Investigating the Language Learning Strategies of Students in the Foundation Program of United Arab Emirates University
Bibliographic record
Abstract
Recently, language learning strategies have gained a lot of importance in different parts of the world, including the United Arab Emirates (UAE). Successful foreign or second language learning attempts are viewed in the light of using appropriate and effective language learning strategies. This study investigated the patterns of language learning strategies (LLS) used by 190 male and female students in the Foundation Program of the United Arab Emirates University (UAEU). It also explored the effects of language proficiency level and gender on the use of these strategies. An Arabic translated version of the Oxford’s (1990) Strategy Inventory for Language Learning (SILL) was used for collecting the data. The results demonstrate that these learners were overall medium strategy users. Metacognitive strategies were the most frequently used among the six strategies followed by social strategies, compensation strategies, affective strategies, cognitive strategies and memory strategies respectively. Proficiency level and gender had no significant effect on the overall strategy use nor on the use of each individual strategy. The findings of this study provide some implications for classroom instruction, curriculum design and teacher training. The study ended with some recommendations to direct future studies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 0.000 |
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".