Creative Learning Design in Social Studies to Promote Productive Citizenship of Secondary School Students
Bibliographic record
Abstract
The purposes of this research are: 1) to study the creative learning in social studies to promote productive citizenship of secondary school students; and 2) to design the guidelines for such learning. This research implements the methodology of action research, consisting of 8 samples: 1) 1 school principal and 2 social studies teachers; and 2) 5 learning management experts. The samples are chosen by the purposive sampling method. Research tools consist of 1) unstructured interview form; and 2) appropriateness assessment form for the guidelines of creative learning in social studies to promote productive citizenship of secondary school students. Methods of data analysis consists of content analysis, and descriptive analysis, in addition to calculation of the means and standard deviation. Study results revealed that: 1. Creative learning in social studies to promote productive citizenship of secondary school students consists of the development of 4 minds, including: 1) critical mind; 2) creative mind; 3) productive mind; and 4) responsible mind. And 2. The guidelines of creative learning in social studies to promote productive citizenship of secondary school students consist of 4 subjects and 8 learning management plans. The effectiveness of the guidelines in terms of the learning management was evaluated as excellent.
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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 | 0.024 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".