Kemahiran dan Penyediaan Bahan Guru Bahasa Arab Terhadap Penggunaan Bahan Bantu Mengajar: Skills and Preparation of Arabic Language Teacher Against Usage Teaching Aids
Bibliographic record
Abstract
This paper aims to identify the level of acceptance teaching aids among teachers who teach Arabic language. The total sample consisted of 427 respondents who teach in secondary schools. These findings were obtained using the survey method (survey) that uses a questionnaire as an instrument. A total of 12 items have been analyzed using descriptive statistics. In detail (mean = 3.82 sp. = 0.45) at a moderate level high. The results of this study are expected to provide recommendations to be made from Arabic language teachers and institutions involved in the training of teachers in improving the use of teaching aids in teaching Arabic. Abstrak Artikel ini bertujuan untuk mengenal pasti tahap Kemahiran dan Inisiatif dalam kalangan guru bahasa Arab. Jumlah sampel kajian ini seramai 427 orang responden yang melibatkan guru yang mengajar di Sekolah Menengah di negeri Selangor. Dapatan kajian ini diperoleh menggunakan kaedah tinjauan (survey) yang menggunakan soal selidik sebagai instrumen kajian. Sebanyak tujuh item telah dianalisis dengan menggunakan statistik deskriptif. Secara terperinci elemen kemahiran guru terhadap penggunaan BBM (min=4.01, sp.=0.56), elemen penyediaan bahan (min= 3.82, sp.= 0.63). Hasil kajian ini diharapkan akan memberikan cadangan tindakan yang perlu dilakukan guru bahasa Arab dan institusi yang terlibat dalam melatih guru dalam menambahbaik penggunaan bahan bantu mengajar dalam pengajaran bahasa Arab.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".