Integrating a hybrid mode into kindergarten STEM education: its impact on young children’s critical thinking skills during the COVID-19 pandemic
Bibliographic record
Abstract
Critical thinking is essential for young children and can be enhanced through appropriate and supportive curricula. With the rich affordance of digitalization, this study evaluated the effects of a hybrid STEM curriculum on critical thinking skills in 74 kindergarteners (42 boys and 32 girls) aged 5.83–7.25 years (Mean = 6.44, SD = 0.31) from a Chinese kindergarten during the COVID-19 pandemic. Employing a quantitative design across two classes, we collaborated with teachers, children, and parents to co-develop a series of STEM activities throughout an academic year. Despite the absence of a pre-test due to pandemic restrictions, no significant age differences or differences in their related assessments prior to participating in this study. Results showed that the experimental class demonstrated significant improvements in interpretation, explanation, inference, and self-regulation skills compared to the control group. These findings suggest the STEM curriculum effectively enhances critical thinking 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".