Empowering Teachers' Learning into Practice: The Case of Flipped Classroom Learning Management
Bibliographic record
Abstract
This research aimed to conduct a study to develop an educational innovation called "Online Self-Training Program to Empower Teachers' Learning into Practice: The Case of Flipped Classroom Learning Management," efficiently using the Research and Development (R&D) methodology. This online self-training program consists of two projects: 1) A Teacher Development Project: It includes self-training modules for teacher learning, comprising 10 modules. 2) A Teacher Implementation Project: It involves a self-training module for teachers to use as a practical guideline, consisting of 1 module. The results of the experimental research in the first project revealed that among the experimental group of 16 teachers, their post-test scores met the standardized criteria of 90/90, and they had statistically significantly higher scores compared to their pretest scores. In the second project's experimental research, it was found that the post-test scores of 640 students showed a statistically significant increase in their perception of flipped classroom learning management when compared to their pre-test scores. The research results were consistent with the predefined research hypotheses, indicating that the educational innovation produced by this research has been confirmed to be effective. It can be beneficially applied to teachers and students in schools that are the target population for disseminating the research findings in the future.
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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".