The Effects of Extensive Reading on English Vocabulary Learning: A Meta-analysis
Bibliographic record
Abstract
Extensive reading has been continuously studied as a promising instructional method for improving students’ language proficiency, including reading proficiency, vocabulary acquisition, and grammar awareness. The present study is a meta–analysis, which synthesized the data of 21 empirical studies (N = 1268). It was designed to explore whether extensive reading instruction was effective in improving students’ vocabulary acquisition, and if so, how the effectiveness varied in terms of the instruction length and teaching methods. Stata 14.0 was utilized to calculate the collected data. The results revealed that: (1) extensive reading has a significant effect on English vocabulary learning; (2) one semester (less than three months) is the most appropriate length of extensive reading instruction for vocabulary learning; (3) Graded Readers, comprehension questions and vocabulary exercise play significant roles as reading materials and education methods in promoting the vocabulary learning of EFL learners.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.032 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".