Evidence-Based Practices of English Language Teaching: A Meta-Analysis of Meta-Analyses
Bibliographic record
Abstract
This meta-analysis aims at investigating the impact of English language teaching practices on language outcomes. The literature search yielded 90 meta-analyses that were published between January, 1995 and December, 2019. The current study analyzed 90 meta-analysis and these studies comprised 3496 studies, 7870 effect sizes and nearly 700,000 students. Three moderator variables were examined: year of publication, setting and educational level. The results showed that a) language learning strategies had medium impact on language outcomes in general and generated the largest impact on speaking (d=0.90), b) technology-based language learning had medium impact on language outcomes in general and generated the largest impact on vocabulary (d=0.98), c) explicit instruction had medium impact on language outcomes in general and generated the largest impact on grammar (d=1.26), d) mobile-based language learning had small impact on language outcomes in general and generated the largest impact on listening (d=0.73), and e) setting and educational level significantly moderated the impact of teaching practices on language outcomes. The findings were discussed and implication and future research were proposed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.054 | 0.135 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.063 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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 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".