Difficulties of Non-Arabic Study Program Students in Arabic Teaching and Learning Process at ITB AAS Indonesia
Bibliographic record
Abstract
This study investigates the difficulties of Non-Arabic study program students in Arabic learning at ITB AAS Indonesia. This research uses descriptive qualitative. This study involves 60 students of the Economy Sharia study program. The analysis shows internal and external factors of students’ difficulties in Arabic learning. The internal factors are 1) difficulty in writing Hijaiyah letters at the beginning of, in the middle, and at the end of the sentence. 2) difficulty in writing Hijaiyah letters in computer typing because it must have an Arabic Typesetting font. 3) belief that writing Arabic letters is more difficult than writing Latin letters, 4) difficulty in reading recitation (Tajwid). 5) belief that learning Arabic is more difficult than Indonesian and English. 6) belief that many Mufrodat includes Isim, Fi'il, and Harf. 7) many rules for changing words and writing an Arabic sentence. Besides, external factors are 1) many foreign expressions or Arabic terms that are different from Indonesia. 2) lack of learning media of Arabic textbooks. 3) lack of facilities in the language laboratory. 4) lack of environment, association, social, and culture in learning Arabic. 5) lack of self-support and motivation in learning Arabic. 6) influence of the first language (regional) and the second language (Indonesian). 7) Arabic learning time is short to 1 week. 8) a lot of Arabic material in one semester. 9) too many students in a class. 10) situation in the Arabic class is not conducive.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 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".