A systematic review and meta‐analysis of approaches to teaching problem‐solving skills in early childhood education and care settings: A focus on science, technology, engineering and mathematics activities
Bibliographic record
Abstract
Abstract This systematic review and meta‐analysis synthesised evidence on teaching problem‐solving skills to children in early childhood education and care settings (ECEC) in the domain of science, technology, engineering and mathematics (STEM). Given the foundational nature of early cognitive development and the growing emphasis on STEM competencies, this review addresses a critical gap by evaluating the effectiveness of intervention strategies in ECEC settings. A total of 13,030 abstracts were screened across PsycInfo, ERIC, Education Source and Child Development and Adolescent Studies. Nineteen studies met inclusion criteria for the systematic review, and 10 of these (comprising 1355 children) were eligible for meta‐analysis. Problem‐solving skills focused interventions in STEM were associated with increased problem‐solving skills for children attending ECEC settings. Specifically, multivariate meta‐analyses for the pooled effects revealed moderately strong effects, r = 0.40. Studies employing an experimental design with random group assignment and author‐created outcome measures showed relatively moderate effect sizes compared to other studies, all falling within a moderately strong range depending on outcome measure types. Analysis of the role of potential moderators and implications for practice were also discussed. This review underscores the importance of intentionally integrating STEM problem‐solving opportunities into ECEC settings. It offers actionable insights for educators, researchers and policy makers aiming to support early learning by equipping young children with foundational skills critical for future academic and workforce success. Implications include the need for curriculum development, professional learning supports, and further research on interventions for infants, toddlers and children facing systemic barriers. Context and implications Rationale for this study: Problem‐solving is foundational to cognitive and academic development, yet little is known about how to best support its development in early STEM learning contexts. Why the new findings matter: This review identifies effective interventions, revealing that structured curricula and addressing equity gaps in STEM access are important features of effective STEM instruction. Implications for practitioners, policy makers and researchers: Practitioners should integrate flexible, evidence‐based strategies into daily routines. Policy makers should fund scalable, inclusive STEM interventions beginning in early childhood. Researchers must prioritise process‐based assessments, longitudinal tracking, and studies involving infants, toddlers, and at‐risk groups to inform equitable practice and policy.
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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 | 0.026 | 0.072 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.030 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".