Microlearning Effectiveness in Higher Education: A Systematic Review and Meta-Analysis of Student Retention and Learning Outcomes
Bibliographic record
Abstract
The proliferation of digital technologies in higher education has necessitated innovative pedagogical approaches to enhance student retention and learning outcomes. Microlearning, characterized by short, focused learning segments, has emerged as a promising strategy for addressing contemporary educational challenges. This systematic review and meta-analysis evaluates the effectiveness of microlearning interventions in higher education settings, specifically examining their impact on student retention rates and learning outcomes from 2020-2025. Following PRISMA guidelines, we comprehensively searched multiple databases, including PubMed, Scopus, Web of Science, ERIC, and IEEE Xplore. Studies were included if they examined microlearning interventions in higher education contexts with quantitative measures of student retention or learning outcomes. Quality assessment was performed using the Newcastle-Ottawa Scale and Cochrane Risk of Bias tool. Of 2,847 initially identified studies, 42 met inclusion criteria, encompassing 15,673 participants across 18 countries. Meta-analysis revealed significant positive effects of microlearning on student retention (pooled OR = 1,87; 95% CI: 1,45-2,41; p < 0,001) and learning outcomes (standardized mean difference = 0,74; 95% CI: 0,58-0,90; p < 0,001). Subgroup analyses indicated greater effectiveness in STEM subjects when combined with mobile technologies. Heterogeneity was moderate (I² = 67% for retention, I² = 71% for learning outcomes). Microlearning significantly positively affects student retention and learning outcomes in higher education. The evidence supports its implementation as an effective pedagogical strategy, particularly in statistics education and technology-enhanced learning environments. Future research should focus on long-term retention effects and optimal design principles.
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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 | 0.021 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| 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".