Empowering Teachers' Learning to Strengthen Students' Teamwork Skills
Bibliographic record
Abstract
This study aimed to create an educational innovation called “Online Self-Training Program for Empowering Teachers' Learning to Strengthen Students' Teamwork Skills” conducted with Research and Development (R&D) to be effective according to specified criteria. It can be broadly applied to the development of teachers and students in target schools in research publications. This online self-training program consists of 2 projects that are continuously operated as follows: 1) a teacher development project involving the use of 7 self-training modules for teachers' learning, and 2) applying learning outcomes to a student development project, with 1 self-training module to be used as a guideline for teachers. The findings of the first online self-training program revealed that twelve teachers in the experimental group achieved 90/90 post-test scores, which were significantly higher than the pre-test scores. Furthermore, the results of the second project revealed that 300 students in the target group obtained statistically significantly higher post-test scores than pre-test scores. The findings are therefore consistent with the hypotheses. It illustrates that the educational innovation, “Online Self-Training Program for Empowering Teachers' Learning to Strengthen Students' Teamwork Skills”, is effective and meets the specified criteria. It can also be disseminated for teacher and student development in the target schools.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".