Empowering Teachers' Learning to Develop Innovative Skills for Students
Bibliographic record
Abstract
This research aims at empowering teachers' learning to develop innovative skills for students at high schools under the Office of the Basic Education Commission. It is one of the research projects in the research series regarding 21st century skills which are the outcomes of the advancement of digital technology as well as society becoming knowledge-based in the 21st century by processing many different views which have been proposed globally across the Internet and sorting them to create educational innovation. The objective of the research and development methodology is to develop an individual which will then lead to the development of the work; provided that a person who has knowledge is then encouraged to put it in action, it will supposedly result in more efficient work according to the concept of "knowledge + action = power". This concept itself has led to the study's idea of "empowering teachers' learning and applying the results in their teaching". Therefore, the result of the study is an educational innovation called “online self-training program to empower teachers' learning in order to develop students' innovative skills" which has been approved of its quality by the stakeholder teachers who have used this educational innovation and approved through experimental field research which was efficient as hypothesized in the research. As a result, this online self-training program can be publicly disseminated to develop the teachers to afterwards develop their students in high schools under the Office of the Basic Education Commission who were primarily the target group of this study across the country.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".