Loose parts play encourages spontaneous science, technology, engineering, and mathematics (STEM) behaviours
Bibliographic record
Abstract
Children incorporate items found in their environment into their play, transforming everyday objects and materials into an opportunity for exploration. Termed loose parts, these versatile, natural, or manufactured materials (e.g., cardboard, pipes, buttons, sticks) are widely recommended for supporting children's early STEM learning. Limited empirical work has documented children's indoor STEM behaviours with loose parts. Using a within-subjects experimental design, we examined children's early STEM behaviours and engagement (N = 60; 32 females, 28 males; Mage = 58.6 months, SD = 10.9) during unstructured solitary play with loose parts and toys that have limited function and affordance (e.g., toy percussion instruments; control). We conducted observations of children's STEM behaviours. Children's cognitive functioning, executive function, and home learning environment were also assessed via standardized measures and parent reports. Children demonstrated significantly more STEM behaviours with loose parts than in the control condition. There was no credible evidence that these behaviours differed by sex. Cognitive functioning predicted STEM Engagement Score with loose parts, with children's verbal comprehension being the strongest predictor in the control condition. Children's executive function and parents' attitudes regarding play and engagement in play activities at home predicted constructing structures, which were the most common STEM behaviours. This study thus demonstrates that loose parts may offer a powerful opportunity for STEM-related early learning; however, children's cognitive capacities and home experiences should be considered, rather than assuming uniform benefits.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".