Moral education in Taiwanese preschools: Importance, concepts and methods
Bibliographic record
Abstract
Early childhood is a period of rapid development and growth, and regardless of physical, psychological, and social abilities, young children at this stage have great plasticity and strong imitation ability. Childhood is a critical period for individual learning; so preschool teachers must provide young children with appropriate moral education at this time. This paper is to discuss the importance of moral education for young children in Taiwan, analyze the relevant literature, and review the teaching concepts and methods of moral education. Moral education of young children is important because the ultimate goal of early childhood education is to shape the character of young children; so its content must focus on moral education, which cultivates moral values in young children. Moral education also develops an understanding of empathy and social responsibility for young children. This paper argues that preschool teachers must (1) respect young children’s moral subjectivity; (2) understand that early childhood is a critical period in which moral behavior is shaped, and (3) organize young children’s moral teaching from the concrete to the abstract. In terms of the optimal teaching methods, this paper proposes that preschool teachers use: (1) moral storytelling, (2) instructional media, (3) picture books, (4) discussion of moral issues, (5) peer learning, (6) moral narratives, (7) modeling moral behavior, (8) incorporate young children’s life experience, and (9) invite young children’s parents to participate in moral education. This study contributes to the literature on early childhood moral education and provides an insight into the praxis of moral education for young children in Taiwan.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".