Exploring the Relationship between Play-Based Learning and Creativity Development in Preschoolers in Ibadan South- West Local Government
Bibliographic record
Abstract
This study explored the relationship between play-based learning and creativity development in preschoolers in Ibadan South- West. The research aimed to understand how digital tools, physical activities, socio-economic and cultural factors, teachers' attitudes, and leadership influence the effectiveness of play-based learning. Six purposes and six research questions guided the study. A mixed-methods research design was employed, incorporating both quantitative and qualitative approaches. A sample of 200 preschool teachers, administrators, and parents was selected using a multistage sampling technique. Data were collected through structured questionnaires, interviews, and observation checklists. Pearson's correlation and regression analysis were used for quantitative data analysis, while thematic analysis was applied to the qualitative data. The results showed a significant positive relationship between play-based learning and creativity development, highlighting the influence of digital tools and physical activities. Challenges related to socio-economic constraints, leadership, and teacher capacity were also identified. The study recommends targeted teacher training, enhanced leadership support, and curriculum reforms to strengthen the integration of play-based learning in early childhood education.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".