Measuring Vocabulary Learning Strategy Use of Turkish EFL Learners in Relation to Academic Success and Vocabulary Size
Bibliographic record
Abstract
The purpose of this study was to investigate Vocabulary Learning Strategy (VLS) use of English Language andLiterature Department students in relation to academic success and vocabulary size. The participants of the study are213 English Language and Literature students. Two data collection tools were used in the study. The first tool wastheVocabulary Learning Strategy (VLS) questionnaire which was adapted from by Gu & Johnson (1996), and thesecond data collection tool was a Vocabulary Level Test (VLT) developed by Nation (1983).Descriptive statisticswere conducted in order to measure the level of vocabulary learning strategy (VLS) use and vocabulary size of theparticipants. In addition, correlation analysis was carried out in order to see which VLSs are more frequently used bylow, middle and upper level vocabulary size students. The results indicated that the participants have a high level ofvocabulary size for 2000 word level, 3000 word level, and academic word levels, a moderate level of vocabulary sizefor 5000 word level and a low level in 10000 word level. The participants were found to have a moderate level ofvocabulary learning strategy use. The study also found that 3rd grade students had larger vocabulary size in terms of2000, 3000 and academic vocabulary level. As for the vocabulary strategy use, 3rd grade students were found to usebottom-up strategies and note-taking strategies more frequently than 2nd grade students. Finally, correlation analysisrevealed that bottom-up strategies, using linguistic clues, and top-down strategies significantly correlated withacademic success.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".