Investigating the Relationship Between Vocabulary Knowledge and FL Speaking Performance
Bibliographic record
Abstract
Research has highlighted the importance of vocabulary learning in order for L2 learners to cope with the linguistic demands of fundamental skills such as reading and listening. However, few empirical studies have investigated the relative strength of the association of a specific construct of vocabulary knowledge has on the skill of speaking. To understand more fully the practical implications of such a relationship, this paper presents empirical evidence gathered to explore a measure of productive vocabulary knowledge and the degree to which this measure correlates with and is able to predict speaking success. A cohort of 18 sophomore university learners of English as a foreign language (EFL) in Saudi Arabia (SA) completed the Productive Vocabulary Levels Test (PVLT), an oral interview and a speaking task. Test scores derived from PVLT were analyzed to produce a range of descriptive statistics, which underwent correlational analyses to determine the relationship between the measure of PVLT and speaking success. Analyses revealed a consistent pattern of declining scores from the highest to the least frequent word levels. A closer examination of the data showed that the participants’ success across the five-word levels of the PVLT showed better performance on the 2,000 and 3,000-word levels, in fact, the results indicated that only these word levels made a contribution to predicting speaking scores. Based on these findings, we draw implications for vocabulary teaching contexts and provide suggestions for future studies on vocabulary and speaking link.
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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 | 0.012 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".