Empowering Teachers' Learning to Enhance Students' Accountability Skills
Bibliographic record
Abstract
This research uses the Research and Development (R&D) methodology to develop “The Online Self-Training Program for Empowering Teachers' Learning to Enhance Students' Accountability Skills” to be effective according to the research hypothesis set out. It is a program that consists of development projects for learning of the teacher and the project for teachers to use learning outcomes to develop students with characteristics according to the specified indicators. Results of testing the program's effectiveness from experimental research in the first project found that teachers in the experimental group had learning results according to the standard criteria of 90/90 and the mean score from the test results after the experiment was significantly higher than before the experiment. The results of the experimental research in the second project found that students who were the target group for development had a considerably higher average score from the post-experiment evaluation than before the experiment. The research results are therefore following the established research hypothesis. It shows that “The Online Self-Training Program for Empowering Teachers' Learning to Enhance Students' Accountability Skills” has confirmed its effectiveness. This program can therefore be used for the benefit of teachers and students in schools who are the target population for disseminating research results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".