Vocabulary Size and Depth of Knowledge: A Study of Bahraini EFL Learners
Bibliographic record
Abstract
This study investigates the size and depth of vocabulary knowledge and its relationship to the general language proficiency of EFL learners. The study sample included 120 students from the University of Bahrain. The sample was randomly selected from the student population and split into two groups in terms of their level of English: intermediate and advanced. The study aims to answer four questions: (1) What is the effect of general language proficiency on the sizes of the receptive and productive vocabularies of learners of English at the University of Bahrain? (2) How does general language proficiency affect the depth of vocabulary knowledge of learners of English at the University of Bahrain? (3) What is the relationship between receptive and productive vocabularies and the depth of vocabulary knowledge? and (4) What is the relationship between vocabulary size and the nature of lexical networking? All the students in the sample completed three vocabulary tasks. The first two tasks were Meara and Jones’s Eurocentres Vocabulary Size Test (1990) and Meara and Fitzpatrick’s Lex30 word association task (2000), which were used to measure the sizes of receptive and productive vocabularies. The third task was Gyllstad’s COLLEX test (2007), which was used to investigate the depth of vocabulary knowledge. A quasi-experimental approach was adopted using a quantitative approach to analyze the data. The data of the study were analyzed by comparing the results of the two groups in relation to the three tasks using SPSS 16.0. The findings of the study have revealed that general language proficiency has a positive effect on learners’ receptive vocabulary size, a moderate effect on learners’ productive vocabulary size, and a very low effect on the depth of vocabulary knowledge. In addition, no relationship was shown between the size of vocabulary and the nature of lexical networking. With reference to these results, pedagogical and future research recommendations are made.
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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 | 0.000 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".