Factors Influencing College-Level EFL Students’ Language Learning Strategies in Saudi Arabia
Bibliographic record
Abstract
This study investigated the factors that influence college-level EFL students’ Language Learning Strategies (LLS) in Saudi Arabia. A survey of 178 participants from different higher education institutions in Saudi Arabia was conducted. A Multivariate Analysis of Variance (MANOVA) was employed to identify the most frequently used LLS and to investigate the difference between students’ demographic variables and their use of LLS. The study’s results revealed that the majority of participants fell in the age category between (18-22) years old, were in their 4th year of college, were Saudi nationals, and majored in TESL/TEFL. The findings also showed that participants’ overall use of LLS was average (medium). The study investigated the six LLS among participants and revealed that Metacognitive Strategies were the most frequently used strategies while Affective Strategies were the least frequently used strategies. The results also indicated that there was an overall statistically significant difference in LLS based on participants’ gender. However, the findings found that age, college level, nationality, and major did not have any statistically significant effect on the six LLS.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".