Development for Teachers’ Learning to Enhance Prosocial Behavior for Students
Bibliographic record
Abstract
The Research and development (R&D) methodology was employed in this research to create an educational innovation, called "Online Self–Training Program for Development for Teachers’ Learning to Enhance Prosocial Behavior for Students”, which was effective according to the specified criteria. This online self–training program consisted of two projects: 1) the development project for teachers’ learning, comprising six self-training modules for teacher development, and 2) the project for teachers applying learning outcomes to learner development, consisting of a self-training module used as a guideline for teachers. The results of the experimental research showed that the developed educational innovation was effective according to the research hypotheses. The results of the experimental research in the first project showed that the post-test scores of 13 teachers met the standard criteria of 90/90 and were significantly higher than their pre-test scores. The results of the experimental research in the second project also revealed that the post-test scores of 55 students who were the target group of the development were significantly higher than the pre-test scores. This indicates that the educational innovation, called "Online Self–Training Program for Development for Teachers' Learning to Enhance Prosocial Behavior for Students" has been confirmed in quality. Therefore, it can be disseminated and used to benefit both teachers and students who are the target population on a large scale.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".