The Effect of Environmental Education Learning for Enhancing Dam Management in the Northeast of Thailand Using Case Study-Based Learning
Bibliographic record
Abstract
The purposes of this research were to develop learning plans for dam management in the Northeast of Thailand using case study-based learning being efficient and effective, to study and compare the knowledge, attitude, and environmental ethics concerning dam management in the Northeast of Thailand before and after learning, and different gender. The sample was 72 2nd-year students in Environmental Education program, Faculty of Environment and Resources Studies, Mahasarakham University, being selected by purposive sampling. Research tools were learning plans for dam management in the Northeast of Thailand, knowledge test, attitude test and an environmental ethics test. The statistics used in the research were frequency, percentage, mean, standard deviation, Paired t-test, and One-way ANOVA. The results of the research were found that: 1) The efficiency of learning plans was equal to 86.38/84.17, and the effectiveness index of learning plans was 0.7466. It showed that the students increased knowledge and resulted in the students progressing from their studies as 74.66 percent. 2) After the learning, the mean score of knowledge, environmental attitude, and environmental ethics of students were significantly higher than before the learning at the .05. 3) There was no different knowledge, environmental attitude, and environmental ethics between students with an different gender.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".