The Outdoor Learning Modules Based on Traditional Games in Improving Prosocial Behaviour of Early Childhood
Bibliographic record
Abstract
Working outside can be harder than inside; one may be physically more active, regularly battling against the elements and often more alert – watchful for potential dangers. Overall education out of doors is physically and mentally taxing. We have to be convinced all the effort is for a good reason (Bilton, 2010, p. 12). The researchers applied the mixed method with covergent parallel design (Creswell, 2016). The results showed that 1) The initial description of prosocial behaviour of young children in Joy Kids Kindergarten, Mangasa Subdistrict, Tamalate Makassar District, showed that teachers paid attention to improve and look for activities in the form of play so that things that were worse towards prosocial behaviour of children could be avoided. 2) The traditional game-based outdoor learning module produced has been accepted from the results of an assessment of the utility, feasibility and accuracy, carried out by two experts in the field of education and an education practitioner. 3) The traditional game-based outdoor learning module that is produced affects the development of prosocial behaviour of young children. Based on the validator’s evaluation, it is declared valid to be used, its practical value is feasible to be used in the field without the need for revision, and all the devices previously presented can be declared valid for use.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".