Enhancing Mathematical Skills of Learners in the Early Childhood Phase Through Play-Based Learning: A Review of Literature
Bibliographic record
Abstract
This theoretical article examines the crucial role of play-based learning (PBL) in enhancing the mathematical skills of children in the Early Childhood Phase, referred to as Foundation Phase (Grade R-3) learners, within a South African context. The article argues that the traditional approach to teaching early childhood mathematics, where teachers typically instruct while learners listen and repeat, is often rigid and lacks engagement. For learners to thrive in mathematics and truly comprehend the subject, they need to be actively involved in ‘doing’ mathematics. PBL is an approach to teaching and learning that uses various forms of play as a medium for learning. The objectives of the article are: a) to explore alternative methods for teaching mathematics to learners in the Early Childhood Phase, and b) to highlight the benefits and value of play as a mode for teaching and learning mathematics. As a theoretical paper, it does not include a methodology section; rather, relevant literature was analysed to support and justify the claims and to address the research questions. The study found that PBL can enhance the development of the vestibular system, which can positively impact learners’ cognitive abilities, including mathematical skills. The study is significant for both learners and teachers in the Early Childhood Phase, as it provides an opportunity for teachers to utilise alternative, play-related, and enjoyable methods to teach mathematics. The paper concludes that a child-centred, play-based curriculum encourages and promotes learning in a playful setting through discovery, investigation, problem-solving, imaginative thinking, and creative thinking.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".