An Evaluation of a Kindergarten Science Education Program in Rural Thailand Using the Triple Ps Model
Bibliographic record
Abstract
This study aimed to evaluate the Little Scientists House Thailand project using the Triple Ps Model, focusing on three components: Philosophy and Purpose, Process, and Product. The participants included 41 individuals comprising a school administrator, project-responsible teachers, parents, and Kindergarten 3 students at a rural school. Research instruments included a science learning activity, questionnaires, and structured interviews. Descriptive statistics (mean and standard deviation), correlation analysis, and content analysis were used to analyze the data. The results showed that the overall satisfaction with the project was at a very high level across all groups. The school staff valued the project’s alignment with national education goals and its effectiveness in developing students’ scientific skills. Parents reported that the activities were age-appropriate and helped foster interest, curiosity, and thinking skills in their children. Students expressed enthusiasm, enjoyment, and improved understanding of science through hands-on experiences. Correlation analysis revealed strong relationships between each component of the Triple Ps Model and overall satisfaction. This study contributes to early childhood education by demonstrating the applicability and effectiveness of the Triple Ps Model in evaluating science learning programs for young learners.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".