The Arabic Language Level of Candidates for Malaysia Religion High Certificate (MRHC): Reading and Grammar
Bibliographic record
Abstract
Malaysia Religion High Certificate (STAM) examination is a Malaysian student’s eligibility to study in the Middle East. STAM was introduced in 2000 as a result of a Memorandum of Understanding Cultural Agreement between the Governments of Malaysia and the Arab Republic of Egypt in November 1999. But many STAM graduates who took the language test at the university had failed to get the level of qualification and had to take Arabic classes at the language center before pursuing studies at the undergraduate level. This study aims to identify the level of text reading in Arabic among STAM candidates and Arabic grammar skills. Therefore, the researchers aim of 52 students who is a STAM candidate to participate in this study. The approach used in this study is quantitative; wherein the information gathered is presented in the Figures. Data collection using a measurement tool based on the study of texts authored by Sheikh Yusuf al-Qaradawi containing 448 words. Comprehension and grammar skills tests were done for collecting data and then presented into numbers. The findings showed that the respondents' reading and grammar level are moderate. Therefore, it is recommended that students who will take the STAM are given proper guidance so that they can improve their Arabic language proficiency before pursuing studies at tertiary institutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".