The Effects of Microlearning on EFL Students’ English Speaking: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Despite advancements in microlearning-based English-speaking education, comprehensive meta-analyses of its effectiveness remain scarce. This study aimed to evaluate the effect of microlearning on English speaking among English as a foreign language (EFL) students through a systematic review and meta-analysis. Following the PRISMA principles, the research was conducted in June 2023 across five phases: problem identification, data collection, screening, evaluation, and extraction. Data were obtained from peer-reviewed journals indexed in databases, including ERIC, Science Direct, Scopus, and Google Scholar. Data analysis was undertaken using the modified Newcastle-Ottawa Scale-Education (NOS-E). Subsequently, the R meta program facilitated a robust meta-analysis, allowing us to comprehensively gauge effect size. A literature review yielded 10 studies (combined sample size = 743) that matched the eligibility guidelines. On the NOS-E, each study scored 4.55 out of 6. The results demonstrate the superiority of microlearning over traditional lectures (total English-speaking scores, SMD = 1.43, 95%CI = 1.27?1.59, p < .05). In the meta-analysis, heterogeneity was revealed (total scores for English speaking, I2 = 66%, p < .01), with no publication bias. Microlearning significantly benefits English language teaching (ELT) and enhances EFL students’ English-speaking skills. However, limitations do exist. By addressing these limitations, educators may refine pedagogical practices for optimal ELT methods for EFL learning.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".