The Role of the Training Program “Making Learning Visible” in Developing Creative Teaching Competencies of Bahrain Teachers College Faculty Members in the University of Bahrain
Bibliographic record
Abstract
This study aims to investigate the role of the training program “Making Learning Visible” (MLV) in developing Creative Teaching Competencies of Bahrain Teachers College (BTC) faculty members at the University of Bahrain. The study sample consists of (35) faculty members in BTC (13 male and 22 female). To achieve the objective of this study, the researchers have developed a measuring scale of Creative Teaching Competencies for BTC Faculty Members, which contains five dimensions: Personal Competencies, Emotional Competencies, Cognitive Competencies, Educational Competencies (Professional), and Social Competencies. Results revealed the training program (MLV) has a significant role in developing Creative Teaching Competencies among faculty members at BTC. Further, there were no statistically significant differences (α≤ 0.05) in the role of the training program in developing the Creative Teaching Competencies of the faculty members at BTC by gender variable. The study also shows that there are statistically significant differences (α≤ 0.05) in the role of the training program in developing Creative Teaching Competencies among faculty members at BTC according to the variable of years of experience in favor of those with more than 20 years of experience.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".