Vocabulary Learning Strategies and Vocabulary Mastery by Indonesian EFL Learners
Bibliographic record
Abstract
This research aimed at unpacking the types of vocabulary learning strategies (VLS), the intensity of use, and their relationship with vocabulary mastery of Indonesian EFL learners. As many as 180 English education department students from three universities in East Java, Indonesia, participated in the study. They were assigned to fill in a 50-item vocabulary learning strategy inventory and a 50-item vocabulary mastery test. The data were analyzed using Principle Component Analysis (PCA) to discern the categories of vocabulary learning strategies. In addition, descriptive and correlational analyses were also employed. The study revealed six categories of vocabulary learning strategies, including cognitive, metacognitive, determination, memory, encoding, and activation strategies, showcasing 63.5% of vocabulary learning strategy variances. In general, the students applied vocabulary learning strategies at a moderate level, with metacognitive and encoding strategies being used the most and cognitive strategies being used the least. The study also unpacked that the six categories of strategies were significant predictors of vocabulary mastery (F=4.391, p<.000), with metacognitive strategies being the best predictor. These findings suggest that overt training on how to make use of vocabulary learning strategies is required for Indonesian EFL learners.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.008 | 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".