Effectiveness Of Outdoor Learning Optimization Program In Learning Social Studies
Bibliographic record
Abstract
Outdoor learning is potential to expose social and natural phenomena related to social studies. However, the social studies teachers seldom to practice outdoor learning on certain occasions. This article has two aims. First, interpreting social studies teacher perspective about the simulation of an outdoor learning laboratory. Second, explain the teacher's obstacle if they are practicing outdoor learning in the school. Step of the research consist of 1) in service-learning 1; 2) on the job learning; 3) in service-learning 2. The participants of this study involved 20 secondary high school active teachers of social studies in Surabaya. The data collecting technique used in this study is a questioner to measuring teacher's perspectives about the outdoor learning simulation in Surabaya, Sidoarjo, Mojokerto, Jombang,Tuban. Then, interview guidelines were used to perceive the data about teacher's obstacles to practice outdoor learning programs. The data analysis technique for examined teachers' perspectives is the percentage and discriminant statistics. The result of this study confirms that learning outdoor program offers a positive outcome. That is, 1) integrity while the social studies learning, extending social studies content, and helpful for difficult content; 2, Motivation improvement, attention and provide the student concrete experience; 3) Social studies learning to be fun, effective, and contextual. However, Social studies teachers assume laboratory outdoor activity is not efficient and hard to do, because they need a big fee, lengthy time, and require teachers' field experience for dominating social studies content
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".