Developing a Green School Training Manual for High School Students Pracharat Wittaya Serm School
Bibliographic record
Abstract
This research aimed to develop a green school training manual for high school students in Pracharat Wittaya Serm School with an efficiency index at a rate of 80/80. The scores of knowledge, attitudes, and participation of the research participants gauged before and after the training were compared. The simple random sampling method was used to recruit a group of 54 student samples from Pracharat Wittayaserm School in the first semester of the academic year 2021. The research instruments consisted of The Green School Training Manual, The Knowledge Testing Form, The Attitude Testing Form, and The Participation Testing Form. The statistics used in the data analysis were frequency, Percentage, mean, standard deviation, and paired t-test. It was found that the efficiency of the training manual was 80.45/82.60, and the efficiency Index (E.I.) was 0.5840. These statistics indicate that the students had a 58.40 percent of knowledge progression. It was also observed that the average post-test scores of knowledge, skills, and environmental management participation were higher than the pre-tests scores with the significance at the level of 0.05.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".