The Relationship between EFL Learners’ Language Learning Strategy Use and Achievement
Bibliographic record
Abstract
The primary purpose of this study was to examine the relationship between language learning strategy use and foreign language achievement, focusing on differences in gender. A total of 263 English as a foreign language students enrolled in English preparatory class program at Necmettin Erbakan University, School of Foreign Languages participated in the study. This was a descriptive study in relational screening model. The Turkish version of “Strategy Inventory of Language Learning (SILL)”, originally developed by Oxford (1990) and adapted into Turkish by Cesur and Fer (2007), was used as the data collection tool. Results from the study indicated that metacognitive strategies were the most frequently used strategies among the participants, while cognitive strategies were the least frequently used. There was no significant difference between the male and female students in terms of language learning strategy use except memory strategies. Also, low but statistically significant positive correlations were observed between foreign language achievement and cognitive (r=0.23; p=0.00), compensation (r=0.16; p=0.01) and metacognitive strategies (r=0.15; p=0.02). The findings reported in the study suggest that high-level strategy use could affect students’ achievement in foreign language preparatory classes.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".