Development Of Professional Competence For Arabic Language Teachers At Madrasah Aliyah Model Hafshawaty Zainul Hasan Genggong
Bibliographic record
Abstract
This community service activity emphasizes the importance of preparing proposals and reports on Classroom Action Research (CAR) to improve the quality of Arabic language learning. It is expected that Arabic teachers at Madrasah Aliyah Model Hafshawaty Zainul Hasan Genggong will understand the steps in preparing CAR, enhance the quality of Arabic language learning, and draft relevant CAR proposals. To achieve this goal, training, and education were conducted at Madrasah Aliyah Model Hafshawaty Zainul Hasan Genggong with 25 participants from May 31 to June 4, 2024, over five days. The methods used included lectures, discussions, and hands-on practice. Participants received material through lectures, engaged in group discussions, and prepared CAR proposals with guidance from facilitators. The training results showed that this activity was highly beneficial for Arabic teachers at Madrasah Aliyah Model Hafshawaty Zainul Hasan Genggong. The teachers gained a better understanding of the importance of professional development through CAR and received guidance in preparing proposals and conducting classroom action research. In conclusion, the program significantly enhanced the teachers' ability to conduct effective CAR, contributing to the overall improvement of Arabic language education at the institution.
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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 | 0.002 | 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 | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".