The Relationship between Vocabulary Size and Reading Comprehension of ESL Learners
Bibliographic record
Abstract
There are many factors that contribute to one’s ability to read effectively. Vocabulary size is one important factor that enhances reading comprehension. The purpose of the study is to examine the relationship between students’ reading comprehension skills and their vocabulary size. A total of 129 pre-university students undergoing an intensive English language programme at a public university in Malaysia participated in this study. A correlational analysis was employed to ascertain the relationship between scores in the reading comprehension component of the institutionalised English Proficiency Test (EPT) and the Vocabulary Levels Tests (Nation, 1990). Based on Pearson product moment correlation coefficient, there was a moderate correlation (r=0.641) between scores in the EPT reading comprehension and Vocabulary Levels Tests. The relationship was statistically significant at p<0.01 level. The findings also indicate that all students (100%) were able to fulfil the minimum admission requirements for the reading skill (Band 5.5) in the EPT even though only half of the students (54.3%) reached the mastery level at the 5,000 word level. The findings provide useful insights into the prediction of ESL students’ performance in reading and the teaching of vocabulary in the ESL context.
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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 | 0.005 |
| 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) | 0.004 | 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".