Indeed, we sought to examine how curriculum developers and decision-makers in education in these jurisdictions prepared the content of the first-grade mathematics in the curriculum writing materials. To do this, by examining the official curricula for grade 1 of these countries and using a procedure called general topic trace mapping, we found a list of 14 topics. The findings of the current paper showed similarities and differences in the topics intended in the mathematics curriculum of these countries. Ontario, Australia, Singapore, New York, and Iran curricula cover 13, 11, 9, 9, and 7 topics of 14 topics, respectively. We also considered five content strands and examined and compared the progress of each intended curriculum in these strands at the end of grade 1. We found that the learning progression in some content strands is different among countries. The results demonstrate the nuanced complexity of these comparisons and the importance of cross-national comparisons. We concluded this article with suggestions for curriculum developers, textbook writers, and teachers.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".