Unlocking Leadership in Young Children: Insights from Teachers, Parents, and Administrators
Bibliographic record
Abstract
Leadership is crucial for success and should be nurtured from an early age. As society rapidly evolves, traditional notions of leadership must adapt to remain relevant. This study aims to identify and prioritize leadership characteristics and skills that parents and teachers consider most in need of development and to investigate approaches for promoting leadership in early childhood through the perspectives of teachers, parents, and administrators. The research included 440 participants—teachers, parents, and administrators from kindergartens under the Office of the Private Education Commission (OPEC) in Bangkok—using stratified random sampling. The perceived needs data were analyzed using mean, standard deviation, and the Modified Priority Needs Index. Single survey questions were assessed through frequency, percentages, averages, and standard deviation. Findings show that both parents and teachers consider communication the most important skill to develop. However, parents prioritize responsibility but often overlook collaboration, while teachers emphasize decision-making and underemphasize responsibility. Parents most frequently develop leadership by praising desirable behavior but least frequently involve children in planning activities to achieve goals. Teachers use positive communication strategies but least frequently encourage children to resolve conflicts independently or assess their own and peers' learning. Administrators focus on creating learning experiences and fostering a school environment that promotes leadership in children, but least frequently reward teachers who excel in this area or encourage collaboration with parents and external organizations. These insights can guide the development of targeted activities and help create tailored leadership strategies that align with the needs and priorities of each group to foster leadership in young children.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".