Integrating Play-Based Learning with Coding for Early Childhood Mathematics Education in Under-Resourced Schools
Bibliographic record
Abstract
Recent advancements in educational technology and mathematics curricula provide early childhood teachers with new opportunities to use coding in their teaching activities. Although the integration of play-based learning with coding holds promise, challenges such as resource availability, teacher training, and curriculum alignment must be addressed. The lens of Kolb’s experiential theory supports that coding involves concrete experiences, reflection on outcomes, interactive problem-solving, and mathematical learning. Furthermore, experiential learning is adaptable to local contexts by leveraging available materials. In a case study within qualitative research, this paper explored teachers’ perceptions of integrating play-based learning with coding for early childhood mathematics in under-resourced schools. Twelve foundation phase teachers teaching mathematics in under-resourced schools from Limpopo, South Africa, were sampled through homogenous purposive sampling. The data were collected using semi-structured interviews and non-participant observations to solicit teachers’ perceptions of integrating play-based learning with coding for early childhood mathematics in under-resourced schools. Thematic data analysis was used to interpret the perceptions of teachers on the subject under the study. The findings indicated that teachers implement play-based learning with LEGO Six Bricks with coding. However, under-resourced schools have limited training, a lack of resources, and insufficient curriculum alignment. Based on these findings, it is recommended that teachers use available coding resources to integrate play-based learning into mathematics classrooms. These findings contribute to the growing literature on coding in teaching mathematics in early childhood. Keywords: Early Childhood, Experiential Learning Theory, Mathematics Education, Play-based Learning, Under-resourced Schools, Coding.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".