Vocabulary Learning Strategies (VLSs) Employed by Learners of English as a Foreign Language (EFL)
Bibliographic record
Abstract
Learning a new language entails various challenges, one of these is grasping the vocabulary of the language. A significant way to tackle the problem is to motivate students to become independent learners during the progression of second language (L2) vocabulary learning. Thus, this study intended to explore the use of different vocabulary learning strategies among adult English as foreign language learners and investigated the various vocabulary learning strategies and found the benefits and drawbacks associated with each strategy. It was able to select the most frequently and least frequently used VLSs by learners who have completed the language program and those who are continuing the course. Further, it found effective strategies that could be used in teaching vocabulary to students. The research used a quantitative method approach with 53 participants who were EFL learners took part in the questionnaire survey. The results of the present study reveal the common strategies that foreign language learners use in vocabulary learning. The VLSs from this study will not only benefit students of the English language but can easily to be used by learners of other second languages globally. Finally, the paper discusses different strategies at length, gives valuable recommendations in the discussion section and concludes with implications for future research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".