Organization of Early Childhood Education Based on Montessori Approach in Thai Social Context
Bibliographic record
Abstract
The purposes of the research and development project were to study appropriate ways to implement the Montessori method in the Thai social context and to present ways to implement the method for the school administrators and those who were interested in the method. One model school, Kornkaew Nursery School and the 4 network schools were selected for study: Amnuaysilpa School, and Pranantanit Kindergarten in Bangkok, Pierra-Navin Child Care, Ayutthaya Branch and Naresuan Palau Border Patrol Police School in rural are3. The samples studied were 5school administrators, 24 teachers/caregivers and 68 parents from the five schools. Five research tools were used: The school Fundamental Data Survey. The Record Form for Supervision and Follow-up of the Montessori Teaching, The Interview Form for Administrators, The Interview Form for teachers/Caregivers, and The Questionnaire for Parents were used for data collecting . The Montessori method was implemented and the data was collected from May to October 2000. The resulted showed: (1) the appropriate way to implement the Montessori method is to study the fundamental information regarding to the social context of the school; (2) 5 models from 5 schools which can be really implemented for the preschool children; and (3) the results from the Interview and the Questionnaire showed that the administrators, the teachers/the caregivers and the parents of five schools were satisfied and confident with the method of teaching.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".