Educational Research Review· 2026Q1
A comprehensive quality assessment of John Hattie's Visible Learning
- 1citations
- Q1SCImago
- 2026year
Short summary
A quality assessment of John Hattie's Visible Learning found only 19% of its 322 influences met PICO criteria and just 2 out of 301 were reproducible, revealing methodological errors that compromise effect size validity and causal interpretations.
AI-generated from the title and abstract; the full text is not read.
Key points
- Only 19% of Visible Learning's 322 influences met PICO inclusion criteria.
- Reproducibility attempts failed for 99% of influences (2 out of 301 successful).
- Methodological errors compromise causal interpretations and effect size validity.
- While 23% of influences are highly relevant, many lack relevance to K-12 education.
AI-generated from the title and abstract; the full text is not read.
Abstract
John Hattie’s Visible Learning aggregates over 2,400 meta-analyses into ranked effect sizes, claiming to reveal the most impactful educational practices. Despite its global influence on education, critics highlight methodological and statistical weaknesses. This study comprehensively assessed Visible Learning’s 322 influences for quality, relevance, and reproducibility. Findings reveal that only 19% meet the inclusion criteria regarding participants, intervention, comparison, and outcome alignment (PICO). Furthermore, in the attempts to reproduce the extractions, only 2 out of 301 influences were successful. The assessment revealed that Visible Learning contained methodological errors that compromised both the causal interpretations and the validity of the reported effect sizes. An expert panel found that while 23% influences are highly relevant, many hold little relevance to K-12 education. These findings underscore significant methodological and statistical shortcomings, raising questions about the validity of Visible Learning as a basis for educational policy and practice.
The authors' abstract, as published at the source. Educational Research Review, 2026 · DOI ↗
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Statistics, Probability and UncertaintyDecision Sciences