Learning Strategies of Arabic Language Vocabulary for Pre-University Students’ in Malaysia
Bibliographic record
Abstract
Vocabulary is a vital aspect in second language learning. The knowledge and mastery of vocabulary are able to give a direct effect on learning and mastery of a second language. The learning of Arabic language in Malaysia has also put the mastery of Arabic language vocabulary as the main goal. The aim of this survey is to explore the learning strategies of Arabic language vocabulary of pre-university students in Malaysia. The objectives of this study are to (a) measure the vocabulary learning strategies (VLS) usage level of pre university students, (b) to identify the highest strategy usage for each main vocabulary learning strategy (VLS) and (c) to identify the lowest strategy usage for each main vocabulary learning strategy (VLS). Questionnaires are used as the instrument which is developed based on the Schmitt’s VLS classification (1997). The sample involved 742 students in 15 religious high school (SMKA) and government-aided religious school (SABK). The study found that pre-university students have been using vocabulary learning strategies (VLS) moderately. Generally, the students used the determination strategy with the highest frequency compared to other strategies whilst the cognitive strategy is the least optimized one. Six strategies are used regularly while 12 strategies are not used frequently. The findings show that pre-university students tend to use strategies that are simpler, not creative and do not require high level of thinking. This situation somehow has displayed that the learning of Arabic language vocabulary in Malaysia is still very far from achieving the vocabulary learning objectives.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".