Vocabulary Levels and Vocabulary learning strategies of Iranian Undergraduate students
Bibliographic record
Abstract
This study tries to investigate the vocabulary learning strategies and vocabulary level of Iranian EFL learners and any potential relation and contribution between these two variables. The research design of the study was quantitative method and the population of the study was Iranian junior EFL students. Thus, 238 participants- both male and female- were selected from Semnan universities according to random cluster sampling. Schmitt’s vocabulary learning strategies questionnaire (VLSQ) and nation’s vocabulary level test (VLT) were used to collect data. The resultsshowed that Iranian junior EFL students were medium strategy users with overall strategy mean score of 2.99. It indicated that the participants of the current study need more training on vocabulary learning strategies to become more familiar with all types of vocabulary earning strategies. Furthermore, memory strategy was found as the most frequently used strategy and cognitive strategy as the least frequently one. The descriptive statistics showed that students had sufficient vocabulary knowledge at 2000 and 3000 word levels. However, they did not have sufficient vocabulary knowledge at 5000, 10000, and academic vocabulary levels. The results indicated significant relationship between all vocabulary learning strategy and overall vocabulary level of the students. However, the strongest correlation was found between memory strategy and overall vocabulary level and the weakest correlation was found between social strategy and overall vocabulary level of Iranian EFL university students. It was found that all vocabulary learning strategy contributed to the overall vocabulary learning of the student. The highest contribution was related to memory strategy and the lowest to social strategy. Key words : Vocabulary; Leaning strategies; Vocabulary learning strategies; Vocabulary level; Vocabularysize
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".