Empowering Teachers' Learning into Action to Enhance Active Learning in the Classroom
Bibliographic record
Abstract
This research is a Research and Development (R&D) study aimed at developing educational innovation entitled "Online Self-Training Program to Empower Teachers' Learning into Action to Enhance Active Learning in the Classroom", and implemented in schools. The online self-training program consists of 2 projects 1) a Development project for teacher learning which includes 7 self-training modules, and 2) the implementation of an online self-training program project which includes 1 self-training module to be used as a teaching guideline. The research consists of 4 steps. The final step was an experimental study. The results of the first project found that 12 participating teachers achieved post-test scores that met the standard of 90/90. The average post-test score was statistically significantly higher than the average pre-test score. Additionally, results from the second project found that, according to students’ perception scores, the average post-test score was statistically significantly higher than the average pre-test score among 500 students. The results confirm that the "Online Self-Training Program to Empower Teachers' Learning into Action to Enhance Active Learning in the Classroom," is an effective educational innovation for teachers. Therefore, it can be disseminated for the benefit of teachers in schools on a broader scale in the future. Additionally, results from the second project found that, according to students' perception scores, the average post-test score was statistically significantly higher than the average pre-test score among 500 students. The results confirm that the "Online Self-Training Program to Empower Teachers' Learning into Action to Enhance Active Learning in the Classroom" is an effective education innovation for teachers. Therefore, it can be disseminated for the benefit of teachers in schools on a broader scale in the future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".