Manajemen Pembelajaran Bahasa Arab di SMP IT Nurul Fikri Makassar
Bibliographic record
Abstract
Penelitian ini membahas tentang manajemen pembelajaran bahasa Arab di SMP IT Nurul Fikri Makassar. Penelitian ini bertujuan untuk, (1) menganalisis dan mendeskripsikan perencanaan, pengorganisasian, pelaksanaan dan evaluasi pembelajaran Bahasa Arab di SMP IT Nurul Fikri Makassar (2) menemukan kendala-kendala yang dihadapi dalam pelaksanaan manajemen pembelajaran Bahasa Arab di SMP IT Nurul Fikri Makassar (3) menemukan dan memberikan solusi terhadap kendala-kendala yang dihadapi dalam pelaksanaan manajemen pembelajaran bahasa Arab di SMP IT Nurul Fikri Makassar. Jenis penelitian ini merupakan jenis penelitian lapangan (field research) dan dilihat dari jenis data analisisnya, penelitian ini termasuk penelitian kualitatif dengan pendekatan studi kasus dan ilmu manajemen yang menerapkan empat fungsinya: perencanaan, pengorganisasian, pelaksanaan dan evaluasi. Hasil penelitian menunjukkan bahwa manajemen pembelajaran bahasa Arab di SMP IT Nurul Fikri Makassar berada pada tahap pengembangan baik dari segi perencanaan, pengorganisasian, pelaksanaan dan evaluasi, namun begitu banyak kendala yang dihadapi seperti waktu pelajaran yang kurang, lingkungan berbahasa, guru tidak sesuai latar belakang pendidikan namun kami memberi solusi seperti membuat pembelajaran semenarik mungkin, menciptakan lingkungan berbahasa, membuat peraturan berbahasa yang ketat dan menghadirkan native speaker.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".