Development for Teacher’s 21st-Century Skills Enhancement into Effective Practices
Bibliographic record
Abstract
This research used the Research and Development (R&D) methodology, which has research steps in the form of R1D1...R2D2...RiDi to get an educational innovation called “Online Self-Training Program to Development for Teacher’s 21st-Century Skills Enhancement into Effective Practices” that is effective. It is an educational innovation with certified research results that can be used and disseminated to benefit development in schools that are the target population on a large scale. The online self–training program consisted of 2 projects: 1) Development Project for Teachers’ Learning with six self-training modules used to develop teachers; and 2) The Project for Teachers to Bring Learning Outcomes into Practice with Students with one self-training module to be used as a guideline for teachers. The results of the experimental research in the first project revealed that among the experimental group of 17 teachers, the learning outcomes had met the standard of the 90/90 criteria. Moreover, the average test scores after the experiment had been significantly higher than before the experiment, according to statistical analysis. In the experimental research of the second project, it was found that among the 510 students involved in the experiment, the average scores from the assessment of perception towards teachers' practices had been significantly higher after the experiment compared to before the experiment. It was found that the research results aligned with the pre-determined research hypotheses, indicating the effectiveness of educational innovations resulting from the research. These findings can be utilized to enhance and cultivate 21st-century skills for teachers in the targeted schools, demonstrating the potential for future research dissemination and application.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".