The Relationship Between Children’s Indoor Loose Parts Play and Cognitive Development: A Systematic Review
Bibliographic record
Abstract
Children's engagement with toys and play materials can contribute to the foundational cognitive processes that drive learning. Loose parts are interactive, open-ended materials originally not designed as toys but can be incorporated into children's play (e.g., acorns, cardboard, and fabric). Practitioners and researchers widely endorse loose parts for fostering creativity, divergent thinking, and problem-solving skills. Despite these recommendations, research on their specific role in young children's cognitive development remains limited. This systematic review examines how indoor loose parts play has been studied in relation to young children's (0-6 years) cognitive development. Following PRISMA guidelines, searches in bibliographic databases and forward and backward citation tracking identified 5721 studies published until December 2024. We identified 25 studies and evaluated the quality and risk of bias. Studies focused on children's general cognitive outcomes, language development, and specific cognitive subdomains, with many reporting positive associations between children's play materials and cognitive development. However, five studies found no such associations, and another seven did not address the relationship between play materials and outcomes. Despite methodological variation across studies, our systematic review identified a relationship between play materials similar to loose parts and children's problem-solving, creativity, academic skills (reading and math), and both convergent and divergent thinking. Notably, only one study explicitly used the term "loose parts."Our review identified empirical and methodological gaps regarding the relationship between play materials and cognitive development, which can inform future research.
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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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".