Development of an Experiential Learning Model for Exploring the Natural Surroundings (EJAS) by Utilizing Forests as a Learning Resource at the Leuser Nature School
Bibliographic record
Abstract
The research objectives are: (1) To produce a learning model product for exploring the surrounding natural environment using the forest as a learning resource for students at Leuser Nature School. (2) To find out the practicality level of the EJAS learning model by utilizing the Forest as a learning resource (3) The effectiveness of the E JAS model by utilizing the Forest as a learning resource on improving learning outcomes and environmentally conscious behavior. Research and development Borg and Gall is a study used in developing the EJAS learning model by utilizing the Forest as a learning resource for Leuser Nature School students. Expert validation of the EJAS learning model shows an average value of 91.7% with a very valid category. The results of the trials in all three stages are in the very practical category with an average value of 92.3%. Learning outcomes appear significant with an average learning completion score of 60.92 and an average N-Gain value of 0.609 (moderate to quite high category). Students' environmental behavior has an average of 4.15. So the EJAS model can be an alternative learning strategy that is not only effective in improving cognitive learning outcomes, but also in forming attitudes and character values of Leuser Nature School students.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".