A study of meta-analyses reporting quality in the large and expanding literature of educational technology
Bibliographic record
Abstract
As the empirical literature in educational technology continues to grow, meta-analyses are increasingly being used to synthesise research to inform practice. However, not all meta-analyses are equal. To examine their evolution over the past 30 years, this study systematically analysed the quality of 52 meta-analyses (1988–2017) on educational technology. Methodological and reporting quality is defined here as the completeness of the descriptive and methodological reporting features of meta-analyses. The study employed the Meta-Analysis Methodological Reporting Quality Guide (MMRQG), an instrument designed to assess 22 areas of reporting quality in meta-analyses. Overall, MMRQG scores were negatively related to average effect size (i.e., the higher the quality, the lower the effect size). Owing to the presence of poor-quality syntheses, the contribution of educational technologies to learning has been overestimated, potentially misleading researchers and practitioners. Nine MMRQG items discriminated between higher and lower average effect sizes. A publication date analysis revealed that older reviews (1988–2009) scored significantly lower on the MMRQG than more recent reviews (2010–2017). Although the increase in quality bodes well for the educational technology literature, many recent meta-analyses still show only moderate levels of quality. Identifying and using only best evidence-based research is thus imperative to avoid bias. Implications for practice or policy: Educational technology practitioners should make use of meta-analytical findings that systematically synthesise primary research. Academics, policymakers and practitioners should consider the methodological quality of meta-analyses as they vary in reliability. Academics, policymakers and practitioners could avoid misleading bias in research evidence by using the MMRQG to evaluate the quality of meta-analyses. Meta-analyses with lower MMRQG scores should be considered with caution as they seem to overestimate the effect of educational technology on 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 | 0.744 | 0.932 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.042 |
| Bibliometrics | 0.028 | 0.034 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".