Kindergarten Children Demonstrating Numeracy Concepts through Drawings and Explanations: Intentional Teaching within Play-based Learning
Bibliographic record
Abstract
Using both child-guided and adult-guided learning, Intentional Teaching in the early years can be a powerful tool for enhancing young children’s numeracy skills. As Epstein (2009) notes, this can include providing “opportunities for children to represent things by drawing, building and moving” (p. 47). This paper investigates how kindergarten (four-five year olds) children represented and demonstrated numeracy concepts through their drawings and explanations, completed for a research study that used arts-based strategies to enhance children’s environmental understanding. This research study involved kindergarten children in Australia creating and exchanging postcards (drawings and explanations) of their local environments with their peers in Canada. Findings include that the kindergarten children, through creating postcards of their physical environments and explanations, demonstrated their growing understanding of numeracy concepts, such as spatial orientation, quantification and attributes of objects. The study argues for quality Intentional Teaching and the development of an ‘early childhood numeracy progress monitoring framework’ that maps and assesses children’s mathematical development.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".