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Record W4415369627 · doi:10.51178/ce.v6i3.2811

Development of an Experiential Learning Model for Exploring the Natural Surroundings (EJAS) by Utilizing Forests as a Learning Resource at the Leuser Nature School

2025· article· W4415369627 on OpenAlexaff
Lola Zeramenda Br Tarigan, Muhammad Iqbal H Tambunan, Sularno Sularno

Bibliographic record

VenueContinuous Education Journal of Science and Research · 2025
Typearticle
Language
FieldComputer Science
TopicEducational Research and Pedagogy
Canadian institutionsRoyal Roads University
Fundersnot available
KeywordsExperiential learningResource (disambiguation)Active learning (machine learning)Value (mathematics)Natural resourceProduct (mathematics)

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0070.003
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.077
GPT teacher head0.428
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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