The Relationship Between Language Learning Strategies and Vocabulary Size Among High School ELL Students
Bibliographic record
Abstract
This study inspects the relationship between language learning strategies (LLS) of 905 Jordanian high school ELL students and their vocabulary size. The data are collected through two instruments: First, a questionnaire of 35 items and 3 types of strategies (metacognitive, cognitive, and social-affective strategies) were adapted from the Strategy Inventory Learning (SILL, Version 7.0) by Oxford (2005) to evaluate language learning strategies. Second, the Vocabulary Levels Test (VLT): Version 2 by Schmitt (2001) to gauge the vocabulary size by measuring the 2,000 word-level, 3,000 word-level, 5,000 word-level, 10,000 word-level, and Academic Word (AWL) level of the students. The results of the descriptive analysis revealed that the students’ overall LLS was at a moderate strategy use. Concerning their use of strategies, the most used strategies were metacognitive, followed by cognitive strategies, and the least used strategies were social-affective strategies. In addition, the effect of their vocabulary size on the use of LLS was identified. Students with high vocabulary size applied more language learning strategies and specific strategies more than students with low vocabulary size. The students’ use of LLS had a positive and significant correlation with their vocabulary size. Students with higher vocabulary size were able to employ strategies to manage and control their learning, in addition, to observe their learning better than students with lower vocabulary size. All together for students to be better in learning English, they are required to enhance their vocabulary because of its substantial relationship with language learning strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".