Supporting kindergarteners’ learning of mathematics through the interactive reading of mathematical stories
Bibliographic record
Abstract
Reading mathematical stories in interactive reading sessions creates ideal settings for social interactions, and this can significantly improve their efficacy as pedagogical tools. This small-scale teaching experiment aimed to investigate the role of interactive reading in promoting mathematical understanding through story-based learning. It focuses on the mathematical learning outcomes that can be achieved through the interactive reading of mathematical stories. A kindergarten teacher delivered a mathematical story to her class of twenty-three children, aged five to seven. The story targeted knowledge of the comparative magnitude of numbers and was delivered in an interactive reading setting. A qualitative analysis of the video recording indicated that the interactive reading played a significant role in promoting the children’s mathematical understanding. While there were certain negative outcomes, their occurrence rates were significantly lower than the positive outcomes. Overall, the implementation of interactive strategies, where the children were encouraged to demonstrate their existing and developing comprehension, and subsequently received affirmation of that understanding, resulted in mainly positive outcomes. An important implication of this study is that incorporating interactive readings of mathematical stories into early childhood classrooms can enhance children’s mathematical understanding, enrich teacher–child interactions, and inform policymakers in the design of more effective reading practices.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".