The Effects of Environmental Education Learning Activities Using Area-Based Learning in Khok Hin Lad Community Forest in Maha Sarakham, Thailand
Bibliographic record
Abstract
The purposes of this research were to study and compare knowledge about Khok Hin Lad community forest, environmental ethics, and environmental volunteers from learning activities of undergraduate students before and after activities, to study and compare knowledge about Khok Hin Lad community forest, environmental ethics, and environmental volunteers of undergraduate students with different gender and GPAs. The sample used in the research was 99 second-year undergraduate students in Environmental Education, Faculty of Environment and Resource Studies, Mahasarakham University, which are derived from purposive sampling. The research instruments were a manual of environmental learning activities in Khok Hin Lad community forest by using area-based learning, knowledge test about Khok Hin Lad Community Forest, Environmental ethics test, and environmental volunteers test. The statistics used in the research were frequency, percentage, mean, standard deviation. And hypothesis testing using t-test, F-test (One – Way MANOVA, One – Way MANCOVA, and Univariate Test). The study found that: 1) The student's knowledge average score of Khok Hin Lad community forest, environmental ethics, and environmental volunteers after learning activities were higher than before the learning activities' statistical significance (p< .05). 2) There was no different knowledge of Khok Hin Lad community forest, environmental ethics, and environmental volunteers between students of different gender (p> .05). 3) There was no different knowledge of Khok Hin Lad community forest, environmental ethics, and environmental volunteers between students with different GPAs (p>. 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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".