A Meta-Analysis of Vocabulary Learning Strategies of EFL Learners
Bibliographic record
Abstract
Vocabulary learning is one of the crucial matters in second language learning. There is a vast body of research in this field which has been done by famous researchers around the world, but still there is no specific solution for extending lexical knowledge in the best way. Therefore, we have conducted a meta-analysis on a body of 30 research projects to investigate the usefulness of different strategies of vocabulary learning. The results showed that the strategies which are used by students are as follows in order: determination, cognitive, memory, meta-cognitive, and social strategies. Besides, it seems that guessing from context and using the dictionary are among the strategies which are most favored by successful students. In addition, the relationship between context, treatment, and methodology by vocabulary learning strategy were studied. It was clear that learners of different contexts would prefer different strategies, and teachers used specific strategies according to their syllabus. For further research, it is suggested to select a larger body of studies. It is advised to make teachers aware of the importance of choosing an appropriate strategy of vocabulary learning for language learners to pave the way of improving lexical knowledge for them.
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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 | 0.044 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.030 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".