Engineering play with blocks as an informal learning context for executive function and planning
Bibliographic record
Abstract
Abstract Background Engineering play is an emerging framework for understanding young children's constructive block play as an engineering design process. Few studies have evaluated engineering thinking, language, or behavior in preschool‐age children, especially quantitative evaluations that systematically document specific early engineering behavior. More research is needed to support diverse children's engineering education in ecologically valid classroom contexts and understand relations with the key cognitive domains that predict school readiness. Purpose/Hypothesis The present study investigated the associations of executive functioning and planning skills with preschoolers' engineering play behaviors with wooden unit blocks, tested the moderating role of disability status in these associations, and provided additional reliability and validity data on the Preschool Engineering Play Behaviors (P‐EPB) measure. Design/Method Participants were 110 preschoolers (44% female; 25% children with disabilities) observed and coded during 15‐min block play sessions with a peer partner. Children completed separate formal assessments of executive function and planning. Results A one‐factor engineering play variable including six behavior categories (i.e., communicating goals, problem‐solving, explaining how things are built/work, following patterns and prototypes, logical and mathematical words, and technical vocabulary) was significantly and positively associated with executive function and planning for children with disabilities. Conclusions Results provide new knowledge about early engineering measurement and implications for teaching and learning engineering across multiple academic disciplines and with children from diverse developmental backgrounds.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".