Enhancing Teachers' Learning to Develop Students to Become Successful Students
Bibliographic record
Abstract
The aim of this research is to enhance teachers' learning towards developing successful students. It is a research project based on advancements in digital technology and the knowledge-based society of the 21st century. Various international perspectives on developing successful students proposed by experts on the internet have gone under the process for this research and development to create educational innovations that could be used to empower teachers and strengthen their students' learning aligned with the concept of “knowledge and action is power”. It is believed that if teachers have learned something, they can bring the knowledge into practice that empowers students’ learning effectively. The results of this research led to educational innovation called “Online Self – Training Program to Enhance Teachers' Learning to Develop Students to Become Successful Students”. This innovation was evaluated by teachers who had a stake in it, and after experimental research was conducted, it was found to be effective according to the established criteria. This evaluation indicated that the innovation can be disseminated to develop teachers, who aim to develop students’ learning, at Mahamakut Buddhist University, which is the target population for this research project, both in the central and regional campuses.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".